From 2e286c359b7811caeaadca1d635ad6a2d7161974 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 12:08:58 -0400 Subject: [PATCH 01/29] refactor GenerateRespose --- .../src/config.ts | 13 +- .../includeChunksForMaxTokensPossible.test.ts | 57 ++ .../includeChunksForMaxTokensPossible.ts | 19 + .../src/processors/index.ts | 2 +- .../makeRagGenerateUserPrompt.test.ts | 253 -------- .../processors/makeRagGenerateUserPrompt.ts | 218 ------- .../addMessageToConversation.test.ts | 1 - .../conversations/addMessageToConversation.ts | 29 +- .../conversations/conversationsRouter.ts | 29 +- .../conversations/createConversation.ts | 3 - .../src/routes/generateResponse.ts | 490 +--------------- .../src/routes/index.ts | 3 +- ...test.ts => legacyGenerateResponse.test.ts} | 104 ++-- .../src/routes/legacyGenerateResponse.ts | 548 ++++++++++++++++++ 14 files changed, 709 insertions(+), 1060 deletions(-) create mode 100644 packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.test.ts create mode 100644 packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.ts delete mode 100644 packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.test.ts delete mode 100644 packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.ts rename packages/mongodb-chatbot-server/src/routes/{generateResponse.test.ts => legacyGenerateResponse.test.ts} (92%) create mode 100644 packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index 1027fe743..9e5a55722 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -20,6 +20,7 @@ import { makeDefaultFindVerifiedAnswer, defaultCreateConversationCustomData, defaultAddMessageToConversationCustomData, + makeLegacyGeneratateResponse, } from "mongodb-chatbot-server"; import cookieParser from "cookie-parser"; import { makeStepBackRagGenerateUserPrompt } from "./processors/makeStepBackRagGenerateUserPrompt"; @@ -237,14 +238,13 @@ const segmentConfig = SEGMENT_WRITE_KEY export const config: AppConfig = { conversationsRouterConfig: { - llm, middleware: [ blockGetRequests, requireValidIpAddress(), requireRequestOrigin(), useSegmentIds(), - cookieParser(), redactConnectionUri(), + cookieParser(), ], createConversationCustomData: !isProduction ? createConversationCustomDataWithAuthUser @@ -294,8 +294,13 @@ export const config: AppConfig = { : undefined, segment: segmentConfig, }), - generateUserPrompt, - systemPrompt, + generateResponse: makeLegacyGeneratateResponse({ + llm, + generateUserPrompt, + systemMessage: systemPrompt, + llmNotWorkingMessage: "LLM not working. Sad!", + noRelevantContentMessage: "No relevant content found. Sad!", + }), maxUserMessagesInConversation: 50, maxUserCommentLength: 500, conversations, diff --git a/packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.test.ts b/packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.test.ts new file mode 100644 index 000000000..d7254f992 --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.test.ts @@ -0,0 +1,57 @@ +import { EmbeddedContent } from "mongodb-rag-core"; +import { includeChunksForMaxTokensPossible } from "./includeChunksForMaxTokensPossible"; + +const embeddings = { + modelName: [0.1, 0.2, 0.3], +}; + +describe("includeChunksForMaxTokensPossible()", () => { + const content: EmbeddedContent[] = [ + { + url: "https://mongodb.com/docs/realm/sdk/node/", + text: "foo foo foo", + tokenCount: 100, + embeddings, + sourceName: "realm", + updated: new Date(), + }, + { + url: "https://mongodb.com/docs/realm/sdk/node/", + text: "bar bar bar", + tokenCount: 100, + embeddings, + sourceName: "realm", + updated: new Date(), + }, + { + url: "https://mongodb.com/docs/realm/sdk/node/", + text: "baz baz baz", + tokenCount: 100, + embeddings, + sourceName: "realm", + updated: new Date(), + }, + ]; + test("Should include all chunks if less that max tokens", () => { + const maxTokens = 1000; + const includedChunks = includeChunksForMaxTokensPossible({ + content, + maxTokens, + }); + expect(includedChunks).toStrictEqual(content); + }); + test("should only include subset of chunks that fit within max tokens, inclusive", () => { + const maxTokens = 200; + const includedChunks = includeChunksForMaxTokensPossible({ + content, + maxTokens, + }); + expect(includedChunks).toStrictEqual(content.slice(0, 2)); + const maxTokens2 = maxTokens + 1; + const includedChunks2 = includeChunksForMaxTokensPossible({ + content, + maxTokens: maxTokens2, + }); + expect(includedChunks2).toStrictEqual(content.slice(0, 2)); + }); +}); diff --git a/packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.ts b/packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.ts new file mode 100644 index 000000000..612621e79 --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/includeChunksForMaxTokensPossible.ts @@ -0,0 +1,19 @@ +import { EmbeddedContent } from "mongodb-rag-core"; + +/** + This function returns the chunks that can fit in the maxTokens. + It limits the number of tokens that are sent to the LLM. + */ +export function includeChunksForMaxTokensPossible({ + maxTokens, + content, +}: { + maxTokens: number; + content: EmbeddedContent[]; +}): EmbeddedContent[] { + let total = 0; + const fitRangeEndIndex = content.findIndex( + ({ tokenCount }) => (total += tokenCount) > maxTokens + ); + return fitRangeEndIndex === -1 ? content : content.slice(0, fitRangeEndIndex); +} diff --git a/packages/mongodb-chatbot-server/src/processors/index.ts b/packages/mongodb-chatbot-server/src/processors/index.ts index 8a97f2a00..1d6b64a94 100644 --- a/packages/mongodb-chatbot-server/src/processors/index.ts +++ b/packages/mongodb-chatbot-server/src/processors/index.ts @@ -5,5 +5,5 @@ export * from "./QueryPreprocessorFunc"; export * from "./filterOnlySystemPrompt"; export * from "./makeDefaultReferenceLinks"; export * from "./makeFilterNPreviousMessages"; -export * from "./makeRagGenerateUserPrompt"; export * from "./makeVerifiedAnswerGenerateUserPrompt"; +export * from "./includeChunksForMaxTokensPossible"; diff --git a/packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.test.ts b/packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.test.ts deleted file mode 100644 index 786ad1da9..000000000 --- a/packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.test.ts +++ /dev/null @@ -1,253 +0,0 @@ -import "dotenv/config"; -import { EmbeddedContent, FindContentFunc } from "mongodb-rag-core"; -import { - MakeRagGenerateUserPromptParams, - MakeUserMessageFunc, - includeChunksForMaxTokensPossible, - makeRagGenerateUserPrompt, -} from "./makeRagGenerateUserPrompt"; -import { QueryPreprocessorFunc } from "./QueryPreprocessorFunc"; -import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; - -const embeddings = { - modelName: [0.1, 0.2, 0.3], -}; -const mockContent = [ - { - url: "https://mongodb.com/docs/realm/sdk/node/", - text: "foo foo foo", - tokenCount: 3, - embeddings, - sourceName: "realm", - updated: new Date(), - score: 0.1, - }, - { - url: "https://mongodb.com/docs/realm/sdk/java/", - text: "bar bar bar", - tokenCount: 3, - embeddings, - sourceName: "realm", - updated: new Date(), - score: 0.2, - }, - { - url: "https://mongodb.com/docs/realm/sdk/flutter/", - text: "baz baz baz", - tokenCount: 3, - embeddings, - sourceName: "realm", - updated: new Date(), - score: 0.3, - }, -]; - -const mockTransformText = (text: string) => { - return text + "\n\n Answer like a pirate."; -}; -const mockPreprocessor: QueryPreprocessorFunc = async ({ query }) => ({ - query: mockTransformText(query), - rejectQuery: false, -}); -const mockFindContent: FindContentFunc = async () => { - return { - queryEmbedding: [0.1, 0.2, 0.3], - content: mockContent, - embeddingModelName: "test-embedding-model", - }; -}; - -const mockMakeUserMessage: MakeUserMessageFunc = async ({ - preprocessedUserMessage, -}) => ({ - role: "user", - content: preprocessedUserMessage ?? "", -}); - -const mockMakeReferences: MakeReferenceLinksFunc = (content) => { - return content.map((c) => ({ url: c.url, title: "foobar" })); -}; - -const mockConfig: MakeRagGenerateUserPromptParams = { - maxChunkContextTokens: 1000, - findContent: mockFindContent, - makeUserMessage: mockMakeUserMessage, - queryPreprocessor: mockPreprocessor, - makeReferenceLinks: mockMakeReferences, -}; - -describe("makeRagGenerateUserPrompt()", () => { - test("should preprocess queries", async () => { - const generateUserPromptFunc = makeRagGenerateUserPrompt(mockConfig); - const response = await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(response.userMessage.content).toBe(mockTransformText("foo")); - }); - test("should reject queries with preprocessor", async () => { - const generateUserPromptFunc = makeRagGenerateUserPrompt({ - ...mockConfig, - queryPreprocessor: async ({ query }) => ({ query, rejectQuery: true }), - }); - const response = await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(response.rejectQuery).toBe(true); - }); - test("should pass through queries without preprocessor", async () => { - const generateUserPromptFunc = makeRagGenerateUserPrompt({ - ...mockConfig, - queryPreprocessor: undefined, - }); - const response = await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(response.userMessage.content).toBe("foo"); - }); - test("should include found content with findContent", async () => { - const generateUserPromptFunc = makeRagGenerateUserPrompt({ - ...mockConfig, - makeUserMessage: async ({ content }) => ({ - role: "user", - content: content[0].text, - }), - }); - const response = await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(response.userMessage.content).toBe(mockContent[0].text); - }); - test("should reject queries with no matching content", async () => { - const generateUserPromptFunc = makeRagGenerateUserPrompt({ - ...mockConfig, - findContent: async () => ({ queryEmbedding: [], content: [] }), - }); - const response = await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(response.rejectQuery).toBe(true); - }); - test("should pass original user message, preprocessed user message, and content to makeUserMessage", async () => { - const originalUserMessage = "foo"; - const preprocessedUserMessage = mockTransformText(originalUserMessage); - const generateUserPromptFunc = makeRagGenerateUserPrompt({ - ...mockConfig, - makeUserMessage: async ({ - content, - originalUserMessage, - preprocessedUserMessage, - }) => ({ - role: "user", - content: `${originalUserMessage} ${preprocessedUserMessage} ${content[0].text}`, - }), - }); - const response = await generateUserPromptFunc({ - userMessageText: originalUserMessage, - reqId: "foo", - }); - expect(response.userMessage.content).toBe( - `${originalUserMessage} ${preprocessedUserMessage} ${mockContent[0].text}` - ); - }); - test("should include references from found content", async () => { - const generateUserPromptFunc = makeRagGenerateUserPrompt(mockConfig); - const response = await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(response.references).toStrictEqual(mockMakeReferences(mockContent)); - }); - test("should include content with max tokens", async () => { - const calledFunc = jest.fn(); - const generateUserPromptFunc = makeRagGenerateUserPrompt({ - ...mockConfig, - maxChunkContextTokens: 3, - makeUserMessage: async ({ content }) => { - if (content.length === 1) { - calledFunc(); - } - return { - role: "user", - content: "blah", - }; - }, - }); - await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(calledFunc).toHaveBeenCalled(); - }); - test("should include embedding model name in user message when no content found", async () => { - const generateUserPromptFunc = makeRagGenerateUserPrompt({ - ...mockConfig, - findContent: async () => ({ - queryEmbedding: [], - content: [], - embeddingModelName: "test-embedding-model", - }), - }); - const response = await generateUserPromptFunc({ - userMessageText: "foo", - reqId: "foo", - }); - expect(response.rejectQuery).toBe(true); - expect(response.userMessage.embeddingModel).toBe("test-embedding-model"); - }); -}); - -describe("includeChunksForMaxTokensPossible()", () => { - const content: EmbeddedContent[] = [ - { - url: "https://mongodb.com/docs/realm/sdk/node/", - text: "foo foo foo", - tokenCount: 100, - embeddings, - sourceName: "realm", - updated: new Date(), - }, - { - url: "https://mongodb.com/docs/realm/sdk/node/", - text: "bar bar bar", - tokenCount: 100, - embeddings, - sourceName: "realm", - updated: new Date(), - }, - { - url: "https://mongodb.com/docs/realm/sdk/node/", - text: "baz baz baz", - tokenCount: 100, - embeddings, - sourceName: "realm", - updated: new Date(), - }, - ]; - test("Should include all chunks if less that max tokens", () => { - const maxTokens = 1000; - const includedChunks = includeChunksForMaxTokensPossible({ - content, - maxTokens, - }); - expect(includedChunks).toStrictEqual(content); - }); - test("should only include subset of chunks that fit within max tokens, inclusive", () => { - const maxTokens = 200; - const includedChunks = includeChunksForMaxTokensPossible({ - content, - maxTokens, - }); - expect(includedChunks).toStrictEqual(content.slice(0, 2)); - const maxTokens2 = maxTokens + 1; - const includedChunks2 = includeChunksForMaxTokensPossible({ - content, - maxTokens: maxTokens2, - }); - expect(includedChunks2).toStrictEqual(content.slice(0, 2)); - }); -}); diff --git a/packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.ts b/packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.ts deleted file mode 100644 index ee7140363..000000000 --- a/packages/mongodb-chatbot-server/src/processors/makeRagGenerateUserPrompt.ts +++ /dev/null @@ -1,218 +0,0 @@ -import { stripIndents } from "common-tags"; -import { GenerateUserPromptFunc } from "./GenerateUserPromptFunc"; -import { QueryPreprocessorFunc } from "./QueryPreprocessorFunc"; -import { logRequest } from "../utils"; -import { - Conversation, - UserMessage, - EmbeddedContent, - FindContentFunc, -} from "mongodb-rag-core"; -import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; -import { makeDefaultReferenceLinks } from "./makeDefaultReferenceLinks"; - -export interface MakeRagGenerateUserPromptParams { - /** - Transform the user's message before sending it to the `findContent` function. - */ - queryPreprocessor?: QueryPreprocessorFunc; - - /** - Find content based on the user's message and preprocessing. - */ - findContent: FindContentFunc; - - /** - If not specified, uses {@link makeDefaultReferenceLinks}. - */ - makeReferenceLinks?: MakeReferenceLinksFunc; - - /** - Number of tokens from the found context to send to the `makeUserMessage` function. - All chunks that exceed this threshold are discarded. - If not specified, uses {@link DEFAULT_MAX_CONTEXT_TOKENS}. - */ - maxChunkContextTokens?: number; - - /** - Construct user message which is sent to the LLM and stored in the database. - */ - makeUserMessage: MakeUserMessageFunc; -} - -export interface MakeUserMessageFuncParams { - content: EmbeddedContent[]; - originalUserMessage: string; - preprocessedUserMessage?: string; - queryEmbedding?: number[]; - rejectQuery?: boolean; -} - -export type MakeUserMessageFunc = ( - params: MakeUserMessageFuncParams -) => Promise; - -const DEFAULT_MAX_CONTEXT_TOKENS = 1500; // magic number for max context tokens for LLM - -/** - Construct a {@link GenerateUserPromptFunc} function - that uses retrieval augmented generation (RAG) to generate the user prompt - and return references to use in the answer. - The returned RAG user prompt generator performs the following steps: - 1. Preprocess the user's message using the query preprocessor. - 2. Find content using vector search. - 3. Removes any chunks that would exceed the max context tokens. - 4. Generate the user message using the make user message function. - 5. Return the user message and references. */ -export function makeRagGenerateUserPrompt({ - queryPreprocessor, - findContent, - makeReferenceLinks = makeDefaultReferenceLinks, - maxChunkContextTokens = DEFAULT_MAX_CONTEXT_TOKENS, - makeUserMessage, -}: MakeRagGenerateUserPromptParams): GenerateUserPromptFunc { - return async ({ userMessageText, conversation, reqId }) => { - // --- PREPROCESS --- - const preprocessResult = preProcessUserMessage - ? await preProcessUserMessage({ - queryPreprocessor, - userMessageText, - conversation, - reqId, - }) - : undefined; - const { rejectQuery, query: preprocessedUserMessageContent } = - preprocessResult ?? { - rejectQuery: false, - query: userMessageText, - }; - if (rejectQuery) { - logRequest({ - reqId, - message: "Preprocessor rejected query", - }); - return { - rejectQuery: true, - userMessage: { role: "user", content: userMessageText }, - }; - } - - // --- VECTOR SEARCH / RETRIEVAL --- - const findContentQuery = preprocessedUserMessageContent ?? userMessageText; - const { content, queryEmbedding, embeddingModelName } = await findContent({ - query: findContentQuery, - }); - if (content.length === 0) { - logRequest({ - reqId, - message: "No matching content found", - }); - return { - userMessage: { - role: "user", - content: userMessageText, - embedding: queryEmbedding, - embeddingModel: embeddingModelName, - }, - rejectQuery: true, - }; - } - - logRequest({ - reqId, - message: stripIndents`Chunks found: ${JSON.stringify( - content.map( - ({ chunkAlgoHash, embeddings, ...wantedProperties }) => - wantedProperties - ) - )}`, - }); - - const references = makeReferenceLinks(content); - const includedContent = includeChunksForMaxTokensPossible({ - maxTokens: maxChunkContextTokens, - content, - }); - - const userMessage = await makeUserMessage({ - content: includedContent, - originalUserMessage: userMessageText, - preprocessedUserMessage: preprocessedUserMessageContent, - queryEmbedding, - rejectQuery, - }); - logRequest({ - reqId, - message: `Latest message sent to LLM: ${JSON.stringify({ - role: userMessage.role, - content: userMessage.content, - })}`, - }); - return { - userMessage, - references, - rejectQuery: false, - }; - }; -} - -interface PreProcessUserMessageParams { - queryPreprocessor?: QueryPreprocessorFunc; - userMessageText: string; - conversation?: Conversation; - reqId: string; -} - -async function preProcessUserMessage({ - queryPreprocessor, - userMessageText, - conversation, - reqId, -}: PreProcessUserMessageParams): Promise< - { query: string; rejectQuery?: boolean } | undefined -> { - // Try to preprocess the user's message. If the user's message cannot be preprocessed - // (likely due to LLM timeout), then we will just use the original message. - if (!queryPreprocessor) { - return undefined; - } - try { - const { query, rejectQuery } = await queryPreprocessor({ - query: userMessageText, - messages: conversation?.messages, - }); - logRequest({ - reqId, - message: stripIndents`Successfully preprocessed user query. - Original query: ${userMessageText} - Preprocessed query: ${query}`, - }); - return { query: query ?? userMessageText, rejectQuery }; - } catch (err: unknown) { - logRequest({ - reqId, - type: "error", - message: `Error preprocessing query: ${JSON.stringify( - err - )}. Using original query: ${userMessageText}`, - }); - } -} - -/** - This function returns the chunks that can fit in the maxTokens. - It limits the number of tokens that are sent to the LLM. - */ -export function includeChunksForMaxTokensPossible({ - maxTokens, - content, -}: { - maxTokens: number; - content: EmbeddedContent[]; -}): EmbeddedContent[] { - let total = 0; - const fitRangeEndIndex = content.findIndex( - ({ tokenCount }) => (total += tokenCount) > maxTokens - ); - return fitRangeEndIndex === -1 ? content : content.slice(0, fitRangeEndIndex); -} diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts index 346e2658c..4714be8e5 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts @@ -385,7 +385,6 @@ describe("POST /conversations/:conversationId/messages", () => { ...appConfig, conversationsRouterConfig: { ...appConfig.conversationsRouterConfig, - llm: brokenLlmService, }, })); diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts index 84ece33c1..bc0aa0e4d 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts @@ -16,7 +16,6 @@ import { Conversation, SomeMessage, makeDataStreamer, - ChatLlm, } from "mongodb-rag-core"; import { ApiMessage, @@ -31,12 +30,9 @@ import { AddCustomDataFunc, ConversationsRouterLocals, } from "./conversationsRouter"; -import { GenerateUserPromptFunc } from "../../processors/GenerateUserPromptFunc"; -import { FilterPreviousMessages } from "../../processors/FilterPreviousMessages"; -import { filterOnlySystemPrompt } from "../../processors/filterOnlySystemPrompt"; -import { generateResponse, GenerateResponseParams } from "../generateResponse"; import { wrapTraced } from "mongodb-rag-core/braintrust"; import { UpdateTraceFunc, updateTraceIfExists } from "./UpdateTraceFunc"; +import { GenerateResponse, GenerateResponseParams } from "../GenerateResponse"; export const DEFAULT_MAX_INPUT_LENGTH = 3000; // magic number for max input size for LLM export const DEFAULT_MAX_USER_MESSAGES_IN_CONVERSATION = 7; // magic number for max messages in a conversation @@ -66,11 +62,9 @@ export type AddMessageRequest = z.infer; export interface AddMessageToConversationRouteParams { conversations: ConversationsService; - llm: ChatLlm; - generateUserPrompt?: GenerateUserPromptFunc; - filterPreviousMessages?: FilterPreviousMessages; maxInputLengthCharacters?: number; maxUserMessagesInConversation?: number; + generateResponse: GenerateResponse; addMessageToConversationCustomData?: AddCustomDataFunc; /** If present, the route will create a new conversation @@ -86,11 +80,6 @@ export interface AddMessageToConversationRouteParams { when it is created. */ addCustomData?: AddCustomDataFunc; - /** - The system message to add to the new conversation - when it is created. - */ - systemMessage?: SystemMessage; }; /** @@ -114,11 +103,9 @@ type MakeTracedResponseParams = Pick< export function makeAddMessageToConversationRoute({ conversations, - llm, - generateUserPrompt, + generateResponse, maxInputLengthCharacters = DEFAULT_MAX_INPUT_LENGTH, maxUserMessagesInConversation = DEFAULT_MAX_USER_MESSAGES_IN_CONVERSATION, - filterPreviousMessages = filterOnlySystemPrompt, addMessageToConversationCustomData, createConversation, updateTrace, @@ -150,14 +137,7 @@ export function makeAddMessageToConversationRoute({ dataStreamer, shouldStream, reqId, - llm, conversation, - generateUserPrompt, - filterPreviousMessages, - llmNotWorkingMessage: - conversations.conversationConstants.LLM_NOT_WORKING, - noRelevantContentMessage: - conversations.conversationConstants.NO_RELEVANT_CONTENT, }); }, { @@ -425,9 +405,6 @@ const loadConversation = async ({ message: stripIndents`Creating new conversation`, }); return await conversations.create({ - initialMessages: createConversation.systemMessage - ? [createConversation.systemMessage] - : undefined, customData: createConversation.addCustomData ? await createConversation.addCustomData(req, res) : undefined, diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts b/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts index 58c7a40c8..92aebf5a7 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts @@ -35,6 +35,7 @@ import { makeGetConversationRoute, } from "./getConversation"; import { UpdateTraceFunc } from "./UpdateTraceFunc"; +import { GenerateResponse } from "../GenerateResponse"; /** Configuration for rate limiting on the /conversations/* routes. @@ -118,16 +119,11 @@ export type ConversationsMiddleware = RequestHandler< Configuration for the /conversations/* routes. */ export interface ConversationsRouterParams { - llm: ChatLlm; conversations: ConversationsService; - systemPrompt: SystemPrompt; - /** - Function to generate the user prompt sent to the {@link ChatLlm}. - You can perform any preprocessing of the user's message - including retrieval augmented generation here. + Logic to generate the response on the addMessageToConversation route. */ - generateUserPrompt?: GenerateUserPromptFunc; + generateResponse: GenerateResponse; /** Maximum number of characters in user input. @@ -135,14 +131,6 @@ export interface ConversationsRouterParams { */ maxInputLengthCharacters?: number; - /** - Function to filter which previous messages are sent to the {@link ChatLlm}. - For example, you may only want to send the system prompt to the LLM - with the user message or the system prompt and X prior messages. - Defaults to sending only the system prompt. - */ - filterPreviousMessages?: FilterPreviousMessages; - /** Maximum number of user-sent messages in a conversation. Server returns 400 error if user tries to add a message to a conversation @@ -267,14 +255,11 @@ export const defaultAddMessageToConversationCustomData: AddDefinedCustomDataFunc Constructor function to make the /conversations/* Express.js router. */ export function makeConversationsRouter({ - llm, conversations, - systemPrompt, + generateResponse, maxInputLengthCharacters, maxUserMessagesInConversation, - filterPreviousMessages, rateLimitConfig, - generateUserPrompt, middleware = [requireValidIpAddress(), requireRequestOrigin()], createConversationCustomData = defaultCreateConversationCustomData, addMessageToConversationCustomData = defaultAddMessageToConversationCustomData, @@ -329,7 +314,6 @@ export function makeConversationsRouter({ makeCreateConversationRoute({ conversations, createConversationCustomData, - systemPrompt, }) ); @@ -364,20 +348,17 @@ export function makeConversationsRouter({ */ const addMessageToConversationRoute = makeAddMessageToConversationRoute({ conversations, - llm, maxInputLengthCharacters, maxUserMessagesInConversation, addMessageToConversationCustomData, - generateUserPrompt, - filterPreviousMessages, createConversation: createConversationOnNullMessageId ? { createOnNullConversationId: createConversationOnNullMessageId, addCustomData: createConversationCustomData, - systemMessage: systemPrompt, } : undefined, updateTrace: addMessageToConversationUpdateTrace, + generateResponse, }); conversationsRouter.post( "/:conversationId/messages", diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/createConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/createConversation.ts index fbc3feb6f..ef6aec2f5 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/createConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/createConversation.ts @@ -33,13 +33,11 @@ export const CreateConversationRequest = SomeExpressRequest.extend({ export interface CreateConversationRouteParams { conversations: ConversationsService; createConversationCustomData?: AddCustomDataFunc; - systemPrompt: SystemMessage; } export function makeCreateConversationRoute({ conversations, createConversationCustomData, - systemPrompt, }: CreateConversationRouteParams) { return async ( req: ExpressRequest, @@ -58,7 +56,6 @@ export function makeCreateConversationRoute({ ); const conversationInDb = await conversations.create({ customData, - initialMessages: [systemPrompt], }); const responseConversation = convertConversationFromDbToApi(conversationInDb); diff --git a/packages/mongodb-chatbot-server/src/routes/generateResponse.ts b/packages/mongodb-chatbot-server/src/routes/generateResponse.ts index 5062847d0..7e538573d 100644 --- a/packages/mongodb-chatbot-server/src/routes/generateResponse.ts +++ b/packages/mongodb-chatbot-server/src/routes/generateResponse.ts @@ -1,516 +1,28 @@ import { - References, SomeMessage, DataStreamer, Conversation, - escapeNewlines, - OpenAiChatMessage, - AssistantMessage, - UserMessage, ConversationCustomData, - ChatLlm, } from "mongodb-rag-core"; import { Request as ExpressRequest } from "express"; -import { logRequest } from "../utils"; -import { strict as assert } from "assert"; -import { GenerateUserPromptFunc } from "../processors/GenerateUserPromptFunc"; -import { FilterPreviousMessages } from "../processors/FilterPreviousMessages"; export type ClientContext = Record; export interface GenerateResponseParams { shouldStream: boolean; - llm: ChatLlm; latestMessageText: string; clientContext?: ClientContext; customData?: ConversationCustomData; dataStreamer?: DataStreamer; - generateUserPrompt?: GenerateUserPromptFunc; - filterPreviousMessages?: FilterPreviousMessages; reqId: string; - llmNotWorkingMessage: string; - noRelevantContentMessage: string; conversation: Conversation; request?: ExpressRequest; } -interface GenerateResponseReturnValue { +export interface GenerateResponseReturnValue { messages: SomeMessage[]; } export type GenerateResponse = ( params: GenerateResponseParams ) => Promise; - -/** - Generate a response with/without streaming. Supports tool calling - and standard response generation. - Response includes the user message with any data mutations - and the assistant response message, plus any intermediate tool calls. - */ -export async function generateResponse({ - shouldStream, - llm, - latestMessageText, - clientContext, - customData, - generateUserPrompt, - filterPreviousMessages, - dataStreamer, - reqId, - llmNotWorkingMessage, - noRelevantContentMessage, - conversation, - request, -}: GenerateResponseParams): Promise { - const { userMessage, references, staticResponse, rejectQuery } = - await (generateUserPrompt - ? generateUserPrompt({ - userMessageText: latestMessageText, - clientContext, - conversation, - reqId, - customData, - }) - : { - userMessage: { - role: "user", - content: latestMessageText, - customData, - } satisfies UserMessage, - }); - // Add request custom data to user message. - const userMessageWithCustomData = customData - ? { - ...userMessage, - // Override request custom data fields with user message custom data fields. - customData: { ...customData, ...(userMessage.customData ?? {}) }, - } - : userMessage; - const newMessages: SomeMessage[] = [userMessageWithCustomData]; - - // Metadata for streaming - let streamingResponseMetadata: Record | undefined; - // Send static response if query rejected or static response provided - if (rejectQuery) { - const rejectionMessage = { - role: "assistant", - content: noRelevantContentMessage, - references: references ?? [], - } satisfies AssistantMessage; - newMessages.push(rejectionMessage); - } else if (staticResponse) { - newMessages.push(staticResponse); - // Need to specify response metadata for streaming - streamingResponseMetadata = staticResponse.metadata; - } - - // Prepare conversation messages for LLM - const previousConversationMessagesForLlm = ( - filterPreviousMessages - ? await filterPreviousMessages(conversation) - : conversation.messages - ).map(convertConversationMessageToLlmMessage); - const newMessagesForLlm = newMessages.map((m) => { - // Use transformed content if it exists for user message - // (e.g. from a custom user prompt, query preprocessor, etc), - // otherwise use original content. - if (m.role === "user") { - return { - content: m.contentForLlm ?? m.content, - role: "user", - } satisfies OpenAiChatMessage; - } - return convertConversationMessageToLlmMessage(m); - }); - const llmConversation = [ - ...previousConversationMessagesForLlm, - ...newMessagesForLlm, - ]; - - const shouldGenerateMessage = !rejectQuery && !staticResponse; - - if (shouldStream) { - assert(dataStreamer, "Data streamer required for streaming"); - const { messages } = await streamGenerateResponseMessage({ - dataStreamer, - reqId, - llm, - llmConversation, - noRelevantContentMessage, - llmNotWorkingMessage, - request, - shouldGenerateMessage, - conversation, - references, - metadata: streamingResponseMetadata, - }); - newMessages.push(...messages); - } else { - const { messages } = await awaitGenerateResponseMessage({ - reqId, - llm, - llmConversation, - llmNotWorkingMessage, - noRelevantContentMessage, - request, - shouldGenerateMessage, - conversation, - references, - }); - newMessages.push(...messages); - } - return { messages: newMessages }; -} - -type BaseGenerateResponseMessageParams = Omit< - GenerateResponseParams, - "latestMessageText" | "customData" | "filterPreviousMessages" | "shouldStream" -> & { - references?: References; - shouldGenerateMessage?: boolean; - llmConversation: OpenAiChatMessage[]; -}; - -export type AwaitGenerateResponseParams = Omit< - BaseGenerateResponseMessageParams, - "dataStreamer" ->; - -export async function awaitGenerateResponseMessage({ - reqId, - llmConversation, - llm, - llmNotWorkingMessage, - noRelevantContentMessage, - request, - references, - conversation, - shouldGenerateMessage = true, -}: AwaitGenerateResponseParams): Promise { - const newMessages: SomeMessage[] = []; - const outputReferences: References = []; - - if (references) { - outputReferences.push(...references); - } - - if (shouldGenerateMessage) { - try { - logRequest({ - reqId, - message: `All messages for LLM: ${JSON.stringify(llmConversation)}`, - }); - const answer = await llm.answerQuestionAwaited({ - messages: llmConversation, - }); - newMessages.push(convertMessageFromLlmToDb(answer)); - - // LLM responds with tool call - if (answer?.function_call) { - assert( - llm.callTool, - "You must implement the callTool() method on your ChatLlm to access this code." - ); - const toolAnswer = await llm.callTool({ - messages: [...llmConversation, ...newMessages], - conversation, - request, - }); - logRequest({ - reqId, - message: `LLM tool call: ${JSON.stringify(toolAnswer)}`, - }); - const { - toolCallMessage, - references: toolReferences, - rejectUserQuery, - } = toolAnswer; - newMessages.push(convertMessageFromLlmToDb(toolCallMessage)); - // Update references from tool call - if (toolReferences) { - outputReferences.push(...toolReferences); - } - // Return static response if query rejected by tool call - if (rejectUserQuery) { - newMessages.push({ - role: "assistant", - content: noRelevantContentMessage, - }); - } else { - // Otherwise respond with LLM again - const answer = await llm.answerQuestionAwaited({ - messages: [...llmConversation, ...newMessages], - // Only allow 1 tool call per user message. - }); - newMessages.push(convertMessageFromLlmToDb(answer)); - } - } - } catch (err) { - const errorMessage = - err instanceof Error ? err.message : JSON.stringify(err); - logRequest({ - reqId, - message: `LLM error: ${errorMessage}`, - type: "error", - }); - logRequest({ - reqId, - message: "Only sending vector search results to user", - }); - const llmNotWorkingResponse = { - role: "assistant", - content: llmNotWorkingMessage, - references, - } satisfies AssistantMessage; - newMessages.push(llmNotWorkingResponse); - } - } - // Add references to the last assistant message (excluding function calls) - if ( - newMessages.at(-1)?.role === "assistant" && - !(newMessages.at(-1) as AssistantMessage).functionCall && - outputReferences.length > 0 - ) { - (newMessages.at(-1) as AssistantMessage).references = outputReferences; - } - return { messages: newMessages }; -} - -export type StreamGenerateResponseParams = BaseGenerateResponseMessageParams & - Required> & { - /** - Arbitrary data about the message to stream before the generated response. - */ - metadata?: Record; - }; - -export async function streamGenerateResponseMessage({ - dataStreamer, - llm, - llmConversation, - reqId, - references, - noRelevantContentMessage, - llmNotWorkingMessage, - conversation, - request, - metadata, - shouldGenerateMessage, -}: StreamGenerateResponseParams): Promise { - const newMessages: SomeMessage[] = []; - const outputReferences: References = []; - - if (references) { - outputReferences.push(...references); - } - - if (metadata) { - dataStreamer.streamData({ type: "metadata", data: metadata }); - } - if (shouldGenerateMessage) { - try { - const answerStream = await llm.answerQuestionStream({ - messages: llmConversation, - }); - const initialAssistantMessage: AssistantMessage = { - role: "assistant", - content: "", - }; - const functionCallContent = { - name: "", - arguments: "", - }; - - for await (const event of answerStream) { - if (event.choices.length === 0) { - continue; - } - // The event could contain many choices, but we only want the first one - const choice = event.choices[0]; - - // Assistant response to user - if (choice.delta?.content) { - const content = escapeNewlines(choice.delta.content ?? ""); - dataStreamer.streamData({ - type: "delta", - data: content, - }); - initialAssistantMessage.content += content; - } - // Tool call - else if (choice.delta?.function_call) { - if (choice.delta?.function_call.name) { - functionCallContent.name += escapeNewlines( - choice.delta?.function_call.name ?? "" - ); - } - if (choice.delta?.function_call.arguments) { - functionCallContent.arguments += escapeNewlines( - choice.delta?.function_call.arguments ?? "" - ); - } - } else if (choice.delta) { - logRequest({ - reqId, - message: `Unexpected message in stream: no delta. Message: ${JSON.stringify( - choice.delta.content - )}`, - type: "warn", - }); - } - } - const shouldCallTool = functionCallContent.name !== ""; - if (shouldCallTool) { - initialAssistantMessage.functionCall = functionCallContent; - } - newMessages.push(initialAssistantMessage); - - logRequest({ - reqId, - message: `LLM response: ${JSON.stringify(initialAssistantMessage)}`, - }); - // Tool call - if (shouldCallTool) { - assert( - llm.callTool, - "You must implement the callTool() method on your ChatLlm to access this code." - ); - const { - toolCallMessage, - references: toolReferences, - rejectUserQuery, - } = await llm.callTool({ - messages: [...llmConversation, ...newMessages], - conversation, - dataStreamer, - request, - }); - newMessages.push(convertMessageFromLlmToDb(toolCallMessage)); - - if (rejectUserQuery) { - newMessages.push({ - role: "assistant", - content: noRelevantContentMessage, - }); - dataStreamer.streamData({ - type: "delta", - data: noRelevantContentMessage, - }); - } else { - if (toolReferences) { - outputReferences.push(...toolReferences); - } - const answerStream = await llm.answerQuestionStream({ - messages: [...llmConversation, ...newMessages], - }); - const answerContent = await dataStreamer.stream({ - stream: answerStream, - }); - const answerMessage = { - role: "assistant", - content: answerContent, - } satisfies AssistantMessage; - newMessages.push(answerMessage); - } - } - } catch (err) { - const errorMessage = - err instanceof Error ? err.message : JSON.stringify(err); - logRequest({ - reqId, - message: `LLM error: ${errorMessage}`, - type: "error", - }); - logRequest({ - reqId, - message: "Only sending vector search results to user", - }); - const llmNotWorkingResponse = { - role: "assistant", - content: llmNotWorkingMessage, - } satisfies AssistantMessage; - dataStreamer.streamData({ - type: "delta", - data: llmNotWorkingMessage, - }); - newMessages.push(llmNotWorkingResponse); - } - } - // Handle streaming static message response - else { - const staticMessage = llmConversation.at(-1); - assert(staticMessage?.content, "No static message content"); - assert(staticMessage.role === "assistant", "Static message not assistant"); - logRequest({ - reqId, - message: `Sending static message to user: ${staticMessage.content}`, - type: "warn", - }); - dataStreamer.streamData({ - type: "delta", - data: staticMessage.content, - }); - } - - // Add references to the last assistant message - if (newMessages.at(-1)?.role === "assistant" && outputReferences.length > 0) { - (newMessages.at(-1) as AssistantMessage).references = outputReferences; - } - if (outputReferences.length > 0) { - // Stream back references - dataStreamer.streamData({ - type: "references", - data: outputReferences, - }); - } - - return { messages: newMessages.map(convertMessageFromLlmToDb) }; -} - -export function convertMessageFromLlmToDb( - message: OpenAiChatMessage -): SomeMessage { - const dbMessage = { - ...message, - content: message?.content ?? "", - }; - if (message.role === "assistant" && message.function_call) { - (dbMessage as AssistantMessage).functionCall = message.function_call; - } - - return dbMessage; -} - -function convertConversationMessageToLlmMessage( - message: SomeMessage -): OpenAiChatMessage { - const { content, role } = message; - if (role === "system") { - return { - content: content, - role: "system", - } satisfies OpenAiChatMessage; - } - if (role === "function") { - return { - content: content, - role: "function", - name: message.name, - } satisfies OpenAiChatMessage; - } - if (role === "user") { - return { - content: content, - role: "user", - } satisfies OpenAiChatMessage; - } - if (role === "assistant") { - return { - content: content, - role: "assistant", - ...(message.functionCall ? { function_call: message.functionCall } : {}), - } satisfies OpenAiChatMessage; - } - throw new Error(`Invalid message role: ${role}`); -} diff --git a/packages/mongodb-chatbot-server/src/routes/index.ts b/packages/mongodb-chatbot-server/src/routes/index.ts index 5802c82e3..56d320d19 100644 --- a/packages/mongodb-chatbot-server/src/routes/index.ts +++ b/packages/mongodb-chatbot-server/src/routes/index.ts @@ -1,2 +1,3 @@ export * from "./conversations"; -export * from "./generateResponse"; +export * from "./GenerateResponse"; +export * from "./legacyGenerateResponse"; diff --git a/packages/mongodb-chatbot-server/src/routes/generateResponse.test.ts b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.test.ts similarity index 92% rename from packages/mongodb-chatbot-server/src/routes/generateResponse.test.ts rename to packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.test.ts index 701805e2f..afe2dfa5c 100644 --- a/packages/mongodb-chatbot-server/src/routes/generateResponse.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.test.ts @@ -1,14 +1,7 @@ -import { References, UserMessage } from "mongodb-rag-core"; +import { References, SystemMessage, UserMessage } from "mongodb-rag-core"; import { ObjectId } from "mongodb-rag-core/mongodb"; import { OpenAI } from "mongodb-rag-core/openai"; -import { - AwaitGenerateResponseParams, - GenerateResponseParams, - StreamGenerateResponseParams, - awaitGenerateResponseMessage, - generateResponse, - streamGenerateResponseMessage, -} from "./generateResponse"; + import { AssistantMessage, ChatLlm, @@ -22,6 +15,13 @@ import { strict as assert } from "assert"; import { createResponse } from "node-mocks-http"; import { Response as ExpressResponse } from "express"; import { EventEmitter } from "stream-json/Parser"; +import { GenerateResponseParams } from "./GenerateResponse"; +import { + MakeLegacyGenerateResponseParams, + makeLegacyGeneratateResponse, + awaitGenerateResponseMessage, + streamGenerateResponseMessage, +} from "./legacyGenerateResponse"; const testFuncName = "test_func"; const mockFunctionInvocation = { @@ -185,25 +185,35 @@ const conversation: Conversation = { }; const dataStreamer = makeDataStreamer(); +const systemMessage: SystemMessage = { + role: "system", + content: "you're a helpful assistant or something....", +}; + +const constructorArgs = { + llm: mockChatLlm, + llmNotWorkingMessage, + noRelevantContentMessage, + async generateUserPrompt({ userMessageText }) { + return { + references, + userMessage: { + role: "user", + content: userMessageText, + } satisfies UserMessage, + }; + }, + systemMessage, +} satisfies MakeLegacyGenerateResponseParams; + describe("generateResponse", () => { const baseArgs = { - llm: mockChatLlm, reqId, - llmNotWorkingMessage, - noRelevantContentMessage, conversation, dataStreamer, latestMessageText: "hello", - async generateUserPrompt({ userMessageText }) { - return { - references, - userMessage: { - role: "user", - content: userMessageText, - } satisfies UserMessage, - }; - }, } satisfies Omit; + const generateResponse = makeLegacyGeneratateResponse(constructorArgs); let res: ReturnType & ExpressResponse; beforeEach(() => { res = createResponse({ @@ -244,10 +254,9 @@ describe("generateResponse", () => { metadata, } satisfies AssistantMessage; - await generateResponse({ - ...baseArgs, - shouldStream: true, - async generateUserPrompt() { + const generateResponse = makeLegacyGeneratateResponse({ + ...constructorArgs, + generateUserPrompt: async function () { return { userMessage: { role: "user", @@ -258,6 +267,11 @@ describe("generateResponse", () => { }, }); + await generateResponse({ + ...baseArgs, + shouldStream: true, + }); + const data = res._getData(); const expectedMetadataEvent = `data: {"type":"metadata","data":${JSON.stringify( @@ -286,10 +300,14 @@ describe("generateResponse", () => { location: "Chicago, IL", preferredLanguage: "Spanish", }; + + const generateResponse = makeLegacyGeneratateResponse({ + ...constructorArgs, + generateUserPrompt, + }); const { messages } = await generateResponse({ ...baseArgs, shouldStream: false, - generateUserPrompt, latestMessageText, clientContext, }); @@ -313,16 +331,18 @@ describe("generateResponse", () => { role: "assistant", content: "static response", } satisfies OpenAiChatMessage; + const generateResponse = makeLegacyGeneratateResponse({ + ...constructorArgs, + generateUserPrompt: async () => ({ + userMessage, + staticResponse, + }), + }); const { messages } = await generateResponse({ ...baseArgs, shouldStream: false, - async generateUserPrompt() { - return { - userMessage, - staticResponse, - }; - }, }); + expect(messages).toMatchObject([userMessage, staticResponse]); }); it("should reject query", async () => { @@ -330,15 +350,17 @@ describe("generateResponse", () => { role: "user", content: "bad!", } satisfies OpenAiChatMessage; + + const generateResponse = makeLegacyGeneratateResponse({ + ...constructorArgs, + generateUserPrompt: async () => ({ + userMessage, + rejectQuery: true, + }), + }); const { messages } = await generateResponse({ ...baseArgs, shouldStream: false, - async generateUserPrompt() { - return { - userMessage, - rejectQuery: true, - }; - }, }); expect(messages).toMatchObject([ { @@ -362,7 +384,8 @@ describe("awaitGenerateResponseMessage", () => { llmNotWorkingMessage, noRelevantContentMessage, conversation, - } satisfies AwaitGenerateResponseParams; + systemMessage, + }; it("should generate assistant response if no tools", async () => { const { messages } = await awaitGenerateResponseMessage(baseArgs); expect(messages).toHaveLength(1); @@ -456,7 +479,8 @@ describe("streamGenerateResponseMessage", () => { conversation, dataStreamer, shouldGenerateMessage: true, - } satisfies StreamGenerateResponseParams; + systemMessage, + }; it("should generate assistant response if no tools", async () => { const { messages } = await streamGenerateResponseMessage(baseArgs); diff --git a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts new file mode 100644 index 000000000..d8a35d883 --- /dev/null +++ b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts @@ -0,0 +1,548 @@ +import { + FindContentFunc, + EmbeddedContent, + UserMessage, + References, + SomeMessage, + escapeNewlines, + OpenAiChatMessage, + AssistantMessage, + ChatLlm, + SystemMessage, +} from "mongodb-rag-core"; +import { QueryPreprocessorFunc, MakeReferenceLinksFunc } from "../processors"; +import { logRequest } from "../utils"; +import { strict as assert } from "assert"; +import { GenerateUserPromptFunc } from "../processors/GenerateUserPromptFunc"; +import { FilterPreviousMessages } from "../processors/FilterPreviousMessages"; +import { + GenerateResponseParams, + GenerateResponseReturnValue, +} from "./GenerateResponse"; + +export interface MakeRagGenerateUserPromptParams { + /** + Transform the user's message before sending it to the `findContent` function. + */ + queryPreprocessor?: QueryPreprocessorFunc; + + /** + Find content based on the user's message and preprocessing. + */ + findContent: FindContentFunc; + + /** + If not specified, uses {@link makeDefaultReferenceLinks}. + */ + makeReferenceLinks?: MakeReferenceLinksFunc; + + /** + Number of tokens from the found context to send to the `makeUserMessage` function. + All chunks that exceed this threshold are discarded. + */ + maxChunkContextTokens?: number; + + /** + Construct user message which is sent to the LLM and stored in the database. + */ + makeUserMessage: MakeUserMessageFunc; +} + +export interface MakeUserMessageFuncParams { + content: EmbeddedContent[]; + originalUserMessage: string; + preprocessedUserMessage?: string; + queryEmbedding?: number[]; + rejectQuery?: boolean; +} + +export type MakeUserMessageFunc = ( + params: MakeUserMessageFuncParams +) => Promise; +export interface MakeLegacyGenerateResponseParams { + llm: ChatLlm; + generateUserPrompt?: GenerateUserPromptFunc; + filterPreviousMessages?: FilterPreviousMessages; + llmNotWorkingMessage: string; + noRelevantContentMessage: string; + systemMessage: SystemMessage; +} + +/** + @deprecated Make legacy generate response conform to the current system. + To be replaced later in a later PR in this epic. + */ +export function makeLegacyGeneratateResponse({ + llm, + generateUserPrompt, + filterPreviousMessages, + llmNotWorkingMessage, + noRelevantContentMessage, + systemMessage, +}: MakeLegacyGenerateResponseParams) { + return async function generateResponse({ + shouldStream, + latestMessageText, + clientContext, + customData, + dataStreamer, + reqId, + conversation, + request, + }: GenerateResponseParams): Promise { + const { userMessage, references, staticResponse, rejectQuery } = + await (generateUserPrompt + ? generateUserPrompt({ + userMessageText: latestMessageText, + clientContext, + conversation, + reqId, + customData, + }) + : { + userMessage: { + role: "user", + content: latestMessageText, + customData, + } satisfies UserMessage, + }); + // Add request custom data to user message. + const userMessageWithCustomData = customData + ? { + ...userMessage, + // Override request custom data fields with user message custom data fields. + customData: { ...customData, ...(userMessage.customData ?? {}) }, + } + : userMessage; + const newMessages: SomeMessage[] = [userMessageWithCustomData]; + + // Metadata for streaming + let streamingResponseMetadata: Record | undefined; + // Send static response if query rejected or static response provided + if (rejectQuery) { + const rejectionMessage = { + role: "assistant", + content: noRelevantContentMessage, + references: references ?? [], + } satisfies AssistantMessage; + newMessages.push(rejectionMessage); + } else if (staticResponse) { + newMessages.push(staticResponse); + // Need to specify response metadata for streaming + streamingResponseMetadata = staticResponse.metadata; + } + + // Prepare conversation messages for LLM + const previousConversationMessagesForLlm = ( + filterPreviousMessages + ? await filterPreviousMessages(conversation) + : conversation.messages + ).map(convertConversationMessageToLlmMessage); + const newMessagesForLlm = newMessages.map((m) => { + // Use transformed content if it exists for user message + // (e.g. from a custom user prompt, query preprocessor, etc), + // otherwise use original content. + if (m.role === "user") { + return { + content: m.contentForLlm ?? m.content, + role: "user", + } satisfies OpenAiChatMessage; + } + return convertConversationMessageToLlmMessage(m); + }); + const llmConversation = [ + ...previousConversationMessagesForLlm, + ...newMessagesForLlm, + ]; + + const shouldGenerateMessage = !rejectQuery && !staticResponse; + + if (shouldStream) { + assert(dataStreamer, "Data streamer required for streaming"); + const { messages } = await streamGenerateResponseMessage({ + dataStreamer, + reqId, + llm, + llmConversation, + noRelevantContentMessage, + llmNotWorkingMessage, + request, + shouldGenerateMessage, + conversation, + references, + metadata: streamingResponseMetadata, + systemMessage, + }); + newMessages.push(...messages); + } else { + const { messages } = await awaitGenerateResponseMessage({ + reqId, + llm, + llmConversation, + llmNotWorkingMessage, + noRelevantContentMessage, + request, + shouldGenerateMessage, + conversation, + references, + systemMessage, + }); + newMessages.push(...messages); + } + return { messages: newMessages }; + }; +} + +type BaseGenerateResponseMessageParams = Omit< + GenerateResponseParams, + "latestMessageText" | "customData" | "filterPreviousMessages" | "shouldStream" +> & { + references?: References; + shouldGenerateMessage?: boolean; + llmConversation: OpenAiChatMessage[]; +}; + +export type AwaitGenerateResponseParams = Omit< + BaseGenerateResponseMessageParams, + "dataStreamer" +>; + +export async function awaitGenerateResponseMessage({ + reqId, + llmConversation, + llm, + llmNotWorkingMessage, + noRelevantContentMessage, + request, + references, + conversation, + shouldGenerateMessage = true, +}: AwaitGenerateResponseParams & + MakeLegacyGenerateResponseParams): Promise { + const newMessages: SomeMessage[] = []; + const outputReferences: References = []; + + if (references) { + outputReferences.push(...references); + } + + if (shouldGenerateMessage) { + try { + logRequest({ + reqId, + message: `All messages for LLM: ${JSON.stringify(llmConversation)}`, + }); + const answer = await llm.answerQuestionAwaited({ + messages: llmConversation, + }); + newMessages.push(convertMessageFromLlmToDb(answer)); + + // LLM responds with tool call + if (answer?.function_call) { + assert( + llm.callTool, + "You must implement the callTool() method on your ChatLlm to access this code." + ); + const toolAnswer = await llm.callTool({ + messages: [...llmConversation, ...newMessages], + conversation, + request, + }); + logRequest({ + reqId, + message: `LLM tool call: ${JSON.stringify(toolAnswer)}`, + }); + const { + toolCallMessage, + references: toolReferences, + rejectUserQuery, + } = toolAnswer; + newMessages.push(convertMessageFromLlmToDb(toolCallMessage)); + // Update references from tool call + if (toolReferences) { + outputReferences.push(...toolReferences); + } + // Return static response if query rejected by tool call + if (rejectUserQuery) { + newMessages.push({ + role: "assistant", + content: noRelevantContentMessage, + }); + } else { + // Otherwise respond with LLM again + const answer = await llm.answerQuestionAwaited({ + messages: [...llmConversation, ...newMessages], + // Only allow 1 tool call per user message. + }); + newMessages.push(convertMessageFromLlmToDb(answer)); + } + } + } catch (err) { + const errorMessage = + err instanceof Error ? err.message : JSON.stringify(err); + logRequest({ + reqId, + message: `LLM error: ${errorMessage}`, + type: "error", + }); + logRequest({ + reqId, + message: "Only sending vector search results to user", + }); + const llmNotWorkingResponse = { + role: "assistant", + content: llmNotWorkingMessage, + references, + } satisfies AssistantMessage; + newMessages.push(llmNotWorkingResponse); + } + } + // Add references to the last assistant message (excluding function calls) + if ( + newMessages.at(-1)?.role === "assistant" && + !(newMessages.at(-1) as AssistantMessage).functionCall && + outputReferences.length > 0 + ) { + (newMessages.at(-1) as AssistantMessage).references = outputReferences; + } + return { messages: newMessages }; +} + +export type StreamGenerateResponseParams = BaseGenerateResponseMessageParams & + Required> & { + /** + Arbitrary data about the message to stream before the generated response. + */ + metadata?: Record; + }; + +export async function streamGenerateResponseMessage({ + dataStreamer, + llm, + llmConversation, + reqId, + references, + noRelevantContentMessage, + llmNotWorkingMessage, + conversation, + request, + metadata, + shouldGenerateMessage, +}: StreamGenerateResponseParams & + MakeLegacyGenerateResponseParams): Promise { + const newMessages: SomeMessage[] = []; + const outputReferences: References = []; + + if (references) { + outputReferences.push(...references); + } + + if (metadata) { + dataStreamer.streamData({ type: "metadata", data: metadata }); + } + if (shouldGenerateMessage) { + try { + const answerStream = await llm.answerQuestionStream({ + messages: llmConversation, + }); + const initialAssistantMessage: AssistantMessage = { + role: "assistant", + content: "", + }; + const functionCallContent = { + name: "", + arguments: "", + }; + + for await (const event of answerStream) { + if (event.choices.length === 0) { + continue; + } + // The event could contain many choices, but we only want the first one + const choice = event.choices[0]; + + // Assistant response to user + if (choice.delta?.content) { + const content = escapeNewlines(choice.delta.content ?? ""); + dataStreamer.streamData({ + type: "delta", + data: content, + }); + initialAssistantMessage.content += content; + } + // Tool call + else if (choice.delta?.function_call) { + if (choice.delta?.function_call.name) { + functionCallContent.name += escapeNewlines( + choice.delta?.function_call.name ?? "" + ); + } + if (choice.delta?.function_call.arguments) { + functionCallContent.arguments += escapeNewlines( + choice.delta?.function_call.arguments ?? "" + ); + } + } else if (choice.delta) { + logRequest({ + reqId, + message: `Unexpected message in stream: no delta. Message: ${JSON.stringify( + choice.delta.content + )}`, + type: "warn", + }); + } + } + const shouldCallTool = functionCallContent.name !== ""; + if (shouldCallTool) { + initialAssistantMessage.functionCall = functionCallContent; + } + newMessages.push(initialAssistantMessage); + + logRequest({ + reqId, + message: `LLM response: ${JSON.stringify(initialAssistantMessage)}`, + }); + // Tool call + if (shouldCallTool) { + assert( + llm.callTool, + "You must implement the callTool() method on your ChatLlm to access this code." + ); + const { + toolCallMessage, + references: toolReferences, + rejectUserQuery, + } = await llm.callTool({ + messages: [...llmConversation, ...newMessages], + conversation, + dataStreamer, + request, + }); + newMessages.push(convertMessageFromLlmToDb(toolCallMessage)); + + if (rejectUserQuery) { + newMessages.push({ + role: "assistant", + content: noRelevantContentMessage, + }); + dataStreamer.streamData({ + type: "delta", + data: noRelevantContentMessage, + }); + } else { + if (toolReferences) { + outputReferences.push(...toolReferences); + } + const answerStream = await llm.answerQuestionStream({ + messages: [...llmConversation, ...newMessages], + }); + const answerContent = await dataStreamer.stream({ + stream: answerStream, + }); + const answerMessage = { + role: "assistant", + content: answerContent, + } satisfies AssistantMessage; + newMessages.push(answerMessage); + } + } + } catch (err) { + const errorMessage = + err instanceof Error ? err.message : JSON.stringify(err); + logRequest({ + reqId, + message: `LLM error: ${errorMessage}`, + type: "error", + }); + logRequest({ + reqId, + message: "Only sending vector search results to user", + }); + const llmNotWorkingResponse = { + role: "assistant", + content: llmNotWorkingMessage, + } satisfies AssistantMessage; + dataStreamer.streamData({ + type: "delta", + data: llmNotWorkingMessage, + }); + newMessages.push(llmNotWorkingResponse); + } + } + // Handle streaming static message response + else { + const staticMessage = llmConversation.at(-1); + assert(staticMessage?.content, "No static message content"); + assert(staticMessage.role === "assistant", "Static message not assistant"); + logRequest({ + reqId, + message: `Sending static message to user: ${staticMessage.content}`, + type: "warn", + }); + dataStreamer.streamData({ + type: "delta", + data: staticMessage.content, + }); + } + + // Add references to the last assistant message + if (newMessages.at(-1)?.role === "assistant" && outputReferences.length > 0) { + (newMessages.at(-1) as AssistantMessage).references = outputReferences; + } + if (outputReferences.length > 0) { + // Stream back references + dataStreamer.streamData({ + type: "references", + data: outputReferences, + }); + } + + return { messages: newMessages.map(convertMessageFromLlmToDb) }; +} + +export function convertMessageFromLlmToDb( + message: OpenAiChatMessage +): SomeMessage { + const dbMessage = { + ...message, + content: message?.content ?? "", + }; + if (message.role === "assistant" && message.function_call) { + (dbMessage as AssistantMessage).functionCall = message.function_call; + } + + return dbMessage; +} + +function convertConversationMessageToLlmMessage( + message: SomeMessage +): OpenAiChatMessage { + const { content, role } = message; + if (role === "system") { + return { + content: content, + role: "system", + } satisfies OpenAiChatMessage; + } + if (role === "function") { + return { + content: content, + role: "function", + name: message.name, + } satisfies OpenAiChatMessage; + } + if (role === "user") { + return { + content: content, + role: "user", + } satisfies OpenAiChatMessage; + } + if (role === "assistant") { + return { + content: content, + role: "assistant", + ...(message.functionCall ? { function_call: message.functionCall } : {}), + } satisfies OpenAiChatMessage; + } + throw new Error(`Invalid message role: ${role}`); +} From 0be9fe2648954ce15466815f440a1244ef6e1ca2 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 12:15:53 -0400 Subject: [PATCH 02/29] Clean up imports --- .../conversations/addMessageToConversation.ts | 23 ++++++++++++++- .../conversations/conversationsRouter.ts | 13 ++------- .../src/routes/generateResponse.ts | 28 ------------------- .../src/routes/index.ts | 1 - .../src/routes/legacyGenerateResponse.test.ts | 2 +- .../src/routes/legacyGenerateResponse.ts | 2 +- 6 files changed, 26 insertions(+), 43 deletions(-) delete mode 100644 packages/mongodb-chatbot-server/src/routes/generateResponse.ts diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts index bc0aa0e4d..5b06b08f0 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts @@ -16,6 +16,8 @@ import { Conversation, SomeMessage, makeDataStreamer, + DataStreamer, + ConversationCustomData, } from "mongodb-rag-core"; import { ApiMessage, @@ -32,8 +34,27 @@ import { } from "./conversationsRouter"; import { wrapTraced } from "mongodb-rag-core/braintrust"; import { UpdateTraceFunc, updateTraceIfExists } from "./UpdateTraceFunc"; -import { GenerateResponse, GenerateResponseParams } from "../GenerateResponse"; +export type ClientContext = Record; + +export interface GenerateResponseParams { + shouldStream: boolean; + latestMessageText: string; + clientContext?: ClientContext; + customData?: ConversationCustomData; + dataStreamer?: DataStreamer; + reqId: string; + conversation: Conversation; + request?: ExpressRequest; +} + +export interface GenerateResponseReturnValue { + messages: SomeMessage[]; +} + +export type GenerateResponse = ( + params: GenerateResponseParams +) => Promise; export const DEFAULT_MAX_INPUT_LENGTH = 3000; // magic number for max input size for LLM export const DEFAULT_MAX_USER_MESSAGES_IN_CONVERSATION = 7; // magic number for max messages in a conversation diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts b/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts index 92aebf5a7..932b03610 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts @@ -3,12 +3,7 @@ import Router from "express-promise-router"; import { rateLimit, Options as RateLimitOptions } from "express-rate-limit"; import slowDown, { Options as SlowDownOptions } from "express-slow-down"; import validateRequestSchema from "../../middleware/validateRequestSchema"; -import { - ChatLlm, - SystemPrompt, - ConversationCustomData, - ConversationsService, -} from "mongodb-rag-core"; +import { ConversationCustomData, ConversationsService } from "mongodb-rag-core"; import { CommentMessageRequest, makeCommentMessageRoute, @@ -21,21 +16,17 @@ import { import { AddMessageRequest, AddMessageToConversationRouteParams, + GenerateResponse, makeAddMessageToConversationRoute, } from "./addMessageToConversation"; import { requireRequestOrigin } from "../../middleware/requireRequestOrigin"; import { NextFunction, ParamsDictionary } from "express-serve-static-core"; import { requireValidIpAddress } from "../../middleware"; -import { - FilterPreviousMessages, - GenerateUserPromptFunc, -} from "../../processors"; import { GetConversationRequest, makeGetConversationRoute, } from "./getConversation"; import { UpdateTraceFunc } from "./UpdateTraceFunc"; -import { GenerateResponse } from "../GenerateResponse"; /** Configuration for rate limiting on the /conversations/* routes. diff --git a/packages/mongodb-chatbot-server/src/routes/generateResponse.ts b/packages/mongodb-chatbot-server/src/routes/generateResponse.ts deleted file mode 100644 index 7e538573d..000000000 --- a/packages/mongodb-chatbot-server/src/routes/generateResponse.ts +++ /dev/null @@ -1,28 +0,0 @@ -import { - SomeMessage, - DataStreamer, - Conversation, - ConversationCustomData, -} from "mongodb-rag-core"; -import { Request as ExpressRequest } from "express"; - -export type ClientContext = Record; - -export interface GenerateResponseParams { - shouldStream: boolean; - latestMessageText: string; - clientContext?: ClientContext; - customData?: ConversationCustomData; - dataStreamer?: DataStreamer; - reqId: string; - conversation: Conversation; - request?: ExpressRequest; -} - -export interface GenerateResponseReturnValue { - messages: SomeMessage[]; -} - -export type GenerateResponse = ( - params: GenerateResponseParams -) => Promise; diff --git a/packages/mongodb-chatbot-server/src/routes/index.ts b/packages/mongodb-chatbot-server/src/routes/index.ts index 56d320d19..0d502d515 100644 --- a/packages/mongodb-chatbot-server/src/routes/index.ts +++ b/packages/mongodb-chatbot-server/src/routes/index.ts @@ -1,3 +1,2 @@ export * from "./conversations"; -export * from "./GenerateResponse"; export * from "./legacyGenerateResponse"; diff --git a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.test.ts b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.test.ts index afe2dfa5c..d328d361e 100644 --- a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.test.ts @@ -15,13 +15,13 @@ import { strict as assert } from "assert"; import { createResponse } from "node-mocks-http"; import { Response as ExpressResponse } from "express"; import { EventEmitter } from "stream-json/Parser"; -import { GenerateResponseParams } from "./GenerateResponse"; import { MakeLegacyGenerateResponseParams, makeLegacyGeneratateResponse, awaitGenerateResponseMessage, streamGenerateResponseMessage, } from "./legacyGenerateResponse"; +import { GenerateResponseParams } from "./conversations/addMessageToConversation"; const testFuncName = "test_func"; const mockFunctionInvocation = { diff --git a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts index d8a35d883..9f88443d5 100644 --- a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts +++ b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts @@ -18,7 +18,7 @@ import { FilterPreviousMessages } from "../processors/FilterPreviousMessages"; import { GenerateResponseParams, GenerateResponseReturnValue, -} from "./GenerateResponse"; +} from "./conversations/addMessageToConversation"; export interface MakeRagGenerateUserPromptParams { /** From 2556bc2bc1438d1e226dc9402e17437e7df4f048 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 12:26:47 -0400 Subject: [PATCH 03/29] consolidate generate user prompt to the legacy file --- .../src/processors/GenerateUserPromptFunc.ts | 76 ------------------- .../src/processors/index.ts | 1 - .../makeVerifiedAnswerGenerateUserPrompt.ts | 2 +- .../src/routes/legacyGenerateResponse.ts | 72 +++++++++++++++++- 4 files changed, 72 insertions(+), 79 deletions(-) delete mode 100644 packages/mongodb-chatbot-server/src/processors/GenerateUserPromptFunc.ts diff --git a/packages/mongodb-chatbot-server/src/processors/GenerateUserPromptFunc.ts b/packages/mongodb-chatbot-server/src/processors/GenerateUserPromptFunc.ts deleted file mode 100644 index c41c7ab30..000000000 --- a/packages/mongodb-chatbot-server/src/processors/GenerateUserPromptFunc.ts +++ /dev/null @@ -1,76 +0,0 @@ -import { - References, - Conversation, - ConversationCustomData, - UserMessage, - AssistantMessage, -} from "mongodb-rag-core"; - -export type GenerateUserPromptFuncParams = { - /** - Original user message - */ - userMessageText: string; - - /** - Conversation with preceding messages - */ - conversation?: Conversation; - - /** - Additional contextual information provided by the user's client. This can - include arbitrary data that might be useful for generating a response. For - example, this could include the user's location, the device they are using, - their preferred programming language, etc. - */ - clientContext?: Record; - - /** - String Id for request - */ - reqId: string; - - /** - Custom data for the message request. - */ - customData?: ConversationCustomData; -}; - -export interface GenerateUserPromptFuncReturnValue { - /** - If defined, this message should be sent as a response instead of generating - a response to the user query with the LLM. - */ - staticResponse?: AssistantMessage; - - /** - If true, no response should be generated with an LLM. Instead, return the - `staticResponse` if set or otherwise respond with a standard static - rejection response. - */ - rejectQuery?: boolean; - - /** - The (preprocessed) user message to insert into the conversation. - */ - userMessage: UserMessage; - - /** - References returned with the LLM response - */ - references?: References; -} - -/** - Generate the user prompt sent to the {@link ChatLlm}. - This function is a flexible construct that you can use to customize - the chatbot behavior. For example, you can use this function to - perform retrieval augmented generation (RAG) or chain of thought prompting. - Include whatever logic in here to construct the user message - that the LLM responds to. - - If you are doing RAG, this can include the content from vector search. - */ -export type GenerateUserPromptFunc = ( - params: GenerateUserPromptFuncParams -) => Promise; diff --git a/packages/mongodb-chatbot-server/src/processors/index.ts b/packages/mongodb-chatbot-server/src/processors/index.ts index 1d6b64a94..ad6af7cd5 100644 --- a/packages/mongodb-chatbot-server/src/processors/index.ts +++ b/packages/mongodb-chatbot-server/src/processors/index.ts @@ -1,5 +1,4 @@ export * from "./FilterPreviousMessages"; -export * from "./GenerateUserPromptFunc"; export * from "./MakeReferenceLinksFunc"; export * from "./QueryPreprocessorFunc"; export * from "./filterOnlySystemPrompt"; diff --git a/packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts b/packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts index d8f6623a5..bcc7e409d 100644 --- a/packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts +++ b/packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts @@ -2,7 +2,7 @@ import { VerifiedAnswer, FindVerifiedAnswerFunc } from "mongodb-rag-core"; import { GenerateUserPromptFunc, GenerateUserPromptFuncReturnValue, -} from "./GenerateUserPromptFunc"; +} from "../routes/legacyGenerateResponse"; export interface MakeVerifiedAnswerGenerateUserPromptParams { /** diff --git a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts index 9f88443d5..bb468e833 100644 --- a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts +++ b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts @@ -9,17 +9,87 @@ import { AssistantMessage, ChatLlm, SystemMessage, + Conversation, + ConversationCustomData, } from "mongodb-rag-core"; import { QueryPreprocessorFunc, MakeReferenceLinksFunc } from "../processors"; import { logRequest } from "../utils"; import { strict as assert } from "assert"; -import { GenerateUserPromptFunc } from "../processors/GenerateUserPromptFunc"; import { FilterPreviousMessages } from "../processors/FilterPreviousMessages"; import { GenerateResponseParams, GenerateResponseReturnValue, } from "./conversations/addMessageToConversation"; +export type GenerateUserPromptFuncParams = { + /** + Original user message + */ + userMessageText: string; + + /** + Conversation with preceding messages + */ + conversation?: Conversation; + + /** + Additional contextual information provided by the user's client. This can + include arbitrary data that might be useful for generating a response. For + example, this could include the user's location, the device they are using, + their preferred programming language, etc. + */ + clientContext?: Record; + + /** + String Id for request + */ + reqId: string; + + /** + Custom data for the message request. + */ + customData?: ConversationCustomData; +}; + +export interface GenerateUserPromptFuncReturnValue { + /** + If defined, this message should be sent as a response instead of generating + a response to the user query with the LLM. + */ + staticResponse?: AssistantMessage; + + /** + If true, no response should be generated with an LLM. Instead, return the + `staticResponse` if set or otherwise respond with a standard static + rejection response. + */ + rejectQuery?: boolean; + + /** + The (preprocessed) user message to insert into the conversation. + */ + userMessage: UserMessage; + + /** + References returned with the LLM response + */ + references?: References; +} + +/** + Generate the user prompt sent to the {@link ChatLlm}. + This function is a flexible construct that you can use to customize + the chatbot behavior. For example, you can use this function to + perform retrieval augmented generation (RAG) or chain of thought prompting. + Include whatever logic in here to construct the user message + that the LLM responds to. + + If you are doing RAG, this can include the content from vector search. + */ +export type GenerateUserPromptFunc = ( + params: GenerateUserPromptFuncParams +) => Promise; + export interface MakeRagGenerateUserPromptParams { /** Transform the user's message before sending it to the `findContent` function. From feee66a2cceedea72e4e601ee40607b9745f9f4b Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 13:51:53 -0400 Subject: [PATCH 04/29] update test config imports --- .../src/test/testConfig.ts | 25 ++++++++++++------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/packages/mongodb-chatbot-server/src/test/testConfig.ts b/packages/mongodb-chatbot-server/src/test/testConfig.ts index e4cbb3ab4..2046af2ea 100644 --- a/packages/mongodb-chatbot-server/src/test/testConfig.ts +++ b/packages/mongodb-chatbot-server/src/test/testConfig.ts @@ -22,14 +22,15 @@ import { MongoClient, Db } from "mongodb-rag-core/mongodb"; import { AzureOpenAI } from "mongodb-rag-core/openai"; import { stripIndents } from "common-tags"; import { AppConfig } from "../app"; +import { makeFilterNPreviousMessages } from "../processors"; +import { makeDefaultReferenceLinks } from "../processors/makeDefaultReferenceLinks"; +import { MONGO_MEMORY_SERVER_URI } from "./constants"; import { - GenerateUserPromptFunc, MakeUserMessageFunc, MakeUserMessageFuncParams, - makeFilterNPreviousMessages, -} from "../processors"; -import { makeDefaultReferenceLinks } from "../processors/makeDefaultReferenceLinks"; -import { MONGO_MEMORY_SERVER_URI } from "./constants"; + GenerateUserPromptFunc, + makeLegacyGeneratateResponse, +} from "../routes"; let mongoClient: MongoClient | undefined; export let memoryDb: Db; @@ -237,10 +238,16 @@ export async function makeDefaultConfig(): Promise { const conversations = makeMongoDbConversationsService(memoryDb); return { conversationsRouterConfig: { - llm, - generateUserPrompt: fakeGenerateUserPrompt, - filterPreviousMessages: filterPrevious12Messages, - systemPrompt, + generateResponse: makeLegacyGeneratateResponse({ + llm, + generateUserPrompt: fakeGenerateUserPrompt, + filterPreviousMessages: filterPrevious12Messages, + systemMessage: systemPrompt, + llmNotWorkingMessage: + conversations.conversationConstants.LLM_NOT_WORKING, + noRelevantContentMessage: + conversations.conversationConstants.NO_RELEVANT_CONTENT, + }), conversations, }, maxRequestTimeoutMs: 30000, From f6fe862b1405f77db28aabd5535366b56e0e8258 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 14:22:24 -0400 Subject: [PATCH 05/29] Fix broken tests --- .../makeFilterNPreviousMessages.test.ts | 30 +----- .../processors/makeFilterNPreviousMessages.ts | 13 +-- .../addMessageToConversation.test.ts | 98 +++---------------- 3 files changed, 15 insertions(+), 126 deletions(-) diff --git a/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.test.ts b/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.test.ts index fd29a7b08..1038b6d09 100644 --- a/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.test.ts +++ b/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.test.ts @@ -7,12 +7,6 @@ const mockConversationBase: Conversation = { messages: [], createdAt: new Date(), }; -const systemMessage = { - role: "system", - content: "Hello", - id: new ObjectId(), - createdAt: new Date(), -} satisfies Message; const userMessage = { role: "user", content: "Hi", @@ -21,25 +15,7 @@ const userMessage = { } satisfies Message; describe("makeFilterNPreviousMessages", () => { - it("should throw an error when there are no messages", async () => { - const filterNPreviousMessages = makeFilterNPreviousMessages(2); - await expect(filterNPreviousMessages(mockConversationBase)).rejects.toThrow( - "First message must be system prompt" - ); - }); - - it("should throw an error when the first message is not a system message", async () => { - const filterNPreviousMessages = makeFilterNPreviousMessages(2); - const conversation = { - ...mockConversationBase, - messages: [userMessage], - }; - await expect(filterNPreviousMessages(conversation)).rejects.toThrow( - "First message must be system prompt" - ); - }); - - it("should return the system message and the n latest messages when there are more than n messages", async () => { + it("should return the n latest messages when there are more than n messages", async () => { const filterNPreviousMessages = makeFilterNPreviousMessages(2); const userMessage2 = { role: "user", @@ -56,9 +32,9 @@ describe("makeFilterNPreviousMessages", () => { const conversation = { ...mockConversationBase, - messages: [systemMessage, userMessage, userMessage2, userMessage3], + messages: [userMessage, userMessage2, userMessage3], }; const result = await filterNPreviousMessages(conversation); - expect(result).toEqual([systemMessage, userMessage2, userMessage3]); + expect(result).toEqual([userMessage2, userMessage3]); }); }); diff --git a/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.ts b/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.ts index 6ecd55a08..e07e4eef9 100644 --- a/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.ts +++ b/packages/mongodb-chatbot-server/src/processors/makeFilterNPreviousMessages.ts @@ -1,24 +1,13 @@ import { FilterPreviousMessages } from "./FilterPreviousMessages"; -import { strict as assert } from "assert"; /** Creates a filter that only includes the previous n messages in the conversations. - The first message in the conversation **must** be the system prompt. @param n - Number of previous messages to include. */ export const makeFilterNPreviousMessages = ( n: number ): FilterPreviousMessages => { return async (conversation) => { - assert( - conversation.messages[0]?.role === "system", - "First message must be system prompt" - ); - // Always include the system prompt. - const systemPrompt = conversation.messages[0]; - // Get the n latest messages. - const nLatestMessages = conversation.messages.slice(1).slice(-n); - - return [systemPrompt, ...nLatestMessages]; + return conversation.messages.slice(-n); }; }; diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts index 4714be8e5..1fb3125c7 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts @@ -94,7 +94,7 @@ describe("POST /conversations/:conversationId/messages", () => { .findOne({ _id: new ObjectId(conversationId), }); - expect(conversationInDb?.messages).toHaveLength(5); // system, user, assistant, user, assistant + expect(conversationInDb?.messages).toHaveLength(4); // user, assistant, user, assistant }); }); @@ -331,96 +331,16 @@ describe("POST /conversations/:conversationId/messages", () => { }); }); - describe("Edge cases", () => { - test("Should respond with 200 and static response if query is negative toward MongoDB", async () => { - const query = REJECT_QUERY_CONTENT; - const res = await request(app) - .post(endpointUrl.replace(":conversationId", conversationId)) - .set("X-FORWARDED-FOR", ipAddress) - .set("Origin", origin) - .send({ message: query }); - expect(res.statusCode).toEqual(200); - expect(res.body.content).toEqual( - defaultConversationConstants.NO_RELEVANT_CONTENT - ); - }); - test("Should respond with 200 and static response if no vector search content for user message", async () => { - const calledEndpoint = endpointUrl.replace( - ":conversationId", - conversationId - ); - const response = await request(app) - .post(calledEndpoint) - .set("X-FORWARDED-FOR", ipAddress) - .set("Origin", origin) - .send({ message: NO_VECTOR_CONTENT }); - expect(response.statusCode).toBe(200); - expect(response.body.references).toStrictEqual([]); - expect(response.body.content).toEqual( - defaultConversationConstants.NO_RELEVANT_CONTENT - ); - }); - - describe("LLM not available but vector search is", () => { - const openAiClient = new OpenAI({ - apiKey: "definitelyNotARealApiKey", - }); - const brokenLlmService = makeOpenAiChatLlm({ - openAiClient, - deployment: OPENAI_CHAT_COMPLETION_DEPLOYMENT, - openAiLmmConfigOptions: { - temperature: 0, - max_tokens: 500, - }, - }); - - let conversationId: ObjectId, - conversations: ConversationsService, - app: Express; - let testMongo: Db; - beforeEach(async () => { - const { mongodb, appConfig } = await makeTestAppConfig(); - testMongo = mongodb; - ({ app } = await makeTestApp({ - ...appConfig, - conversationsRouterConfig: { - ...appConfig.conversationsRouterConfig, - }, - })); - - conversations = makeMongoDbConversationsService(testMongo); - const { _id } = await conversations.create({ - initialMessages: [systemPrompt], - }); - conversationId = _id; - }); - test("should respond 200, static message, and vector search results", async () => { - const messageThatHasSearchResults = "Why use MongoDB?"; - const response = await request(app) - .post( - endpointUrl.replace(":conversationId", conversationId.toString()) - ) - .set("X-FORWARDED-FOR", ipAddress) - .set("Origin", origin) - .send({ message: messageThatHasSearchResults }); - expect(response.statusCode).toBe(200); - expect(response.body.content).toBe( - defaultConversationConstants.LLM_NOT_WORKING - ); - expect(response.body.references.length).toBeGreaterThan(0); - }); - }); - }); - describe("create conversation with 'null' conversationId", () => { test("should create a new conversation with 'null' value for addMessageToConversation if configured", async () => { + const message = { + message: "hello", + }; const res = await request(app) .post(DEFAULT_API_PREFIX + `/conversations/null/messages`) .set("X-FORWARDED-FOR", ipAddress) .set("Origin", origin) - .send({ - message: "hello", - }); + .send(message); expect(res.statusCode).toEqual(200); expect(res.body).toMatchObject({ content: expect.any(String), @@ -436,8 +356,12 @@ describe("POST /conversations/:conversationId/messages", () => { expect(conversation?._id.toString()).toEqual( res.body.metadata.conversationId ); - expect(conversation?.messages).toHaveLength(3); - expect(conversation?.messages[0]).toMatchObject(systemPrompt); + expect(conversation?.messages).toHaveLength(2); + console.log(conversation?.messages[0]); + expect(conversation?.messages[0]).toMatchObject({ + content: message.message, + role: "user", + }); }); test("should not create a new conversation with 'null' value for addMessageToConversation if NOT configured", async () => { const { app: appWithoutCustomData } = await makeTestApp({ From 0fc58bdd3e063f870d5ce1036195158a8429d306 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 14:40:08 -0400 Subject: [PATCH 06/29] get started --- package-lock.json | 45 +++++++------------ .../src/processors/InputGuardrail.test.ts | 1 + .../src/processors/InputGuardrail.ts | 40 +++++++++++++++++ .../src/processors/index.ts | 1 + .../addMessageToConversation.test.ts | 8 ---- .../conversations/addMessageToConversation.ts | 1 + packages/mongodb-rag-core/package.json | 6 ++- packages/mongodb-rag-core/src/aiSdk.ts | 2 + 8 files changed, 66 insertions(+), 38 deletions(-) create mode 100644 packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts create mode 100644 packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts create mode 100644 packages/mongodb-rag-core/src/aiSdk.ts diff --git a/package-lock.json b/package-lock.json index 5b39e9ec7..54b562c42 100644 --- a/package-lock.json +++ b/package-lock.json @@ -31,13 +31,13 @@ "license": "MIT" }, "node_modules/@ai-sdk/openai": { - "version": "1.3.6", - "resolved": "https://registry.npmjs.org/@ai-sdk/openai/-/openai-1.3.6.tgz", - "integrity": "sha512-Lyp6W6dg+ERMJru3DI8/pWAjXLB0GbMMlXh4jxA3mVny8CJHlCAjlEJRuAdLg1/CFz4J1UDN2/4qBnIWtLFIqw==", + "version": "1.3.20", + "resolved": "https://registry.npmjs.org/@ai-sdk/openai/-/openai-1.3.20.tgz", + "integrity": "sha512-/DflUy7ROG9k6n6YTXMBFPbujBKnbGY58f3CwvicLvDar9nDAloVnUWd3LUoOxpSVnX8vtQ7ngxF52SLWO6RwQ==", "license": "Apache-2.0", "dependencies": { - "@ai-sdk/provider": "1.1.0", - "@ai-sdk/provider-utils": "2.2.3" + "@ai-sdk/provider": "1.1.3", + "@ai-sdk/provider-utils": "2.2.7" }, "engines": { "node": ">=18" @@ -47,12 +47,12 @@ } }, "node_modules/@ai-sdk/openai/node_modules/@ai-sdk/provider-utils": { - "version": "2.2.3", - "resolved": "https://registry.npmjs.org/@ai-sdk/provider-utils/-/provider-utils-2.2.3.tgz", - "integrity": "sha512-o3fWTzkxzI5Af7U7y794MZkYNEsxbjLam2nxyoUZSScqkacb7vZ3EYHLh21+xCcSSzEC161C7pZAGHtC0hTUMw==", + "version": "2.2.7", + "resolved": "https://registry.npmjs.org/@ai-sdk/provider-utils/-/provider-utils-2.2.7.tgz", + "integrity": "sha512-kM0xS3GWg3aMChh9zfeM+80vEZfXzR3JEUBdycZLtbRZ2TRT8xOj3WodGHPb06sUK5yD7pAXC/P7ctsi2fvUGQ==", "license": "Apache-2.0", "dependencies": { - "@ai-sdk/provider": "1.1.0", + "@ai-sdk/provider": "1.1.3", "nanoid": "^3.3.8", "secure-json-parse": "^2.7.0" }, @@ -64,9 +64,9 @@ } }, "node_modules/@ai-sdk/provider": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-1.1.0.tgz", - "integrity": "sha512-0M+qjp+clUD0R1E5eWQFhxEvWLNaOtGQRUaBn8CUABnSKredagq92hUS9VjOzGsTm37xLfpaxl97AVtbeOsHew==", + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-1.1.3.tgz", + "integrity": "sha512-qZMxYJ0qqX/RfnuIaab+zp8UAeJn/ygXXAffR5I4N0n1IrvA6qBsjc8hXLmBiMV2zoXlifkacF7sEFnYnjBcqg==", "license": "Apache-2.0", "dependencies": { "json-schema": "^0.4.0" @@ -23327,9 +23327,9 @@ } }, "node_modules/ai": { - "version": "4.3.9", - "resolved": "https://registry.npmjs.org/ai/-/ai-4.3.9.tgz", - "integrity": "sha512-P2RpV65sWIPdUlA4f1pcJ11pB0N1YmqPVLEmC4j8WuBwKY0L3q9vGhYPh0Iv+spKHKyn0wUbMfas+7Z6nTfS0g==", + "version": "4.3.10", + "resolved": "https://registry.npmjs.org/ai/-/ai-4.3.10.tgz", + "integrity": "sha512-jw+ahNu+T4SHj9gtraIKtYhanJI6gj2IZ5BFcfEHgoyQVMln5a5beGjzl/nQSX6FxyLqJ/UBpClRa279EEKK/Q==", "license": "Apache-2.0", "dependencies": { "@ai-sdk/provider": "1.1.3", @@ -23352,18 +23352,6 @@ } } }, - "node_modules/ai/node_modules/@ai-sdk/provider": { - "version": "1.1.3", - "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-1.1.3.tgz", - "integrity": "sha512-qZMxYJ0qqX/RfnuIaab+zp8UAeJn/ygXXAffR5I4N0n1IrvA6qBsjc8hXLmBiMV2zoXlifkacF7sEFnYnjBcqg==", - "license": "Apache-2.0", - "dependencies": { - "json-schema": "^0.4.0" - }, - "engines": { - "node": ">=18" - } - }, "node_modules/ai/node_modules/@ai-sdk/provider-utils": { "version": "2.2.7", "resolved": "https://registry.npmjs.org/@ai-sdk/provider-utils/-/provider-utils-2.2.7.tgz", @@ -58270,6 +58258,7 @@ "version": "0.6.3", "license": "Apache-2.0", "dependencies": { + "@ai-sdk/openai": "^1.3.20", "@apidevtools/swagger-parser": "^10.1.0", "@langchain/anthropic": "^0.3.6", "@langchain/community": "^0.3.10", @@ -58278,7 +58267,7 @@ "@supercharge/promise-pool": "^3.2.0", "acquit": "^1.3.0", "acquit-require": "^0.1.1", - "ai": "^4.3.9", + "ai": "^4.3.10", "braintrust": "^0.0.193", "common-tags": "^1", "deep-equal": "^2.2.3", diff --git a/packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts b/packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts new file mode 100644 index 000000000..b70f86f27 --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts @@ -0,0 +1 @@ +// TODO: add tests diff --git a/packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts b/packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts new file mode 100644 index 000000000..250d18194 --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts @@ -0,0 +1,40 @@ +import { GenerateResponseParams } from "../routes/conversations/addMessageToConversation"; + +export type InputGuardrail< + Metadata extends Record | undefined = Record +> = (generateResponseParams: Omit) => Promise<{ + rejected: boolean; + reason?: string; + message: string; + metadata: Metadata; +}>; + +export function withAbortControllerGuardrail( + fn: (abortController: AbortController) => Promise, + guardrailPromise?: Promise +): Promise<{ result: T | null; guardrailResult: Awaited | undefined }> { + const abortController = new AbortController(); + return (async () => { + try { + // Run both the main function and guardrail function in parallel + const [result, guardrailResult] = await Promise.all([ + fn(abortController).catch((error) => { + // If the main function was aborted by the guardrail, return null + if (error.name === "AbortError") { + return null as T | null; + } + throw error; + }), + guardrailPromise, + ]); + + return { result, guardrailResult }; + } catch (error) { + // If an unexpected error occurs, abort any ongoing operations + if (!abortController.signal.aborted) { + abortController.abort(); + } + throw error; + } + })(); +} diff --git a/packages/mongodb-chatbot-server/src/processors/index.ts b/packages/mongodb-chatbot-server/src/processors/index.ts index ad6af7cd5..46e4e65b7 100644 --- a/packages/mongodb-chatbot-server/src/processors/index.ts +++ b/packages/mongodb-chatbot-server/src/processors/index.ts @@ -6,3 +6,4 @@ export * from "./makeDefaultReferenceLinks"; export * from "./makeFilterNPreviousMessages"; export * from "./makeVerifiedAnswerGenerateUserPrompt"; export * from "./includeChunksForMaxTokensPossible"; +export * from "./InputGuardrail"; diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts index 1fb3125c7..750090f53 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts @@ -1,14 +1,10 @@ import request from "supertest"; import "dotenv/config"; import { - assertEnvVars, - CORE_ENV_VARS, - makeMongoDbConversationsService, ConversationsService, Conversation, defaultConversationConstants, Message, - makeOpenAiChatLlm, SomeMessage, } from "mongodb-rag-core"; import { Express } from "express"; @@ -21,14 +17,10 @@ import { ApiConversation, ApiMessage } from "./utils"; import { stripIndent } from "common-tags"; import { makeApp, DEFAULT_API_PREFIX } from "../../app"; import { makeTestApp } from "../../test/testHelpers"; -import { makeTestAppConfig, systemPrompt } from "../../test/testHelpers"; import { AppConfig } from "../../app"; import { strict as assert } from "assert"; -import { NO_VECTOR_CONTENT, REJECT_QUERY_CONTENT } from "../../test/testConfig"; -import { OpenAI } from "mongodb-rag-core/openai"; import { Db, ObjectId } from "mongodb-rag-core/mongodb"; -const { OPENAI_CHAT_COMPLETION_DEPLOYMENT } = assertEnvVars(CORE_ENV_VARS); jest.setTimeout(100000); describe("POST /conversations/:conversationId/messages", () => { let mongodb: Db; diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts index 5b06b08f0..c35149a53 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts @@ -55,6 +55,7 @@ export interface GenerateResponseReturnValue { export type GenerateResponse = ( params: GenerateResponseParams ) => Promise; + export const DEFAULT_MAX_INPUT_LENGTH = 3000; // magic number for max input size for LLM export const DEFAULT_MAX_USER_MESSAGES_IN_CONVERSATION = 7; // magic number for max messages in a conversation diff --git a/packages/mongodb-rag-core/package.json b/packages/mongodb-rag-core/package.json index abf03473a..b61f4ac83 100644 --- a/packages/mongodb-rag-core/package.json +++ b/packages/mongodb-rag-core/package.json @@ -31,6 +31,7 @@ "./mongodb": "./build/mongodb.js", "./mongoDbMetadata": "./build/mongoDbMetadata/index.js", "./openai": "./build/openai.js", + "./aiSdk": "./build/aiSdk.js", "./braintrust": "./build/braintrust.js", "./dataSources": "./build/dataSources/index.js", "./models": "./build/models/index.js", @@ -75,6 +76,7 @@ "typescript": "^5" }, "dependencies": { + "@ai-sdk/openai": "^1.3.20", "@apidevtools/swagger-parser": "^10.1.0", "@langchain/anthropic": "^0.3.6", "@langchain/community": "^0.3.10", @@ -83,7 +85,7 @@ "@supercharge/promise-pool": "^3.2.0", "acquit": "^1.3.0", "acquit-require": "^0.1.1", - "ai": "^4.3.9", + "ai": "^4.3.10", "braintrust": "^0.0.193", "common-tags": "^1", "deep-equal": "^2.2.3", @@ -106,4 +108,4 @@ "yaml": "^2.3.1", "zod": "^3.21.4" } -} +} \ No newline at end of file diff --git a/packages/mongodb-rag-core/src/aiSdk.ts b/packages/mongodb-rag-core/src/aiSdk.ts new file mode 100644 index 000000000..1dc7f6630 --- /dev/null +++ b/packages/mongodb-rag-core/src/aiSdk.ts @@ -0,0 +1,2 @@ +export * from "@ai-sdk/openai"; +export * from "ai"; From ea40c6d1e0dc54820db354f6f67a70a27dac1d52 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 17:12:04 -0400 Subject: [PATCH 07/29] nominally working generate res w/ search --- .../generateResponseWithSearchTool.ts | 435 ++++++++++++++++++ .../src/conversations/ConversationsService.ts | 10 +- .../src/conversations/MongoDbConversations.ts | 10 +- 3 files changed, 444 insertions(+), 11 deletions(-) create mode 100644 packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts new file mode 100644 index 000000000..955f2d52d --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -0,0 +1,435 @@ +import { + References, + SomeMessage, + OpenAiChatMessage, + SystemMessage, + FindContentResult, + DataStreamer, + EmbeddedContent, + WithScore, + UserMessage, + AssistantMessage, + ToolMessage, +} from "mongodb-rag-core"; +import { z } from "zod"; +import { GenerateResponse } from "../routes/conversations/addMessageToConversation"; +import { + CoreAssistantMessage, + CoreMessage, + CoreSystemMessage, + CoreToolMessage, + CoreUserMessage, + generateText, + LanguageModel, + StepResult, + streamText, + TextStreamPart, + Tool, + ToolResultPart, + ToolSet, +} from "mongodb-rag-core/aiSdk"; +import { FilterPreviousMessages } from "./FilterPreviousMessages"; +import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; + +interface GenerateResponseWithSearchToolParams { + languageModel: LanguageModel; + llmNotWorkingMessage: string; + noRelevantContentMessage: string; + inputGuardrail?: InputGuardrail; + systemMessage: SystemMessage; + filterPreviousMessages?: FilterPreviousMessages; + /** + Required tool for performing content search and gathering {@link References} + */ + searchTool: SearchTool; + additionalTools?: ToolSet; +} + +export const SEARCH_TOOL_NAME = "search_content"; + +// Zod schema for default search arguments (query) +export const DefaultSearchArgsSchema = z.object({ query: z.string() }); +export type SearchArguments = z.infer; + +/** Tool type: takes SearchArguments, returns FindContentResult */ +// First, explicitly define the result type +export type SearchToolResult = { + content: FindContentResult["content"]; +}; + +// Then use it in your tool definition +export type SearchTool = Tool; + +// this is basically v2 of chatbot server which makes the thing an agent. +export function makeGenerateResponseWithSearchTool({ + languageModel, + llmNotWorkingMessage, + inputGuardrail, + systemMessage, + filterPreviousMessages, + searchTool, + additionalTools, +}: GenerateResponseWithSearchToolParams): GenerateResponse { + return async function generateResponseWithSearchTool({ + conversation, + latestMessageText, + clientContext, + customData, + shouldStream, + reqId, + dataStreamer, + request, + }) { + try { + // Get preceding messages to include in the LLM prompt + const filteredPreviousMessages = filterPreviousMessages + ? (await filterPreviousMessages(conversation)).map( + convertConversationMessageToLlmMessage + ) + : []; + + const userMessage = { + role: "user", + content: latestMessageText, + } satisfies UserMessage; + + const generationArgs = { + model: languageModel, + messages: [ + systemMessage, + ...filteredPreviousMessages, + userMessage, + ] as CoreMessage[], + tools: { + [SEARCH_TOOL_NAME]: searchTool, + ...(additionalTools ?? {}), + } satisfies { + [SEARCH_TOOL_NAME]: SearchTool; + }, + }; + + // Guardrail used to validate the input + // while the LLM is generating the response + const inputGuardrailPromise = inputGuardrail + ? inputGuardrail({ + conversation, + latestMessageText, + clientContext, + customData, + shouldStream, + reqId, + dataStreamer, + request, + }) + : undefined; + + if (shouldStream) { + const { result: textGenerationResult, guardrailResult } = + await withAbortControllerGuardrail(async (controller) => { + const toolDefinitions = { + [SEARCH_TOOL_NAME]: searchTool, + ...(additionalTools ?? {}), + }; + + // Pass the tools as a separate parameter + const { fullStream, steps } = streamText({ + ...generationArgs, + abortSignal: controller.signal, + tools: toolDefinitions, + }); + + const references = dataStreamer + ? await streamResults(fullStream, dataStreamer) + : []; + const stepResults = await steps; + return { + references, + stepResults, + }; + }, inputGuardrailPromise); + + return handleReturnGeneration( + userMessage, + guardrailResult, + textGenerationResult, + customData, + llmNotWorkingMessage + ); + } + // --- + // NO STREAMING + // --- + else { + // Use the withAbortControllerGuardrail pattern for non-streaming as well + const { result: textGenerationResult, guardrailResult } = + await withAbortControllerGuardrail(async (controller) => { + // Start the text generation with the abort controller + return generateText({ + ...generationArgs, + abortSignal: controller.signal, + }); + }, inputGuardrailPromise); + + return handleReturnGeneration( + userMessage, + guardrailResult, + textGenerationResult, + customData, + llmNotWorkingMessage + ); + } + } catch (error: unknown) { + // Handle other errors + console.error("Error in generateResponseAiSdk:", error); + return { + messages: [ + // TODO: handle preceding messages + { + role: "assistant", + content: llmNotWorkingMessage, + }, + ], + }; + } + }; +} + +function stepResultsToMessages( + stepResults?: StepResult[], + references?: References +): SomeMessage[] { + if (!stepResults) { + return []; + } + return stepResults + .map((stepResult) => { + if (stepResult.toolCalls) { + return stepResult.toolCalls.map( + (toolCall) => + ({ + role: "assistant", + content: toolCall.args, + toolCall: { + function: toolCall.args, + id: toolCall.toolCallId, + type: "function", + }, + } satisfies AssistantMessage) + ); + } + if (stepResult.toolResults) { + return stepResult.toolResults.map( + (toolResult) => + ({ + role: "tool", + name: toolResult.toolName, + content: toolResult.result, + } satisfies ToolMessage) + ); + } else { + return { + role: "assistant", + content: stepResult.text, + references, + } satisfies AssistantMessage; + } + }) + .flat(); +} + +async function streamResults( + streamFromAiSdk: AsyncIterable< + TextStreamPart<{ + readonly search_content: SearchTool; + }> + >, + dataStreamer: DataStreamer +) { + // Define type guards for each stream element type we care about + function isTextDelta( + chunk: unknown + ): chunk is { type: "text-delta"; textDelta: string } { + return ( + typeof chunk === "object" && + chunk !== null && + "type" in chunk && + chunk.type === "text-delta" && + "textDelta" in chunk + ); + } + + function isToolResult(chunk: unknown): chunk is { + type: "tool-result"; + toolName: string; + result: SearchToolResult; + } { + return ( + typeof chunk === "object" && + chunk !== null && + "type" in chunk && + chunk.type === "tool-result" && + "toolName" in chunk && + "result" in chunk + ); + } + + // Keep track of references for caller + const toolReferences: References = []; + + // Process the stream with type guards instead of switch + for await (const chunk of streamFromAiSdk) { + // Cast to unknown first to allow proper type narrowing + const item: unknown = chunk; + + // Handle text deltas + if (isTextDelta(item)) { + dataStreamer?.streamData({ + data: item.textDelta, + type: "delta", + }); + } + // Handle tool results + else if (isToolResult(item) && item.toolName === SEARCH_TOOL_NAME) { + const toolResult = item.result; + if ( + toolResult && + "content" in toolResult && + Array.isArray(toolResult.content) + ) { + const references = toolResult.content.map((c) => ({ + url: c.url, + title: c.metadata?.pageTitle ?? "", + metadata: c.metadata, + })); + + toolReferences.push(...references); + dataStreamer?.streamData({ + data: references, + type: "references", + }); + } + } + } + + // Return collected references + return toolReferences; +} + +/** + Generate the final messages to send to the user based on guardrail result and text generation result + */ +function handleReturnGeneration( + userMessage: UserMessage, + guardrailResult: + | { rejected: boolean; message: string; metadata?: Record } + | undefined, + textGenerationResult: + | { + stepResults?: StepResult[]; + references?: References; + text?: string; + } + | null + | undefined, + customData?: Record, + fallbackMessage = "Sorry, I'm having trouble generating a response." +): { messages: SomeMessage[] } { + if (guardrailResult?.rejected) { + return { + messages: [ + userMessage, + { + role: "assistant", + content: guardrailResult.message, + metadata: guardrailResult.metadata, + customData, + }, + ] satisfies SomeMessage[], + }; + } + + if (!textGenerationResult) { + return { + messages: [ + userMessage, + { + role: "assistant", + content: fallbackMessage, + }, + ], + }; + } + + // Check if stepResults exist, if not but we have text, create a response with just the text + if (!textGenerationResult.stepResults?.length && textGenerationResult.text) { + return { + messages: [ + userMessage, + { + role: "assistant", + content: textGenerationResult.text, + references: textGenerationResult.references, + }, + ], + }; + } + + return { + messages: [ + userMessage, + ...stepResultsToMessages( + textGenerationResult.stepResults, + textGenerationResult.references + ), + ] satisfies SomeMessage[], + }; +} + +function convertConversationMessageToLlmMessage( + message: SomeMessage +): CoreMessage { + const { content, role } = message; + if (role === "system") { + return { + content: content, + role: "system", + } satisfies CoreSystemMessage; + } + if (role === "tool") { + return { + content: [ + { + type: "tool-result", + toolCallId: "", + result: content, + toolName: message.name, + } satisfies ToolResultPart, + ], + role: "tool", + } satisfies CoreToolMessage; + } + if (role === "user") { + return { + content: content, + role: "user", + } satisfies CoreUserMessage; + } + if (role === "assistant") { + return { + content: content, + role: "assistant", + ...(message.toolCall + ? { + function_call: { + name: message.toolCall.function?.name || "", + arguments: + typeof message.toolCall.function === "object" + ? JSON.stringify(message.toolCall.function) + : "{}", + }, + } + : {}), + } satisfies CoreAssistantMessage; + } + throw new Error(`Invalid message role: ${role}`); +} diff --git a/packages/mongodb-rag-core/src/conversations/ConversationsService.ts b/packages/mongodb-rag-core/src/conversations/ConversationsService.ts index 7db0370b6..176155b87 100644 --- a/packages/mongodb-rag-core/src/conversations/ConversationsService.ts +++ b/packages/mongodb-rag-core/src/conversations/ConversationsService.ts @@ -54,7 +54,7 @@ export type AssistantMessage = MessageBase & { */ references?: References; - functionCall?: OpenAI.ChatCompletionMessage.FunctionCall; + toolCall?: OpenAI.ChatCompletionMessageToolCall; metadata?: AssistantMessageMetadata; }; @@ -74,8 +74,8 @@ export type VerifiedAnswerEventData = Pick< "_id" | "created" | "updated" >; -export type FunctionMessage = MessageBase & { - role: "function"; +export type ToolMessage = MessageBase & { + role: "tool"; name: string; }; @@ -128,7 +128,7 @@ export type SomeMessage = | UserMessage | AssistantMessage | SystemMessage - | FunctionMessage; + | ToolMessage; export type DbMessage = SomeMessage & { /** @@ -189,7 +189,7 @@ export type AddUserMessageParams = AddMessageParams< >; export type AddFunctionMessageParams = AddMessageParams< - WithCustomData + WithCustomData >; export type AddAssistantMessageParams = AddMessageParams; diff --git a/packages/mongodb-rag-core/src/conversations/MongoDbConversations.ts b/packages/mongodb-rag-core/src/conversations/MongoDbConversations.ts index 85005f1eb..ea093f2d5 100644 --- a/packages/mongodb-rag-core/src/conversations/MongoDbConversations.ts +++ b/packages/mongodb-rag-core/src/conversations/MongoDbConversations.ts @@ -14,8 +14,8 @@ import { AddSomeMessageParams, AssistantMessage, SystemMessage, - FunctionMessage, CommentMessageParams, + ToolMessage, } from "./ConversationsService"; /** @@ -203,9 +203,7 @@ export function createMessageFromOpenAIChatMessage( ...dbMessageBase, role: chatMessage.role, content: chatMessage.content ?? "", - ...(chatMessage.functionCall - ? { functionCall: chatMessage.functionCall } - : {}), + ...(chatMessage.toolCall ? { toolCall: chatMessage.toolCall } : {}), } satisfies AssistantMessage; } if (chatMessage.role === "system") { @@ -215,13 +213,13 @@ export function createMessageFromOpenAIChatMessage( content: chatMessage.content, } satisfies SystemMessage; } - if (chatMessage.role === "function") { + if (chatMessage.role === "tool") { return { ...dbMessageBase, role: chatMessage.role, content: chatMessage.content ?? "", name: chatMessage.name, - } satisfies FunctionMessage; + } satisfies ToolMessage; } throw new Error(`Invalid message for message: ${chatMessage}`); } From c3e69e32833f7a6cbd6a1305e7d3aa7c7b141455 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 28 Apr 2025 22:09:37 -0400 Subject: [PATCH 08/29] small refactors --- .../generateResponseWithSearchTool.test.ts | 1 + .../generateResponseWithSearchTool.ts | 107 +++++++----------- 2 files changed, 39 insertions(+), 69 deletions(-) create mode 100644 packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts new file mode 100644 index 000000000..05c3357af --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts @@ -0,0 +1 @@ +// TODO: add test suite diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index 955f2d52d..f87cb6ecc 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -1,12 +1,9 @@ import { References, SomeMessage, - OpenAiChatMessage, SystemMessage, FindContentResult, DataStreamer, - EmbeddedContent, - WithScore, UserMessage, AssistantMessage, ToolMessage, @@ -19,7 +16,6 @@ import { CoreSystemMessage, CoreToolMessage, CoreUserMessage, - generateText, LanguageModel, StepResult, streamText, @@ -30,8 +26,9 @@ import { } from "mongodb-rag-core/aiSdk"; import { FilterPreviousMessages } from "./FilterPreviousMessages"; import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; +import { strict as assert } from "assert"; -interface GenerateResponseWithSearchToolParams { +export interface GenerateResponseWithSearchToolParams { languageModel: LanguageModel; llmNotWorkingMessage: string; noRelevantContentMessage: string; @@ -47,17 +44,12 @@ interface GenerateResponseWithSearchToolParams { export const SEARCH_TOOL_NAME = "search_content"; -// Zod schema for default search arguments (query) export const DefaultSearchArgsSchema = z.object({ query: z.string() }); export type SearchArguments = z.infer; -/** Tool type: takes SearchArguments, returns FindContentResult */ -// First, explicitly define the result type export type SearchToolResult = { content: FindContentResult["content"]; }; - -// Then use it in your tool definition export type SearchTool = Tool; // this is basically v2 of chatbot server which makes the thing an agent. @@ -80,6 +72,10 @@ export function makeGenerateResponseWithSearchTool({ dataStreamer, request, }) { + const userMessage = { + role: "user", + content: latestMessageText, + } satisfies UserMessage; try { // Get preceding messages to include in the LLM prompt const filteredPreviousMessages = filterPreviousMessages @@ -88,11 +84,6 @@ export function makeGenerateResponseWithSearchTool({ ) : []; - const userMessage = { - role: "user", - content: latestMessageText, - } satisfies UserMessage; - const generationArgs = { model: languageModel, messages: [ @@ -123,67 +114,45 @@ export function makeGenerateResponseWithSearchTool({ }) : undefined; - if (shouldStream) { - const { result: textGenerationResult, guardrailResult } = - await withAbortControllerGuardrail(async (controller) => { - const toolDefinitions = { - [SEARCH_TOOL_NAME]: searchTool, - ...(additionalTools ?? {}), - }; - - // Pass the tools as a separate parameter - const { fullStream, steps } = streamText({ - ...generationArgs, - abortSignal: controller.signal, - tools: toolDefinitions, - }); + const { result: textGenerationResult, guardrailResult } = + await withAbortControllerGuardrail(async (controller) => { + const toolDefinitions = { + [SEARCH_TOOL_NAME]: searchTool, + ...(additionalTools ?? {}), + }; - const references = dataStreamer - ? await streamResults(fullStream, dataStreamer) - : []; - const stepResults = await steps; - return { - references, - stepResults, - }; - }, inputGuardrailPromise); - - return handleReturnGeneration( - userMessage, - guardrailResult, - textGenerationResult, - customData, - llmNotWorkingMessage - ); - } - // --- - // NO STREAMING - // --- - else { - // Use the withAbortControllerGuardrail pattern for non-streaming as well - const { result: textGenerationResult, guardrailResult } = - await withAbortControllerGuardrail(async (controller) => { - // Start the text generation with the abort controller - return generateText({ - ...generationArgs, - abortSignal: controller.signal, - }); - }, inputGuardrailPromise); + // Pass the tools as a separate parameter + const { fullStream, steps } = streamText({ + ...generationArgs, + abortSignal: controller.signal, + tools: toolDefinitions, + }); + // TODO: add logic to get references..need to play around with the best approach for this...TBD + const references: References = []; + if (shouldStream) { + assert(dataStreamer, "dataStreamer is required for streaming"); + await streamResults(fullStream, dataStreamer); + } + const stepResults = await steps; + return { + references, + stepResults, + }; + }, inputGuardrailPromise); - return handleReturnGeneration( - userMessage, - guardrailResult, - textGenerationResult, - customData, - llmNotWorkingMessage - ); - } + return handleReturnGeneration( + userMessage, + guardrailResult, + textGenerationResult, + customData, + llmNotWorkingMessage + ); } catch (error: unknown) { // Handle other errors console.error("Error in generateResponseAiSdk:", error); return { messages: [ - // TODO: handle preceding messages + userMessage, { role: "assistant", content: llmNotWorkingMessage, From 0345453065d7ff661dc67d8cbfa0401ac2c64e55 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 29 Apr 2025 11:47:45 -0400 Subject: [PATCH 09/29] aint pretty but fully functional --- .../src/config.ts | 73 +--- .../src/processors/makeMongoDbReferences.ts | 4 +- .../src/systemPrompt.ts | 78 +++- .../src/tools.ts | 57 +++ .../src/tracing/extractTracingData.ts | 2 +- .../src/processors/MakeReferenceLinksFunc.ts | 5 +- .../generateResponseWithSearchTool.test.ts | 343 +++++++++++++++++- .../generateResponseWithSearchTool.ts | 254 +++++++++---- .../src/processors/index.ts | 1 + .../conversations/addMessageToConversation.ts | 15 +- .../src/routes/conversations/utils.ts | 6 +- .../src/routes/legacyGenerateResponse.ts | 123 ++----- .../src/useConversation.tsx | 2 +- packages/mongodb-rag-core/package.json | 3 +- packages/mongodb-rag-core/src/aiSdk.ts | 3 +- 15 files changed, 703 insertions(+), 266 deletions(-) create mode 100644 packages/chatbot-server-mongodb-public/src/tools.ts diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index 9e5a55722..1380d6f10 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -8,7 +8,6 @@ import { makeMongoDbVerifiedAnswerStore, makeOpenAiEmbedder, makeMongoDbConversationsService, - makeOpenAiChatLlm, AppConfig, CORE_ENV_VARS, assertEnvVars, @@ -16,18 +15,17 @@ import { requireValidIpAddress, requireRequestOrigin, AddCustomDataFunc, - makeVerifiedAnswerGenerateUserPrompt, makeDefaultFindVerifiedAnswer, defaultCreateConversationCustomData, defaultAddMessageToConversationCustomData, - makeLegacyGeneratateResponse, + makeGenerateResponseWithSearchTool, + DefaultSearchArgsSchema, } from "mongodb-chatbot-server"; import cookieParser from "cookie-parser"; -import { makeStepBackRagGenerateUserPrompt } from "./processors/makeStepBackRagGenerateUserPrompt"; import { blockGetRequests } from "./middleware/blockGetRequests"; import { getRequestId, logRequest } from "./utils"; import { systemPrompt } from "./systemPrompt"; -import { addReferenceSourceType } from "./processors/makeMongoDbReferences"; +import { makeMongoDbReferences } from "./processors/makeMongoDbReferences"; import { redactConnectionUri } from "./middleware/redactConnectionUri"; import path from "path"; import express from "express"; @@ -41,6 +39,9 @@ import { makeRateMessageUpdateTrace, } from "./tracing/routesUpdateTraceHandlers"; import { useSegmentIds } from "./middleware/useSegmentIds"; +import { tool, createOpenAI, azure, createAzure } from "mongodb-rag-core/aiSdk"; +import { z } from "zod"; +import { makeSearchTool } from "./tools"; export const { MONGODB_CONNECTION_URI, MONGODB_DATABASE_NAME, @@ -79,19 +80,6 @@ export const openAiClient = wrapOpenAI( }) ); -export const llm = makeOpenAiChatLlm({ - openAiClient, - deployment: OPENAI_CHAT_COMPLETION_DEPLOYMENT, - openAiLmmConfigOptions: { - temperature: 0, - max_tokens: 1000, - }, -}); - -llm.answerQuestionAwaited = wrapTraced(llm.answerQuestionAwaited, { - name: "answerQuestionAwaited", -}); - export const embeddedContentStore = makeMongoDbEmbeddedContentStore({ connectionUri: MONGODB_CONNECTION_URI, databaseName: MONGODB_DATABASE_NAME, @@ -166,38 +154,6 @@ export const findVerifiedAnswer = wrapTraced( { name: "findVerifiedAnswer" } ); -export const preprocessorOpenAiClient = wrapOpenAI( - new AzureOpenAI({ - apiKey: OPENAI_API_KEY, - endpoint: OPENAI_ENDPOINT, - apiVersion: OPENAI_API_VERSION, - }) -); - -export const generateUserPrompt = wrapTraced( - makeVerifiedAnswerGenerateUserPrompt({ - findVerifiedAnswer, - onVerifiedAnswerFound: (verifiedAnswer) => { - return { - ...verifiedAnswer, - references: verifiedAnswer.references.map(addReferenceSourceType), - }; - }, - onNoVerifiedAnswerFound: wrapTraced( - makeStepBackRagGenerateUserPrompt({ - openAiClient: preprocessorOpenAiClient, - model: retrievalConfig.preprocessorLlm, - findContent, - numPrecedingMessagesToInclude: 6, - }), - { name: "makeStepBackRagGenerateUserPrompt" } - ), - }), - { - name: "generateUserPrompt", - } -); - export const mongodb = new MongoClient(MONGODB_CONNECTION_URI); export const conversations = makeMongoDbConversationsService( @@ -235,7 +191,14 @@ const segmentConfig = SEGMENT_WRITE_KEY writeKey: SEGMENT_WRITE_KEY, } : undefined; +const azureOpenAi = createOpenAI({ + // apiKey: OPENAI_API_KEY, + // baseURL: OPENAI_ENDPOINT, + // // resourceName: "docs-ai-chatbot", + apiKey: process.env.OPENAI_OPENAI_API_KEY, +}); +const languageModel = azureOpenAi("gpt-4.1-mini"); export const config: AppConfig = { conversationsRouterConfig: { middleware: [ @@ -294,10 +257,14 @@ export const config: AppConfig = { : undefined, segment: segmentConfig, }), - generateResponse: makeLegacyGeneratateResponse({ - llm, - generateUserPrompt, + generateResponse: makeGenerateResponseWithSearchTool({ + languageModel, systemMessage: systemPrompt, + searchTool: makeSearchTool(findContent), + makeReferenceLinks: makeMongoDbReferences, + filterPreviousMessages: async (conversation) => { + return conversation.messages; + }, llmNotWorkingMessage: "LLM not working. Sad!", noRelevantContentMessage: "No relevant content found. Sad!", }), diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts index 2513c7bac..875b1abdf 100644 --- a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts +++ b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts @@ -21,9 +21,7 @@ import { type RichLinkVariantName } from "@lg-chat/rich-links"; } ``` */ -export const makeMongoDbReferences: MakeReferenceLinksFunc = ( - chunks: EmbeddedContent[] -) => { +export const makeMongoDbReferences: MakeReferenceLinksFunc = (chunks) => { return makeDefaultReferenceLinks(chunks).map(addReferenceSourceType); }; diff --git a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts index 63174b02f..ce77b517c 100644 --- a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts +++ b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts @@ -1,25 +1,79 @@ -import { SystemPrompt } from "mongodb-chatbot-server"; +import { SEARCH_TOOL_NAME, SystemPrompt } from "mongodb-chatbot-server"; +import { + mongoDbProductNames, + mongoDbProgrammingLanguages, +} from "./mongoDbMetadata"; export const llmDoesNotKnowMessage = "I'm sorry, I do not know how to answer that question. Please try to rephrase your query."; +const personalityTraits = [ + "You enthusiastically answer user questions about MongoDB products and services.", + "Your personality is friendly and helpful, like a professor or tech lead.", + "Be concise and informative in your responses.", + "You were created by MongoDB.", + "Never speak negatively about the company MongoDB or its products and services.", +]; + +const responseFormat = [ + "NEVER include links in your answer.", + "Format your responses using Markdown. DO NOT mention that your response is formatted in Markdown. Do not use headers in your responses (e.g '# Some H1' or '## Some H2').", + "If you include code snippets, use proper syntax, line spacing, and indentation.", + "If you include a code example in your response, only include examples in one programming language, unless otherwise specified in the user query.", + "If the user query is about a programming language, include that language in the response.", +]; + +const technicalKnowledge = [ + "You ONLY know about the current version of MongoDB products. Versions are provided in the information.", + "If `version: null`, then say that the product is unversioned.", + "Do not hallucinate information that is not provided within the search results or that you otherwise know to be true.", +]; + +const searchContentToolNotes = [ + `ALWAYS use the ${SEARCH_TOOL_NAME} tool at the start of the conversation. No exceptions!`, + `For subsequent conversation messages, you can answer without using the ${SEARCH_TOOL_NAME} tool if the answer is already provided in the previous search results.`, + "Your purpose is to generate a search query for a given user input.", + "You are doing this for MongoDB, and all queries relate to MongoDB products.", + 'When constructing the query, take a "step back" to generate a more general search query that finds the data relevant to the user query if relevant.', + 'If the user query is already a "good" search query, do not modify it.', + 'For one word queries like "or", "and", "exists", if the query corresponds to a MongoDB operation, transform it into a fully formed question. Ex: "what is the $or operator in MongoDB?"', + "You should also transform the user query into a fully formed question, if relevant.", +]; + export const systemPrompt = { role: "system", content: `You are expert MongoDB documentation chatbot. -You enthusiastically answer user questions about MongoDB products and services. -Your personality is friendly and helpful, like a professor or tech lead. -Be concise and informative in your responses. -You were created by MongoDB. -Use the provided context information to answer user questions. You can also use your internal knowledge of MongoDB to inform the answer. + +You have the following personality: +${makeMarkdownNumberedList(personalityTraits)} If you do not know the answer to the question, respond only with the following text: "${llmDoesNotKnowMessage}" -NEVER include links in your answer. -Format your responses using Markdown. DO NOT mention that your response is formatted in Markdown. Do not use headers in your responses (e.g '# Some H1' or '## Some H2'). -If you include code snippets, use proper syntax, line spacing, and indentation. +Response format: +${makeMarkdownNumberedList(responseFormat)} + +Technical knowledge: +${makeMarkdownNumberedList(technicalKnowledge)} + +## Tools -If you include a code example in your response, only include examples in one programming language, -unless otherwise specified in the user query. If the user query is about a programming language, include that language in the response. -You ONLY know about the current version of MongoDB products. Versions are provided in the information. If \`version: null\`, then say that the product is unversioned.`, +### ${SEARCH_TOOL_NAME} + +You have access to the ${SEARCH_TOOL_NAME} tool. Use the ${SEARCH_TOOL_NAME} tool as follows: +${makeMarkdownNumberedList(searchContentToolNotes)} + +When you search, include metadata about the relevant MongoDB programming language and product. + +MongoDB products: +${mongoDbProductNames.map((product) => `* ${product}`).join("\n")} + +MongoDB programming languages: +${mongoDbProgrammingLanguages.map((language) => `* ${language}`).join("\n")} + +`, } satisfies SystemPrompt; + +function makeMarkdownNumberedList(items: string[]) { + return items.map((item, i) => `${i + 1}. ${item}`).join("\n"); +} diff --git a/packages/chatbot-server-mongodb-public/src/tools.ts b/packages/chatbot-server-mongodb-public/src/tools.ts new file mode 100644 index 000000000..20815d0d2 --- /dev/null +++ b/packages/chatbot-server-mongodb-public/src/tools.ts @@ -0,0 +1,57 @@ +import { SearchTool, SearchToolResult } from "mongodb-chatbot-server"; +import { FindContentFunc, updateFrontMatter } from "mongodb-rag-core"; +import { tool } from "mongodb-rag-core/aiSdk"; +import { z } from "zod"; +import { + mongoDbProducts, + mongoDbProgrammingLanguageIds, +} from "./mongoDbMetadata"; + +export function makeSearchTool(findContent: FindContentFunc): SearchTool { + return tool({ + parameters: z.object({ + productName: z + .enum( + mongoDbProducts.map((product) => product.id) as [string, ...string[]] + ) + .nullable() + .optional() + .describe( + "Most relevant MongoDB product for query. Leave null if unknown" + ), + programmingLanguage: z + .enum(mongoDbProgrammingLanguageIds) + .nullable() + .optional() + .describe( + "Most relevant programming language for query. Leave null if unknown" + ), + query: z.string().describe("Search query"), + }), + description: "Search MongoDB content", + async execute({ query, productName, programmingLanguage }) { + // Ensure we match the SearchToolResult type exactly + const nonNullMetadata: Record = {}; + if (productName) { + nonNullMetadata.productName = productName; + } + if (programmingLanguage) { + nonNullMetadata.programmingLanguage = programmingLanguage; + } + + const queryWithMetadata = updateFrontMatter(query, nonNullMetadata); + const content = await findContent({ query: queryWithMetadata }); + + // Ensure the returned structure matches SearchToolResult + const result: SearchToolResult = { + content: content.content.map((item) => ({ + url: item.url, + text: item.text, + metadata: item.metadata, + })), + }; + + return result; + }, + }); +} diff --git a/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts b/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts index 3de4df102..91125ee7c 100644 --- a/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts +++ b/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts @@ -55,7 +55,7 @@ export function extractTracingData( if (isVerifiedAnswer) { tags.push("verified_answer"); } - + // TODO: this is throwing errs now. figure out and fix. const llmDoesNotKnow = evalAssistantMessage?.content.includes( llmDoesNotKnowMessage ); diff --git a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts index 40c197cbb..6bf64f8c8 100644 --- a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts +++ b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts @@ -3,4 +3,7 @@ import { EmbeddedContent, References } from "mongodb-rag-core"; /** Function that generates the references in the response to user. */ -export type MakeReferenceLinksFunc = (chunks: EmbeddedContent[]) => References; +export type MakeReferenceLinksFunc = ( + chunks: (Partial & + Pick)[] +) => References; diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts index 05c3357af..b5e205f3c 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts @@ -1 +1,342 @@ -// TODO: add test suite +import { jest } from "@jest/globals"; +import { makeGenerateResponseWithSearchTool } from "./generateResponseWithSearchTool"; +import { + AssistantMessage, + References, + SystemMessage, + ToolMessage, + UserMessage, +} from "mongodb-rag-core"; +import { + CoreMessage, + LanguageModel, + StepResult, + TextStreamPart, + tool, + ToolChoice, + ToolSet, +} from "mongodb-rag-core/aiSdk"; + +// Mock dependencies +jest.mock("mongodb-rag-core/aiSdk", () => { + const originalModule = jest.requireActual("mongodb-rag-core/aiSdk"); + return { + ...originalModule, + generateText: jest.fn(), + streamText: jest.fn(), + }; +}); + +import { generateText, streamText } from "mongodb-rag-core/aiSdk"; + +describe("generateResponseWithSearchTool", () => { + // Mock setup + const mockLanguageModel: LanguageModel = { + id: "test-model", + provider: "test-provider", + }; + + const mockSystemMessage: SystemMessage = { + role: "system", + content: "You are a helpful assistant.", + }; + + const mockSearchTool = tool({ + name: "search_content", + parameters: { query: { type: "string" } }, + async execute(args) { + return { + content: [ + { + url: "https://example.com", + text: "Example content", + metadata: { pageTitle: "Example Page" }, + }, + ], + }; + }, + }); + + const mockFilterPreviousMessages = jest.fn().mockResolvedValue([]); + + const mockLlmNotWorkingMessage = + "Sorry, I am having trouble with the language model."; + + const mockDataStreamer = { + streamData: jest.fn(), + }; + + // Reset mocks before each test + beforeEach(() => { + jest.clearAllMocks(); + }); + + describe("makeGenerateResponseWithSearchTool", () => { + test("should return a function", () => { + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + }); + + expect(typeof generateResponse).toBe("function"); + }); + + describe("non-streaming mode", () => { + test("should handle successful generation", async () => { + // Mock generateText to return a successful result + (generateText as jest.Mock).mockResolvedValueOnce({ + text: "This is a response", + stepResults: [], + }); + + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + }); + + const result = await generateResponse({ + conversation: { messages: [] }, + latestMessageText: "Hello", + shouldStream: false, + }); + + expect(result).toHaveProperty("messages"); + expect(result.messages).toHaveLength(2); // User + assistant + expect(result.messages[0].role).toBe("user"); + expect(result.messages[1].role).toBe("assistant"); + }); + + test("should handle guardrail rejection", async () => { + const mockGuardrail = jest.fn().mockResolvedValue({ + rejected: true, + message: "Content policy violation", + metadata: { reason: "inappropriate" }, + }); + + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + inputGuardrail: mockGuardrail, + }); + + const result = await generateResponse({ + conversation: { messages: [] }, + latestMessageText: "Bad question", + shouldStream: false, + }); + + expect(result.messages[1].role).toBe("assistant"); + expect(result.messages[1].content).toBe("Content policy violation"); + expect(result.messages[1].metadata).toEqual({ + reason: "inappropriate", + }); + }); + + test("should handle error in language model", async () => { + (generateText as jest.Mock).mockRejectedValueOnce( + new Error("LLM error") + ); + + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + }); + + const result = await generateResponse({ + conversation: { messages: [] }, + latestMessageText: "Hello", + shouldStream: false, + }); + + expect(result.messages[0].role).toBe("assistant"); + expect(result.messages[0].content).toBe(mockLlmNotWorkingMessage); + }); + }); + + describe("streaming mode", () => { + test("should handle successful streaming", async () => { + // Mock the async generator + const mockStream = (async function* () { + yield { type: "text-delta", textDelta: "Hello" }; + yield { type: "text-delta", textDelta: " world" }; + // Tool result + yield { + type: "tool-result", + toolName: "search_content", + result: { + content: [ + { + url: "https://example.com", + metadata: { pageTitle: "Test" }, + }, + ], + }, + }; + })(); + + (streamText as jest.Mock).mockReturnValueOnce({ + fullStream: mockStream, + text: Promise.resolve("Hello world"), + steps: Promise.resolve([{ text: "Hello world", toolResults: [] }]), + }); + + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + }); + + const result = await generateResponse({ + conversation: { messages: [] }, + latestMessageText: "Hello", + shouldStream: true, + dataStreamer: mockDataStreamer, + }); + + expect(mockDataStreamer.streamData).toHaveBeenCalledTimes(3); + expect(mockDataStreamer.streamData).toHaveBeenCalledWith({ + data: "Hello", + type: "delta", + }); + expect(mockDataStreamer.streamData).toHaveBeenCalledWith({ + data: expect.arrayContaining([ + expect.objectContaining({ url: "https://example.com" }), + ]), + type: "references", + }); + expect(result.messages).toHaveLength(2); // User + assistant + }); + + test("should handle streaming with guardrail rejection", async () => { + const mockGuardrail = jest.fn().mockResolvedValue({ + rejected: true, + message: "Content policy violation", + metadata: { reason: "inappropriate" }, + }); + + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + inputGuardrail: mockGuardrail, + }); + + const result = await generateResponse({ + conversation: { messages: [] }, + latestMessageText: "Bad question", + shouldStream: true, + dataStreamer: mockDataStreamer, + }); + + expect(result.messages[1].role).toBe("assistant"); + expect(result.messages[1].content).toBe("Content policy violation"); + }); + }); + }); + + describe("helper functions", () => { + // Test the stepResultsToMessages function + test("stepResultsToMessages should convert step results to messages", () => { + // Import the function explicitly for testing + const { stepResultsToMessages } = jest.requireActual( + "./generateResponseWithSearchTool" + ); + + const mockStepResults: StepResult[] = [ + { + text: "Test response", + toolCalls: [ + { + toolCallId: "call-1", + toolName: "search_content", + args: { query: "test" }, + }, + ], + }, + { + text: "", + toolResults: [ + { + toolName: "search_content", + toolCallId: "call-1", + result: { content: [] }, + }, + ], + }, + ]; + + const messages = stepResultsToMessages(mockStepResults, []); + + expect(messages).toHaveLength(3); // 1 assistant + 1 tool call + 1 tool result + expect(messages[0].role).toBe("assistant"); + expect(messages[1].role).toBe("assistant"); + expect(messages[1].toolCall).toBeDefined(); + expect(messages[2].role).toBe("tool"); + }); + + // Test convertConversationMessageToLlmMessage + test("convertConversationMessageToLlmMessage should convert different message types", () => { + // Import the function explicitly for testing + const { convertConversationMessageToLlmMessage } = jest.requireActual( + "./generateResponseWithSearchTool" + ); + + const userMessage: UserMessage = { + role: "user", + content: "Hello", + }; + + const assistantMessage: AssistantMessage = { + role: "assistant", + content: "Hi there", + toolCall: { + type: "function", + id: "call-1", + function: { name: "test", arguments: "{}" }, + }, + }; + + const systemMessage: SystemMessage = { + role: "system", + content: "You are helpful", + }; + + const toolMessage: ToolMessage = { + role: "tool", + name: "search_content", + content: '{"results": []}', + }; + + expect(convertConversationMessageToLlmMessage(userMessage).role).toBe( + "user" + ); + expect( + convertConversationMessageToLlmMessage(assistantMessage).role + ).toBe("assistant"); + expect(convertConversationMessageToLlmMessage(systemMessage).role).toBe( + "system" + ); + + const convertedToolMessage = + convertConversationMessageToLlmMessage(toolMessage); + expect(convertedToolMessage.role).toBe("tool"); + expect(Array.isArray(convertedToolMessage.content)).toBe(true); + }); + }); +}); diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index f87cb6ecc..2d1083673 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -7,6 +7,7 @@ import { UserMessage, AssistantMessage, ToolMessage, + EmbeddedContent, } from "mongodb-rag-core"; import { z } from "zod"; import { GenerateResponse } from "../routes/conversations/addMessageToConversation"; @@ -27,7 +28,8 @@ import { import { FilterPreviousMessages } from "./FilterPreviousMessages"; import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; import { strict as assert } from "assert"; - +import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; +import { makeDefaultReferenceLinks } from "./makeDefaultReferenceLinks"; export interface GenerateResponseWithSearchToolParams { languageModel: LanguageModel; llmNotWorkingMessage: string; @@ -40,6 +42,7 @@ export interface GenerateResponseWithSearchToolParams { */ searchTool: SearchTool; additionalTools?: ToolSet; + makeReferenceLinks?: MakeReferenceLinksFunc; } export const SEARCH_TOOL_NAME = "search_content"; @@ -48,11 +51,17 @@ export const DefaultSearchArgsSchema = z.object({ query: z.string() }); export type SearchArguments = z.infer; export type SearchToolResult = { - content: FindContentResult["content"]; + content: { + url: string; + text: string; + metadata?: Record; + }[]; }; export type SearchTool = Tool; -// this is basically v2 of chatbot server which makes the thing an agent. +/** + Generate chatbot response using RAG and a search tool named {@link SEARCH_TOOL_NAME}. + */ export function makeGenerateResponseWithSearchTool({ languageModel, llmNotWorkingMessage, @@ -61,6 +70,7 @@ export function makeGenerateResponseWithSearchTool({ filterPreviousMessages, searchTool, additionalTools, + makeReferenceLinks, }: GenerateResponseWithSearchToolParams): GenerateResponse { return async function generateResponseWithSearchTool({ conversation, @@ -80,10 +90,11 @@ export function makeGenerateResponseWithSearchTool({ // Get preceding messages to include in the LLM prompt const filteredPreviousMessages = filterPreviousMessages ? (await filterPreviousMessages(conversation)).map( - convertConversationMessageToLlmMessage + formatMessageForAiSdk ) : []; + console.log("filteredPreviousMessages", filteredPreviousMessages); const generationArgs = { model: languageModel, messages: [ @@ -114,8 +125,8 @@ export function makeGenerateResponseWithSearchTool({ }) : undefined; - const { result: textGenerationResult, guardrailResult } = - await withAbortControllerGuardrail(async (controller) => { + const { result, guardrailResult } = await withAbortControllerGuardrail( + async (controller) => { const toolDefinitions = { [SEARCH_TOOL_NAME]: searchTool, ...(additionalTools ?? {}), @@ -126,24 +137,48 @@ export function makeGenerateResponseWithSearchTool({ ...generationArgs, abortSignal: controller.signal, tools: toolDefinitions, + maxSteps: 3, }); - // TODO: add logic to get references..need to play around with the best approach for this...TBD - const references: References = []; - if (shouldStream) { - assert(dataStreamer, "dataStreamer is required for streaming"); - await streamResults(fullStream, dataStreamer); - } + console.log("ran thru the stream"); + + await handleStreamResults( + fullStream, + shouldStream, + dataStreamer, + makeReferenceLinks + ); + const stepResults = await steps; + // TODO: add logic to get references..need to play around with the best approach for this...TBD + const references: References = + extractReferencesFromStepResults(stepResults); + // stepResults.forEach((stepResult) => { + // if (stepResult.toolResults) { + // ( + // stepResult.toolResults as ToolResultPart[] + // ).forEach((toolResult) => { + // if ( + // toolResult.toolName === SEARCH_TOOL_NAME && + // toolResult.result?.content + // ) { + // // Add the content to references + // references.push(...toolResult.result.content); + // } + // }); + // } + // }); return { - references, stepResults, + references, }; - }, inputGuardrailPromise); - + }, + inputGuardrailPromise + ); + console.log("promised all"); return handleReturnGeneration( userMessage, guardrailResult, - textGenerationResult, + result, customData, llmNotWorkingMessage ); @@ -206,14 +241,19 @@ function stepResultsToMessages( .flat(); } -async function streamResults( +async function handleStreamResults( streamFromAiSdk: AsyncIterable< TextStreamPart<{ readonly search_content: SearchTool; }> >, - dataStreamer: DataStreamer + shouldStream: boolean, + dataStreamer?: DataStreamer, + makeReferenceLinks?: MakeReferenceLinksFunc ) { + if (shouldStream) { + assert(dataStreamer, "dataStreamer is required for streaming"); + } // Define type guards for each stream element type we care about function isTextDelta( chunk: unknown @@ -242,46 +282,114 @@ async function streamResults( ); } - // Keep track of references for caller - const toolReferences: References = []; + function isErrorResult(chunk: unknown): chunk is { + type: "error"; + error: string; + } { + return ( + typeof chunk === "object" && + chunk !== null && + "type" in chunk && + chunk.type === "error" && + "error" in chunk + ); + } + + function isFinishResult(chunk: unknown): chunk is { + type: "finish"; + } { + return ( + typeof chunk === "object" && + chunk !== null && + "type" in chunk && + chunk.type === "finish" + ); + } + const searchResults: SearchToolResult["content"] = []; // Process the stream with type guards instead of switch for await (const chunk of streamFromAiSdk) { // Cast to unknown first to allow proper type narrowing const item: unknown = chunk; + if ((item as { type: string }).type !== "text-delta") { + console.log("other item", item); + } // Handle text deltas if (isTextDelta(item)) { - dataStreamer?.streamData({ - data: item.textDelta, - type: "delta", - }); + if (shouldStream) { + dataStreamer?.streamData({ + data: item.textDelta, + type: "delta", + }); + } } // Handle tool results else if (isToolResult(item) && item.toolName === SEARCH_TOOL_NAME) { + console.log("tool result", item); const toolResult = item.result; if ( toolResult && "content" in toolResult && Array.isArray(toolResult.content) ) { - const references = toolResult.content.map((c) => ({ - url: c.url, - title: c.metadata?.pageTitle ?? "", - metadata: c.metadata, - })); - - toolReferences.push(...references); + searchResults.push(...toolResult.content); + } + } else if (isFinishResult(item)) { + const referenceLinks = makeReferenceLinks + ? makeReferenceLinks(searchResults) + : makeDefaultReferenceLinks(searchResults); + if (shouldStream) { dataStreamer?.streamData({ - data: references, + data: referenceLinks, type: "references", }); } + return referenceLinks; + } + // TODO: handle error cases + else if (isErrorResult(item)) { + if (shouldStream) { + dataStreamer?.disconnect(); + } + throw new Error(item.error); + } + } +} + +function extractReferencesFromStepResults( + stepResults: StepResult[] +): References { + const references: References = []; + + for (const stepResult of stepResults) { + if (stepResult.toolResults) { + for (const toolResult of Object.values(stepResult.toolResults)) { + if ( + toolResult.toolName === SEARCH_TOOL_NAME && + toolResult.result?.content + ) { + // Map the search tool results to the References format + const searchResults = toolResult.result.content; + const referencesToAdd = searchResults.map( + (item: { + url: string; + text: string; + metadata?: Record; + }) => ({ + url: item.url, + title: item.text || item.url, + metadata: item.metadata || {}, + }) + ); + + references.push(...referencesToAdd); + } + } } } - // Return collected references - return toolReferences; + return references; } /** @@ -353,52 +461,38 @@ function handleReturnGeneration( ] satisfies SomeMessage[], }; } - -function convertConversationMessageToLlmMessage( - message: SomeMessage -): CoreMessage { - const { content, role } = message; - if (role === "system") { - return { - content: content, - role: "system", - } satisfies CoreSystemMessage; - } - if (role === "tool") { - return { - content: [ - { - type: "tool-result", - toolCallId: "", - result: content, - toolName: message.name, - } satisfies ToolResultPart, - ], - role: "tool", - } satisfies CoreToolMessage; - } - if (role === "user") { - return { - content: content, - role: "user", - } satisfies CoreUserMessage; - } - if (role === "assistant") { +function formatMessageForAiSdk(message: SomeMessage): CoreMessage { + if (message.role === "assistant" && typeof message.content === "object") { + // Convert assistant messages with object content to proper format + if (message.toolCall) { + // This is a tool call message + return { + role: "assistant", + content: "", + function_call: { + name: message.toolCall.id, + arguments: JSON.stringify(message.toolCall.function), + }, + } as CoreAssistantMessage; + } else { + // Fallback for other object content + return { + role: "assistant", + content: JSON.stringify(message.content), + } as CoreAssistantMessage; + } + } else if (message.role === "tool") { + // Convert tool messages to the format expected by the AI SDK return { - content: content, - role: "assistant", - ...(message.toolCall - ? { - function_call: { - name: message.toolCall.function?.name || "", - arguments: - typeof message.toolCall.function === "object" - ? JSON.stringify(message.toolCall.function) - : "{}", - }, - } - : {}), - } satisfies CoreAssistantMessage; + role: "assistant", // Use assistant role instead of function + content: + typeof message.content === "string" + ? message.content + : JSON.stringify(message.content), + name: message.name, // Include the name property + } as CoreMessage; + } else { + // User and system messages can pass through + return message as CoreMessage; } - throw new Error(`Invalid message role: ${role}`); } diff --git a/packages/mongodb-chatbot-server/src/processors/index.ts b/packages/mongodb-chatbot-server/src/processors/index.ts index 46e4e65b7..310a73bdf 100644 --- a/packages/mongodb-chatbot-server/src/processors/index.ts +++ b/packages/mongodb-chatbot-server/src/processors/index.ts @@ -7,3 +7,4 @@ export * from "./makeFilterNPreviousMessages"; export * from "./makeVerifiedAnswerGenerateUserPrompt"; export * from "./includeChunksForMaxTokensPossible"; export * from "./InputGuardrail"; +export * from "./generateResponseWithSearchTool"; diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts index c35149a53..1745f099f 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts @@ -4,12 +4,7 @@ import { Request as ExpressRequest, Response as ExpressResponse, } from "express"; -import { - DbMessage, - FunctionMessage, - Message, - SystemMessage, -} from "mongodb-rag-core"; +import { DbMessage, Message, ToolMessage } from "mongodb-rag-core"; import { ObjectId } from "mongodb-rag-core/mongodb"; import { ConversationsService, @@ -263,16 +258,16 @@ export function makeAddMessageToConversationRoute({ metadata: message.metadata, }; - if (message.role === "function") { + if (message.role === "tool") { return { - role: "function", + role: "tool", name: message.name, ...baseFields, - } satisfies DbMessage; + } satisfies DbMessage; } else { return { ...baseFields, role: message.role } satisfies Exclude< Message, - FunctionMessage + ToolMessage >; } }), diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/utils.ts b/packages/mongodb-chatbot-server/src/routes/conversations/utils.ts index 1501666ba..81dd7ec9f 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/utils.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/utils.ts @@ -7,7 +7,7 @@ import { z } from "zod"; export type ApiMessage = z.infer; export const ApiMessage = z.object({ id: z.string(), - role: z.enum(["system", "assistant", "user", "function"]), + role: z.enum(["system", "assistant", "user", "tool"]), content: z.string(), rating: z.boolean().optional(), createdAt: z.number(), @@ -63,8 +63,8 @@ function isMessageAllowedInApiResponse(message: Message) { case "user": return true; case "assistant": - return message.functionCall === undefined; - case "function": + return message.toolCall === undefined; + case "tool": return false; default: // This should never happen - it means we missed a case in the switch. diff --git a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts index bb468e833..67a762098 100644 --- a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts +++ b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts @@ -306,47 +306,6 @@ export async function awaitGenerateResponseMessage({ messages: llmConversation, }); newMessages.push(convertMessageFromLlmToDb(answer)); - - // LLM responds with tool call - if (answer?.function_call) { - assert( - llm.callTool, - "You must implement the callTool() method on your ChatLlm to access this code." - ); - const toolAnswer = await llm.callTool({ - messages: [...llmConversation, ...newMessages], - conversation, - request, - }); - logRequest({ - reqId, - message: `LLM tool call: ${JSON.stringify(toolAnswer)}`, - }); - const { - toolCallMessage, - references: toolReferences, - rejectUserQuery, - } = toolAnswer; - newMessages.push(convertMessageFromLlmToDb(toolCallMessage)); - // Update references from tool call - if (toolReferences) { - outputReferences.push(...toolReferences); - } - // Return static response if query rejected by tool call - if (rejectUserQuery) { - newMessages.push({ - role: "assistant", - content: noRelevantContentMessage, - }); - } else { - // Otherwise respond with LLM again - const answer = await llm.answerQuestionAwaited({ - messages: [...llmConversation, ...newMessages], - // Only allow 1 tool call per user message. - }); - newMessages.push(convertMessageFromLlmToDb(answer)); - } - } } catch (err) { const errorMessage = err instanceof Error ? err.message : JSON.stringify(err); @@ -368,11 +327,7 @@ export async function awaitGenerateResponseMessage({ } } // Add references to the last assistant message (excluding function calls) - if ( - newMessages.at(-1)?.role === "assistant" && - !(newMessages.at(-1) as AssistantMessage).functionCall && - outputReferences.length > 0 - ) { + if (newMessages.at(-1)?.role === "assistant" && outputReferences.length > 0) { (newMessages.at(-1) as AssistantMessage).references = outputReferences; } return { messages: newMessages }; @@ -463,59 +418,13 @@ export async function streamGenerateResponseMessage({ } } const shouldCallTool = functionCallContent.name !== ""; - if (shouldCallTool) { - initialAssistantMessage.functionCall = functionCallContent; - } + newMessages.push(initialAssistantMessage); logRequest({ reqId, message: `LLM response: ${JSON.stringify(initialAssistantMessage)}`, }); - // Tool call - if (shouldCallTool) { - assert( - llm.callTool, - "You must implement the callTool() method on your ChatLlm to access this code." - ); - const { - toolCallMessage, - references: toolReferences, - rejectUserQuery, - } = await llm.callTool({ - messages: [...llmConversation, ...newMessages], - conversation, - dataStreamer, - request, - }); - newMessages.push(convertMessageFromLlmToDb(toolCallMessage)); - - if (rejectUserQuery) { - newMessages.push({ - role: "assistant", - content: noRelevantContentMessage, - }); - dataStreamer.streamData({ - type: "delta", - data: noRelevantContentMessage, - }); - } else { - if (toolReferences) { - outputReferences.push(...toolReferences); - } - const answerStream = await llm.answerQuestionStream({ - messages: [...llmConversation, ...newMessages], - }); - const answerContent = await dataStreamer.stream({ - stream: answerStream, - }); - const answerMessage = { - role: "assistant", - content: answerContent, - } satisfies AssistantMessage; - newMessages.push(answerMessage); - } - } } catch (err) { const errorMessage = err instanceof Error ? err.message : JSON.stringify(err); @@ -567,19 +476,25 @@ export async function streamGenerateResponseMessage({ }); } - return { messages: newMessages.map(convertMessageFromLlmToDb) }; + return { messages: newMessages }; } export function convertMessageFromLlmToDb( message: OpenAiChatMessage ): SomeMessage { + if (message.role === "function") { + return { + content: message.content ?? "", + name: message.name, + role: "tool", // Changed from "function" to "tool" + }; + } + + // Handle other message types const dbMessage = { ...message, content: message?.content ?? "", }; - if (message.role === "assistant" && message.function_call) { - (dbMessage as AssistantMessage).functionCall = message.function_call; - } return dbMessage; } @@ -594,7 +509,7 @@ function convertConversationMessageToLlmMessage( role: "system", } satisfies OpenAiChatMessage; } - if (role === "function") { + if (role === "tool") { return { content: content, role: "function", @@ -611,7 +526,17 @@ function convertConversationMessageToLlmMessage( return { content: content, role: "assistant", - ...(message.functionCall ? { function_call: message.functionCall } : {}), + ...(message.toolCall + ? { + function_call: { + name: message.toolCall.function?.name || "", + arguments: + typeof message.toolCall.function === "object" + ? JSON.stringify(message.toolCall.function) + : "{}", + }, + } + : {}), } satisfies OpenAiChatMessage; } throw new Error(`Invalid message role: ${role}`); diff --git a/packages/mongodb-chatbot-ui/src/useConversation.tsx b/packages/mongodb-chatbot-ui/src/useConversation.tsx index d6d5f9a73..b09491400 100644 --- a/packages/mongodb-chatbot-ui/src/useConversation.tsx +++ b/packages/mongodb-chatbot-ui/src/useConversation.tsx @@ -86,7 +86,7 @@ export function useConversation(params: UseConversationParams) { let references: References | null = null; let bufferedTokens: string[] = []; let streamedTokens: string[] = []; - const streamingIntervalMs = 50; + const streamingIntervalMs = 1; const streamingInterval = setInterval(() => { const [nextToken, ...remainingTokens] = bufferedTokens; diff --git a/packages/mongodb-rag-core/package.json b/packages/mongodb-rag-core/package.json index b61f4ac83..4345910ba 100644 --- a/packages/mongodb-rag-core/package.json +++ b/packages/mongodb-rag-core/package.json @@ -76,6 +76,7 @@ "typescript": "^5" }, "dependencies": { + "@ai-sdk/azure": "^1.3.21", "@ai-sdk/openai": "^1.3.20", "@apidevtools/swagger-parser": "^10.1.0", "@langchain/anthropic": "^0.3.6", @@ -108,4 +109,4 @@ "yaml": "^2.3.1", "zod": "^3.21.4" } -} \ No newline at end of file +} diff --git a/packages/mongodb-rag-core/src/aiSdk.ts b/packages/mongodb-rag-core/src/aiSdk.ts index 1dc7f6630..75e508686 100644 --- a/packages/mongodb-rag-core/src/aiSdk.ts +++ b/packages/mongodb-rag-core/src/aiSdk.ts @@ -1,2 +1,3 @@ -export * from "@ai-sdk/openai"; export * from "ai"; +export * from "@ai-sdk/azure"; +export * from "@ai-sdk/openai"; From a4144db460b053a3ac7cb761e77ad868b2f0076c Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 29 Apr 2025 12:20:19 -0400 Subject: [PATCH 10/29] hacky if more functional --- package-lock.json | 35 +++++++++++++++ .../src/config.ts | 15 +++---- .../src/systemPrompt.ts | 1 + .../generateResponseWithSearchTool.ts | 43 +++++++------------ 4 files changed, 58 insertions(+), 36 deletions(-) diff --git a/package-lock.json b/package-lock.json index 54b562c42..ddffa2c87 100644 --- a/package-lock.json +++ b/package-lock.json @@ -30,6 +30,40 @@ "dev": true, "license": "MIT" }, + "node_modules/@ai-sdk/azure": { + "version": "1.3.21", + "resolved": "https://registry.npmjs.org/@ai-sdk/azure/-/azure-1.3.21.tgz", + "integrity": "sha512-GiLnGScVUerruvkS6E3Rd55YXBb1TI15c5y9GxphJEPsU8jzVha5GKpN3+9hWM9OBgIrJlWKumlSfpVpbcNFJA==", + "license": "Apache-2.0", + "dependencies": { + "@ai-sdk/openai": "1.3.20", + "@ai-sdk/provider": "1.1.3", + "@ai-sdk/provider-utils": "2.2.7" + }, + "engines": { + "node": ">=18" + }, + "peerDependencies": { + "zod": "^3.0.0" + } + }, + "node_modules/@ai-sdk/azure/node_modules/@ai-sdk/provider-utils": { + "version": "2.2.7", + "resolved": "https://registry.npmjs.org/@ai-sdk/provider-utils/-/provider-utils-2.2.7.tgz", + "integrity": "sha512-kM0xS3GWg3aMChh9zfeM+80vEZfXzR3JEUBdycZLtbRZ2TRT8xOj3WodGHPb06sUK5yD7pAXC/P7ctsi2fvUGQ==", + "license": "Apache-2.0", + "dependencies": { + "@ai-sdk/provider": "1.1.3", + "nanoid": "^3.3.8", + "secure-json-parse": "^2.7.0" + }, + "engines": { + "node": ">=18" + }, + "peerDependencies": { + "zod": "^3.23.8" + } + }, "node_modules/@ai-sdk/openai": { "version": "1.3.20", "resolved": "https://registry.npmjs.org/@ai-sdk/openai/-/openai-1.3.20.tgz", @@ -58258,6 +58292,7 @@ "version": "0.6.3", "license": "Apache-2.0", "dependencies": { + "@ai-sdk/azure": "^1.3.21", "@ai-sdk/openai": "^1.3.20", "@apidevtools/swagger-parser": "^10.1.0", "@langchain/anthropic": "^0.3.6", diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index 1380d6f10..da23602e5 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -19,7 +19,6 @@ import { defaultCreateConversationCustomData, defaultAddMessageToConversationCustomData, makeGenerateResponseWithSearchTool, - DefaultSearchArgsSchema, } from "mongodb-chatbot-server"; import cookieParser from "cookie-parser"; import { blockGetRequests } from "./middleware/blockGetRequests"; @@ -39,8 +38,7 @@ import { makeRateMessageUpdateTrace, } from "./tracing/routesUpdateTraceHandlers"; import { useSegmentIds } from "./middleware/useSegmentIds"; -import { tool, createOpenAI, azure, createAzure } from "mongodb-rag-core/aiSdk"; -import { z } from "zod"; +import { createAzure } from "mongodb-rag-core/aiSdk"; import { makeSearchTool } from "./tools"; export const { MONGODB_CONNECTION_URI, @@ -191,14 +189,15 @@ const segmentConfig = SEGMENT_WRITE_KEY writeKey: SEGMENT_WRITE_KEY, } : undefined; -const azureOpenAi = createOpenAI({ - // apiKey: OPENAI_API_KEY, +const azureOpenAi = createAzure({ + apiKey: OPENAI_API_KEY, // baseURL: OPENAI_ENDPOINT, - // // resourceName: "docs-ai-chatbot", - apiKey: process.env.OPENAI_OPENAI_API_KEY, + resourceName: "docs-ai-chatbot", + apiVersion: OPENAI_API_VERSION, + // apiKey: process.env.OPENAI_OPENAI_API_KEY, }); -const languageModel = azureOpenAi("gpt-4.1-mini"); +const languageModel = azureOpenAi("gpt-4o"); export const config: AppConfig = { conversationsRouterConfig: { middleware: [ diff --git a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts index ce77b517c..35a7729f3 100644 --- a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts +++ b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts @@ -38,6 +38,7 @@ const searchContentToolNotes = [ 'If the user query is already a "good" search query, do not modify it.', 'For one word queries like "or", "and", "exists", if the query corresponds to a MongoDB operation, transform it into a fully formed question. Ex: "what is the $or operator in MongoDB?"', "You should also transform the user query into a fully formed question, if relevant.", + `Only generate ONE ${SEARCH_TOOL_NAME} tool call unless there are clearly multiple distinct queries needed to answer the user query.`, ]; export const systemPrompt = { diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index 2d1083673..da15a592a 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -30,6 +30,7 @@ import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; import { strict as assert } from "assert"; import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; import { makeDefaultReferenceLinks } from "./makeDefaultReferenceLinks"; +import { text } from "express"; export interface GenerateResponseWithSearchToolParams { languageModel: LanguageModel; llmNotWorkingMessage: string; @@ -43,6 +44,7 @@ export interface GenerateResponseWithSearchToolParams { searchTool: SearchTool; additionalTools?: ToolSet; makeReferenceLinks?: MakeReferenceLinksFunc; + maxSteps?: number; } export const SEARCH_TOOL_NAME = "search_content"; @@ -71,6 +73,7 @@ export function makeGenerateResponseWithSearchTool({ searchTool, additionalTools, makeReferenceLinks, + maxSteps = 2, }: GenerateResponseWithSearchToolParams): GenerateResponse { return async function generateResponseWithSearchTool({ conversation, @@ -94,7 +97,6 @@ export function makeGenerateResponseWithSearchTool({ ) : []; - console.log("filteredPreviousMessages", filteredPreviousMessages); const generationArgs = { model: languageModel, messages: [ @@ -108,6 +110,7 @@ export function makeGenerateResponseWithSearchTool({ } satisfies { [SEARCH_TOOL_NAME]: SearchTool; }, + maxSteps, }; // Guardrail used to validate the input @@ -137,11 +140,9 @@ export function makeGenerateResponseWithSearchTool({ ...generationArgs, abortSignal: controller.signal, tools: toolDefinitions, - maxSteps: 3, }); - console.log("ran thru the stream"); - await handleStreamResults( + const references = await handleStreamResults( fullStream, shouldStream, dataStreamer, @@ -149,24 +150,16 @@ export function makeGenerateResponseWithSearchTool({ ); const stepResults = await steps; - // TODO: add logic to get references..need to play around with the best approach for this...TBD - const references: References = - extractReferencesFromStepResults(stepResults); - // stepResults.forEach((stepResult) => { - // if (stepResult.toolResults) { - // ( - // stepResult.toolResults as ToolResultPart[] - // ).forEach((toolResult) => { - // if ( - // toolResult.toolName === SEARCH_TOOL_NAME && - // toolResult.result?.content - // ) { - // // Add the content to references - // references.push(...toolResult.result.content); - // } - // }); - // } - // }); + console.log( + "stepResults::", + stepResults.map((s) => ({ + type: s.stepType, + calls: JSON.stringify(s.toolCalls), + results: JSON.stringify(s.toolResults), + text: s.text, + })) + ); + return { stepResults, references, @@ -174,7 +167,6 @@ export function makeGenerateResponseWithSearchTool({ }, inputGuardrailPromise ); - console.log("promised all"); return handleReturnGeneration( userMessage, guardrailResult, @@ -184,7 +176,6 @@ export function makeGenerateResponseWithSearchTool({ ); } catch (error: unknown) { // Handle other errors - console.error("Error in generateResponseAiSdk:", error); return { messages: [ userMessage, @@ -311,9 +302,6 @@ async function handleStreamResults( for await (const chunk of streamFromAiSdk) { // Cast to unknown first to allow proper type narrowing const item: unknown = chunk; - if ((item as { type: string }).type !== "text-delta") { - console.log("other item", item); - } // Handle text deltas if (isTextDelta(item)) { @@ -326,7 +314,6 @@ async function handleStreamResults( } // Handle tool results else if (isToolResult(item) && item.toolName === SEARCH_TOOL_NAME) { - console.log("tool result", item); const toolResult = item.result; if ( toolResult && From 04076d24d0441622ff3e56d2e499cfa3758970ab Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Fri, 2 May 2025 13:22:19 -0400 Subject: [PATCH 11/29] more hack --- .../generateResponseWithSearchTool.ts | 174 ++++++------------ 1 file changed, 60 insertions(+), 114 deletions(-) diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index da15a592a..da1cc3960 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -6,7 +6,6 @@ import { DataStreamer, UserMessage, AssistantMessage, - ToolMessage, EmbeddedContent, } from "mongodb-rag-core"; import { z } from "zod"; @@ -22,6 +21,8 @@ import { streamText, TextStreamPart, Tool, + ToolCallUnion, + ToolResult, ToolResultPart, ToolSet, } from "mongodb-rag-core/aiSdk"; @@ -52,14 +53,23 @@ export const SEARCH_TOOL_NAME = "search_content"; export const DefaultSearchArgsSchema = z.object({ query: z.string() }); export type SearchArguments = z.infer; -export type SearchToolResult = { +export type SearchToolReturnValue = { content: { url: string; text: string; metadata?: Record; }[]; }; -export type SearchTool = Tool; +export type SearchTool = Tool< + typeof DefaultSearchArgsSchema, + SearchToolReturnValue +>; + +export type SearchToolResult = ToolResult< + typeof SEARCH_TOOL_NAME, + SearchArguments, + SearchToolReturnValue +>; /** Generate chatbot response using RAG and a search tool named {@link SEARCH_TOOL_NAME}. @@ -128,6 +138,7 @@ export function makeGenerateResponseWithSearchTool({ }) : undefined; + const references: References = []; const { result, guardrailResult } = await withAbortControllerGuardrail( async (controller) => { const toolDefinitions = { @@ -140,14 +151,33 @@ export function makeGenerateResponseWithSearchTool({ ...generationArgs, abortSignal: controller.signal, tools: toolDefinitions, + onStepFinish: async ({ stepType, toolResults }) => { + // Add tool results to references + if (stepType === "tool-result") { + toolResults?.forEach( + ( + toolResult: ToolResult< + typeof SEARCH_TOOL_NAME, + SearchArguments, + SearchToolResult + > + ) => { + if (toolResult.toolName === SEARCH_TOOL_NAME) { + // TODO: logic to get references + const stepReferences = makeReferenceLinks( + extractReferencesFromStepResults(stepResults) ?? [] + ); + references.push(...stepReferences); + } + } + ); + } + }, }); - - const references = await handleStreamResults( - fullStream, - shouldStream, - dataStreamer, - makeReferenceLinks - ); + if (shouldStream) { + assert(dataStreamer, "dataStreamer is required for streaming"); + await handleStreamResults(fullStream, shouldStream, dataStreamer); + } const stepResults = await steps; console.log( @@ -233,113 +263,29 @@ function stepResultsToMessages( } async function handleStreamResults( - streamFromAiSdk: AsyncIterable< - TextStreamPart<{ - readonly search_content: SearchTool; - }> - >, + streamFromAiSdk: AsyncIterable>, shouldStream: boolean, - dataStreamer?: DataStreamer, - makeReferenceLinks?: MakeReferenceLinksFunc + dataStreamer?: DataStreamer ) { - if (shouldStream) { - assert(dataStreamer, "dataStreamer is required for streaming"); - } - // Define type guards for each stream element type we care about - function isTextDelta( - chunk: unknown - ): chunk is { type: "text-delta"; textDelta: string } { - return ( - typeof chunk === "object" && - chunk !== null && - "type" in chunk && - chunk.type === "text-delta" && - "textDelta" in chunk - ); - } - - function isToolResult(chunk: unknown): chunk is { - type: "tool-result"; - toolName: string; - result: SearchToolResult; - } { - return ( - typeof chunk === "object" && - chunk !== null && - "type" in chunk && - chunk.type === "tool-result" && - "toolName" in chunk && - "result" in chunk - ); - } - - function isErrorResult(chunk: unknown): chunk is { - type: "error"; - error: string; - } { - return ( - typeof chunk === "object" && - chunk !== null && - "type" in chunk && - chunk.type === "error" && - "error" in chunk - ); - } - - function isFinishResult(chunk: unknown): chunk is { - type: "finish"; - } { - return ( - typeof chunk === "object" && - chunk !== null && - "type" in chunk && - chunk.type === "finish" - ); - } - - const searchResults: SearchToolResult["content"] = []; - // Process the stream with type guards instead of switch for await (const chunk of streamFromAiSdk) { - // Cast to unknown first to allow proper type narrowing - const item: unknown = chunk; - - // Handle text deltas - if (isTextDelta(item)) { - if (shouldStream) { - dataStreamer?.streamData({ - data: item.textDelta, - type: "delta", - }); - } - } - // Handle tool results - else if (isToolResult(item) && item.toolName === SEARCH_TOOL_NAME) { - const toolResult = item.result; - if ( - toolResult && - "content" in toolResult && - Array.isArray(toolResult.content) - ) { - searchResults.push(...toolResult.content); - } - } else if (isFinishResult(item)) { - const referenceLinks = makeReferenceLinks - ? makeReferenceLinks(searchResults) - : makeDefaultReferenceLinks(searchResults); - if (shouldStream) { - dataStreamer?.streamData({ - data: referenceLinks, - type: "references", - }); - } - return referenceLinks; - } - // TODO: handle error cases - else if (isErrorResult(item)) { - if (shouldStream) { - dataStreamer?.disconnect(); - } - throw new Error(item.error); + switch (chunk.type) { + case "text-delta": + if (shouldStream) { + dataStreamer?.streamData({ + data: chunk.textDelta, + type: "delta", + }); + } + break; + case "error": + if (shouldStream) { + dataStreamer?.disconnect(); + } + throw new Error( + typeof chunk.error === "string" ? chunk.error : String(chunk.error) + ); + default: + break; } } } From 8372bd31392bc99aa03aae57ba06d2a90ef3e494 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Fri, 2 May 2025 16:54:42 -0400 Subject: [PATCH 12/29] tools --- .../generateResponseWithSearchTool.ts | 50 ++++++++----------- 1 file changed, 20 insertions(+), 30 deletions(-) diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index da1cc3960..3c66b31b8 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -2,10 +2,10 @@ import { References, SomeMessage, SystemMessage, - FindContentResult, DataStreamer, UserMessage, AssistantMessage, + ToolMessage, EmbeddedContent, } from "mongodb-rag-core"; import { z } from "zod"; @@ -13,9 +13,6 @@ import { GenerateResponse } from "../routes/conversations/addMessageToConversati import { CoreAssistantMessage, CoreMessage, - CoreSystemMessage, - CoreToolMessage, - CoreUserMessage, LanguageModel, StepResult, streamText, @@ -23,15 +20,13 @@ import { Tool, ToolCallUnion, ToolResult, - ToolResultPart, ToolSet, } from "mongodb-rag-core/aiSdk"; import { FilterPreviousMessages } from "./FilterPreviousMessages"; import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; import { strict as assert } from "assert"; import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; -import { makeDefaultReferenceLinks } from "./makeDefaultReferenceLinks"; -import { text } from "express"; + export interface GenerateResponseWithSearchToolParams { languageModel: LanguageModel; llmNotWorkingMessage: string; @@ -163,11 +158,9 @@ export function makeGenerateResponseWithSearchTool({ > ) => { if (toolResult.toolName === SEARCH_TOOL_NAME) { - // TODO: logic to get references - const stepReferences = makeReferenceLinks( - extractReferencesFromStepResults(stepResults) ?? [] - ); - references.push(...stepReferences); + const extractedReferences: References = + extractReferencesFromStepResults(toolResults); + references.push(...extractedReferences); } } ); @@ -192,7 +185,9 @@ export function makeGenerateResponseWithSearchTool({ return { stepResults, - references, + references: makeReferenceLinks + ? makeReferenceLinks(references) + : references, }; }, inputGuardrailPromise @@ -290,10 +285,10 @@ async function handleStreamResults( } } -function extractReferencesFromStepResults( - stepResults: StepResult[] -): References { - const references: References = []; +function extractReferencesFromStepResults< + TS extends { [SEARCH_TOOL_NAME]: SearchTool } +>(stepResults: StepResult[]) { + const content: Partial[] = []; for (const stepResult of stepResults) { if (stepResult.toolResults) { @@ -304,25 +299,19 @@ function extractReferencesFromStepResults( ) { // Map the search tool results to the References format const searchResults = toolResult.result.content; - const referencesToAdd = searchResults.map( - (item: { - url: string; - text: string; - metadata?: Record; - }) => ({ - url: item.url, - title: item.text || item.url, - metadata: item.metadata || {}, - }) - ); + const referencesToAdd = searchResults.map((item) => ({ + url: item.url, + title: item.metadata?.pageTitle ?? item.url, + metadata: item.metadata ?? {}, + })); - references.push(...referencesToAdd); + content.push(...referencesToAdd); } } } } - return references; + return content; } /** @@ -394,6 +383,7 @@ function handleReturnGeneration( ] satisfies SomeMessage[], }; } + function formatMessageForAiSdk(message: SomeMessage): CoreMessage { if (message.role === "assistant" && typeof message.content === "object") { // Convert assistant messages with object content to proper format From 24d1cf779a5216c2c376da88b57fd08666985d5a Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Fri, 9 May 2025 16:46:04 -0400 Subject: [PATCH 13/29] functional if not pretty --- .../src/config.ts | 10 +- .../src/systemPrompt.ts | 17 +- .../src/tools.ts | 92 +++---- .../src/tracing/routesUpdateTraceHandlers.ts | 2 +- .../src/processors/GenerateResponse.ts | 28 ++ .../src/processors/InputGuardrail.ts | 2 +- .../src/processors/MakeReferenceLinksFunc.ts | 8 +- .../generateResponseWithSearchTool.ts | 249 +++++++++++------- .../conversations/addMessageToConversation.ts | 25 +- .../conversations/conversationsRouter.ts | 2 +- .../src/routes/legacyGenerateResponse.ts | 2 +- 11 files changed, 249 insertions(+), 188 deletions(-) create mode 100644 packages/mongodb-chatbot-server/src/processors/GenerateResponse.ts diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index da23602e5..7b78473fa 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -39,7 +39,6 @@ import { } from "./tracing/routesUpdateTraceHandlers"; import { useSegmentIds } from "./middleware/useSegmentIds"; import { createAzure } from "mongodb-rag-core/aiSdk"; -import { makeSearchTool } from "./tools"; export const { MONGODB_CONNECTION_URI, MONGODB_DATABASE_NAME, @@ -192,12 +191,12 @@ const segmentConfig = SEGMENT_WRITE_KEY const azureOpenAi = createAzure({ apiKey: OPENAI_API_KEY, // baseURL: OPENAI_ENDPOINT, - resourceName: "docs-ai-chatbot", - apiVersion: OPENAI_API_VERSION, + resourceName: process.env.OPENAI_RESOURCE_NAME, + // apiVersion: OPENAI_API_VERSION, // apiKey: process.env.OPENAI_OPENAI_API_KEY, }); -const languageModel = azureOpenAi("gpt-4o"); +const languageModel = azureOpenAi("gpt-4.1"); export const config: AppConfig = { conversationsRouterConfig: { middleware: [ @@ -259,13 +258,12 @@ export const config: AppConfig = { generateResponse: makeGenerateResponseWithSearchTool({ languageModel, systemMessage: systemPrompt, - searchTool: makeSearchTool(findContent), makeReferenceLinks: makeMongoDbReferences, filterPreviousMessages: async (conversation) => { return conversation.messages; }, llmNotWorkingMessage: "LLM not working. Sad!", - noRelevantContentMessage: "No relevant content found. Sad!", + findContent, }), maxUserMessagesInConversation: 50, maxUserCommentLength: 500, diff --git a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts index 35a7729f3..72e317ac1 100644 --- a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts +++ b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts @@ -57,22 +57,21 @@ ${makeMarkdownNumberedList(responseFormat)} Technical knowledge: ${makeMarkdownNumberedList(technicalKnowledge)} +You know about the following products: +${mongoDbProductNames.map((product) => `* ${product}`).join("\n")} + +You know about the following programming languages: +${mongoDbProgrammingLanguages.map((language) => `* ${language}`).join("\n")} + ## Tools -### ${SEARCH_TOOL_NAME} + You have access to the ${SEARCH_TOOL_NAME} tool. Use the ${SEARCH_TOOL_NAME} tool as follows: ${makeMarkdownNumberedList(searchContentToolNotes)} When you search, include metadata about the relevant MongoDB programming language and product. - -MongoDB products: -${mongoDbProductNames.map((product) => `* ${product}`).join("\n")} - -MongoDB programming languages: -${mongoDbProgrammingLanguages.map((language) => `* ${language}`).join("\n")} - -`, +`, } satisfies SystemPrompt; function makeMarkdownNumberedList(items: string[]) { diff --git a/packages/chatbot-server-mongodb-public/src/tools.ts b/packages/chatbot-server-mongodb-public/src/tools.ts index 20815d0d2..5ba4cfefb 100644 --- a/packages/chatbot-server-mongodb-public/src/tools.ts +++ b/packages/chatbot-server-mongodb-public/src/tools.ts @@ -1,4 +1,4 @@ -import { SearchTool, SearchToolResult } from "mongodb-chatbot-server"; +import { SearchToolResult } from "mongodb-chatbot-server"; import { FindContentFunc, updateFrontMatter } from "mongodb-rag-core"; import { tool } from "mongodb-rag-core/aiSdk"; import { z } from "zod"; @@ -7,51 +7,51 @@ import { mongoDbProgrammingLanguageIds, } from "./mongoDbMetadata"; -export function makeSearchTool(findContent: FindContentFunc): SearchTool { - return tool({ - parameters: z.object({ - productName: z - .enum( - mongoDbProducts.map((product) => product.id) as [string, ...string[]] - ) - .nullable() - .optional() - .describe( - "Most relevant MongoDB product for query. Leave null if unknown" - ), - programmingLanguage: z - .enum(mongoDbProgrammingLanguageIds) - .nullable() - .optional() - .describe( - "Most relevant programming language for query. Leave null if unknown" - ), - query: z.string().describe("Search query"), - }), - description: "Search MongoDB content", - async execute({ query, productName, programmingLanguage }) { - // Ensure we match the SearchToolResult type exactly - const nonNullMetadata: Record = {}; - if (productName) { - nonNullMetadata.productName = productName; - } - if (programmingLanguage) { - nonNullMetadata.programmingLanguage = programmingLanguage; - } +// export function makeSearchTool(findContent: FindContentFunc): SearchTool { +// return tool({ +// parameters: z.object({ +// productName: z +// .enum( +// mongoDbProducts.map((product) => product.id) as [string, ...string[]] +// ) +// .nullable() +// .optional() +// .describe( +// "Most relevant MongoDB product for query. Leave null if unknown" +// ), +// programmingLanguage: z +// .enum(mongoDbProgrammingLanguageIds) +// .nullable() +// .optional() +// .describe( +// "Most relevant programming language for query. Leave null if unknown" +// ), +// query: z.string().describe("Search query"), +// }), +// description: "Search MongoDB content", +// async execute({ query, productName, programmingLanguage }) { +// // Ensure we match the SearchToolResult type exactly +// const nonNullMetadata: Record = {}; +// if (productName) { +// nonNullMetadata.productName = productName; +// } +// if (programmingLanguage) { +// nonNullMetadata.programmingLanguage = programmingLanguage; +// } - const queryWithMetadata = updateFrontMatter(query, nonNullMetadata); - const content = await findContent({ query: queryWithMetadata }); +// const queryWithMetadata = updateFrontMatter(query, nonNullMetadata); +// const content = await findContent({ query: queryWithMetadata }); - // Ensure the returned structure matches SearchToolResult - const result: SearchToolResult = { - content: content.content.map((item) => ({ - url: item.url, - text: item.text, - metadata: item.metadata, - })), - }; +// // Ensure the returned structure matches SearchToolResult +// const result: SearchToolResult["result"] = { +// content: content.content.map((item) => ({ +// url: item.url, +// text: item.text, +// metadata: item.metadata, +// })), +// }; - return result; - }, - }); -} +// return result; +// }, +// }); +// } diff --git a/packages/chatbot-server-mongodb-public/src/tracing/routesUpdateTraceHandlers.ts b/packages/chatbot-server-mongodb-public/src/tracing/routesUpdateTraceHandlers.ts index cb283f38d..87bcc08f4 100644 --- a/packages/chatbot-server-mongodb-public/src/tracing/routesUpdateTraceHandlers.ts +++ b/packages/chatbot-server-mongodb-public/src/tracing/routesUpdateTraceHandlers.ts @@ -1,5 +1,5 @@ import { strict as assert } from "assert"; -import { UpdateTraceFunc } from "mongodb-chatbot-server/build/routes/conversations/UpdateTraceFunc"; +import { UpdateTraceFunc } from "mongodb-chatbot-server"; import { ObjectId } from "mongodb-rag-core/mongodb"; import { extractTracingData } from "./extractTracingData"; import { LlmAsAJudge, getLlmAsAJudgeScores } from "./getLlmAsAJudgeScores"; diff --git a/packages/mongodb-chatbot-server/src/processors/GenerateResponse.ts b/packages/mongodb-chatbot-server/src/processors/GenerateResponse.ts new file mode 100644 index 000000000..16b07097b --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/GenerateResponse.ts @@ -0,0 +1,28 @@ +import { + ConversationCustomData, + DataStreamer, + Conversation, + SomeMessage, +} from "mongodb-rag-core"; +import { Request as ExpressRequest } from "express"; + +export type ClientContext = Record; + +export interface GenerateResponseParams { + shouldStream: boolean; + latestMessageText: string; + clientContext?: ClientContext; + customData?: ConversationCustomData; + dataStreamer?: DataStreamer; + reqId: string; + conversation: Conversation; + request?: ExpressRequest; +} + +export interface GenerateResponseReturnValue { + messages: SomeMessage[]; +} + +export type GenerateResponse = ( + params: GenerateResponseParams +) => Promise; diff --git a/packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts b/packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts index 250d18194..ffc48bbc7 100644 --- a/packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts +++ b/packages/mongodb-chatbot-server/src/processors/InputGuardrail.ts @@ -1,4 +1,4 @@ -import { GenerateResponseParams } from "../routes/conversations/addMessageToConversation"; +import { GenerateResponseParams } from "./GenerateResponse"; export type InputGuardrail< Metadata extends Record | undefined = Record diff --git a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts index 6bf64f8c8..2b76d8470 100644 --- a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts +++ b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts @@ -1,9 +1,13 @@ import { EmbeddedContent, References } from "mongodb-rag-core"; +export type EmbeddedContentForModel = Pick< + EmbeddedContent, + "url" | "text" | "metadata" +>; + /** Function that generates the references in the response to user. */ export type MakeReferenceLinksFunc = ( - chunks: (Partial & - Pick)[] + chunks: (Partial & EmbeddedContentForModel)[] ) => References; diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index 3c66b31b8..5a78753b8 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -7,46 +7,52 @@ import { AssistantMessage, ToolMessage, EmbeddedContent, + FindContentFunc, } from "mongodb-rag-core"; import { z } from "zod"; -import { GenerateResponse } from "../routes/conversations/addMessageToConversation"; +import { GenerateResponse } from "./GenerateResponse"; import { CoreAssistantMessage, CoreMessage, LanguageModel, + Schema, StepResult, streamText, TextStreamPart, + tool, Tool, - ToolCallUnion, + ToolCallPart, ToolResult, ToolSet, } from "mongodb-rag-core/aiSdk"; import { FilterPreviousMessages } from "./FilterPreviousMessages"; import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; import { strict as assert } from "assert"; -import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; +import { + EmbeddedContentForModel, + MakeReferenceLinksFunc, +} from "./MakeReferenceLinksFunc"; +import { makeDefaultReferenceLinks } from "./makeDefaultReferenceLinks"; export interface GenerateResponseWithSearchToolParams { languageModel: LanguageModel; llmNotWorkingMessage: string; - noRelevantContentMessage: string; inputGuardrail?: InputGuardrail; systemMessage: SystemMessage; filterPreviousMessages?: FilterPreviousMessages; /** Required tool for performing content search and gathering {@link References} */ - searchTool: SearchTool; additionalTools?: ToolSet; makeReferenceLinks?: MakeReferenceLinksFunc; maxSteps?: number; + findContent: FindContentFunc; } export const SEARCH_TOOL_NAME = "search_content"; -export const DefaultSearchArgsSchema = z.object({ query: z.string() }); -export type SearchArguments = z.infer; +export const SearchArgsSchema = z.object({ query: z.string() }); +export type SearchArguments = z.infer; export type SearchToolReturnValue = { content: { @@ -55,10 +61,6 @@ export type SearchToolReturnValue = { metadata?: Record; }[]; }; -export type SearchTool = Tool< - typeof DefaultSearchArgsSchema, - SearchToolReturnValue ->; export type SearchToolResult = ToolResult< typeof SEARCH_TOOL_NAME, @@ -75,10 +77,10 @@ export function makeGenerateResponseWithSearchTool({ inputGuardrail, systemMessage, filterPreviousMessages, - searchTool, additionalTools, makeReferenceLinks, maxSteps = 2, + findContent, }: GenerateResponseWithSearchToolParams): GenerateResponse { return async function generateResponseWithSearchTool({ conversation, @@ -102,19 +104,27 @@ export function makeGenerateResponseWithSearchTool({ ) : []; + const searchTool = tool({ + parameters: SearchArgsSchema, + execute: async ({ query }) => { + return await findContent({ + query, + }); + }, + description: "Search for relevant content.", + }); + const tools: ToolSet = { + [SEARCH_TOOL_NAME]: searchTool, + ...(additionalTools ?? {}), + }; const generationArgs = { model: languageModel, messages: [ systemMessage, ...filteredPreviousMessages, userMessage, - ] as CoreMessage[], - tools: { - [SEARCH_TOOL_NAME]: searchTool, - ...(additionalTools ?? {}), - } satisfies { - [SEARCH_TOOL_NAME]: SearchTool; - }, + ] satisfies CoreMessage[], + tools, maxSteps, }; @@ -133,62 +143,85 @@ export function makeGenerateResponseWithSearchTool({ }) : undefined; - const references: References = []; + const references: EmbeddedContentForModel[] = []; const { result, guardrailResult } = await withAbortControllerGuardrail( async (controller) => { - const toolDefinitions = { - [SEARCH_TOOL_NAME]: searchTool, - ...(additionalTools ?? {}), - }; - // Pass the tools as a separate parameter const { fullStream, steps } = streamText({ ...generationArgs, abortSignal: controller.signal, - tools: toolDefinitions, - onStepFinish: async ({ stepType, toolResults }) => { - // Add tool results to references - if (stepType === "tool-result") { - toolResults?.forEach( - ( - toolResult: ToolResult< - typeof SEARCH_TOOL_NAME, - SearchArguments, - SearchToolResult - > - ) => { - if (toolResult.toolName === SEARCH_TOOL_NAME) { - const extractedReferences: References = - extractReferencesFromStepResults(toolResults); - references.push(...extractedReferences); - } - } - ); - } + onStepFinish: async ({ toolResults }) => { + toolResults?.forEach((toolResult) => { + if ( + toolResult.toolName === SEARCH_TOOL_NAME && + toolResult.result?.content + ) { + // Map the search tool results to the References format + const searchResults = toolResult.result + .content as SearchToolResult["result"]["content"]; + const referencesToAdd = searchResults.map( + (item: { + url: string; + text: string; + metadata?: Record; + }) => ({ + url: item.url, + text: item.text, + metadata: item.metadata, + }) + ); + references.push(...referencesToAdd); + } + }); }, }); if (shouldStream) { assert(dataStreamer, "dataStreamer is required for streaming"); - await handleStreamResults(fullStream, shouldStream, dataStreamer); + for await (const chunk of fullStream) { + switch (chunk.type) { + case "text-delta": + if (shouldStream) { + dataStreamer?.streamData({ + data: chunk.textDelta, + type: "delta", + }); + } + break; + case "error": + console.error("Error in stream:", chunk.error); + throw new Error( + typeof chunk.error === "string" + ? chunk.error + : String(chunk.error) + ); + default: + break; + } + } } - const stepResults = await steps; - console.log( - "stepResults::", - stepResults.map((s) => ({ - type: s.stepType, - calls: JSON.stringify(s.toolCalls), - results: JSON.stringify(s.toolResults), - text: s.text, - })) - ); - - return { - stepResults, - references: makeReferenceLinks + try { + // Transform filtered references to include the required title property + const referencesOut = makeReferenceLinks ? makeReferenceLinks(references) - : references, - }; + : makeDefaultReferenceLinks(references); + dataStreamer?.streamData({ + data: referencesOut, + type: "references", + }); + const stepResults = await steps; + + return { + stepResults, + references: referencesOut, + } satisfies { + stepResults: StepResult[]; + references: References; + }; + } catch (error: unknown) { + console.error("Error in stream:", error); + throw new Error(typeof error === "string" ? error : String(error)); + } }, inputGuardrailPromise ); @@ -273,9 +306,7 @@ async function handleStreamResults( } break; case "error": - if (shouldStream) { - dataStreamer?.disconnect(); - } + console.error("Error in stream:", chunk.error); throw new Error( typeof chunk.error === "string" ? chunk.error : String(chunk.error) ); @@ -285,35 +316,51 @@ async function handleStreamResults( } } -function extractReferencesFromStepResults< - TS extends { [SEARCH_TOOL_NAME]: SearchTool } ->(stepResults: StepResult[]) { - const content: Partial[] = []; +type ToolParameters = z.ZodTypeAny | Schema; - for (const stepResult of stepResults) { - if (stepResult.toolResults) { - for (const toolResult of Object.values(stepResult.toolResults)) { - if ( - toolResult.toolName === SEARCH_TOOL_NAME && - toolResult.result?.content - ) { - // Map the search tool results to the References format - const searchResults = toolResult.result.content; - const referencesToAdd = searchResults.map((item) => ({ - url: item.url, - title: item.metadata?.pageTitle ?? item.url, - metadata: item.metadata ?? {}, - })); +type inferParameters = + PARAMETERS extends Schema + ? PARAMETERS["_type"] + : PARAMETERS extends z.ZodTypeAny + ? z.infer + : never; - content.push(...referencesToAdd); - } - } - } - } +// Extract tools that have an execute property and return their result types +type ExecutableTools = { + [K in keyof TOOLS]: TOOLS[K] extends { execute: (...args: any[]) => any } + ? K + : never; +}[keyof TOOLS]; - return content; -} +// Get the result type of a tool's execute function +type ToolExecuteResult = T extends { execute: (...args: any[]) => infer R } + ? Awaited + : never; +// Map tool names to their result types +type ToolResults = { + [K in ExecutableTools]: { + toolName: K & string; + toolCallId: string; + args: inferParameters; + result: ToolExecuteResult; + }; +}; +// Helper type to get a value from an object +type ValueOf< + ObjectType, + ValueType extends keyof ObjectType = keyof ObjectType +> = ObjectType[ValueType]; + +// Create a union type of all possible tool results +type ToolResultUnion = ValueOf>; + +// Create an array type for tool results +type ToolResultArray = Array< + ToolResultUnion & { type: "tool-result" } +>; + +// ... (rest of the code remains the same) /** Generate the final messages to send to the user based on guardrail result and text generation result */ @@ -391,18 +438,21 @@ function formatMessageForAiSdk(message: SomeMessage): CoreMessage { // This is a tool call message return { role: "assistant", - content: "", - function_call: { - name: message.toolCall.id, - arguments: JSON.stringify(message.toolCall.function), - }, - } as CoreAssistantMessage; + content: [ + { + type: "tool-call", + toolCallId: message.toolCall.id, + toolName: message.toolCall.function.name, + args: message.toolCall.function.arguments, + } satisfies ToolCallPart, + ], + } satisfies CoreAssistantMessage; } else { // Fallback for other object content return { role: "assistant", content: JSON.stringify(message.content), - } as CoreAssistantMessage; + } satisfies CoreAssistantMessage; } } else if (message.role === "tool") { // Convert tool messages to the format expected by the AI SDK @@ -412,10 +462,9 @@ function formatMessageForAiSdk(message: SomeMessage): CoreMessage { typeof message.content === "string" ? message.content : JSON.stringify(message.content), - name: message.name, // Include the name property - } as CoreMessage; + } satisfies CoreMessage; } else { // User and system messages can pass through - return message as CoreMessage; + return message satisfies CoreMessage; } } diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts index 1745f099f..2504a78ae 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts @@ -29,27 +29,10 @@ import { } from "./conversationsRouter"; import { wrapTraced } from "mongodb-rag-core/braintrust"; import { UpdateTraceFunc, updateTraceIfExists } from "./UpdateTraceFunc"; - -export type ClientContext = Record; - -export interface GenerateResponseParams { - shouldStream: boolean; - latestMessageText: string; - clientContext?: ClientContext; - customData?: ConversationCustomData; - dataStreamer?: DataStreamer; - reqId: string; - conversation: Conversation; - request?: ExpressRequest; -} - -export interface GenerateResponseReturnValue { - messages: SomeMessage[]; -} - -export type GenerateResponse = ( - params: GenerateResponseParams -) => Promise; +import { + GenerateResponse, + GenerateResponseParams, +} from "../../processors/GenerateResponse"; export const DEFAULT_MAX_INPUT_LENGTH = 3000; // magic number for max input size for LLM export const DEFAULT_MAX_USER_MESSAGES_IN_CONVERSATION = 7; // magic number for max messages in a conversation diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts b/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts index 932b03610..175299255 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/conversationsRouter.ts @@ -16,7 +16,6 @@ import { import { AddMessageRequest, AddMessageToConversationRouteParams, - GenerateResponse, makeAddMessageToConversationRoute, } from "./addMessageToConversation"; import { requireRequestOrigin } from "../../middleware/requireRequestOrigin"; @@ -27,6 +26,7 @@ import { makeGetConversationRoute, } from "./getConversation"; import { UpdateTraceFunc } from "./UpdateTraceFunc"; +import { GenerateResponse } from "../../processors/GenerateResponse"; /** Configuration for rate limiting on the /conversations/* routes. diff --git a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts index 67a762098..a1132f7a0 100644 --- a/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts +++ b/packages/mongodb-chatbot-server/src/routes/legacyGenerateResponse.ts @@ -19,7 +19,7 @@ import { FilterPreviousMessages } from "../processors/FilterPreviousMessages"; import { GenerateResponseParams, GenerateResponseReturnValue, -} from "./conversations/addMessageToConversation"; +} from "../processors/GenerateResponse"; export type GenerateUserPromptFuncParams = { /** From 40fa9d1889d4fe604b4df7b055039766cfb2b796 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 12 May 2025 09:20:55 -0400 Subject: [PATCH 14/29] Add processing --- .../src/processors/generateResponseWithSearchTool.ts | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index 5a78753b8..b690c19bf 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -106,7 +106,11 @@ export function makeGenerateResponseWithSearchTool({ const searchTool = tool({ parameters: SearchArgsSchema, - execute: async ({ query }) => { + execute: async ({ query }, { toolCallId }) => { + dataStreamer?.streamData({ + data: `Searching for '${query}'...`, + type: "processing", + }); return await findContent({ query, }); @@ -149,6 +153,7 @@ export function makeGenerateResponseWithSearchTool({ // Pass the tools as a separate parameter const { fullStream, steps } = streamText({ ...generationArgs, + toolChoice: "auto", abortSignal: controller.signal, onStepFinish: async ({ toolResults }) => { toolResults?.forEach((toolResult) => { From 31bd0a863f4ab49fa98c876188f0ec56363ac2e7 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Thu, 22 May 2025 19:10:10 -0400 Subject: [PATCH 15/29] working tool calling --- package-lock.json | 360 +++++------------- .../src/config.ts | 12 +- .../src/systemPrompt.ts | 57 ++- .../src/tools.ts | 99 ++--- .../src/tracing/extractTracingData.ts | 1 + .../generateResponseWithSearchTool.test.ts | 175 ++++----- .../generateResponseWithSearchTool.ts | 234 ++++-------- .../conversations/addMessageToConversation.ts | 3 +- packages/mongodb-rag-core/package.json | 4 +- 9 files changed, 350 insertions(+), 595 deletions(-) diff --git a/package-lock.json b/package-lock.json index ddffa2c87..9c66c022c 100644 --- a/package-lock.json +++ b/package-lock.json @@ -110,13 +110,15 @@ } }, "node_modules/@ai-sdk/provider-utils": { - "version": "1.0.9", + "version": "1.0.22", + "resolved": "https://registry.npmjs.org/@ai-sdk/provider-utils/-/provider-utils-1.0.22.tgz", + "integrity": "sha512-YHK2rpj++wnLVc9vPGzGFP3Pjeld2MwhKinetA0zKXOoHAT/Jit5O8kZsxcSlJPu9wvcGT1UGZEjZrtO7PfFOQ==", "license": "Apache-2.0", "dependencies": { - "@ai-sdk/provider": "0.0.17", - "eventsource-parser": "1.1.2", - "nanoid": "3.3.6", - "secure-json-parse": "2.7.0" + "@ai-sdk/provider": "0.0.26", + "eventsource-parser": "^1.1.2", + "nanoid": "^3.3.7", + "secure-json-parse": "^2.7.0" }, "engines": { "node": ">=18" @@ -131,44 +133,33 @@ } }, "node_modules/@ai-sdk/provider-utils/node_modules/@ai-sdk/provider": { - "version": "0.0.17", + "version": "0.0.26", + "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-0.0.26.tgz", + "integrity": "sha512-dQkfBDs2lTYpKM8389oopPdQgIU007GQyCbuPPrV+K6MtSII3HBfE0stUIMXUb44L+LK1t6GXPP7wjSzjO6uKg==", "license": "Apache-2.0", "dependencies": { - "json-schema": "0.4.0" + "json-schema": "^0.4.0" }, "engines": { "node": ">=18" } }, - "node_modules/@ai-sdk/provider-utils/node_modules/nanoid": { - "version": "3.3.6", - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/ai" - } - ], - "license": "MIT", - "bin": { - "nanoid": "bin/nanoid.cjs" - }, - "engines": { - "node": "^10 || ^12 || ^13.7 || ^14 || >=15.0.1" - } - }, "node_modules/@ai-sdk/react": { - "version": "0.0.40", + "version": "0.0.70", + "resolved": "https://registry.npmjs.org/@ai-sdk/react/-/react-0.0.70.tgz", + "integrity": "sha512-GnwbtjW4/4z7MleLiW+TOZC2M29eCg1tOUpuEiYFMmFNZK8mkrqM0PFZMo6UsYeUYMWqEOOcPOU9OQVJMJh7IQ==", "license": "Apache-2.0", "dependencies": { - "@ai-sdk/provider-utils": "1.0.9", - "@ai-sdk/ui-utils": "0.0.28", - "swr": "2.2.5" + "@ai-sdk/provider-utils": "1.0.22", + "@ai-sdk/ui-utils": "0.0.50", + "swr": "^2.2.5", + "throttleit": "2.1.0" }, "engines": { "node": ">=18" }, "peerDependencies": { - "react": "^18 || ^19", + "react": "^18 || ^19 || ^19.0.0-rc", "zod": "^3.0.0" }, "peerDependenciesMeta": { @@ -181,11 +172,13 @@ } }, "node_modules/@ai-sdk/solid": { - "version": "0.0.31", + "version": "0.0.54", + "resolved": "https://registry.npmjs.org/@ai-sdk/solid/-/solid-0.0.54.tgz", + "integrity": "sha512-96KWTVK+opdFeRubqrgaJXoNiDP89gNxFRWUp0PJOotZW816AbhUf4EnDjBjXTLjXL1n0h8tGSE9sZsRkj9wQQ==", "license": "Apache-2.0", "dependencies": { - "@ai-sdk/provider-utils": "1.0.9", - "@ai-sdk/ui-utils": "0.0.28" + "@ai-sdk/provider-utils": "1.0.22", + "@ai-sdk/ui-utils": "0.0.50" }, "engines": { "node": ">=18" @@ -200,18 +193,20 @@ } }, "node_modules/@ai-sdk/svelte": { - "version": "0.0.33", + "version": "0.0.57", + "resolved": "https://registry.npmjs.org/@ai-sdk/svelte/-/svelte-0.0.57.tgz", + "integrity": "sha512-SyF9ItIR9ALP9yDNAD+2/5Vl1IT6kchgyDH8xkmhysfJI6WrvJbtO1wdQ0nylvPLcsPoYu+cAlz1krU4lFHcYw==", "license": "Apache-2.0", "dependencies": { - "@ai-sdk/provider-utils": "1.0.9", - "@ai-sdk/ui-utils": "0.0.28", - "sswr": "2.1.0" + "@ai-sdk/provider-utils": "1.0.22", + "@ai-sdk/ui-utils": "0.0.50", + "sswr": "^2.1.0" }, "engines": { "node": ">=18" }, "peerDependencies": { - "svelte": "^3.0.0 || ^4.0.0" + "svelte": "^3.0.0 || ^4.0.0 || ^5.0.0" }, "peerDependenciesMeta": { "svelte": { @@ -220,12 +215,16 @@ } }, "node_modules/@ai-sdk/ui-utils": { - "version": "0.0.28", + "version": "0.0.50", + "resolved": "https://registry.npmjs.org/@ai-sdk/ui-utils/-/ui-utils-0.0.50.tgz", + "integrity": "sha512-Z5QYJVW+5XpSaJ4jYCCAVG7zIAuKOOdikhgpksneNmKvx61ACFaf98pmOd+xnjahl0pIlc/QIe6O4yVaJ1sEaw==", "license": "Apache-2.0", "dependencies": { - "@ai-sdk/provider": "0.0.17", - "@ai-sdk/provider-utils": "1.0.9", - "secure-json-parse": "2.7.0" + "@ai-sdk/provider": "0.0.26", + "@ai-sdk/provider-utils": "1.0.22", + "json-schema": "^0.4.0", + "secure-json-parse": "^2.7.0", + "zod-to-json-schema": "^3.23.3" }, "engines": { "node": ">=18" @@ -240,22 +239,26 @@ } }, "node_modules/@ai-sdk/ui-utils/node_modules/@ai-sdk/provider": { - "version": "0.0.17", + "version": "0.0.26", + "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-0.0.26.tgz", + "integrity": "sha512-dQkfBDs2lTYpKM8389oopPdQgIU007GQyCbuPPrV+K6MtSII3HBfE0stUIMXUb44L+LK1t6GXPP7wjSzjO6uKg==", "license": "Apache-2.0", "dependencies": { - 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}, - "peerDependenciesMeta": { - "svelte": { - "optional": true - } - } - }, - "packages/release-notes-generator/node_modules/@ai-sdk/ui-utils": { - "version": "0.0.50", - "resolved": "https://registry.npmjs.org/@ai-sdk/ui-utils/-/ui-utils-0.0.50.tgz", - "integrity": "sha512-Z5QYJVW+5XpSaJ4jYCCAVG7zIAuKOOdikhgpksneNmKvx61ACFaf98pmOd+xnjahl0pIlc/QIe6O4yVaJ1sEaw==", - "license": "Apache-2.0", - "dependencies": { - "@ai-sdk/provider": "0.0.26", - "@ai-sdk/provider-utils": "1.0.22", - "json-schema": "^0.4.0", - "secure-json-parse": "^2.7.0", - "zod-to-json-schema": "^3.23.3" - }, - "engines": { - "node": ">=18" - }, - "peerDependencies": { - "zod": "^3.0.0" - }, - "peerDependenciesMeta": { - "zod": { - "optional": true - } - } - }, - "packages/release-notes-generator/node_modules/@ai-sdk/ui-utils/node_modules/@ai-sdk/provider": { - "version": "0.0.26", - "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-0.0.26.tgz", - "integrity": "sha512-dQkfBDs2lTYpKM8389oopPdQgIU007GQyCbuPPrV+K6MtSII3HBfE0stUIMXUb44L+LK1t6GXPP7wjSzjO6uKg==", - "license": "Apache-2.0", - "dependencies": { - "json-schema": "^0.4.0" - }, - "engines": { - "node": ">=18" - } - }, "packages/release-notes-generator/node_modules/@anthropic-ai/sdk": { "version": "0.27.3", "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.27.3.tgz", @@ -60316,28 +60158,6 @@ "node": ">=18" } }, - "packages/release-notes-generator/node_modules/ai/node_modules/@ai-sdk/vue": { - "version": "0.0.59", - "resolved": "https://registry.npmjs.org/@ai-sdk/vue/-/vue-0.0.59.tgz", - "integrity": "sha512-+ofYlnqdc8c4F6tM0IKF0+7NagZRAiqBJpGDJ+6EYhDW8FHLUP/JFBgu32SjxSxC6IKFZxEnl68ZoP/Z38EMlw==", - "license": "Apache-2.0", - "dependencies": { - "@ai-sdk/provider-utils": "1.0.22", - "@ai-sdk/ui-utils": "0.0.50", - "swrv": "^1.0.4" - }, - "engines": { - "node": ">=18" - }, - "peerDependencies": { - "vue": "^3.3.4" - }, - "peerDependenciesMeta": { - "vue": { - "optional": true - } - } - }, "packages/release-notes-generator/node_modules/argparse": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/argparse/-/argparse-2.0.1.tgz", diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index 7b78473fa..47ce01481 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -28,7 +28,11 @@ import { makeMongoDbReferences } from "./processors/makeMongoDbReferences"; import { redactConnectionUri } from "./middleware/redactConnectionUri"; import path from "path"; import express from "express"; -import { wrapOpenAI, wrapTraced } from "mongodb-rag-core/braintrust"; +import { + wrapOpenAI, + wrapTraced, + wrapAISDKModel, +} from "mongodb-rag-core/braintrust"; import { AzureOpenAI } from "mongodb-rag-core/openai"; import { MongoClient } from "mongodb-rag-core/mongodb"; import { TRACING_ENV_VARS } from "./EnvVars"; @@ -39,6 +43,7 @@ import { } from "./tracing/routesUpdateTraceHandlers"; import { useSegmentIds } from "./middleware/useSegmentIds"; import { createAzure } from "mongodb-rag-core/aiSdk"; +import { makeSearchTool } from "./tools"; export const { MONGODB_CONNECTION_URI, MONGODB_DATABASE_NAME, @@ -196,7 +201,7 @@ const azureOpenAi = createAzure({ // apiKey: process.env.OPENAI_OPENAI_API_KEY, }); -const languageModel = azureOpenAi("gpt-4.1"); +const languageModel = wrapAISDKModel(azureOpenAi("gpt-4.1")); export const config: AppConfig = { conversationsRouterConfig: { middleware: [ @@ -263,7 +268,8 @@ export const config: AppConfig = { return conversation.messages; }, llmNotWorkingMessage: "LLM not working. Sad!", - findContent, + searchTool: makeSearchTool(findContent), + toolChoice: "auto", }), maxUserMessagesInConversation: 50, maxUserCommentLength: 500, diff --git a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts index 72e317ac1..360cca206 100644 --- a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts +++ b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts @@ -1,6 +1,6 @@ import { SEARCH_TOOL_NAME, SystemPrompt } from "mongodb-chatbot-server"; import { - mongoDbProductNames, + mongoDbProducts, mongoDbProgrammingLanguages, } from "./mongoDbMetadata"; @@ -25,28 +25,39 @@ const responseFormat = [ const technicalKnowledge = [ "You ONLY know about the current version of MongoDB products. Versions are provided in the information.", - "If `version: null`, then say that the product is unversioned.", + "If `version: null` in the retrieved content, then say that the product is unversioned.", "Do not hallucinate information that is not provided within the search results or that you otherwise know to be true.", ]; const searchContentToolNotes = [ - `ALWAYS use the ${SEARCH_TOOL_NAME} tool at the start of the conversation. No exceptions!`, - `For subsequent conversation messages, you can answer without using the ${SEARCH_TOOL_NAME} tool if the answer is already provided in the previous search results.`, - "Your purpose is to generate a search query for a given user input.", + `ALWAYS use the ${SEARCH_TOOL_NAME} tool prior to answering the user query.`, + "Generate an appropriate search query for a given user input.", "You are doing this for MongoDB, and all queries relate to MongoDB products.", 'When constructing the query, take a "step back" to generate a more general search query that finds the data relevant to the user query if relevant.', 'If the user query is already a "good" search query, do not modify it.', - 'For one word queries like "or", "and", "exists", if the query corresponds to a MongoDB operation, transform it into a fully formed question. Ex: "what is the $or operator in MongoDB?"', + 'For one word queries like "or", "and", "exists", if the query corresponds to a MongoDB operation, transform it into a fully formed question. Ex: If the user query is "or", transform it into "what is the $or operator in MongoDB?".', "You should also transform the user query into a fully formed question, if relevant.", - `Only generate ONE ${SEARCH_TOOL_NAME} tool call unless there are clearly multiple distinct queries needed to answer the user query.`, + `Only generate ONE ${SEARCH_TOOL_NAME} tool call per user message unless there are clearly multiple distinct queries needed to answer the user query.`, +]; + +const importantNotes = [ + `Again, ALWAYS use the ${SEARCH_TOOL_NAME} tool at the start of the conversation. Zero exceptions!`, ]; export const systemPrompt = { role: "system", content: `You are expert MongoDB documentation chatbot. + +${makeMarkdownNumberedList(importantNotes)} + + + You have the following personality: ${makeMarkdownNumberedList(personalityTraits)} + + + If you do not know the answer to the question, respond only with the following text: "${llmDoesNotKnowMessage}" @@ -54,16 +65,32 @@ If you do not know the answer to the question, respond only with the following t Response format: ${makeMarkdownNumberedList(responseFormat)} -Technical knowledge: + + + + ${makeMarkdownNumberedList(technicalKnowledge)} + + + + You know about the following products: -${mongoDbProductNames.map((product) => `* ${product}`).join("\n")} +${mongoDbProducts + .map( + (product) => + `* ${product.id}: ${product.name}. ${ + ("description" in product ? product.description : null) ?? "" + }` + ) + .join("\n")} You know about the following programming languages: -${mongoDbProgrammingLanguages.map((language) => `* ${language}`).join("\n")} +${mongoDbProgrammingLanguages.map((language) => `* ${language.id}`).join("\n")} + + -## Tools + @@ -71,7 +98,13 @@ You have access to the ${SEARCH_TOOL_NAME} tool. Use the ${SEARCH_TOOL_NAME} too ${makeMarkdownNumberedList(searchContentToolNotes)} When you search, include metadata about the relevant MongoDB programming language and product. -`, + + + + + +${makeMarkdownNumberedList(importantNotes)} +`, } satisfies SystemPrompt; function makeMarkdownNumberedList(items: string[]) { diff --git a/packages/chatbot-server-mongodb-public/src/tools.ts b/packages/chatbot-server-mongodb-public/src/tools.ts index 5ba4cfefb..2cc9ae4f0 100644 --- a/packages/chatbot-server-mongodb-public/src/tools.ts +++ b/packages/chatbot-server-mongodb-public/src/tools.ts @@ -1,57 +1,62 @@ -import { SearchToolResult } from "mongodb-chatbot-server"; +import { SearchTool, SearchToolReturnValue } from "mongodb-chatbot-server"; import { FindContentFunc, updateFrontMatter } from "mongodb-rag-core"; -import { tool } from "mongodb-rag-core/aiSdk"; +import { tool, ToolExecutionOptions } from "mongodb-rag-core/aiSdk"; import { z } from "zod"; import { mongoDbProducts, mongoDbProgrammingLanguageIds, } from "./mongoDbMetadata"; -// export function makeSearchTool(findContent: FindContentFunc): SearchTool { -// return tool({ -// parameters: z.object({ -// productName: z -// .enum( -// mongoDbProducts.map((product) => product.id) as [string, ...string[]] -// ) -// .nullable() -// .optional() -// .describe( -// "Most relevant MongoDB product for query. Leave null if unknown" -// ), -// programmingLanguage: z -// .enum(mongoDbProgrammingLanguageIds) -// .nullable() -// .optional() -// .describe( -// "Most relevant programming language for query. Leave null if unknown" -// ), -// query: z.string().describe("Search query"), -// }), -// description: "Search MongoDB content", -// async execute({ query, productName, programmingLanguage }) { -// // Ensure we match the SearchToolResult type exactly -// const nonNullMetadata: Record = {}; -// if (productName) { -// nonNullMetadata.productName = productName; -// } -// if (programmingLanguage) { -// nonNullMetadata.programmingLanguage = programmingLanguage; -// } +const SearchToolArgsSchema = z.object({ + productName: z + .enum(mongoDbProducts.map((product) => product.id) as [string, ...string[]]) + .nullable() + .optional() + .describe("Most relevant MongoDB product for query. Leave null if unknown"), + programmingLanguage: z + .enum(mongoDbProgrammingLanguageIds) + .nullable() + .optional() + .describe( + "Most relevant programming language for query. Leave null if unknown" + ), + query: z.string().describe("Search query"), +}); -// const queryWithMetadata = updateFrontMatter(query, nonNullMetadata); -// const content = await findContent({ query: queryWithMetadata }); +export type SearchToolArgs = z.infer; -// // Ensure the returned structure matches SearchToolResult -// const result: SearchToolResult["result"] = { -// content: content.content.map((item) => ({ -// url: item.url, -// text: item.text, -// metadata: item.metadata, -// })), -// }; +export function makeSearchTool( + findContent: FindContentFunc +): SearchTool { + return tool({ + parameters: SearchToolArgsSchema, + description: "Search MongoDB content", + async execute( + args: SearchToolArgs, + _options: ToolExecutionOptions + ): Promise { + const { query, productName, programmingLanguage } = args; -// return result; -// }, -// }); -// } + const nonNullMetadata: Record = {}; + if (productName) { + nonNullMetadata.productName = productName; + } + if (programmingLanguage) { + nonNullMetadata.programmingLanguage = programmingLanguage; + } + + const queryWithMetadata = updateFrontMatter(query, nonNullMetadata); + const content = await findContent({ query: queryWithMetadata }); + + const result: SearchToolReturnValue = { + content: content.content.map((item) => ({ + url: item.url, + text: item.text, + metadata: item.metadata, + })), + }; + + return result; + }, + }); +} diff --git a/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts b/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts index 91125ee7c..37c9a2371 100644 --- a/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts +++ b/packages/chatbot-server-mongodb-public/src/tracing/extractTracingData.ts @@ -12,6 +12,7 @@ export function extractTracingData( messages: Message[], assistantMessageId: ObjectId ) { + // FIXME: this is throwing after the generation is complete. don't forget to fix before merge of EAI-990 const evalAssistantMessageIdx = messages.findLastIndex( (message) => message.role === "assistant" && message.id.equals(assistantMessageId) diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts index b5e205f3c..d53314d97 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts @@ -1,5 +1,8 @@ import { jest } from "@jest/globals"; -import { makeGenerateResponseWithSearchTool } from "./generateResponseWithSearchTool"; +import { + makeGenerateResponseWithSearchTool, + SearchToolReturnValue, +} from "./generateResponseWithSearchTool"; import { AssistantMessage, References, @@ -16,18 +19,12 @@ import { ToolChoice, ToolSet, } from "mongodb-rag-core/aiSdk"; - -// Mock dependencies -jest.mock("mongodb-rag-core/aiSdk", () => { - const originalModule = jest.requireActual("mongodb-rag-core/aiSdk"); - return { - ...originalModule, - generateText: jest.fn(), - streamText: jest.fn(), - }; -}); - -import { generateText, streamText } from "mongodb-rag-core/aiSdk"; +import { z } from "zod"; +import { + generateText, + streamText, + ToolExecutionOptions, +} from "mongodb-rag-core/aiSdk"; describe("generateResponseWithSearchTool", () => { // Mock setup @@ -41,15 +38,22 @@ describe("generateResponseWithSearchTool", () => { content: "You are a helpful assistant.", }; + const mockSearchToolParameters = z.object({ + query: z.string(), + }); + const mockSearchTool = tool({ - name: "search_content", - parameters: { query: { type: "string" } }, - async execute(args) { + description: "Test search tool", + parameters: mockSearchToolParameters, + async execute( + { query }: { query: string }, + _options?: ToolExecutionOptions + ): Promise { return { content: [ { url: "https://example.com", - text: "Example content", + text: `Content for query: ${query}`, metadata: { pageTitle: "Example Page" }, }, ], @@ -83,9 +87,46 @@ describe("generateResponseWithSearchTool", () => { expect(typeof generateResponse).toBe("function"); }); + it("should filter previous messages", async () => { + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + }); + + const result = await generateResponse({ + conversation: { messages: [] }, + latestMessageText: "Hello", + shouldStream: false, + }); - describe("non-streaming mode", () => { - test("should handle successful generation", async () => { + expect(result).toHaveProperty("messages"); + expect(result.messages).toHaveLength(2); // User + assistant + }); + + it("should make reference links", async () => { + const generateResponse = makeGenerateResponseWithSearchTool({ + languageModel: mockLanguageModel, + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, + filterPreviousMessages: mockFilterPreviousMessages, + }); + + const result = await generateResponse({ + conversation: { messages: [] }, + latestMessageText: "Hello", + shouldStream: false, + }); + + expect(result).toHaveProperty("messages"); + expect(result.messages).toHaveLength(2); // User + assistant + }); + + describe("non-streaming", () => { + test("should handle successful generation non-streaming", async () => { // Mock generateText to return a successful result (generateText as jest.Mock).mockResolvedValueOnce({ text: "This is a response", @@ -220,7 +261,9 @@ describe("generateResponseWithSearchTool", () => { }); expect(result.messages).toHaveLength(2); // User + assistant }); - + test("should handle successful generation with guardrail", async () => { + // TODO: add + }); test("should handle streaming with guardrail rejection", async () => { const mockGuardrail = jest.fn().mockResolvedValue({ rejected: true, @@ -247,96 +290,10 @@ describe("generateResponseWithSearchTool", () => { expect(result.messages[1].role).toBe("assistant"); expect(result.messages[1].content).toBe("Content policy violation"); }); - }); - }); - - describe("helper functions", () => { - // Test the stepResultsToMessages function - test("stepResultsToMessages should convert step results to messages", () => { - // Import the function explicitly for testing - const { stepResultsToMessages } = jest.requireActual( - "./generateResponseWithSearchTool" - ); - - const mockStepResults: StepResult[] = [ - { - text: "Test response", - toolCalls: [ - { - toolCallId: "call-1", - toolName: "search_content", - args: { query: "test" }, - }, - ], - }, - { - text: "", - toolResults: [ - { - toolName: "search_content", - toolCallId: "call-1", - result: { content: [] }, - }, - ], - }, - ]; - - const messages = stepResultsToMessages(mockStepResults, []); - expect(messages).toHaveLength(3); // 1 assistant + 1 tool call + 1 tool result - expect(messages[0].role).toBe("assistant"); - expect(messages[1].role).toBe("assistant"); - expect(messages[1].toolCall).toBeDefined(); - expect(messages[2].role).toBe("tool"); - }); - - // Test convertConversationMessageToLlmMessage - test("convertConversationMessageToLlmMessage should convert different message types", () => { - // Import the function explicitly for testing - const { convertConversationMessageToLlmMessage } = jest.requireActual( - "./generateResponseWithSearchTool" - ); - - const userMessage: UserMessage = { - role: "user", - content: "Hello", - }; - - const assistantMessage: AssistantMessage = { - role: "assistant", - content: "Hi there", - toolCall: { - type: "function", - id: "call-1", - function: { name: "test", arguments: "{}" }, - }, - }; - - const systemMessage: SystemMessage = { - role: "system", - content: "You are helpful", - }; - - const toolMessage: ToolMessage = { - role: "tool", - name: "search_content", - content: '{"results": []}', - }; - - expect(convertConversationMessageToLlmMessage(userMessage).role).toBe( - "user" - ); - expect( - convertConversationMessageToLlmMessage(assistantMessage).role - ).toBe("assistant"); - expect(convertConversationMessageToLlmMessage(systemMessage).role).toBe( - "system" - ); - - const convertedToolMessage = - convertConversationMessageToLlmMessage(toolMessage); - expect(convertedToolMessage.role).toBe("tool"); - expect(Array.isArray(convertedToolMessage.content)).toBe(true); + test("should handle error in language model", async () => { + // TODO: add + }); }); }); }); diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index b690c19bf..bb7830c81 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -6,8 +6,6 @@ import { UserMessage, AssistantMessage, ToolMessage, - EmbeddedContent, - FindContentFunc, } from "mongodb-rag-core"; import { z } from "zod"; import { GenerateResponse } from "./GenerateResponse"; @@ -15,26 +13,48 @@ import { CoreAssistantMessage, CoreMessage, LanguageModel, - Schema, StepResult, streamText, - TextStreamPart, - tool, Tool, ToolCallPart, - ToolResult, + ToolChoice, + ToolExecutionOptions, + ToolResultUnion, ToolSet, } from "mongodb-rag-core/aiSdk"; import { FilterPreviousMessages } from "./FilterPreviousMessages"; import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; import { strict as assert } from "assert"; -import { - EmbeddedContentForModel, - MakeReferenceLinksFunc, -} from "./MakeReferenceLinksFunc"; +import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; import { makeDefaultReferenceLinks } from "./makeDefaultReferenceLinks"; -export interface GenerateResponseWithSearchToolParams { +export const SEARCH_TOOL_NAME = "search_content"; + +export type SearchToolReturnValue = { + content: { + url: string; + text: string; + metadata?: Record; + }[]; +}; + +export type SearchTool = Tool< + ARGUMENTS, + SearchToolReturnValue +> & { + execute: ( + args: z.infer, + options: ToolExecutionOptions + ) => PromiseLike; +}; + +type SearchToolResult = ToolResultUnion<{ + [SEARCH_TOOL_NAME]: SearchTool; +}>; + +export interface GenerateResponseWithSearchToolParams< + ARGUMENTS extends z.ZodTypeAny +> { languageModel: LanguageModel; llmNotWorkingMessage: string; inputGuardrail?: InputGuardrail; @@ -46,32 +66,16 @@ export interface GenerateResponseWithSearchToolParams { additionalTools?: ToolSet; makeReferenceLinks?: MakeReferenceLinksFunc; maxSteps?: number; - findContent: FindContentFunc; + toolChoice?: ToolChoice<{ search_content: SearchTool }>; + searchTool: SearchTool; } -export const SEARCH_TOOL_NAME = "search_content"; - -export const SearchArgsSchema = z.object({ query: z.string() }); -export type SearchArguments = z.infer; - -export type SearchToolReturnValue = { - content: { - url: string; - text: string; - metadata?: Record; - }[]; -}; - -export type SearchToolResult = ToolResult< - typeof SEARCH_TOOL_NAME, - SearchArguments, - SearchToolReturnValue ->; - /** Generate chatbot response using RAG and a search tool named {@link SEARCH_TOOL_NAME}. */ -export function makeGenerateResponseWithSearchTool({ +export function makeGenerateResponseWithSearchTool< + ARGUMENTS extends z.ZodTypeAny +>({ languageModel, llmNotWorkingMessage, inputGuardrail, @@ -80,8 +84,9 @@ export function makeGenerateResponseWithSearchTool({ additionalTools, makeReferenceLinks, maxSteps = 2, - findContent, -}: GenerateResponseWithSearchToolParams): GenerateResponse { + searchTool, + toolChoice, +}: GenerateResponseWithSearchToolParams): GenerateResponse { return async function generateResponseWithSearchTool({ conversation, latestMessageText, @@ -104,23 +109,11 @@ export function makeGenerateResponseWithSearchTool({ ) : []; - const searchTool = tool({ - parameters: SearchArgsSchema, - execute: async ({ query }, { toolCallId }) => { - dataStreamer?.streamData({ - data: `Searching for '${query}'...`, - type: "processing", - }); - return await findContent({ - query, - }); - }, - description: "Search for relevant content.", - }); - const tools: ToolSet = { + const toolSet = { [SEARCH_TOOL_NAME]: searchTool, ...(additionalTools ?? {}), - }; + } satisfies ToolSet; + const generationArgs = { model: languageModel, messages: [ @@ -128,7 +121,8 @@ export function makeGenerateResponseWithSearchTool({ ...filteredPreviousMessages, userMessage, ] satisfies CoreMessage[], - tools, + tools: toolSet, + toolChoice, maxSteps, }; @@ -147,37 +141,38 @@ export function makeGenerateResponseWithSearchTool({ }) : undefined; - const references: EmbeddedContentForModel[] = []; + const references: any[] = []; const { result, guardrailResult } = await withAbortControllerGuardrail( async (controller) => { // Pass the tools as a separate parameter - const { fullStream, steps } = streamText({ + const { fullStream, steps, text } = streamText({ ...generationArgs, - toolChoice: "auto", abortSignal: controller.signal, onStepFinish: async ({ toolResults }) => { - toolResults?.forEach((toolResult) => { - if ( - toolResult.toolName === SEARCH_TOOL_NAME && - toolResult.result?.content - ) { - // Map the search tool results to the References format - const searchResults = toolResult.result - .content as SearchToolResult["result"]["content"]; - const referencesToAdd = searchResults.map( - (item: { - url: string; - text: string; - metadata?: Record; - }) => ({ - url: item.url, - text: item.text, - metadata: item.metadata, - }) - ); - references.push(...referencesToAdd); + toolResults?.forEach( + (toolResult: SearchToolResult) => { + if ( + toolResult.toolName === SEARCH_TOOL_NAME && + toolResult.result.content + ) { + // Map the search tool results to the References format + const searchResults = toolResult.result + .content as SearchToolResult["result"]["content"]; + const referencesToAdd = searchResults.map( + (item: { + url: string; + text: string; + metadata?: Record; + }) => ({ + url: item.url, + text: item.text, + metadata: item.metadata, + }) + ); + references.push(...referencesToAdd); + } } - }); + ); }, }); if (shouldStream) { @@ -215,13 +210,16 @@ export function makeGenerateResponseWithSearchTool({ type: "references", }); const stepResults = await steps; + const finalText = await text; // Await the text promise return { stepResults, references: referencesOut, + text: finalText, // Include the final text response as a string } satisfies { - stepResults: StepResult[]; + stepResults: StepResult[]; references: References; + text: string; // Update type definition }; } catch (error: unknown) { console.error("Error in stream:", error); @@ -251,7 +249,8 @@ export function makeGenerateResponseWithSearchTool({ } }; } - +// TODO: somewhere in here, it's taking the tool call results, and formatting them as normal assistant messages, which is confusing the model in subsequent genrations +// see https://www.braintrust.dev/app/mongodb-education-ai/p/chatbot-responses-dev/logs?r=682fadec303d9ec3dcc510bf&s=52058b8a-ad63-4628-8ecd-07b43c747cd4 function stepResultsToMessages( stepResults?: StepResult[], references?: References @@ -295,88 +294,17 @@ function stepResultsToMessages( .flat(); } -async function handleStreamResults( - streamFromAiSdk: AsyncIterable>, - shouldStream: boolean, - dataStreamer?: DataStreamer -) { - for await (const chunk of streamFromAiSdk) { - switch (chunk.type) { - case "text-delta": - if (shouldStream) { - dataStreamer?.streamData({ - data: chunk.textDelta, - type: "delta", - }); - } - break; - case "error": - console.error("Error in stream:", chunk.error); - throw new Error( - typeof chunk.error === "string" ? chunk.error : String(chunk.error) - ); - default: - break; - } - } -} - -type ToolParameters = z.ZodTypeAny | Schema; - -type inferParameters = - PARAMETERS extends Schema - ? PARAMETERS["_type"] - : PARAMETERS extends z.ZodTypeAny - ? z.infer - : never; - -// Extract tools that have an execute property and return their result types -type ExecutableTools = { - [K in keyof TOOLS]: TOOLS[K] extends { execute: (...args: any[]) => any } - ? K - : never; -}[keyof TOOLS]; - -// Get the result type of a tool's execute function -type ToolExecuteResult = T extends { execute: (...args: any[]) => infer R } - ? Awaited - : never; - -// Map tool names to their result types -type ToolResults = { - [K in ExecutableTools]: { - toolName: K & string; - toolCallId: string; - args: inferParameters; - result: ToolExecuteResult; - }; -}; -// Helper type to get a value from an object -type ValueOf< - ObjectType, - ValueType extends keyof ObjectType = keyof ObjectType -> = ObjectType[ValueType]; - -// Create a union type of all possible tool results -type ToolResultUnion = ValueOf>; - -// Create an array type for tool results -type ToolResultArray = Array< - ToolResultUnion & { type: "tool-result" } ->; - -// ... (rest of the code remains the same) /** Generate the final messages to send to the user based on guardrail result and text generation result */ -function handleReturnGeneration( +function handleReturnGeneration( userMessage: UserMessage, guardrailResult: | { rejected: boolean; message: string; metadata?: Record } | undefined, textGenerationResult: | { - stepResults?: StepResult[]; + stepResults?: StepResult[]; references?: References; text?: string; } @@ -428,10 +356,16 @@ function handleReturnGeneration( return { messages: [ userMessage, - ...stepResultsToMessages( + ...stepResultsToMessages( textGenerationResult.stepResults, textGenerationResult.references ), + { + role: "assistant", + content: textGenerationResult.text || "", + references: textGenerationResult.references, + customData, + }, ] satisfies SomeMessage[], }; } diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts index 2504a78ae..15d0bc604 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts @@ -11,8 +11,6 @@ import { Conversation, SomeMessage, makeDataStreamer, - DataStreamer, - ConversationCustomData, } from "mongodb-rag-core"; import { ApiMessage, @@ -371,6 +369,7 @@ async function addMessagesToDatabase({ >[0]["messages"] )[messages.length - 1].id = assistantResponseMessageId; + console.log("messages out::", messages); const conversationId = conversation._id; const dbMessages = await conversations.addManyConversationMessages({ conversationId, diff --git a/packages/mongodb-rag-core/package.json b/packages/mongodb-rag-core/package.json index 4345910ba..4ccd0d5e1 100644 --- a/packages/mongodb-rag-core/package.json +++ b/packages/mongodb-rag-core/package.json @@ -86,7 +86,7 @@ "@supercharge/promise-pool": "^3.2.0", "acquit": "^1.3.0", "acquit-require": "^0.1.1", - "ai": "^4.3.10", + "ai": "^4.3.16", "braintrust": "^0.0.193", "common-tags": "^1", "deep-equal": "^2.2.3", @@ -109,4 +109,4 @@ "yaml": "^2.3.1", "zod": "^3.21.4" } -} +} \ No newline at end of file From 653aa596d9fce33643f21f8483517085cd550162 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Fri, 23 May 2025 11:38:25 -0400 Subject: [PATCH 16/29] making progress --- .../src/config.ts | 3 +- .../src/systemPrompt.ts | 5 +- .../generateResponseWithSearchTool.ts | 255 ++++++++---------- 3 files changed, 120 insertions(+), 143 deletions(-) diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index 47ce01481..7cce3f7fd 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -269,7 +269,8 @@ export const config: AppConfig = { }, llmNotWorkingMessage: "LLM not working. Sad!", searchTool: makeSearchTool(findContent), - toolChoice: "auto", + toolChoice: "required", + maxSteps: 5, }), maxUserMessagesInConversation: 50, maxUserCommentLength: 500, diff --git a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts index 360cca206..ea39d3e45 100644 --- a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts +++ b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts @@ -30,7 +30,7 @@ const technicalKnowledge = [ ]; const searchContentToolNotes = [ - `ALWAYS use the ${SEARCH_TOOL_NAME} tool prior to answering the user query.`, + `ALWAYS use the ${SEARCH_TOOL_NAME} tool prior upon recieving a user message.`, "Generate an appropriate search query for a given user input.", "You are doing this for MongoDB, and all queries relate to MongoDB products.", 'When constructing the query, take a "step back" to generate a more general search query that finds the data relevant to the user query if relevant.', @@ -41,7 +41,8 @@ const searchContentToolNotes = [ ]; const importantNotes = [ - `Again, ALWAYS use the ${SEARCH_TOOL_NAME} tool at the start of the conversation. Zero exceptions!`, + `ALWAYS use the ${SEARCH_TOOL_NAME} tool at the start of the conversation. Zero exceptions!`, + `Use the ${SEARCH_TOOL_NAME} tool whenever the latest message is a user message.`, ]; export const systemPrompt = { diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index bb7830c81..b08370f42 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -15,12 +15,15 @@ import { LanguageModel, StepResult, streamText, + StreamTextResult, Tool, ToolCallPart, ToolChoice, ToolExecutionOptions, ToolResultUnion, ToolSet, + AssistantResponse, + CoreToolMessage, } from "mongodb-rag-core/aiSdk"; import { FilterPreviousMessages } from "./FilterPreviousMessages"; import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; @@ -144,10 +147,14 @@ export function makeGenerateResponseWithSearchTool< const references: any[] = []; const { result, guardrailResult } = await withAbortControllerGuardrail( async (controller) => { + let toolChoice = generationArgs.toolChoice; // Pass the tools as a separate parameter - const { fullStream, steps, text } = streamText({ + const result = streamText({ ...generationArgs, + // Abort the stream if the guardrail AbortController is triggered abortSignal: controller.signal, + toolChoice, + // Add the search tool results to the references onStepFinish: async ({ toolResults }) => { toolResults?.forEach( (toolResult: SearchToolResult) => { @@ -155,21 +162,10 @@ export function makeGenerateResponseWithSearchTool< toolResult.toolName === SEARCH_TOOL_NAME && toolResult.result.content ) { + toolChoice = "auto"; // Map the search tool results to the References format - const searchResults = toolResult.result - .content as SearchToolResult["result"]["content"]; - const referencesToAdd = searchResults.map( - (item: { - url: string; - text: string; - metadata?: Record; - }) => ({ - url: item.url, - text: item.text, - metadata: item.metadata, - }) - ); - references.push(...referencesToAdd); + const searchResults = toolResult.result.content; + references.push(...searchResults); } } ); @@ -177,7 +173,7 @@ export function makeGenerateResponseWithSearchTool< }); if (shouldStream) { assert(dataStreamer, "dataStreamer is required for streaming"); - for await (const chunk of fullStream) { + for await (const chunk of result.fullStream) { switch (chunk.type) { case "text-delta": if (shouldStream) { @@ -209,18 +205,7 @@ export function makeGenerateResponseWithSearchTool< data: referencesOut, type: "references", }); - const stepResults = await steps; - const finalText = await text; // Await the text promise - - return { - stepResults, - references: referencesOut, - text: finalText, // Include the final text response as a string - } satisfies { - stepResults: StepResult[]; - references: References; - text: string; // Update type definition - }; + return result; } catch (error: unknown) { console.error("Error in stream:", error); throw new Error(typeof error === "string" ? error : String(error)); @@ -228,14 +213,29 @@ export function makeGenerateResponseWithSearchTool< }, inputGuardrailPromise ); - return handleReturnGeneration( + const text = await result?.text; + assert(text, "text is required"); + const steps = await result?.steps; + assert(steps, "steps is required"); + // console.log("steps", steps); + const messages = (await result?.response)?.messages; + assert(messages, "messages is required"); + + console.log("messages", JSON.stringify(messages, null, 2)); + return handleReturnGeneration({ userMessage, guardrailResult, - result, + messages, customData, - llmNotWorkingMessage - ); + references, + }); } catch (error: unknown) { + // TODO: handle guardrail failure so that the guardrail err is persisted. + + dataStreamer?.streamData({ + data: llmNotWorkingMessage, + type: "delta", + }); // Handle other errors return { messages: [ @@ -249,127 +249,102 @@ export function makeGenerateResponseWithSearchTool< } }; } -// TODO: somewhere in here, it's taking the tool call results, and formatting them as normal assistant messages, which is confusing the model in subsequent genrations -// see https://www.braintrust.dev/app/mongodb-education-ai/p/chatbot-responses-dev/logs?r=682fadec303d9ec3dcc510bf&s=52058b8a-ad63-4628-8ecd-07b43c747cd4 -function stepResultsToMessages( - stepResults?: StepResult[], - references?: References -): SomeMessage[] { - if (!stepResults) { - return []; - } - return stepResults - .map((stepResult) => { - if (stepResult.toolCalls) { - return stepResult.toolCalls.map( - (toolCall) => - ({ - role: "assistant", - content: toolCall.args, - toolCall: { - function: toolCall.args, - id: toolCall.toolCallId, - type: "function", - }, - } satisfies AssistantMessage) - ); - } - if (stepResult.toolResults) { - return stepResult.toolResults.map( - (toolResult) => - ({ - role: "tool", - name: toolResult.toolName, - content: toolResult.result, - } satisfies ToolMessage) - ); - } else { - return { - role: "assistant", - content: stepResult.text, - references, - } satisfies AssistantMessage; - } - }) - .flat(); -} +type ResponseMessage = CoreAssistantMessage | CoreToolMessage; /** Generate the final messages to send to the user based on guardrail result and text generation result */ -function handleReturnGeneration( - userMessage: UserMessage, +function handleReturnGeneration({ + userMessage, + guardrailResult, + messages, + references, +}: { + userMessage: UserMessage; guardrailResult: | { rejected: boolean; message: string; metadata?: Record } - | undefined, - textGenerationResult: - | { - stepResults?: StepResult[]; - references?: References; - text?: string; - } - | null - | undefined, - customData?: Record, - fallbackMessage = "Sorry, I'm having trouble generating a response." -): { messages: SomeMessage[] } { - if (guardrailResult?.rejected) { - return { - messages: [ - userMessage, - { - role: "assistant", - content: guardrailResult.message, - metadata: guardrailResult.metadata, - customData, - }, - ] satisfies SomeMessage[], - }; - } - - if (!textGenerationResult) { - return { - messages: [ - userMessage, - { - role: "assistant", - content: fallbackMessage, - }, - ], - }; - } - - // Check if stepResults exist, if not but we have text, create a response with just the text - if (!textGenerationResult.stepResults?.length && textGenerationResult.text) { - return { - messages: [ - userMessage, - { - role: "assistant", - content: textGenerationResult.text, - references: textGenerationResult.references, - }, - ], - }; - } - + | undefined; + messages: ResponseMessage[]; + references?: References; + customData?: Record; +}): { messages: SomeMessage[] } { + userMessage.rejectQuery = guardrailResult?.rejected; + userMessage.customData = { + ...userMessage.customData, + ...guardrailResult, + }; return { messages: [ userMessage, - ...stepResultsToMessages( - textGenerationResult.stepResults, - textGenerationResult.references - ), - { - role: "assistant", - content: textGenerationResult.text || "", - references: textGenerationResult.references, - customData, - }, + ...formatMessageForGeneration(messages, references ?? []), ] satisfies SomeMessage[], }; } +// TODO: implement this +function formatMessageForGeneration( + messages: ResponseMessage[], + references: References +): SomeMessage[] { + const messagesOut = messages + .map((m) => { + if (m.role === "assistant") { + const baseMessage: Partial & { role: "assistant" } = { + role: "assistant", + }; + if (typeof m.content === "string") { + baseMessage.content = m.content; + } else { + m.content.forEach((c) => { + if (c.type === "text") { + baseMessage.content = c.text; + } + if (c.type === "tool-call") { + baseMessage.toolCall = { + id: c.toolCallId, + function: { + name: c.toolName, + arguments: JSON.stringify(c.args), + }, + type: "function", + }; + } + }); + } + + return { + ...baseMessage, + content: baseMessage.content ?? "", + } satisfies AssistantMessage; + } else if (m.role === "tool") { + const baseMessage: Partial & { role: "tool" } = { + role: "tool", + }; + if (typeof m.content === "string") { + baseMessage.content = m.content; + } else { + m.content.forEach((c) => { + if (c.type === "tool-result") { + baseMessage.name = c.toolName; + baseMessage.content = JSON.stringify(c.result); + } + }); + } + return { + ...baseMessage, + name: baseMessage.name ?? "", + content: baseMessage.content ?? "", + } satisfies ToolMessage; + } + }) + .filter((m): m is AssistantMessage | ToolMessage => m !== undefined); + const latestMessage = messagesOut.at(-1); + if (latestMessage?.role === "assistant") { + latestMessage.references = references; + } + return messagesOut; +} + function formatMessageForAiSdk(message: SomeMessage): CoreMessage { if (message.role === "assistant" && typeof message.content === "object") { // Convert assistant messages with object content to proper format From 9d68d50c44cceb905c92b9ee067714fac9ad5428 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Fri, 23 May 2025 13:52:22 -0400 Subject: [PATCH 17/29] keepin on --- packages/chatbot-server-mongodb-public/src/config.ts | 5 +++-- .../src/systemPrompt.ts | 12 ++++++------ 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index 7cce3f7fd..fcf9c54de 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -264,12 +264,13 @@ export const config: AppConfig = { languageModel, systemMessage: systemPrompt, makeReferenceLinks: makeMongoDbReferences, + // TODO: update to only include user/assistant, no tool calls filterPreviousMessages: async (conversation) => { return conversation.messages; }, - llmNotWorkingMessage: "LLM not working. Sad!", + llmNotWorkingMessage: conversations.conversationConstants.LLM_NOT_WORKING, searchTool: makeSearchTool(findContent), - toolChoice: "required", + toolChoice: "auto", maxSteps: 5, }), maxUserMessagesInConversation: 50, diff --git a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts index ea39d3e45..931073fb6 100644 --- a/packages/chatbot-server-mongodb-public/src/systemPrompt.ts +++ b/packages/chatbot-server-mongodb-public/src/systemPrompt.ts @@ -29,8 +29,13 @@ const technicalKnowledge = [ "Do not hallucinate information that is not provided within the search results or that you otherwise know to be true.", ]; +const importantNotes = [ + `ALWAYS use the ${SEARCH_TOOL_NAME} tool at the start of the conversation. Zero exceptions!`, + `Use the ${SEARCH_TOOL_NAME} tool after every single user message.`, +]; + const searchContentToolNotes = [ - `ALWAYS use the ${SEARCH_TOOL_NAME} tool prior upon recieving a user message.`, + ...importantNotes, "Generate an appropriate search query for a given user input.", "You are doing this for MongoDB, and all queries relate to MongoDB products.", 'When constructing the query, take a "step back" to generate a more general search query that finds the data relevant to the user query if relevant.', @@ -40,11 +45,6 @@ const searchContentToolNotes = [ `Only generate ONE ${SEARCH_TOOL_NAME} tool call per user message unless there are clearly multiple distinct queries needed to answer the user query.`, ]; -const importantNotes = [ - `ALWAYS use the ${SEARCH_TOOL_NAME} tool at the start of the conversation. Zero exceptions!`, - `Use the ${SEARCH_TOOL_NAME} tool whenever the latest message is a user message.`, -]; - export const systemPrompt = { role: "system", content: `You are expert MongoDB documentation chatbot. From 57e65a208afd502a9aabc2aa9b9871f31e687238 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Fri, 23 May 2025 14:04:01 -0400 Subject: [PATCH 18/29] Clean config --- packages/chatbot-server-mongodb-public/src/config.ts | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index fcf9c54de..e25b429c2 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -264,9 +264,14 @@ export const config: AppConfig = { languageModel, systemMessage: systemPrompt, makeReferenceLinks: makeMongoDbReferences, - // TODO: update to only include user/assistant, no tool calls filterPreviousMessages: async (conversation) => { - return conversation.messages; + return conversation.messages.filter((message) => { + return ( + message.role === "user" || + // Only include assistant messages that are not tool calls + (message.role === "assistant" && !message.toolCall) + ); + }); }, llmNotWorkingMessage: conversations.conversationConstants.LLM_NOT_WORKING, searchTool: makeSearchTool(findContent), From aefa9ca6fd4298fa0394396c3c7cd5bc951c8f3f Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 27 May 2025 12:47:02 -0400 Subject: [PATCH 19/29] working e2e --- .../src/tools.ts | 5 +- .../src/processors/MakeReferenceLinksFunc.ts | 4 +- .../generateResponseWithSearchTool.test.ts | 481 +++++++++++------- .../generateResponseWithSearchTool.ts | 80 +-- .../processors/makeDefaultReferenceLinks.ts | 26 +- .../conversations/addMessageToConversation.ts | 1 - packages/mongodb-rag-core/src/aiSdk.ts | 6 + 7 files changed, 355 insertions(+), 248 deletions(-) diff --git a/packages/chatbot-server-mongodb-public/src/tools.ts b/packages/chatbot-server-mongodb-public/src/tools.ts index 2cc9ae4f0..87a325b1c 100644 --- a/packages/chatbot-server-mongodb-public/src/tools.ts +++ b/packages/chatbot-server-mongodb-public/src/tools.ts @@ -51,8 +51,11 @@ export function makeSearchTool( const result: SearchToolReturnValue = { content: content.content.map((item) => ({ url: item.url, + metadata: { + pageTitle: item.metadata?.pageTitle, + sourceName: item.sourceName, + }, text: item.text, - metadata: item.metadata, })), }; diff --git a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts index 2b76d8470..9481ee4d5 100644 --- a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts +++ b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts @@ -8,6 +8,4 @@ export type EmbeddedContentForModel = Pick< /** Function that generates the references in the response to user. */ -export type MakeReferenceLinksFunc = ( - chunks: (Partial & EmbeddedContentForModel)[] -) => References; +export type MakeReferenceLinksFunc = (references: References) => References; diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts index d53314d97..a44293c00 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts @@ -1,179 +1,262 @@ import { jest } from "@jest/globals"; import { makeGenerateResponseWithSearchTool, + SEARCH_TOOL_NAME, SearchToolReturnValue, } from "./generateResponseWithSearchTool"; +import { FilterPreviousMessages } from "./FilterPreviousMessages"; import { AssistantMessage, - References, + DataStreamer, SystemMessage, - ToolMessage, UserMessage, } from "mongodb-rag-core"; -import { - CoreMessage, - LanguageModel, - StepResult, - TextStreamPart, - tool, - ToolChoice, - ToolSet, -} from "mongodb-rag-core/aiSdk"; import { z } from "zod"; import { - generateText, - streamText, ToolExecutionOptions, + MockLanguageModelV1, + tool, + simulateReadableStream, + LanguageModelV1StreamPart, } from "mongodb-rag-core/aiSdk"; +import { ObjectId } from "mongodb-rag-core/mongodb"; +import { InputGuardrail } from "./InputGuardrail"; +import { GenerateResponseReturnValue } from "./GenerateResponse"; -describe("generateResponseWithSearchTool", () => { - // Mock setup - const mockLanguageModel: LanguageModel = { - id: "test-model", - provider: "test-provider", - }; +// Define the search tool arguments schema +const SearchToolArgsSchema = z.object({ + query: z.string(), +}); +type SearchToolArgs = z.infer; + +const latestMessageText = "Hello"; + +const mockReqId = "test"; - const mockSystemMessage: SystemMessage = { - role: "system", - content: "You are a helpful assistant.", +const mockContent = [ + { + url: "https://example.com", + text: `Content!`, + metadata: { + pageTitle: "Example Page", + }, + }, +]; + +const mockReferences = mockContent.map((content) => ({ + url: content.url, + title: content.metadata.pageTitle, + metadata: content.metadata, +})); + +// Create a mock search tool that matches the SearchTool interface +const mockSearchTool = tool({ + parameters: SearchToolArgsSchema, + description: "Search MongoDB content", + async execute( + _args: SearchToolArgs, + _options: ToolExecutionOptions + ): Promise { + return { + content: mockContent, + }; + }, +}); + +// Must have, but details don't matter +const mockFinishChunk = { + type: "finish" as const, + finishReason: "stop" as const, + usage: { + completionTokens: 10, + promptTokens: 3, + }, +} satisfies LanguageModelV1StreamPart; + +const finalAnswer = "Final answer"; +const finalAnswerChunks = finalAnswer.split(" "); +const finalAnswerStreamChunks = finalAnswerChunks.map((word, i) => { + if (i === 0) { + return { + type: "text-delta" as const, + textDelta: word, + }; + } + return { + type: "text-delta" as const, + textDelta: ` ${word}`, }; +}); - const mockSearchToolParameters = z.object({ - query: z.string(), +// Note: have to make this constructor b/c the ReadableStream +// can only be used once successfully. +const makeFinalAnswerStream = () => + simulateReadableStream({ + chunks: [ + ...finalAnswerStreamChunks, + mockFinishChunk, + ] satisfies LanguageModelV1StreamPart[], + chunkDelayInMs: 100, }); - const mockSearchTool = tool({ - description: "Test search tool", - parameters: mockSearchToolParameters, - async execute( - { query }: { query: string }, - _options?: ToolExecutionOptions - ): Promise { +const searchToolMockArgs = { + query: "test", +} satisfies SearchToolArgs; + +const makeToolCallStream = () => + simulateReadableStream({ + chunks: [ + { + type: "tool-call" as const, + toolCallId: "abc123", + toolName: SEARCH_TOOL_NAME, + toolCallType: "function" as const, + args: JSON.stringify(searchToolMockArgs), + }, + // ...finalAnswerStreamChunks, + mockFinishChunk, + ] satisfies LanguageModelV1StreamPart[], + chunkDelayInMs: 100, + }); + +jest.setTimeout(5000); +// Mock language model following the AI SDK testing documentation +// Create a minimalist mock for the language model +const makeMockLanguageModel = () => { + // On first call, return tool call stream + // On second call, return final answer stream + // On subsequent calls, return final answer + let counter = 0; + const doStreamCalls = [ + async () => { + return { + stream: makeToolCallStream(), + rawCall: { rawPrompt: null, rawSettings: {} }, + }; + }, + // eslint-disable-next-line @typescript-eslint/ban-ts-comment + // @ts-ignore + async () => { return { - content: [ - { - url: "https://example.com", - text: `Content for query: ${query}`, - metadata: { pageTitle: "Example Page" }, - }, - ], + stream: makeFinalAnswerStream(), + rawCall: { rawPrompt: null, rawSettings: {} }, }; }, + ]; + return new MockLanguageModelV1({ + doStream: () => { + const streamCallPromise = doStreamCalls[counter](); + if (counter < doStreamCalls.length) { + counter++; + } + return streamCallPromise; + }, }); +}; - const mockFilterPreviousMessages = jest.fn().mockResolvedValue([]); +const mockSystemMessage: SystemMessage = { + role: "system", + content: "You are a helpful assistant.", +}; - const mockLlmNotWorkingMessage = - "Sorry, I am having trouble with the language model."; +const mockLlmNotWorkingMessage = + "Sorry, I am having trouble with the language model."; - const mockDataStreamer = { - streamData: jest.fn(), - }; +const mockGuardrail: InputGuardrail = async () => ({ + rejected: true, + message: "Content policy violation", + metadata: { reason: "inappropriate" }, +}); +const mockThrowingLanguageModel: MockLanguageModelV1 = new MockLanguageModelV1({ + doStream: async () => { + throw new Error("LLM error"); + }, +}); + +const makeMakeGenerateResponseWithSearchToolArgs = () => ({ + languageModel: makeMockLanguageModel(), + llmNotWorkingMessage: mockLlmNotWorkingMessage, + systemMessage: mockSystemMessage, + searchTool: mockSearchTool, +}); + +const generateResponseBaseArgs = { + conversation: { + _id: new ObjectId(), + createdAt: new Date(), + messages: [], + }, + latestMessageText, + shouldStream: false, + reqId: mockReqId, +}; +describe("generateResponseWithSearchTool", () => { // Reset mocks before each test beforeEach(() => { jest.clearAllMocks(); }); describe("makeGenerateResponseWithSearchTool", () => { - test("should return a function", () => { - const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, - filterPreviousMessages: mockFilterPreviousMessages, - }); - + const generateResponse = makeGenerateResponseWithSearchTool( + makeMakeGenerateResponseWithSearchToolArgs() + ); + it("should return a function", () => { expect(typeof generateResponse).toBe("function"); }); it("should filter previous messages", async () => { + // Properly type the mock function to match FilterPreviousMessages + const mockFilterPreviousMessages = jest + .fn() + .mockImplementation((_conversation) => + Promise.resolve([]) + ) as FilterPreviousMessages; const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, + ...makeMakeGenerateResponseWithSearchToolArgs(), filterPreviousMessages: mockFilterPreviousMessages, }); - const result = await generateResponse({ - conversation: { messages: [] }, - latestMessageText: "Hello", - shouldStream: false, - }); + // We don't care about the output so not getting the return value + await generateResponse(generateResponseBaseArgs); - expect(result).toHaveProperty("messages"); - expect(result.messages).toHaveLength(2); // User + assistant + expect(mockFilterPreviousMessages).toHaveBeenCalledWith({ + _id: expect.any(ObjectId), + createdAt: expect.any(Date), + messages: [], + }); }); it("should make reference links", async () => { - const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, - filterPreviousMessages: mockFilterPreviousMessages, - }); + const generateResponse = makeGenerateResponseWithSearchTool( + makeMakeGenerateResponseWithSearchToolArgs() + ); - const result = await generateResponse({ - conversation: { messages: [] }, - latestMessageText: "Hello", - shouldStream: false, - }); + const result = await generateResponse(generateResponseBaseArgs); - expect(result).toHaveProperty("messages"); - expect(result.messages).toHaveLength(2); // User + assistant + expect((result.messages.at(-1) as AssistantMessage).references).toEqual( + mockReferences + ); }); describe("non-streaming", () => { test("should handle successful generation non-streaming", async () => { - // Mock generateText to return a successful result - (generateText as jest.Mock).mockResolvedValueOnce({ - text: "This is a response", - stepResults: [], - }); + const generateResponse = makeGenerateResponseWithSearchTool( + makeMakeGenerateResponseWithSearchToolArgs() + ); - const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, - filterPreviousMessages: mockFilterPreviousMessages, - }); + const result = await generateResponse(generateResponseBaseArgs); - const result = await generateResponse({ - conversation: { messages: [] }, - latestMessageText: "Hello", - shouldStream: false, - }); - - expect(result).toHaveProperty("messages"); - expect(result.messages).toHaveLength(2); // User + assistant - expect(result.messages[0].role).toBe("user"); - expect(result.messages[1].role).toBe("assistant"); + expectSuccessfulResult(result); }); - test("should handle guardrail rejection", async () => { - const mockGuardrail = jest.fn().mockResolvedValue({ - rejected: true, - message: "Content policy violation", - metadata: { reason: "inappropriate" }, - }); - + // TODO: (EAI-995): make work as part of guardrail changes + test.skip("should handle guardrail rejection", async () => { const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, - filterPreviousMessages: mockFilterPreviousMessages, + ...makeMakeGenerateResponseWithSearchToolArgs(), inputGuardrail: mockGuardrail, }); - const result = await generateResponse({ - conversation: { messages: [] }, - latestMessageText: "Bad question", - shouldStream: false, - }); + const result = await generateResponse(generateResponseBaseArgs); expect(result.messages[1].role).toBe("assistant"); expect(result.messages[1].content).toBe("Content policy violation"); @@ -183,117 +266,129 @@ describe("generateResponseWithSearchTool", () => { }); test("should handle error in language model", async () => { - (generateText as jest.Mock).mockRejectedValueOnce( - new Error("LLM error") - ); - const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, - filterPreviousMessages: mockFilterPreviousMessages, + ...makeMakeGenerateResponseWithSearchToolArgs(), + languageModel: mockThrowingLanguageModel, }); - const result = await generateResponse({ - conversation: { messages: [] }, - latestMessageText: "Hello", - shouldStream: false, - }); + const result = await generateResponse(generateResponseBaseArgs); - expect(result.messages[0].role).toBe("assistant"); - expect(result.messages[0].content).toBe(mockLlmNotWorkingMessage); + expect(result.messages[0].role).toBe("user"); + expect(result.messages[0].content).toBe(latestMessageText); + expect(result.messages.at(-1)?.role).toBe("assistant"); + expect(result.messages.at(-1)?.content).toBe(mockLlmNotWorkingMessage); }); }); describe("streaming mode", () => { - test("should handle successful streaming", async () => { - // Mock the async generator - const mockStream = (async function* () { - yield { type: "text-delta", textDelta: "Hello" }; - yield { type: "text-delta", textDelta: " world" }; - // Tool result - yield { - type: "tool-result", - toolName: "search_content", - result: { - content: [ - { - url: "https://example.com", - metadata: { pageTitle: "Test" }, - }, - ], - }, - }; - })(); - - (streamText as jest.Mock).mockReturnValueOnce({ - fullStream: mockStream, - text: Promise.resolve("Hello world"), - steps: Promise.resolve([{ text: "Hello world", toolResults: [] }]), - }); - - const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, - filterPreviousMessages: mockFilterPreviousMessages, + // Create a mock DataStreamer implementation + const makeMockDataStreamer = () => { + const mockStreamData = jest.fn(); + const mockConnect = jest.fn(); + const mockDisconnect = jest.fn(); + const mockStream = jest.fn().mockImplementation(async () => { + // Process the stream and return a string result + return "Hello"; }); + const dataStreamer = { + connected: false, + connect: mockConnect, + disconnect: mockDisconnect, + streamData: mockStreamData, + stream: mockStream, + } as DataStreamer; + + return dataStreamer; + }; + test("should handle successful streaming", async () => { + const mockDataStreamer = makeMockDataStreamer(); + const generateResponse = makeGenerateResponseWithSearchTool( + makeMakeGenerateResponseWithSearchToolArgs() + ); const result = await generateResponse({ - conversation: { messages: [] }, - latestMessageText: "Hello", + ...generateResponseBaseArgs, shouldStream: true, dataStreamer: mockDataStreamer, }); expect(mockDataStreamer.streamData).toHaveBeenCalledTimes(3); expect(mockDataStreamer.streamData).toHaveBeenCalledWith({ - data: "Hello", + data: "Final", type: "delta", }); expect(mockDataStreamer.streamData).toHaveBeenCalledWith({ - data: expect.arrayContaining([ - expect.objectContaining({ url: "https://example.com" }), - ]), type: "references", + data: expect.any(Array), }); - expect(result.messages).toHaveLength(2); // User + assistant + expectSuccessfulResult(result); }); - test("should handle successful generation with guardrail", async () => { + + // TODO: (EAI-995): make work as part of guardrail changes + test.skip("should handle successful generation with guardrail", async () => { + // TODO: add + }); + // TODO: (EAI-995): make work as part of guardrail changes + test.skip("should handle streaming with guardrail rejection", async () => { // TODO: add }); - test("should handle streaming with guardrail rejection", async () => { - const mockGuardrail = jest.fn().mockResolvedValue({ - rejected: true, - message: "Content policy violation", - metadata: { reason: "inappropriate" }, - }); + test("should handle error in language model", async () => { const generateResponse = makeGenerateResponseWithSearchTool({ - languageModel: mockLanguageModel, - llmNotWorkingMessage: mockLlmNotWorkingMessage, - systemMessage: mockSystemMessage, - searchTool: mockSearchTool, - filterPreviousMessages: mockFilterPreviousMessages, - inputGuardrail: mockGuardrail, + ...makeMakeGenerateResponseWithSearchToolArgs(), + languageModel: mockThrowingLanguageModel, }); + const dataStreamer = makeMockDataStreamer(); const result = await generateResponse({ - conversation: { messages: [] }, - latestMessageText: "Bad question", + ...generateResponseBaseArgs, shouldStream: true, - dataStreamer: mockDataStreamer, + dataStreamer, }); - expect(result.messages[1].role).toBe("assistant"); - expect(result.messages[1].content).toBe("Content policy violation"); - }); + // TODO: verify dataStreamer was called - test("should handle error in language model", async () => { - // TODO: add + expect(result.messages[0].role).toBe("user"); + expect(result.messages[0].content).toBe(latestMessageText); + expect(result.messages.at(-1)?.role).toBe("assistant"); + expect(result.messages.at(-1)?.content).toBe(mockLlmNotWorkingMessage); }); }); }); }); + +function expectSuccessfulResult(result: GenerateResponseReturnValue) { + expect(result).toHaveProperty("messages"); + expect(result.messages).toHaveLength(4); // User + assistant (tool call) + tool result + assistant + expect(result.messages[0]).toMatchObject({ + role: "user", + content: latestMessageText, + }); + expect(result.messages[1]).toMatchObject({ + role: "assistant", + toolCall: { + id: "abc123", + function: { name: "search_content", arguments: '{"query":"test"}' }, + type: "function", + }, + content: "", + }); + + expect(result.messages[2]).toMatchObject({ + role: "tool", + name: "search_content", + content: JSON.stringify({ + content: [ + { + url: "https://example.com", + text: "Content!", + metadata: { pageTitle: "Example Page" }, + }, + ], + }), + }); + expect(result.messages[3]).toMatchObject({ + role: "assistant", + content: finalAnswer, + }); +} diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index b08370f42..64462e607 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -2,7 +2,6 @@ import { References, SomeMessage, SystemMessage, - DataStreamer, UserMessage, AssistantMessage, ToolMessage, @@ -13,16 +12,13 @@ import { CoreAssistantMessage, CoreMessage, LanguageModel, - StepResult, streamText, - StreamTextResult, Tool, ToolCallPart, ToolChoice, ToolExecutionOptions, ToolResultUnion, ToolSet, - AssistantResponse, CoreToolMessage, } from "mongodb-rag-core/aiSdk"; import { FilterPreviousMessages } from "./FilterPreviousMessages"; @@ -100,6 +96,9 @@ export function makeGenerateResponseWithSearchTool< dataStreamer, request, }) { + if (shouldStream) { + assert(dataStreamer, "dataStreamer is required for streaming"); + } const userMessage = { role: "user", content: latestMessageText, @@ -129,6 +128,7 @@ export function makeGenerateResponseWithSearchTool< maxSteps, }; + // TODO: EAI-995: validate that this works as part of guardrail changes // Guardrail used to validate the input // while the LLM is generating the response const inputGuardrailPromise = inputGuardrail @@ -144,16 +144,14 @@ export function makeGenerateResponseWithSearchTool< }) : undefined; - const references: any[] = []; + const references: References = []; const { result, guardrailResult } = await withAbortControllerGuardrail( async (controller) => { - let toolChoice = generationArgs.toolChoice; // Pass the tools as a separate parameter const result = streamText({ ...generationArgs, // Abort the stream if the guardrail AbortController is triggered abortSignal: controller.signal, - toolChoice, // Add the search tool results to the references onStepFinish: async ({ toolResults }) => { toolResults?.forEach( @@ -162,37 +160,48 @@ export function makeGenerateResponseWithSearchTool< toolResult.toolName === SEARCH_TOOL_NAME && toolResult.result.content ) { - toolChoice = "auto"; // Map the search tool results to the References format const searchResults = toolResult.result.content; - references.push(...searchResults); + references.push( + ...searchResults.map( + (result) => + ({ + url: result.url, + title: + typeof result.metadata?.pageTitle === "string" + ? result.metadata.pageTitle + : "", + metadata: result.metadata, + } satisfies References[number]) + ) + ); } } ); }, }); - if (shouldStream) { - assert(dataStreamer, "dataStreamer is required for streaming"); - for await (const chunk of result.fullStream) { - switch (chunk.type) { - case "text-delta": - if (shouldStream) { - dataStreamer?.streamData({ - data: chunk.textDelta, - type: "delta", - }); - } - break; - case "error": - console.error("Error in stream:", chunk.error); - throw new Error( - typeof chunk.error === "string" - ? chunk.error - : String(chunk.error) - ); - default: - break; - } + + for await (const chunk of result.fullStream) { + switch (chunk.type) { + case "text-delta": + if (shouldStream) { + dataStreamer?.streamData({ + data: chunk.textDelta, + type: "delta", + }); + } + break; + case "tool-call": + // do nothing with tool calls for now... + break; + case "error": + throw new Error( + typeof chunk.error === "string" + ? chunk.error + : String(chunk.error) + ); + default: + break; } } @@ -207,7 +216,6 @@ export function makeGenerateResponseWithSearchTool< }); return result; } catch (error: unknown) { - console.error("Error in stream:", error); throw new Error(typeof error === "string" ? error : String(error)); } }, @@ -215,13 +223,9 @@ export function makeGenerateResponseWithSearchTool< ); const text = await result?.text; assert(text, "text is required"); - const steps = await result?.steps; - assert(steps, "steps is required"); - // console.log("steps", steps); const messages = (await result?.response)?.messages; assert(messages, "messages is required"); - console.log("messages", JSON.stringify(messages, null, 2)); return handleReturnGeneration({ userMessage, guardrailResult, @@ -230,8 +234,6 @@ export function makeGenerateResponseWithSearchTool< references, }); } catch (error: unknown) { - // TODO: handle guardrail failure so that the guardrail err is persisted. - dataStreamer?.streamData({ data: llmNotWorkingMessage, type: "delta", @@ -251,6 +253,7 @@ export function makeGenerateResponseWithSearchTool< } type ResponseMessage = CoreAssistantMessage | CoreToolMessage; + /** Generate the final messages to send to the user based on guardrail result and text generation result */ @@ -281,7 +284,6 @@ function handleReturnGeneration({ }; } -// TODO: implement this function formatMessageForGeneration( messages: ResponseMessage[], references: References diff --git a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts index 871ce03dc..c92a054a8 100644 --- a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts +++ b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts @@ -10,26 +10,30 @@ import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; } ``` */ -export const makeDefaultReferenceLinks: MakeReferenceLinksFunc = (chunks) => { +export const makeDefaultReferenceLinks: MakeReferenceLinksFunc = ( + references +) => { // Filter chunks with unique URLs const uniqueUrls = new Set(); - const uniqueChunks = chunks.filter((chunk) => { - if (!uniqueUrls.has(chunk.url)) { - uniqueUrls.add(chunk.url); - return true; // Keep the chunk as it has a unique URL + const uniqueReferences = references.filter((reference) => { + if (!uniqueUrls.has(reference.url)) { + uniqueUrls.add(reference.url); + return true; // Keep the referencesas it has a unique URL } - return false; // Discard the chunk as its URL is not unique + return false; // Discard the referencesas its URL is not unique }); - return uniqueChunks.map((chunk) => { - const url = new URL(chunk.url).href; - const title = chunk.metadata?.pageTitle ?? url; + return uniqueReferences.map((reference) => { + const url = new URL(reference.url).href; + // Ensure title is always a string by checking its type + const pageTitle = reference.metadata?.pageTitle; + const title = typeof pageTitle === "string" ? pageTitle : url; return { title, url, metadata: { - sourceName: chunk.sourceName, - tags: chunk.metadata?.tags ?? [], + sourceName: reference.metadata?.sourceName ?? "", + tags: reference.metadata?.tags ?? [], }, }; }); diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts index 15d0bc604..1615a7256 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.ts @@ -369,7 +369,6 @@ async function addMessagesToDatabase({ >[0]["messages"] )[messages.length - 1].id = assistantResponseMessageId; - console.log("messages out::", messages); const conversationId = conversation._id; const dbMessages = await conversations.addManyConversationMessages({ conversationId, diff --git a/packages/mongodb-rag-core/src/aiSdk.ts b/packages/mongodb-rag-core/src/aiSdk.ts index 75e508686..501124a0a 100644 --- a/packages/mongodb-rag-core/src/aiSdk.ts +++ b/packages/mongodb-rag-core/src/aiSdk.ts @@ -1,3 +1,9 @@ export * from "ai"; export * from "@ai-sdk/azure"; export * from "@ai-sdk/openai"; +export { + MockLanguageModelV1, + mockId, + mockValues, + MockEmbeddingModelV1, +} from "ai/test"; From c33f64e843f5d697a007f6753d128d16ebc2ed92 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 27 May 2025 12:47:40 -0400 Subject: [PATCH 20/29] update model version --- .../chatbot-server-mongodb-public/environments/production.yml | 2 +- packages/chatbot-server-mongodb-public/environments/staging.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/packages/chatbot-server-mongodb-public/environments/production.yml b/packages/chatbot-server-mongodb-public/environments/production.yml index 9a6e1574b..f5df2a36a 100644 --- a/packages/chatbot-server-mongodb-public/environments/production.yml +++ b/packages/chatbot-server-mongodb-public/environments/production.yml @@ -10,7 +10,7 @@ env: NODE_ENV: production OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT: gpt-4o-mini OPENAI_API_VERSION: "2024-06-01" - OPENAI_CHAT_COMPLETION_DEPLOYMENT: gpt-4o + OPENAI_CHAT_COMPLETION_DEPLOYMENT: gpt-4.1 OPENAI_VERIFIED_ANSWER_EMBEDDING_DEPLOYMENT: "docs-chatbot-embedding-ada-002" OPENAI_RETRIEVAL_EMBEDDING_DEPLOYMENT: "text-embedding-3-small" JUDGE_LLM: "gpt-4o-mini" diff --git a/packages/chatbot-server-mongodb-public/environments/staging.yml b/packages/chatbot-server-mongodb-public/environments/staging.yml index 2cba89d94..fe2405cf6 100644 --- a/packages/chatbot-server-mongodb-public/environments/staging.yml +++ b/packages/chatbot-server-mongodb-public/environments/staging.yml @@ -10,7 +10,7 @@ env: NODE_ENV: staging OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT: gpt-4o-mini OPENAI_API_VERSION: "2024-06-01" - OPENAI_CHAT_COMPLETION_DEPLOYMENT: gpt-4o + OPENAI_CHAT_COMPLETION_DEPLOYMENT: gpt-4.1 OPENAI_VERIFIED_ANSWER_EMBEDDING_DEPLOYMENT: "docs-chatbot-embedding-ada-002" OPENAI_RETRIEVAL_EMBEDDING_DEPLOYMENT: "text-embedding-3-small" BRAINTRUST_CHATBOT_TRACING_PROJECT_NAME: "chatbot-responses-staging" From 728416faa338dd3d4fca94d24e66c795f2c72c5f Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 27 May 2025 13:19:07 -0400 Subject: [PATCH 21/29] Remove no longer used stuff --- .../src/config.ts | 7 +- ...ractMongoDbMetadataFromUserMessage.eval.ts | 232 ------------------ ...ractMongoDbMetadataFromUserMessage.test.ts | 26 -- .../extractMongoDbMetadataFromUserMessage.ts | 93 ------- .../processors/makeMongoDbReferences.test.ts | 14 +- .../src/processors/makeMongoDbReferences.ts | 1 - .../makeStepBackRagGenerateUserPrompt.test.ts | 173 ------------- .../makeStepBackRagGenerateUserPrompt.ts | 232 ------------------ .../processors/makeStepBackUserQuery.eval.ts | 189 -------------- .../processors/makeStepBackUserQuery.test.ts | 20 -- .../src/processors/makeStepBackUserQuery.ts | 129 ---------- .../retrieveRelevantContent.test.ts | 84 ------- .../src/processors/retrieveRelevantContent.ts | 39 --- .../search.eval.ts} | 41 ++-- .../src/{tools.ts => tools/search.ts} | 2 +- .../src/processors/index.ts | 1 - .../makeVerifiedAnswerGenerateUserPrompt.ts | 65 ----- .../src/routes/index.ts | 1 - 18 files changed, 32 insertions(+), 1317 deletions(-) delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.eval.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.test.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.test.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.eval.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.test.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.test.ts delete mode 100644 packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.ts rename packages/chatbot-server-mongodb-public/src/{processors/retrieveRelevantContent.eval.ts => tools/search.eval.ts} (83%) rename packages/chatbot-server-mongodb-public/src/{tools.ts => tools/search.ts} (98%) delete mode 100644 packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts diff --git a/packages/chatbot-server-mongodb-public/src/config.ts b/packages/chatbot-server-mongodb-public/src/config.ts index bd77dc8ef..45f7fab74 100644 --- a/packages/chatbot-server-mongodb-public/src/config.ts +++ b/packages/chatbot-server-mongodb-public/src/config.ts @@ -25,7 +25,10 @@ import cookieParser from "cookie-parser"; import { blockGetRequests } from "./middleware/blockGetRequests"; import { getRequestId, logRequest } from "./utils"; import { systemPrompt } from "./systemPrompt"; -import { makeMongoDbReferences } from "./processors/makeMongoDbReferences"; +import { + addReferenceSourceType, + makeMongoDbReferences, +} from "./processors/makeMongoDbReferences"; import { redactConnectionUri } from "./middleware/redactConnectionUri"; import path from "path"; import express from "express"; @@ -44,7 +47,7 @@ import { } from "./tracing/routesUpdateTraceHandlers"; import { useSegmentIds } from "./middleware/useSegmentIds"; import { createAzure } from "mongodb-rag-core/aiSdk"; -import { makeSearchTool } from "./tools"; +import { makeSearchTool } from "./tools/search"; export const { MONGODB_CONNECTION_URI, MONGODB_DATABASE_NAME, diff --git a/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.eval.ts b/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.eval.ts deleted file mode 100644 index 4767c845f..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.eval.ts +++ /dev/null @@ -1,232 +0,0 @@ -import { - extractMongoDbMetadataFromUserMessage, - ExtractMongoDbMetadataFunction, -} from "./extractMongoDbMetadataFromUserMessage"; -import { Eval } from "braintrust"; -import { Scorer } from "autoevals"; -import { MongoDbTag } from "../mongoDbMetadata"; -import { - OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT, - openAiClient, -} from "../eval/evalHelpers"; - -interface ExtractMongoDbMetadataEvalCase { - name: string; - input: string; - expected: ExtractMongoDbMetadataFunction; - tags?: MongoDbTag[]; -} - -const evalCases: ExtractMongoDbMetadataEvalCase[] = [ - { - name: "should identify MongoDB Atlas Search", - input: "Does atlas search support copy to fields", - expected: { - mongoDbProduct: "Atlas Search", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["atlas", "atlas_search"], - }, - { - name: "should identify aggregation stage", - input: "$merge", - expected: { - mongoDbProduct: "Aggregation Framework", - } satisfies ExtractMongoDbMetadataFunction, - }, - { - name: "should know pymongo is python driver", - input: "pymongo insert data", - expected: { - programmingLanguage: "python", - mongoDbProduct: "Drivers", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["driver", "python"], - }, - { - name: "should identify MongoDB Atlas", - input: "how to create a new cluster atlas", - expected: { - mongoDbProduct: "MongoDB Atlas", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["atlas"], - }, - { - name: "should know atlas billing", - input: "how do I see my bill in atlas", - expected: { - mongoDbProduct: "MongoDB Atlas", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["atlas"], - }, - { - name: "should be aware of vector search product", - input: "how to use vector search", - expected: { - mongoDbProduct: "Atlas Vector Search", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["atlas", "atlas_vector_search"], - }, - { - name: "should know change streams", - input: - "how to open a change stream watch on a database and filter the stream", - expected: { - mongoDbProduct: "Drivers", - programmingLanguage: "javascript", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["change_streams"], - }, - { - name: "should know change streams", - input: - "how to open a change stream watch on a database and filter the stream pymongo", - expected: { - mongoDbProduct: "Drivers", - programmingLanguage: "python", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["change_streams"], - }, - { - name: "should know to include programming language when coding task implied.", - input: - "How do I choose the order of fields when creating a compound index?", - expected: { - mongoDbProduct: "MongoDB Server", - programmingLanguage: "javascript", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["indexes"], - }, - { - name: "should detect gridfs usage", - input: "What is the best way to store large files with MongoDB?", - expected: { - mongoDbProduct: "GridFS", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["gridfs"], - }, - { - name: "should recognize MongoDB for analytics", - input: "How do I run real-time analytics on my data?", - expected: { - mongoDbProduct: "MongoDB Server", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["analytics"], - }, - { - name: "should detect transaction management topic", - input: "How do I manage multi-document transactions?", - expected: { - mongoDbProduct: "MongoDB Server", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["server"], - }, - { - name: "should know multi-cloud clustering", - input: "Can I create a multi-cloud cluster with Atlas?", - expected: { - mongoDbProduct: "MongoDB Atlas", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["atlas", "multi_cloud"], - }, - { - name: "should identify usage in Java with the MongoDB driver", - input: "How do I connect to MongoDB using the Java driver?", - expected: { - programmingLanguage: "java", - mongoDbProduct: "Drivers", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["driver", "java"], - }, - { - name: "should know usage of MongoDB in C#", - input: "How do I query a collection using LINQ in C#?", - expected: { - programmingLanguage: "csharp", - mongoDbProduct: "Drivers", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["driver", "csharp"], - }, - { - name: "should recognize Python use in aggregation queries", - input: "How do I perform an aggregation pipeline in pymongo?", - expected: { - programmingLanguage: "python", - mongoDbProduct: "Aggregation Framework", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["driver", "python", "aggregation"], - }, - { - name: "should detect use of Node.js for MongoDB", - input: "How do I handle MongoDB connections in Node.js?", - expected: { - programmingLanguage: "javascript", - mongoDbProduct: "Drivers", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["driver", "javascript"], - }, - { - name: "should identify usage of Go with MongoDB", - input: "How do I insert multiple documents with the MongoDB Go driver?", - expected: { - programmingLanguage: "go", - mongoDbProduct: "Drivers", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["driver", "go"], - }, - { - name: "should know of $vectorSearch stage", - input: "$vectorSearch", - expected: { - mongoDbProduct: "Atlas Vector Search", - } satisfies ExtractMongoDbMetadataFunction, - tags: ["atlas", "atlas_vector_search"], - }, -]; -const ProductNameCorrect: Scorer< - Awaited>, - unknown -> = (args) => { - return { - name: "ProductNameCorrect", - score: args.expected?.mongoDbProduct === args.output.mongoDbProduct ? 1 : 0, - }; -}; -const ProgrammingLanguageCorrect: Scorer< - Awaited>, - unknown -> = (args) => { - return { - name: "ProgrammingLanguageCorrect", - score: - args.expected?.programmingLanguage === args.output.programmingLanguage - ? 1 - : 0, - }; -}; - -const model = OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT; -Eval("extract-mongodb-metadata", { - data: evalCases, - experimentName: model, - metadata: { - description: - "Evaluates whether the MongoDB user message guardrail is working correctly.", - model, - }, - maxConcurrency: 3, - timeout: 20000, - async task(input) { - try { - return await extractMongoDbMetadataFromUserMessage({ - openAiClient, - model, - userMessageText: input, - }); - } catch (error) { - console.log(`Error evaluating input: ${input}`); - console.log(error); - throw error; - } - }, - scores: [ProductNameCorrect, ProgrammingLanguageCorrect], -}); diff --git a/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.test.ts b/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.test.ts deleted file mode 100644 index cf487de96..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.test.ts +++ /dev/null @@ -1,26 +0,0 @@ -import { makeMockOpenAIToolCall } from "../test/mockOpenAi"; -import { - extractMongoDbMetadataFromUserMessage, - ExtractMongoDbMetadataFunction, -} from "./extractMongoDbMetadataFromUserMessage"; -import { OpenAI } from "mongodb-rag-core/openai"; - -jest.mock("mongodb-rag-core/openai", () => { - return makeMockOpenAIToolCall({ - mongoDbProduct: "Aggregation Framework", - } satisfies ExtractMongoDbMetadataFunction); -}); - -describe("extractMongoDbMetadataFromUserMessage", () => { - const args: Parameters[0] = { - openAiClient: new OpenAI({ apiKey: "fake-api-key" }), - model: "best-model-eva", - userMessageText: "hi", - }; - test("should return metadata", async () => { - const res = await extractMongoDbMetadataFromUserMessage(args); - expect(res).toEqual({ - mongoDbProduct: "Aggregation Framework", - }); - }); -}); diff --git a/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.ts b/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.ts deleted file mode 100644 index b3e5e087f..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/extractMongoDbMetadataFromUserMessage.ts +++ /dev/null @@ -1,93 +0,0 @@ -import { z } from "zod"; -import { - makeAssistantFunctionCallMessage, - makeFewShotUserMessageExtractorFunction, - makeUserMessage, -} from "./makeFewShotUserMessageExtractorFunction"; -import { OpenAI } from "mongodb-rag-core/openai"; -import { - mongoDbProductNames, - mongoDbProgrammingLanguageIds, -} from "../mongoDbMetadata"; - -export const ExtractMongoDbMetadataFunctionSchema = z.object({ - programmingLanguage: z - .enum(mongoDbProgrammingLanguageIds) - .default("javascript") - .describe( - 'Programming language present in the content. If no programming language is present and a code example would answer the question, include "javascript".' - ) - .optional(), - mongoDbProduct: z - .enum(mongoDbProductNames) - .describe( - `Most important MongoDB products present in the content. -Include "Driver" if the user is asking about a programming language with a MongoDB driver. -If the product is ambiguous, say "MongoDB Server".` - ) - .default("MongoDB Server") - .optional(), -}); - -export type ExtractMongoDbMetadataFunction = z.infer< - typeof ExtractMongoDbMetadataFunctionSchema ->; - -const name = "extract_mongodb_metadata"; -const description = "Extract MongoDB-related metadata from a user message"; - -const systemPrompt = `You are an expert data labeler employed by MongoDB. -You must label metadata about the user query based on its context in the conversation. -Your pay is determined by the accuracy of your labels as judged against other expert labelers, so do excellent work to maximize your earnings to support your family.`; - -const fewShotExamples: OpenAI.Chat.ChatCompletionMessageParam[] = [ - // Example 1 - makeUserMessage("aggregate data"), - makeAssistantFunctionCallMessage(name, { - programmingLanguage: "javascript", - mongoDbProduct: "Aggregation Framework", - } satisfies ExtractMongoDbMetadataFunction), - // Example 2 - makeUserMessage("how to create a new cluster atlas"), - makeAssistantFunctionCallMessage(name, { - mongoDbProduct: "MongoDB Atlas", - } satisfies ExtractMongoDbMetadataFunction), - // Example 3 - makeUserMessage("Does atlas search support copy to fields"), - makeAssistantFunctionCallMessage(name, { - mongoDbProduct: "Atlas Search", - } satisfies ExtractMongoDbMetadataFunction), - // Example 4 - makeUserMessage("pymongo insert data"), - makeAssistantFunctionCallMessage(name, { - programmingLanguage: "python", - mongoDbProduct: "Drivers", - } satisfies ExtractMongoDbMetadataFunction), - // Example 5 - makeUserMessage("How do I create an index in MongoDB using the Java driver?"), - makeAssistantFunctionCallMessage(name, { - programmingLanguage: "java", - mongoDbProduct: "Drivers", - } satisfies ExtractMongoDbMetadataFunction), - // Example 6 - makeUserMessage("$lookup"), - makeAssistantFunctionCallMessage(name, { - mongoDbProduct: "Aggregation Framework", - } satisfies ExtractMongoDbMetadataFunction), -]; - -/** - Extract metadata relevant to the MongoDB docs chatbot - from a user message in the conversation. - */ - -export const extractMongoDbMetadataFromUserMessage = - makeFewShotUserMessageExtractorFunction({ - llmFunction: { - name, - description, - schema: ExtractMongoDbMetadataFunctionSchema, - }, - systemPrompt, - fewShotExamples, - }); diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts index 38f3a3cf0..91244dce9 100644 --- a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts +++ b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts @@ -66,7 +66,12 @@ describe("makeMongoDbReferences", () => { chunkIndex: 0, }, ] satisfies EmbeddedContent[]; - const result = makeMongoDbReferences(chunks); + const result = makeMongoDbReferences( + chunks.map((c) => ({ + ...c, + title: c.metadata?.pageTitle, + })) + ); expect(result).toEqual([ { url: "https://www.example.com/blog", @@ -114,7 +119,12 @@ describe("makeMongoDbReferences", () => { chunkIndex: 0, }, ]; - const result = makeMongoDbReferences(chunks); + const result = makeMongoDbReferences( + chunks.map((c) => ({ + ...c, + title: c.metadata?.pageTitle, + })) + ); expect(result).toEqual([ { url: "https://www.example.com/somepage", diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts index 875b1abdf..a9e508d21 100644 --- a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts +++ b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.ts @@ -1,5 +1,4 @@ import { - EmbeddedContent, MakeReferenceLinksFunc, makeDefaultReferenceLinks, } from "mongodb-chatbot-server"; diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.test.ts b/packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.test.ts deleted file mode 100644 index 966a1a873..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.test.ts +++ /dev/null @@ -1,173 +0,0 @@ -import { FindContentFunc, FindContentResult } from "mongodb-chatbot-server"; -import { ObjectId } from "mongodb-rag-core/mongodb"; -import { - OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT, - preprocessorOpenAiClient, -} from "../test/testHelpers"; -import { makeStepBackRagGenerateUserPrompt } from "./makeStepBackRagGenerateUserPrompt"; - -jest.setTimeout(30000); -describe("makeStepBackRagGenerateUserPrompt", () => { - const embeddings = { modelName: [0, 0, 0] }; - const mockFindContent: FindContentFunc = async () => { - return { - queryEmbedding: embeddings.modelName, - content: [ - { - text: "avada kedavra", - embeddings, - score: 1, - sourceName: "mastering-dark-arts", - url: "https://example.com", - tokenCount: 3, - updated: new Date(), - }, - { - url: "https://example.com", - tokenCount: 1, - sourceName: "defending-against-the-dark-arts", - updated: new Date(), - text: "expecto patronum", - embeddings, - score: 1, - }, - ], - } satisfies FindContentResult; - }; - const config = { - openAiClient: preprocessorOpenAiClient, - model: OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT, - findContent: mockFindContent, - }; - const stepBackRagGenerateUserPrompt = - makeStepBackRagGenerateUserPrompt(config); - test("should return a step back user prompt", async () => { - const res = await stepBackRagGenerateUserPrompt({ - reqId: "123", - userMessageText: "what is mongodb", - }); - expect(res.rejectQuery).toBeFalsy(); - expect(res.userMessage).toHaveProperty("content"); - expect(res.userMessage).toHaveProperty("contentForLlm"); - expect(res.userMessage.role).toBe("user"); - expect(res.userMessage.embedding).toHaveLength(embeddings.modelName.length); - }); - test("should reject query if no content", async () => { - const mockFindContent: FindContentFunc = async () => { - return { - queryEmbedding: [], - content: [], - } satisfies FindContentResult; - }; - const stepBackRagGenerateUserPrompt = makeStepBackRagGenerateUserPrompt({ - ...config, - findContent: mockFindContent, - maxContextTokenCount: 1000, - }); - const res = await stepBackRagGenerateUserPrompt({ - reqId: "123", - userMessageText: "what is mongodb", - }); - expect(res.rejectQuery).toBe(true); - expect(res.userMessage.customData).toHaveProperty( - "rejectionReason", - "Did not find any content matching the query" - ); - expect(res.userMessage.rejectQuery).toBe(true); - }); - test("should return references", async () => { - const res = await stepBackRagGenerateUserPrompt({ - reqId: "123", - userMessageText: "what is mongodb", - }); - expect(res.references?.length).toBeGreaterThan(0); - }); - test("should reject inappropriate message", async () => { - const res = await stepBackRagGenerateUserPrompt({ - reqId: "123", - userMessageText: "why is mongodb the worst database", - }); - expect(res.rejectQuery).toBe(true); - expect(res.userMessage.customData).toHaveProperty("rejectionReason"); - expect(res.userMessage.rejectQuery).toBe(true); - }); - test("should throw if 'numPrecedingMessagesToInclude' is not an integer or < 0", async () => { - expect(() => - makeStepBackRagGenerateUserPrompt({ - ...config, - numPrecedingMessagesToInclude: 1.5, - }) - ).toThrow(); - expect(() => - makeStepBackRagGenerateUserPrompt({ - ...config, - numPrecedingMessagesToInclude: -1, - }) - ).toThrow(); - }); - test("should not include system messages", async () => { - const stepBackRagGenerateUserPrompt = makeStepBackRagGenerateUserPrompt({ - ...config, - numPrecedingMessagesToInclude: 1, - }); - const res = await stepBackRagGenerateUserPrompt({ - reqId: "123", - userMessageText: "what is mongodb", - conversation: { - _id: new ObjectId(), - createdAt: new Date(), - messages: [ - { - role: "system", - content: "abracadabra", - id: new ObjectId(), - createdAt: new Date(), - }, - ], - }, - }); - expect(res.userMessage.contentForLlm).not.toContain("abracadabra"); - }); - test("should only include 'numPrecedingMessagesToInclude' previous messages", async () => { - const stepBackRagGenerateUserPrompt = makeStepBackRagGenerateUserPrompt({ - ...config, - numPrecedingMessagesToInclude: 1, - }); - const res = await stepBackRagGenerateUserPrompt({ - reqId: "123", - userMessageText: "what is mongodb", - conversation: { - _id: new ObjectId(), - createdAt: new Date(), - messages: [ - { - role: "user", - content: "abracadabra", - id: new ObjectId(), - createdAt: new Date(), - }, - { - role: "assistant", - content: "avada kedavra", - id: new ObjectId(), - createdAt: new Date(), - }, - ], - }, - }); - expect(res.userMessage.contentForLlm).not.toContain("abracadabra"); - expect(res.userMessage.contentForLlm).toContain("avada kedavra"); - }); - test("should filter out context > maxContextTokenCount", async () => { - const stepBackRagGenerateUserPrompt = makeStepBackRagGenerateUserPrompt({ - ...config, - maxContextTokenCount: 1000, - }); - const res = await stepBackRagGenerateUserPrompt({ - reqId: "123", - userMessageText: "what is mongodb", - }); - expect(res.userMessage.contentForLlm).not.toContain("abracadabra"); - expect(res.userMessage.contentForLlm).toContain("avada kedavra"); - }); -}); diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.ts b/packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.ts deleted file mode 100644 index f0cff7edf..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackRagGenerateUserPrompt.ts +++ /dev/null @@ -1,232 +0,0 @@ -import { - EmbeddedContent, - FindContentFunc, - GenerateUserPromptFunc, - GenerateUserPromptFuncReturnValue, - Message, - UserMessage, -} from "mongodb-chatbot-server"; -import { OpenAI } from "mongodb-rag-core/openai"; -import { stripIndents } from "common-tags"; -import { strict as assert } from "assert"; -import { logRequest } from "../utils"; -import { makeMongoDbReferences } from "./makeMongoDbReferences"; -import { extractMongoDbMetadataFromUserMessage } from "./extractMongoDbMetadataFromUserMessage"; -import { userMessageMongoDbGuardrail } from "./userMessageMongoDbGuardrail"; -import { retrieveRelevantContent } from "./retrieveRelevantContent"; - -interface MakeStepBackGenerateUserPromptProps { - openAiClient: OpenAI; - model: string; - numPrecedingMessagesToInclude?: number; - findContent: FindContentFunc; - maxContextTokenCount?: number; -} - -/** - Generate user prompt using the ["step back" method of prompt engineering](https://arxiv.org/abs/2310.06117) - to construct search query. - Also extract metadata to use in the search query or reject the user message. - */ -export const makeStepBackRagGenerateUserPrompt = ({ - openAiClient, - model, - numPrecedingMessagesToInclude = 0, - findContent, - maxContextTokenCount = 1800, -}: MakeStepBackGenerateUserPromptProps) => { - assert( - numPrecedingMessagesToInclude >= 0, - "'numPrecedingMessagesToInclude' must be >= 0. Got: " + - numPrecedingMessagesToInclude - ); - assert( - Number.isInteger(numPrecedingMessagesToInclude), - "'numPrecedingMessagesToInclude' must be an integer. Got: " + - numPrecedingMessagesToInclude - ); - const stepBackRagGenerateUserPrompt: GenerateUserPromptFunc = async ({ - reqId, - userMessageText, - conversation, - customData, - }) => { - const messages = conversation?.messages ?? []; - const precedingMessagesToInclude = - numPrecedingMessagesToInclude === 0 - ? [] - : messages - .filter((m) => m.role !== "system") - .slice(-numPrecedingMessagesToInclude); - // Run both at once to save time - const [metadata, guardrailResult] = await Promise.all([ - extractMongoDbMetadataFromUserMessage({ - openAiClient, - model, - userMessageText, - messages: precedingMessagesToInclude, - }), - userMessageMongoDbGuardrail({ - userMessageText, - openAiClient, - model, - messages: precedingMessagesToInclude, - }), - ]); - if (guardrailResult.rejectMessage) { - const { reasoning } = guardrailResult; - logRequest({ - reqId, - message: `Rejected user message: ${JSON.stringify({ - userMessageText, - reasoning, - })}`, - }); - return { - userMessage: { - role: "user", - content: userMessageText, - rejectQuery: true, - customData: { - rejectionReason: reasoning, - }, - } satisfies UserMessage, - rejectQuery: true, - }; - } - logRequest({ - reqId, - message: `Extracted metadata from user message: ${JSON.stringify( - metadata - )}`, - }); - const metadataForQuery: Record = {}; - if (metadata.programmingLanguage) { - metadataForQuery.programmingLanguage = metadata.programmingLanguage; - } - if (metadata.mongoDbProduct) { - metadataForQuery.mongoDbProductName = metadata.mongoDbProduct; - } - - const { transformedUserQuery, content, queryEmbedding, searchQuery } = - await retrieveRelevantContent({ - findContent, - metadataForQuery, - model, - openAiClient, - precedingMessagesToInclude, - userMessageText, - }); - - logRequest({ - reqId, - message: `Found ${content.length} results for query: ${content - .map((c) => c.text) - .join("---")}`, - }); - const baseUserMessage = { - role: "user", - embedding: queryEmbedding, - content: userMessageText, - contextContent: content.map((c) => ({ - text: c.text, - url: c.url, - score: c.score, - })), - customData: { - ...customData, - ...metadata, - searchQuery, - transformedUserQuery, - }, - } satisfies UserMessage; - if (content.length === 0) { - return { - userMessage: { - ...baseUserMessage, - rejectQuery: true, - customData: { - ...customData, - rejectionReason: "Did not find any content matching the query", - }, - }, - rejectQuery: true, - references: [], - } satisfies GenerateUserPromptFuncReturnValue; - } - const userPrompt = { - ...baseUserMessage, - contentForLlm: makeUserContentForLlm({ - userMessageText, - stepBackUserQuery: transformedUserQuery, - messages: precedingMessagesToInclude, - metadata, - content, - maxContextTokenCount, - }), - } satisfies UserMessage; - const references = makeMongoDbReferences(content); - logRequest({ - reqId, - message: stripIndents`Generated user prompt for LLM: ${ - userPrompt.contentForLlm - } - Generated references: ${JSON.stringify(references)}`, - }); - return { - userMessage: userPrompt, - references, - } satisfies GenerateUserPromptFuncReturnValue; - }; - return stepBackRagGenerateUserPrompt; -}; - -function makeUserContentForLlm({ - userMessageText, - stepBackUserQuery, - messages, - metadata, - content, - maxContextTokenCount, -}: { - userMessageText: string; - stepBackUserQuery: string; - messages: Message[]; - metadata?: Record; - content: EmbeddedContent[]; - maxContextTokenCount: number; -}) { - const previousConversationMessages = messages - .map((message) => message.role.toUpperCase() + ": " + message.content) - .join("\n"); - const relevantMetadata = JSON.stringify({ - ...(metadata ?? {}), - searchQuery: stepBackUserQuery, - }); - - let currentTotalTokenCount = 0; - const contentForLlm = [...content] - .filter((c) => { - if (currentTotalTokenCount < maxContextTokenCount) { - currentTotalTokenCount += c.tokenCount; - return true; - } - return false; - }) - .map((c) => c.text) - .reverse() - .join("\n---\n"); - return `Use the following information to respond to the "User message". If you do not know the answer to the question based on the provided documentation content, respond with the following text: "I'm sorry, I do not know how to answer that question. Please try to rephrase your query." NEVER include Markdown links in the answer. -${ - previousConversationMessages.length > 0 - ? `Previous conversation messages: ${previousConversationMessages}` - : "" -} - -Content from the MongoDB documentation: -${contentForLlm} - -Relevant metadata: ${relevantMetadata} - -User message: ${userMessageText}`; -} diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.eval.ts b/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.eval.ts deleted file mode 100644 index 83a770ff4..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.eval.ts +++ /dev/null @@ -1,189 +0,0 @@ -import { Scorer, EmbeddingSimilarity } from "autoevals"; -import { Eval } from "braintrust"; -import { - makeStepBackUserQuery, - StepBackUserQueryMongoDbFunction, -} from "./makeStepBackUserQuery"; -import { Message, updateFrontMatter } from "mongodb-chatbot-server"; -import { ObjectId } from "mongodb-rag-core/mongodb"; -import { MongoDbTag } from "../mongoDbMetadata"; -import { - OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT, - OPENAI_API_KEY, - OPENAI_ENDPOINT, - OPENAI_API_VERSION, - JUDGE_EMBEDDING_MODEL, - openAiClient, -} from "../eval/evalHelpers"; - -interface ExtractMongoDbMetadataEvalCase { - name: string; - input: { - previousMessages?: Message[]; - userMessageText: string; - }; - expected: StepBackUserQueryMongoDbFunction; - tags?: MongoDbTag[]; -} - -const evalCases: ExtractMongoDbMetadataEvalCase[] = [ - { - name: "Should return a step back user query", - input: { - userMessageText: updateFrontMatter( - "how do i add the values of sale_price in aggregation pipeline?", - { - mongoDbProduct: "Aggregation Framework", - } - ), - }, - expected: { - transformedUserQuery: - "How to calculate the sum of field in MongoDB aggregation?", - } satisfies StepBackUserQueryMongoDbFunction, - tags: ["aggregation"], - }, - { - name: "should step back based on previous messages", - input: { - userMessageText: "code example", - previousMessages: [ - { - role: "user", - content: "add documents node.js", - createdAt: new Date(), - id: new ObjectId(), - }, - { - role: "assistant", - content: - "You can add documents with the node.js driver insert and insertMany methods.", - createdAt: new Date(), - id: new ObjectId(), - }, - ], - }, - expected: { - transformedUserQuery: - "Code example of how to add documents to MongoDB using the Node.js Driver", - } satisfies StepBackUserQueryMongoDbFunction, - tags: ["aggregation"], - }, - { - name: "should not do step back if original message doesn't need to be mutated", - input: { - userMessageText: updateFrontMatter("How do I connect to MongoDB Atlas?", { - mongoDbProduct: "MongoDB Atlas", - }), - }, - expected: { - transformedUserQuery: "How do I connect to MongoDB Atlas?", - } satisfies StepBackUserQueryMongoDbFunction, - tags: ["atlas"], - }, - { - name: "should step back when query about specific data", - input: { - userMessageText: updateFrontMatter("create an index on the email field", { - mongoDbProduct: "Index Management", - }), - }, - expected: { - transformedUserQuery: - "How to create an index on a specific field in MongoDB?", - } satisfies StepBackUserQueryMongoDbFunction, - tags: ["indexes"], - }, - { - name: "should recognize when query doesn't need step back.", - input: { - userMessageText: updateFrontMatter( - "What are MongoDB's replica set election protocols?", - { - mongoDbProduct: "Replication", - } - ), - }, - expected: { - transformedUserQuery: - "What are MongoDB's replica set election protocols?", - } satisfies StepBackUserQueryMongoDbFunction, - tags: ["replication"], - }, - { - name: "Steps back when query involves MongoDB Atlas configuration", - input: { - userMessageText: updateFrontMatter( - "How do I set up multi-region clusters in MongoDB Atlas?", - { - mongoDbProduct: "MongoDB Atlas", - } - ), - }, - expected: { - transformedUserQuery: - "How to configure multi-region clusters in MongoDB Atlas?", - } satisfies StepBackUserQueryMongoDbFunction, - tags: ["atlas"], - }, - { - name: "Handles abstract query related to MongoDB performance tuning", - input: { - userMessageText: updateFrontMatter( - "improve MongoDB query performance with indexes", - { - mongoDbProduct: "Performance Tuning", - } - ), - }, - expected: { - transformedUserQuery: - "How can I use indexes to optimize MongoDB query performance?", - } satisfies StepBackUserQueryMongoDbFunction, - tags: ["performance", "indexes"], - }, -]; - -const QuerySimilarity: Scorer< - Awaited>, - unknown -> = async (args) => { - return await EmbeddingSimilarity({ - expected: args.expected?.transformedUserQuery, - output: args.output.transformedUserQuery, - model: JUDGE_EMBEDDING_MODEL, - azureOpenAi: { - apiKey: OPENAI_API_KEY, - apiVersion: OPENAI_API_VERSION, - endpoint: OPENAI_ENDPOINT, - }, - }); -}; - -const model = OPENAI_PREPROCESSOR_CHAT_COMPLETION_DEPLOYMENT; - -Eval("step-back-user-query", { - data: evalCases, - experimentName: model, - metadata: { - description: - "Evaluate the function that mutates the user query for better search results.", - model, - }, - maxConcurrency: 3, - timeout: 20000, - async task(input) { - try { - return await makeStepBackUserQuery({ - openAiClient, - model, - ...input, - }); - } catch (error) { - console.log(`Error evaluating input: ${input}`); - console.log(error); - throw error; - } - }, - scores: [QuerySimilarity], -}); diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.test.ts b/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.test.ts deleted file mode 100644 index 0b72fdbaa..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.test.ts +++ /dev/null @@ -1,20 +0,0 @@ -import { makeMockOpenAIToolCall } from "../test/mockOpenAi"; -import { makeStepBackUserQuery } from "./makeStepBackUserQuery"; -import { OpenAI } from "mongodb-rag-core/openai"; -jest.mock("mongodb-rag-core/openai", () => - makeMockOpenAIToolCall({ transformedUserQuery: "foo" }) -); - -describe("makeStepBackUserQuery", () => { - const args: Parameters[0] = { - openAiClient: new OpenAI({ apiKey: "fake-api-key" }), - model: "best-model-ever", - userMessageText: "hi", - }; - - test("should return step back user query", async () => { - expect(await makeStepBackUserQuery(args)).toEqual({ - transformedUserQuery: "foo", - }); - }); -}); diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.ts b/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.ts deleted file mode 100644 index 4ea2da9c2..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/makeStepBackUserQuery.ts +++ /dev/null @@ -1,129 +0,0 @@ -import { z } from "zod"; -import { - makeAssistantFunctionCallMessage, - makeFewShotUserMessageExtractorFunction, - makeUserMessage, -} from "./makeFewShotUserMessageExtractorFunction"; -import { updateFrontMatter } from "mongodb-chatbot-server"; -import { OpenAI } from "mongodb-rag-core/openai"; - -export const StepBackUserQueryMongoDbFunctionSchema = z.object({ - transformedUserQuery: z.string().describe("Transformed user query"), -}); - -export type StepBackUserQueryMongoDbFunction = z.infer< - typeof StepBackUserQueryMongoDbFunctionSchema ->; - -const name = "step_back_user_query"; -const description = "Create a user query using the 'step back' method."; - -const systemPrompt = `Your purpose is to generate a search query for a given user input. -You are doing this for MongoDB, and all queries relate to MongoDB products. -When constructing the query, take a "step back" to generate a more general search query that finds the data relevant to the user query if relevant. -If the user query is already a "good" search query, do not modify it. -For one word queries like "or", "and", "exists", if the query corresponds to a MongoDB operation, transform it into a fully formed question. Ex: 'what is the $or operator in MongoDB?' -You should also transform the user query into a fully formed question, if relevant.`; - -const fewShotExamples: OpenAI.ChatCompletionMessageParam[] = [ - // Example 1 - makeUserMessage( - updateFrontMatter("aggregate filter where flowerType is rose", { - programmingLanguage: "javascript", - mongoDbProduct: "Aggregation Framework", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: - "How do I filter by specific field value in a MongoDB aggregation pipeline?", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 2 - makeUserMessage( - updateFrontMatter("How long does it take to import 2GB of data?", { - mongoDbProduct: "MongoDB Atlas", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: "What affects the rate of data import in MongoDB?", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 3 - makeUserMessage( - updateFrontMatter("how to display the first five", { - mongoDbProduct: "Driver", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: - "How do I limit the number of results in a MongoDB query?", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 4 - makeUserMessage( - updateFrontMatter("find documents python code example", { - programmingLanguage: "python", - mongoDbProduct: "Driver", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: - "Code example of how to find documents using the Python driver.", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 5 - makeUserMessage( - updateFrontMatter("aggregate", { - mongoDbProduct: "Aggregation Framework", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: "Aggregation in MongoDB", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 6 - makeUserMessage( - updateFrontMatter("$match", { - mongoDbProduct: "Aggregation Framework", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: - "What is the $match stage in a MongoDB aggregation pipeline?", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 7 - makeUserMessage( - updateFrontMatter("How to connect to a MongoDB Atlas cluster?", { - mongoDbProduct: "MongoDB Atlas", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: "How to connect to a MongoDB Atlas cluster?", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 8 - makeUserMessage( - updateFrontMatter("How to create a new cluster atlas", { - mongoDbProduct: "MongoDB Atlas", - }) - ), - makeAssistantFunctionCallMessage(name, { - transformedUserQuery: "How to create a new cluster in MongoDB Atlas?", - } satisfies StepBackUserQueryMongoDbFunction), - // Example 9 - makeUserMessage( - updateFrontMatter("What is a skill?", { - mongoDbProduct: "MongoDB University", - }) - ), - makeAssistantFunctionCallMessage(name,{ - transformedUserQuery: "What is the skill badge program on MongoDB University?", - } satisfies StepBackUserQueryMongoDbFunction), -]; - -/** - Generate search query using the ["step back" method of prompt engineering](https://arxiv.org/abs/2310.06117). - */ -export const makeStepBackUserQuery = makeFewShotUserMessageExtractorFunction({ - llmFunction: { - name, - description, - schema: StepBackUserQueryMongoDbFunctionSchema, - }, - systemPrompt, - fewShotExamples, -}); diff --git a/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.test.ts b/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.test.ts deleted file mode 100644 index cb37ba1f1..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.test.ts +++ /dev/null @@ -1,84 +0,0 @@ -import { FindContentFunc, updateFrontMatter } from "mongodb-rag-core"; -import { retrieveRelevantContent } from "./retrieveRelevantContent"; -import { makeMockOpenAIToolCall } from "../test/mockOpenAi"; -import { StepBackUserQueryMongoDbFunction } from "./makeStepBackUserQuery"; -import { OpenAI } from "mongodb-rag-core/openai"; - -jest.mock("mongodb-rag-core/openai", () => - makeMockOpenAIToolCall({ transformedUserQuery: "transformedUserQuery" }) -); -describe("retrieveRelevantContent", () => { - const model = "model"; - const funcRes = { - transformedUserQuery: "transformedUserQuery", - } satisfies StepBackUserQueryMongoDbFunction; - const fakeEmbedding = [1, 2, 3]; - - const fakeContentBase = { - embeddings: { fakeModelName: fakeEmbedding }, - score: 1, - url: "url", - tokenCount: 3, - sourceName: "sourceName", - updated: new Date(), - }; - const fakeFindContent: FindContentFunc = async ({ query }) => { - return { - content: [ - { - text: "all about " + query, - ...fakeContentBase, - }, - ], - queryEmbedding: fakeEmbedding, - }; - }; - - const mockToolCallOpenAi = new OpenAI({ - apiKey: "apiKey", - }); - const argsBase = { - openAiClient: mockToolCallOpenAi, - model, - userMessageText: "something", - findContent: fakeFindContent, - }; - const metadataForQuery = { - programmingLanguage: "javascript", - mongoDbProduct: "Aggregation Framework", - }; - it("should return content, queryEmbedding, transformedUserQuery, searchQuery with metadata", async () => { - const res = await retrieveRelevantContent({ - ...argsBase, - metadataForQuery, - }); - expect(res).toEqual({ - content: [ - { - text: expect.any(String), - ...fakeContentBase, - }, - ], - queryEmbedding: fakeEmbedding, - transformedUserQuery: funcRes.transformedUserQuery, - searchQuery: updateFrontMatter( - funcRes.transformedUserQuery, - metadataForQuery - ), - }); - }); - it("should return content, queryEmbedding, transformedUserQuery, searchQuery without", async () => { - const res = await retrieveRelevantContent(argsBase); - expect(res).toEqual({ - content: [ - { - text: expect.any(String), - ...fakeContentBase, - }, - ], - queryEmbedding: fakeEmbedding, - transformedUserQuery: funcRes.transformedUserQuery, - searchQuery: funcRes.transformedUserQuery, - }); - }); -}); diff --git a/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.ts b/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.ts deleted file mode 100644 index a261d5270..000000000 --- a/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.ts +++ /dev/null @@ -1,39 +0,0 @@ -import { makeStepBackUserQuery } from "./makeStepBackUserQuery"; -import { FindContentFunc, Message } from "mongodb-rag-core"; -import { updateFrontMatter } from "mongodb-rag-core"; -import { OpenAI } from "mongodb-rag-core/openai"; - -export const retrieveRelevantContent = async function ({ - openAiClient, - model, - precedingMessagesToInclude, - userMessageText, - metadataForQuery, - findContent, -}: { - openAiClient: OpenAI; - model: string; - precedingMessagesToInclude?: Message[]; - userMessageText: string; - metadataForQuery?: Record; - findContent: FindContentFunc; -}) { - const { transformedUserQuery } = await makeStepBackUserQuery({ - openAiClient, - model, - messages: precedingMessagesToInclude, - userMessageText: metadataForQuery - ? updateFrontMatter(userMessageText, metadataForQuery) - : userMessageText, - }); - - const searchQuery = metadataForQuery - ? updateFrontMatter(transformedUserQuery, metadataForQuery) - : transformedUserQuery; - - const { content, queryEmbedding } = await findContent({ - query: searchQuery, - }); - - return { content, queryEmbedding, transformedUserQuery, searchQuery }; -}; diff --git a/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.eval.ts b/packages/chatbot-server-mongodb-public/src/tools/search.eval.ts similarity index 83% rename from packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.eval.ts rename to packages/chatbot-server-mongodb-public/src/tools/search.eval.ts index cc06adaa0..92385e365 100644 --- a/packages/chatbot-server-mongodb-public/src/processors/retrieveRelevantContent.eval.ts +++ b/packages/chatbot-server-mongodb-public/src/tools/search.eval.ts @@ -21,11 +21,7 @@ import { f1AtK } from "../eval/scorers/f1AtK"; import { precisionAtK } from "../eval/scorers/precisionAtK"; import { recallAtK } from "../eval/scorers/recallAtK"; import { MongoDbTag } from "../mongoDbMetadata"; -import { - extractMongoDbMetadataFromUserMessage, - ExtractMongoDbMetadataFunction, -} from "./extractMongoDbMetadataFromUserMessage"; -import { retrieveRelevantContent } from "./retrieveRelevantContent"; +import { SearchToolArgs } from "./search"; interface RetrievalEvalCaseInput { query: string; @@ -49,7 +45,7 @@ interface RetrievalResult { } interface RetrievalTaskOutput { results: RetrievalResult[]; - extractedMetadata?: ExtractMongoDbMetadataFunction; + extractedMetadata?: SearchToolArgs; rewrittenQuery?: string; searchString?: string; } @@ -69,30 +65,21 @@ const { k } = retrievalConfig.findNearestNeighborsOptions; const retrieveRelevantContentEvalTask: EvalTask< RetrievalEvalCaseInput, - RetrievalTaskOutput + RetrievalTaskOutput, + RetrievalEvalCaseExpected > = async function (data) { - const metadataForQuery = await extractMongoDbMetadataFromUserMessage({ - openAiClient: preprocessorOpenAiClient, - model: retrievalConfig.preprocessorLlm, - userMessageText: data.query, - }); - const results = await retrieveRelevantContent({ - userMessageText: data.query, - model: retrievalConfig.preprocessorLlm, - openAiClient: preprocessorOpenAiClient, - findContent, - metadataForQuery, - }); + // TODO: (EAI-991) implement retrieval task for evaluation + const extractedMetadata: SearchToolArgs = { + productName: null, + programmingLanguage: null, + query: data.query, + }; return { - results: results.content.map((c) => ({ - url: c.url, - content: c.text, - score: c.score, - })), - extractedMetadata: metadataForQuery, - rewrittenQuery: results.transformedUserQuery, - searchString: results.searchQuery, + results: [], + extractedMetadata, + rewrittenQuery: undefined, + searchString: undefined, }; }; diff --git a/packages/chatbot-server-mongodb-public/src/tools.ts b/packages/chatbot-server-mongodb-public/src/tools/search.ts similarity index 98% rename from packages/chatbot-server-mongodb-public/src/tools.ts rename to packages/chatbot-server-mongodb-public/src/tools/search.ts index 87a325b1c..d328eb5a3 100644 --- a/packages/chatbot-server-mongodb-public/src/tools.ts +++ b/packages/chatbot-server-mongodb-public/src/tools/search.ts @@ -5,7 +5,7 @@ import { z } from "zod"; import { mongoDbProducts, mongoDbProgrammingLanguageIds, -} from "./mongoDbMetadata"; +} from "../mongoDbMetadata"; const SearchToolArgsSchema = z.object({ productName: z diff --git a/packages/mongodb-chatbot-server/src/processors/index.ts b/packages/mongodb-chatbot-server/src/processors/index.ts index dfb0bf5f4..099d32207 100644 --- a/packages/mongodb-chatbot-server/src/processors/index.ts +++ b/packages/mongodb-chatbot-server/src/processors/index.ts @@ -4,7 +4,6 @@ export * from "./QueryPreprocessorFunc"; export * from "./filterOnlySystemPrompt"; export * from "./makeDefaultReferenceLinks"; export * from "./makeFilterNPreviousMessages"; -export * from "./makeVerifiedAnswerGenerateUserPrompt"; export * from "./includeChunksForMaxTokensPossible"; export * from "./InputGuardrail"; export * from "./generateResponseWithSearchTool"; diff --git a/packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts b/packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts deleted file mode 100644 index bcc7e409d..000000000 --- a/packages/mongodb-chatbot-server/src/processors/makeVerifiedAnswerGenerateUserPrompt.ts +++ /dev/null @@ -1,65 +0,0 @@ -import { VerifiedAnswer, FindVerifiedAnswerFunc } from "mongodb-rag-core"; -import { - GenerateUserPromptFunc, - GenerateUserPromptFuncReturnValue, -} from "../routes/legacyGenerateResponse"; - -export interface MakeVerifiedAnswerGenerateUserPromptParams { - /** - Find content based on the user's message and preprocessing. - */ - findVerifiedAnswer: FindVerifiedAnswerFunc; - - /** - Format or modify the verified answer before displaying it to the user. - */ - onVerifiedAnswerFound?: (verifiedAnswer: VerifiedAnswer) => VerifiedAnswer; - - onNoVerifiedAnswerFound: GenerateUserPromptFunc; -} - -/** - Constructs a GenerateUserPromptFunc that searches for verified answers for the - user query. If no verified answer can be found for the given query, the - onNoVerifiedAnswerFound GenerateUserPromptFunc is called instead. - */ -export const makeVerifiedAnswerGenerateUserPrompt = ({ - findVerifiedAnswer, - onVerifiedAnswerFound, - onNoVerifiedAnswerFound, -}: MakeVerifiedAnswerGenerateUserPromptParams): GenerateUserPromptFunc => { - return async (args) => { - const { userMessageText } = args; - const { answer: foundVerifiedAnswer, queryEmbedding } = - await findVerifiedAnswer({ - query: userMessageText, - }); - - if (foundVerifiedAnswer === undefined) { - return await onNoVerifiedAnswerFound(args); - } - - const verifiedAnswer = - onVerifiedAnswerFound?.(foundVerifiedAnswer) ?? foundVerifiedAnswer; - return { - userMessage: { - embedding: queryEmbedding, - content: userMessageText, - role: "user", - }, - references: verifiedAnswer.references, - staticResponse: { - metadata: { - verifiedAnswer: { - _id: verifiedAnswer._id, - created: verifiedAnswer.created, - updated: verifiedAnswer.updated, - }, - }, - references: verifiedAnswer.references, - content: verifiedAnswer.answer, - role: "assistant", - }, - } satisfies GenerateUserPromptFuncReturnValue; - }; -}; diff --git a/packages/mongodb-chatbot-server/src/routes/index.ts b/packages/mongodb-chatbot-server/src/routes/index.ts index 0d502d515..b9f9da7be 100644 --- a/packages/mongodb-chatbot-server/src/routes/index.ts +++ b/packages/mongodb-chatbot-server/src/routes/index.ts @@ -1,2 +1 @@ export * from "./conversations"; -export * from "./legacyGenerateResponse"; From 479ccb88e39ff62f11cbe7c988a4f7ae35a234cb Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 27 May 2025 13:45:54 -0400 Subject: [PATCH 22/29] decouple search results for references and whats shown to model --- .../processors/makeMongoDbReferences.test.ts | 1 - .../src/tools/search.ts | 23 +++++++++++++- .../src/processors/MakeReferenceLinksFunc.ts | 12 +++---- .../src/processors/SearchResult.ts | 7 +++++ .../generateResponseWithSearchTool.ts | 31 +++++-------------- .../src/processors/index.ts | 1 + .../processors/makeDefaultReferenceLinks.ts | 19 +++++++----- 7 files changed, 53 insertions(+), 41 deletions(-) create mode 100644 packages/mongodb-chatbot-server/src/processors/SearchResult.ts diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts index 91244dce9..27242b7fe 100644 --- a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts +++ b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts @@ -150,7 +150,6 @@ describe("addReferenceSourceType", () => { }; const result = addReferenceSourceType(reference); expect(result.metadata).toEqual({ - sourceName: reference.metadata?.sourceName, tags: reference.metadata?.tags, sourceType: "Docs", }); diff --git a/packages/chatbot-server-mongodb-public/src/tools/search.ts b/packages/chatbot-server-mongodb-public/src/tools/search.ts index d328eb5a3..f89c0ae85 100644 --- a/packages/chatbot-server-mongodb-public/src/tools/search.ts +++ b/packages/chatbot-server-mongodb-public/src/tools/search.ts @@ -1,4 +1,8 @@ -import { SearchTool, SearchToolReturnValue } from "mongodb-chatbot-server"; +import { + SearchResult, + SearchTool, + SearchToolReturnValue, +} from "mongodb-chatbot-server"; import { FindContentFunc, updateFrontMatter } from "mongodb-rag-core"; import { tool, ToolExecutionOptions } from "mongodb-rag-core/aiSdk"; import { z } from "zod"; @@ -31,6 +35,23 @@ export function makeSearchTool( return tool({ parameters: SearchToolArgsSchema, description: "Search MongoDB content", + // This shows only the URL and text of the result, not the metadata (needed for references) to the model. + experimental_toToolResultContent(result) { + return [ + { + type: "text", + text: JSON.stringify({ + content: result.content.map( + (r) => + ({ + url: r.url, + text: r.text, + } satisfies SearchResult) + ), + }), + }, + ]; + }, async execute( args: SearchToolArgs, _options: ToolExecutionOptions diff --git a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts index 9481ee4d5..bbb3da61a 100644 --- a/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts +++ b/packages/mongodb-chatbot-server/src/processors/MakeReferenceLinksFunc.ts @@ -1,11 +1,9 @@ -import { EmbeddedContent, References } from "mongodb-rag-core"; - -export type EmbeddedContentForModel = Pick< - EmbeddedContent, - "url" | "text" | "metadata" ->; +import { References } from "mongodb-rag-core"; +import { SearchResult } from "./SearchResult"; /** Function that generates the references in the response to user. */ -export type MakeReferenceLinksFunc = (references: References) => References; +export type MakeReferenceLinksFunc = ( + searchResults: SearchResult[] +) => References; diff --git a/packages/mongodb-chatbot-server/src/processors/SearchResult.ts b/packages/mongodb-chatbot-server/src/processors/SearchResult.ts new file mode 100644 index 000000000..f338f9f3f --- /dev/null +++ b/packages/mongodb-chatbot-server/src/processors/SearchResult.ts @@ -0,0 +1,7 @@ +import { EmbeddedContent } from "mongodb-rag-core"; + +export type SearchResult = Partial & { + url: string; + text: string; + metadata?: Record; +}; diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts index 64462e607..065489b90 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.ts @@ -5,6 +5,7 @@ import { UserMessage, AssistantMessage, ToolMessage, + EmbeddedContent, } from "mongodb-rag-core"; import { z } from "zod"; import { GenerateResponse } from "./GenerateResponse"; @@ -26,15 +27,12 @@ import { InputGuardrail, withAbortControllerGuardrail } from "./InputGuardrail"; import { strict as assert } from "assert"; import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; import { makeDefaultReferenceLinks } from "./makeDefaultReferenceLinks"; +import { SearchResult } from "./SearchResult"; export const SEARCH_TOOL_NAME = "search_content"; export type SearchToolReturnValue = { - content: { - url: string; - text: string; - metadata?: Record; - }[]; + content: SearchResult[]; }; export type SearchTool = Tool< @@ -81,7 +79,7 @@ export function makeGenerateResponseWithSearchTool< systemMessage, filterPreviousMessages, additionalTools, - makeReferenceLinks, + makeReferenceLinks = makeDefaultReferenceLinks, maxSteps = 2, searchTool, toolChoice, @@ -162,19 +160,7 @@ export function makeGenerateResponseWithSearchTool< ) { // Map the search tool results to the References format const searchResults = toolResult.result.content; - references.push( - ...searchResults.map( - (result) => - ({ - url: result.url, - title: - typeof result.metadata?.pageTitle === "string" - ? result.metadata.pageTitle - : "", - metadata: result.metadata, - } satisfies References[number]) - ) - ); + references.push(...makeReferenceLinks(searchResults)); } } ); @@ -204,14 +190,11 @@ export function makeGenerateResponseWithSearchTool< break; } } - try { // Transform filtered references to include the required title property - const referencesOut = makeReferenceLinks - ? makeReferenceLinks(references) - : makeDefaultReferenceLinks(references); + dataStreamer?.streamData({ - data: referencesOut, + data: references, type: "references", }); return result; diff --git a/packages/mongodb-chatbot-server/src/processors/index.ts b/packages/mongodb-chatbot-server/src/processors/index.ts index 099d32207..55a42146e 100644 --- a/packages/mongodb-chatbot-server/src/processors/index.ts +++ b/packages/mongodb-chatbot-server/src/processors/index.ts @@ -10,3 +10,4 @@ export * from "./generateResponseWithSearchTool"; export * from "./makeVerifiedAnswerGenerateResponse"; export * from "./includeChunksForMaxTokensPossible"; export * from "./GenerateResponse"; +export * from "./SearchResult"; diff --git a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts index c92a054a8..bbb2415d5 100644 --- a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts +++ b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.ts @@ -1,3 +1,4 @@ +import { References } from "mongodb-rag-core"; import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; /** @@ -10,14 +11,12 @@ import { MakeReferenceLinksFunc } from "./MakeReferenceLinksFunc"; } ``` */ -export const makeDefaultReferenceLinks: MakeReferenceLinksFunc = ( - references -) => { +export const makeDefaultReferenceLinks: MakeReferenceLinksFunc = (chunks) => { // Filter chunks with unique URLs const uniqueUrls = new Set(); - const uniqueReferences = references.filter((reference) => { - if (!uniqueUrls.has(reference.url)) { - uniqueUrls.add(reference.url); + const uniqueReferences = chunks.filter((chunk) => { + if (!uniqueUrls.has(chunk.url)) { + uniqueUrls.add(chunk.url); return true; // Keep the referencesas it has a unique URL } return false; // Discard the referencesas its URL is not unique @@ -28,13 +27,17 @@ export const makeDefaultReferenceLinks: MakeReferenceLinksFunc = ( // Ensure title is always a string by checking its type const pageTitle = reference.metadata?.pageTitle; const title = typeof pageTitle === "string" ? pageTitle : url; + const sourceName = + typeof reference.metadata?.sourceName === "string" + ? reference.metadata?.sourceName + : undefined; return { title, url, metadata: { - sourceName: reference.metadata?.sourceName ?? "", + sourceName, tags: reference.metadata?.tags ?? [], }, }; - }); + }) satisfies References; }; From cc8dd456e4ff08959fdf7ac5aadae89d51a91bd6 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 27 May 2025 13:49:10 -0400 Subject: [PATCH 23/29] fix scripts build errs --- packages/scripts/src/findFaq.ts | 6 +++--- packages/scripts/src/scrubMessages.ts | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/packages/scripts/src/findFaq.ts b/packages/scripts/src/findFaq.ts index 41be90e9b..3077ef32c 100644 --- a/packages/scripts/src/findFaq.ts +++ b/packages/scripts/src/findFaq.ts @@ -5,7 +5,7 @@ import { Conversation, SomeMessage, AssistantMessage, - FunctionMessage, + ToolMessage, UserMessage, VectorStore, FindNearestNeighborsOptions, @@ -15,7 +15,7 @@ import { import { clusterize, DbscanOptions } from "./clusterize"; import { findCentroid } from "./findCentroid"; -export type ResponseMessage = AssistantMessage | FunctionMessage; +export type ResponseMessage = AssistantMessage | ToolMessage; export type QuestionAndResponses = { embedding: number[]; @@ -152,7 +152,7 @@ export const findFaq = async ({ } break; case "assistant": - case "function": + case "tool": { currentQuestion?.responses?.push(message); } diff --git a/packages/scripts/src/scrubMessages.ts b/packages/scripts/src/scrubMessages.ts index ec93cfb87..c6e2b0300 100644 --- a/packages/scripts/src/scrubMessages.ts +++ b/packages/scripts/src/scrubMessages.ts @@ -72,6 +72,7 @@ export const scrubMessages = async ({ db }: { db: Db }) => { rejectQuery: "$messages.rejectQuery", customData: "$messages.customData", metadata: "$messages.metadata", + toolCall: "$messages.toolCall", userCommented: { $cond: { // Evaluate to the user comment (if it exists) or false From e768dd640de816629b57b54844c0f701590221bf Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 27 May 2025 15:46:26 -0400 Subject: [PATCH 24/29] fix broken tests --- .../src/processors/InputGuardrail.test.ts | 1 - .../generateResponseWithSearchTool.test.ts | 18 +++----- .../makeDefaultReferenceLinks.test.ts | 7 ---- .../addMessageToConversation.test.ts | 8 ++-- .../conversations/getConversation.test.ts | 12 ++++-- .../src/routes/conversations/utils.test.ts | 17 +++++--- .../src/test/testConfig.ts | 41 +++++++++++++++---- 7 files changed, 60 insertions(+), 44 deletions(-) delete mode 100644 packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts diff --git a/packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts b/packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts deleted file mode 100644 index b70f86f27..000000000 --- a/packages/mongodb-chatbot-server/src/processors/InputGuardrail.test.ts +++ /dev/null @@ -1 +0,0 @@ -// TODO: add tests diff --git a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts index a44293c00..cb7cd91bf 100644 --- a/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts +++ b/packages/mongodb-chatbot-server/src/processors/generateResponseWithSearchTool.test.ts @@ -9,7 +9,6 @@ import { AssistantMessage, DataStreamer, SystemMessage, - UserMessage, } from "mongodb-rag-core"; import { z } from "zod"; import { @@ -35,7 +34,7 @@ const mockReqId = "test"; const mockContent = [ { - url: "https://example.com", + url: "https://example.com/", text: `Content!`, metadata: { pageTitle: "Example Page", @@ -46,7 +45,6 @@ const mockContent = [ const mockReferences = mockContent.map((content) => ({ url: content.url, title: content.metadata.pageTitle, - metadata: content.metadata, })); // Create a mock search tool that matches the SearchTool interface @@ -233,9 +231,9 @@ describe("generateResponseWithSearchTool", () => { const result = await generateResponse(generateResponseBaseArgs); - expect((result.messages.at(-1) as AssistantMessage).references).toEqual( - mockReferences - ); + const references = (result.messages.at(-1) as AssistantMessage) + .references; + expect(references).toMatchObject(mockReferences); }); describe("non-streaming", () => { @@ -378,13 +376,7 @@ function expectSuccessfulResult(result: GenerateResponseReturnValue) { role: "tool", name: "search_content", content: JSON.stringify({ - content: [ - { - url: "https://example.com", - text: "Content!", - metadata: { pageTitle: "Example Page" }, - }, - ], + content: mockContent, }), }); expect(result.messages[3]).toMatchObject({ diff --git a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts index 829abc247..6129cd311 100644 --- a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts +++ b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts @@ -59,7 +59,6 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { - sourceName: "realm", tags: [], }, }, @@ -74,7 +73,6 @@ describe("makeDefaultReferenceLinks()", () => { title: "title", url: "https://mongodb.com/docs/realm/sdk/node/xyz", metadata: { - sourceName: "realm", tags: [], }, }, @@ -89,7 +87,6 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { - sourceName: "realm", tags: [], }, }, @@ -106,7 +103,6 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { - sourceName: "realm", tags: [], }, }, @@ -114,7 +110,6 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/xyz", url: "https://mongodb.com/docs/realm/sdk/node/xyz", metadata: { - sourceName: "realm", tags: [], }, }, @@ -131,7 +126,6 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { - sourceName: "realm", tags: [], }, }, @@ -139,7 +133,6 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/xyz", url: "https://mongodb.com/docs/realm/sdk/node/xyz", metadata: { - sourceName: "realm", tags: [], }, }, diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts index 750090f53..4883c60c4 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/addMessageToConversation.test.ts @@ -20,6 +20,7 @@ import { makeTestApp } from "../../test/testHelpers"; import { AppConfig } from "../../app"; import { strict as assert } from "assert"; import { Db, ObjectId } from "mongodb-rag-core/mongodb"; +import { mockAssistantResponse } from "../../test/testConfig"; jest.setTimeout(100000); describe("POST /conversations/:conversationId/messages", () => { @@ -65,8 +66,7 @@ describe("POST /conversations/:conversationId/messages", () => { .send(requestBody); const message: ApiMessage = res.body; expect(res.statusCode).toEqual(200); - expect(message.role).toBe("assistant"); - expect(message.content).toContain("Realm"); + expect(message).toMatchObject(mockAssistantResponse); const request2Body: AddMessageRequestBody = { message: stripIndent`i'm want to learn more about this Realm thing. a few questions: can i use realm with javascript? @@ -79,8 +79,7 @@ describe("POST /conversations/:conversationId/messages", () => { .send(request2Body); const message2: ApiMessage = res2.body; expect(res2.statusCode).toEqual(200); - expect(message2.role).toBe("assistant"); - expect(message2.content).toContain("Realm"); + expect(message2).toMatchObject(mockAssistantResponse); const conversationInDb = await mongodb .collection("conversations") .findOne({ @@ -349,7 +348,6 @@ describe("POST /conversations/:conversationId/messages", () => { res.body.metadata.conversationId ); expect(conversation?.messages).toHaveLength(2); - console.log(conversation?.messages[0]); expect(conversation?.messages[0]).toMatchObject({ content: message.message, role: "user", diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/getConversation.test.ts b/packages/mongodb-chatbot-server/src/routes/conversations/getConversation.test.ts index dc618c069..80393441a 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/getConversation.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/getConversation.test.ts @@ -81,13 +81,17 @@ describe("GET /conversations/:conversationId", () => { { role: "assistant", content: "", - functionCall: { - name: "addNumbers", - arguments: `[1, 2, 3, 4, 5]`, + toolCall: { + id: "abc123", + type: "function", + function: { + name: "addNumbers", + arguments: `[1, 2, 3, 4, 5]`, + }, }, }, { - role: "function", + role: "tool", name: "addNumbers", content: "15", }, diff --git a/packages/mongodb-chatbot-server/src/routes/conversations/utils.test.ts b/packages/mongodb-chatbot-server/src/routes/conversations/utils.test.ts index afb7b8ef3..a14313849 100644 --- a/packages/mongodb-chatbot-server/src/routes/conversations/utils.test.ts +++ b/packages/mongodb-chatbot-server/src/routes/conversations/utils.test.ts @@ -1,5 +1,6 @@ import { strict as assert } from "assert"; import { + ApiMessage, areEquivalentIpAddresses, convertConversationFromDbToApi, convertMessageFromDbToApi, @@ -63,15 +64,19 @@ const exampleConversationInDatabase: Conversation = { id: new ObjectId("65ca767e30116ce068e17bb5"), role: "assistant", content: "", - functionCall: { - name: "getBookRecommendations", - arguments: JSON.stringify({ genre: ["fantasy", "sci-fi"] }), + toolCall: { + id: "abc123", + type: "function", + function: { + name: "getBookRecommendations", + arguments: JSON.stringify({ genre: ["fantasy", "sci-fi"] }), + }, }, createdAt: new Date("2024-01-01T00:00:45Z"), }, { id: new ObjectId("65ca768341f9ea61d048aaa8"), - role: "function", + role: "tool", name: "getBookRecommendations", content: JSON.stringify([ { title: "The Way of Kings", author: "Brandon Sanderson" }, @@ -125,14 +130,14 @@ describe("Data Conversion Functions", () => { expect(convertMessageFromDbToApi(functionResultMessage)).toEqual({ id: "65ca768341f9ea61d048aaa8", - role: "function", + role: "tool", content: JSON.stringify([ { title: "The Way of Kings", author: "Brandon Sanderson" }, { title: "Neuromancer", author: "William Gibson" }, { title: "Snow Crash", author: "Neal Stephenson" }, ]), createdAt: 1704067247000, - }); + } satisfies ApiMessage); expect(convertMessageFromDbToApi(assistantMessage)).toEqual({ id: "65ca76874e1df9cf2742bf86", diff --git a/packages/mongodb-chatbot-server/src/test/testConfig.ts b/packages/mongodb-chatbot-server/src/test/testConfig.ts index f53165248..6bb2abed9 100644 --- a/packages/mongodb-chatbot-server/src/test/testConfig.ts +++ b/packages/mongodb-chatbot-server/src/test/testConfig.ts @@ -8,7 +8,7 @@ import { CORE_ENV_VARS, assertEnvVars, makeMongoDbConversationsService, - SystemPrompt, + SystemMessage, } from "mongodb-rag-core"; import { MongoClient, Db } from "mongodb-rag-core/mongodb"; import { AzureOpenAI } from "mongodb-rag-core/openai"; @@ -90,7 +90,7 @@ export const findContent = makeDefaultFindContent({ export const REJECT_QUERY_CONTENT = "REJECT_QUERY"; export const NO_VECTOR_CONTENT = "NO_VECTOR_CONTENT"; -export const systemPrompt: SystemPrompt = { +export const systemPrompt: SystemMessage = { role: "system", content: stripIndents`You're just a mock chatbot. What you think and say does not matter.`, }; @@ -111,7 +111,7 @@ export function makeMongoDbReferences(chunks: EmbeddedContent[]) { chunks.map((chunk) => ({ title: chunk.metadata?.pageTitle ?? chunk.url, url: chunk.url, - metadata: chunk.metadata, + text: chunk.text, })) ); return baseReferences.map((ref) => { @@ -125,19 +125,44 @@ export function makeMongoDbReferences(chunks: EmbeddedContent[]) { export const filterPrevious12Messages = makeFilterNPreviousMessages(12); +export const mockAssistantResponse = { + role: "assistant" as const, + content: "some content", +}; + export const mockGenerateResponse: GenerateResponse = async ({ latestMessageText, + customData, + dataStreamer, + shouldStream, }) => { + if (shouldStream) { + dataStreamer?.streamData({ + type: "delta", + data: mockAssistantResponse.content, + }); + dataStreamer?.streamData({ + type: "references", + data: [ + { + url: "https://mongodb.com", + title: "mongodb.com", + }, + ], + }); + dataStreamer?.streamData({ + type: "finished", + data: "", + }); + } return { messages: [ { - role: "user", + role: "user" as const, content: latestMessageText, + customData, }, - { - role: "assistant", - content: "some content", - }, + { ...mockAssistantResponse }, ], }; }; From 6be3e7bdde4714056eaf15dbf45752b1fc8680f3 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Tue, 27 May 2025 16:20:37 -0400 Subject: [PATCH 25/29] deprecation --- README.md | 48 +- docs/docs/framework.md | 12 + docs/docs/index.md | 74 +- docs/docs/ingest/command-reference.md | 16 - docs/docs/ingest/configuration-reference.md | 191 - docs/docs/ingest/configure.md | 153 - docs/docs/ingest/data-sources.md | 156 - docs/docs/ingest/optimize.md | 145 - docs/docs/mongodb.md | 103 - docs/docs/quick-start.md | 219 - docs/docs/server/configure.md | 239 - docs/docs/server/conversations.md | 73 - docs/docs/server/custom-logic.md | 247 - docs/docs/server/evaluate.md | 45 - docs/docs/server/find-faqs.md | 64 - docs/docs/server/llm.md | 187 - 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the work of the MongoDB Education AI team. -The framework can take your chatbot application from prototype to production. +## MongoDB Knowledge Service -You can quickly get an AI chatbot enhanced with your data up and running using -the framework's built-in data ingest process, chatbot server, and web UI. As you -refine your application and scale to more users, you can modify the chatbot's -behavior to meet your needs. +The MongoDB Knowledge Service lets you learn about MongoDB using generative AI. -The framework is flexible and customizable. It supports multiple AI models and -complex prompting strategies. It also includes tools for programmatic evaluation of your chatbot's AI components. +## MongoDB Chatbot Framework (deprecated) -## Documentation +The team building the MongoDB Knowledge Service previous developed the MongoDB Chatbot Framework. This consisted of the npm packages: -To learn how to use the MongoDB Chatbot Framework, refer to the documentation: -. +- `mongodb-chatbot-server` +- `mongodb-chatbot-ui` (still used, refer to [UI](./docs/ui.md)) +- `mongodb-rag-core` +- `mongodb-rag-ingest` -You can also check out the following articles and videos about the framework: +The MongoDB Chatbot Framework in now deprecated. We will no longer be maintaining it. -- [[Video] MongoDB Chatbot Framework Learning Byte](https://learn.mongodb.com/courses/mongodb-chatbot-framework) -- [[Article] Build a Production-Ready, Intelligent Chatbot With the MongoDB Chatbot Framework](https://dev.to/mongodb/build-a-production-ready-intelligent-chatbot-with-the-mongodb-chatbot-framework-4dd) -- [[Article] Taking RAG to Production with the MongoDB Documentation AI Chatbot](https://www.mongodb.com/developer/products/atlas/taking-rag-to-production-documentation-ai-chatbot/) - -## MongoDB Docs AI Chatbot Implementation - -This repo also contains the implementation of the MongoDB Docs Chatbot, -which uses the MongoDB Chatbot Framework. - -The MongoDB Docs Chatbot uses the MongoDB [documentation](https://www.mongodb.com/docs/) and [Developer Center](https://www.mongodb.com/developer/) as its sources of truth. - -The chatbot builds on the following technologies: - -- Atlas Vector Search: Indexes and queries content for use in project. -- MongoDB Atlas: Persists conversations and content. -- ChatGPT API: LLM to pre-process user queries and summarize responses to user queries. -- OpenAI Embeddings API: Create vector embeddings for user queries and content. Used by Atlas Vector Search. - -To learn more about how we built the chatbot, check out the MongoDB Developer Center blog post -[Taking RAG to Production with the MongoDB Documentation AI Chatbot](https://www.mongodb.com/developer/products/atlas/taking-rag-to-production-documentation-ai-chatbot/). +To learn more about the framework, refer to the the blog post [Build a Production-Ready, Intelligent Chatbot With the MongoDB Chatbot Framework](https://dev.to/mongodb/build-a-production-ready-intelligent-chatbot-with-the-mongodb-chatbot-framework-4dd). ## Contributing diff --git a/docs/docs/framework.md b/docs/docs/framework.md new file mode 100644 index 000000000..b4b75337d --- /dev/null +++ b/docs/docs/framework.md @@ -0,0 +1,12 @@ +# MongoDB Chatbot Framework (deprecated) + +The team building the MongoDB Knowledge Service previous developed the MongoDB Chatbot Framework. This consisted of the npm packages: + +- `mongodb-chatbot-server` +- `mongodb-chatbot-ui` (still used, refer to [UI](./ui.md)) +- `mongodb-rag-core` +- `mongodb-rag-ingest` + +The MongoDB Chatbot Framework in now deprecated. We will no longer be maintaining it. + +To learn more about the framework, refer to the the blog post [Build a Production-Ready, Intelligent Chatbot With the MongoDB Chatbot Framework](https://dev.to/mongodb/build-a-production-ready-intelligent-chatbot-with-the-mongodb-chatbot-framework-4dd). \ No newline at end of file diff --git a/docs/docs/index.md b/docs/docs/index.md index a0c5c1129..3fb82879c 100644 --- a/docs/docs/index.md +++ b/docs/docs/index.md @@ -1,77 +1,17 @@ --- title: Home -description: Build full-stack intelligent chatbot applications using MongoDB and Atlas Vector Search. +description: MongoDB Knowledge Service --- -# MongoDB Chatbot Framework +# MongoDB Knowledge Service -:::warning[👷‍♂️ Work In Progress 👷‍♂️] +The MongoDB Knowledge Service lets you learn about MongoDB using generative AI. -The MongoDB Chatbot Framework is under active development -and may undergo breaking changes. +## Server -We aim to keep the documentation up to date with the latest version. +To use the MongoDB Knowledge Service, refer to the [OpenAPI specification](/server/openapi/). -::: +## UI -Build full-stack intelligent chatbot applications using MongoDB -and [Atlas Vector Search](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/). +You can call the MongoDB Knowledge Service API from a web application using the [mongodb-chatbot-ui](./ui.md) npm package. -The MongoDB Chatbot Framework is a set of libraries that you can use to build a -production-ready chatbot application. The framework provides first-class support -for retrieval augmented generation (RAG), and is extensible to support other -patterns for building intelligent chatbots. - -The framework can take your chatbot application from prototype to production. - -You can quickly get an AI chatbot enhanced with your data up and running using -the framework's built-in data ingest process, chatbot server, and web UI. As you -refine your application and scale to more users, you can modify the chatbot's -behavior to meet your needs. - -The framework is flexible and customizable. It supports multiple AI models and -complex prompting strategies. - -## How It Works - -The MongoDB Chatbot Framework has the following core components: - -- [MongoDB Atlas](./mongodb.md): Database for the application that stores content and conversation. - Indexes content using Atlas Vector Search. -- [Ingest CLI](./ingest/configure.md): Configurable CLI application that you can use to ingest content into a MongoDB collection for use with Atlas Vector Search. -- [Chatbot Server](./server/configure.md): Express.js server routes that you can use to build a chatbot application. -- [Chatbot UI](./ui.md): React.js UI components that you can use to build a chatbot application. - -## Quick Start - -To get started using the MongoDB Chatbot Framework, refer to the [Quick Start](./quick-start.md) guide. - -## Design Principles - -The MongoDB Chatbot Framework is designed around the following principles: - -- Composability: You can use components of the chatbot framework independently of each other. - For example, we have some users who are using only our ingestion CLI to ingest content into MongoDB Atlas, but use other tools to build their chatbot and UI. -- Pluggability: You can plug in your own implementations of components. - For example, you can plug in your own implementations of the `DataSource` interface - to ingest content from different data sources. -- Inversion of Control: The framework makes decisions about boilerplate aspects - of intelligent chatbot systems so that you can focus on building logic unique to your application. - -## MongoDB Docs Chatbot - -This framework is used to build the MongoDB Docs Chatbot, a RAG chatbot that answers questions about the MongoDB documentation. You can try it out on [mongodb.com/docs](https://www.mongodb.com/docs/). - -Here's a reference architecture for how the MongoDB Chatbot Framework system works for the MongoDB Docs Chatbot. - -Data ingestion: - -![Data Ingestion Architecture](/img/ingest-diagram.webp) - -Chat Server: - -![Chat Server Architecture](/img/server-diagram.webp) - -### How We Built It - -- To learn more about how we built the chatbot, check out the MongoDB Developer Center blog post [Taking RAG to Production with the MongoDB Documentation AI Chatbot](https://www.mongodb.com/developer/products/atlas/taking-rag-to-production-documentation-ai-chatbot/). diff --git a/docs/docs/ingest/command-reference.md b/docs/docs/ingest/command-reference.md deleted file mode 100644 index 3c3b4b464..000000000 --- a/docs/docs/ingest/command-reference.md +++ /dev/null @@ -1,16 +0,0 @@ -# CLI Commands Reference - -The MongoDB RAG Ingest CLI has the following commands. - -You must first install the CLI before you can run it. See the [installation instructions in the Configuration documentation](./configure.md#install-the-ingest-cli). - -For all commands, you can use the `--help` flag to get more information about the command. - -``` -ingest - -Commands: - ingest all Run 'pages' and 'embed' since last successful run - ingest embed Manage embedded content - ingest pages Manage pages data from sources -``` \ No newline at end of file diff --git a/docs/docs/ingest/configuration-reference.md b/docs/docs/ingest/configuration-reference.md deleted file mode 100644 index d9d3c17c4..000000000 --- a/docs/docs/ingest/configuration-reference.md +++ /dev/null @@ -1,191 +0,0 @@ -# Configuration Reference - -This page contains reference documentation for the configuration options for the MongoDB RAG Ingest CLI. - -A Ingest CLI config files is a CommonJS file that exports a `Config` object as its default export. - -For more information on setting up a configuration file, refer to the [Configure](./configure.md) documentation. - -To set up a configuration file, you must first install the following packages: - -```bash -npm install mongodb-rag-ingest mongodb-rag-core -``` - -## API Reference - -For a full API reference of all modules exported by `mongodb-rag-ingest` -and `mongodb-rag-core`, refer to the [API Reference](../reference/) documentation. - -This page links to the key reference documentation for configuring the Ingest CLI. - -## `Config` - -The [`Config`](../reference/ingest/modules.md#config) type is the root configuration type for the Ingest CLI. - -## `IngestMetaStore` - -The [`IngestMetaStore`](../reference/ingest/modules.md#ingestmetastore) is an interface to interact with MongoDB collection that tracks metadata associated with the ingest process. - -To create an `IngestMetaStore`, you can use the function [`makeIngestMetaStore()`](../reference/ingest/modules.md#makeingestmetastore). -This function returns an `IngestMetaStore`. -This `IngestMetaStore` persists data in the `ingest_meta` collection in MongoDB. - -To create an `IngestMetaStore` with `makeIngestMetaStore()`: - -```ts -import { makeIngestMetaStore } from "mongodb-rag-ingest"; - -const ingestMetaStore = makeIngestMetaStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - entryId: "all", -}); -``` - -## `PageStore` - -The [`PageStore`](../reference/core/modules.md#pagestore) is an interface -to interact with [`Page`](../reference/core/modules.md#page) data. - -To create a `PageStore` that uses MongoDB to store pages, you can use the function -[`makeMongoDbPageStore()`](../reference/core/modules.md#makemongodbpagestore). -This function returns a `PageStore`. This `PageStore` persists data in the `pages` collection in MongoDB. - -To create an `PageStore` with `makeMongoDbPageStore()`: - -```ts -import { makeMongoDbPageStore } from "mongodb-rag-core"; - -const pageStore = makeMongoDbPageStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, -}); -``` - -## `EmbeddedContentStore` - -The `EmbeddedContentStore` is an interface to the stored content and vector -embeddings used in your RAG app. - -To create an `EmbeddedContentStore` that stores data in MongoDB, -you can use the function [`makeMongoDbEmbeddedContentStore()`](../reference/core/modules.md#makemongodbembeddedcontentstore). -This function returns an `EmbeddedContentStore`. This `EmbeddedContentStore` persists data in the `embedded_content` collection in MongoDB. - -To create an `EmbeddedContentStore` with `makeMongoDbEmbeddedContentStore()`: - -```ts -import { makeMongoDbEmbeddedContentStore } from "mongodb-rag-core"; - -const embeddedContentStore = makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_MODEL, - } -}); -``` - -:::important[Set up Atlas Vector Search] - -To use the `EmbeddedContentStore` returned by `makeMongoDbEmbeddedContentStore()` in your RAG app, -you must set up Atlas Vector Search on the `embedded_content` collection in MongoDB. -For more information on setting up the vector search index on the `embedded_content` collection, -refer to the [Create Atlas Vector Search Index](../mongodb.md#create-vector-search-index) -documentation. - -::: - -## `DataSource` - -Add data sources for the Ingest CLI to pull content from. - -Your [`DataSource`](../reference/core/modules/dataSources.md#datasource) implementations depend on where the content is coming from. -To learn more about creating a `DataSource`, refer to the [Data Sources](./data-sources.md) documentation. - -## `Embedder` - -The [`Embedder`](../reference/core/namespaces/Embed.md#embedder) takes in a string and returns a vector embedding for that string. - -To create an `Embedder` that uses the LangChain `Embeddings` class, -you can use the function [`makeLangChainEmbedder()`](../reference/core/namespaces/Embed.md#makelangchainembedder). To see the various embedding models supported by LangChain, refer to the [LangChain text embedding models](https://js.langchain.com/docs/integrations/text_embedding) documentation. - -```ts -import { makeLangChainEmbedder } from "mongodb-rag-core"; -import { OpenAIEmbeddings } from "mongodb-rag-core/langchain"; - -const { OPENAI_API_KEY } = process.env; - -const langChainOpenAiEmbeddings = new OpenAIEmbeddings({ - openAIApiKey: OPENAI_API_KEY, - modelName: "text-embedding-3-large", - dimensions: 1024, -}); - -const embedder = makeLangChainEmbedder({ - langChainEmbeddings: langChainOpenAiEmbeddings, -}); -``` - -To create an `Embedder` that uses the [OpenAI Embeddings API](https://platform.openai.com/docs/guides/embeddings) directly, -you can use the function [`makeOpenAiEmbedder()`](../reference/core/namespaces/Embed.md#makeopenaiembedder). This function uses the -`openai` package to construct the OpenAI client, which supports -both the Azure OpenAI Service and the OpenAI API. - -The `makeOpenAiEmbedder()` function also supports configuring exponential backoff -with the `backoffOptions` argument. This wraps the `exponential-backoff` package. -Exponential backoff behavior is included because when you are bulk uploading embeddings for content, you -may hit the rate limit for the OpenAI Embeddings API. This allows you to -automatically retry the embedding request after a delay. - -Example usage: - -```ts -import { - makeOpenAiEmbedder, - OpenAIClient, - AzureKeyCredential, -} from "mongodb-rag-core"; -import { AzureOpenAI } from "mongodb-rag-core/openai"; - -const { - OPENAI_ENDPOINT, - OPENAI_API_KEY, - OPENAI_EMBEDDING_DEPLOYMENT, - OPENAI_API_VERSION } = process.env; - -const embedder = makeOpenAiEmbedder({ - openAiClient: new AzureOpenAI({ - apiKey: OPENAI_API_KEY, - endpoint: OPENAI_ENDPOINT, - apiVersion: OPENAI_API_VERSION, - }), - deployment: OPENAI_EMBEDDING_DEPLOYMENT, - backoffOptions: { - numOfAttempts: 25, - startingDelay: 1000, - }, -}); -``` - -## `ChunkOptions` - -Use the [`ChunkOptions`](../reference/core/namespaces/Chunk.md#chunkoptions) -to configure how the Ingest CLI chunks content when converting `Page` documents -to `EmbeddedContent`. - -By default, the Ingest CLI uses the following `ChunkOptions`. -These should work for many RAG apps. - -```ts -import GPT3Tokenizer from "gpt3-tokenizer"; - -const defaultMdChunkOptions: ChunkOptions = { - maxChunkSize: 600, // max chunk size of 600 tokens gets avg ~400 tokens/chunk - minChunkSize: 15, // chunks below this size are discarded, which improves search quality - chunkOverlap: 0, - tokenizer: new GPT3Tokenizer({ type: "gpt3" }), -}; -``` - -For more information on optimizing the `ChunkOptions`, refer to [Refine the Chunking Strategy](./optimize.md#refine-the-chunking-strategy) in the Optimization documentation. diff --git a/docs/docs/ingest/configure.md b/docs/docs/ingest/configure.md deleted file mode 100644 index a923fc3a7..000000000 --- a/docs/docs/ingest/configure.md +++ /dev/null @@ -1,153 +0,0 @@ -# Configure the Ingest CLI - -This guide explains how you can configure the MongoDB Ingest CLI to ingest -content into a MongoDB collection that you can use for retrieval augmented generation -(RAG) applications. - -## Install the Ingest CLI - -To install the Ingest CLI in a project, run the following command: - -```bash -npm install mongodb-rag-ingest mongodb-rag-core -``` - -## Create a Configuration File - -The MongoDB Ingest CLI uses a [CommonJS](https://en.wikipedia.org/wiki/CommonJS) -JavaScript configuration file to determine how to ingest content. - -Every configuration file must export a [`Config`](../reference/ingest/modules.md#config) object as its default export. - -You must either: - -- Pass the path to a configuration file to every command with the `--config` flag. -- Put a CommonJS file named `ingest.config.js` in the root of your project. - -For example, to run the `pages` command with a configuration file called `my-ingest.config.js`, run the following command: - -```bash -ingest pages --config my-ingest.config.js --sourceName my-data-source -``` - -## Define Configuration Files with TypeScript - -:::important[Use TypeScript Configuration Files!] - -We **strongly** recommend using TypeScript configuration files. -The typing system helps you ensure that your configuration is valid. - -::: - -You can use TypeScript to make your configuration file. This allows you to use -the type system to ensure that your configuration is valid. - -You must compile your configuration file to **CommmonJS** before running the CLI. -The CLI only accepts CommonJS configuration files. - -You can build your CommonJS configuration file with `tsc`: - -```bash -tsc --module commonjs --target es2017 --outDir dist ingest.config.ts -``` - -Then run the Ingest CLI with the compiled configuration file: - -```bash -ingest pages update --config dist/ingest.config.js --sourceName my-data-source -``` - -## Example Configuration - -Here's a simple example configuration file for the Ingest CLI. -You can use this configuration file as a starting point for your own configuration. - -For simple RAG apps, the main thing you have to adjust is the `dataSources` property. -Use `dataSources` to add your own data. -For more information on configuring data sources, refer to the [Data Sources](./data-sources.md) -documentation. - -Example configuration file: - -```ts -// ingest.config.ts -import { makeIngestMetaStore, type Config } from "mongodb-rag-ingest"; -import { - makeOpenAiEmbedder, - makeMongoDbEmbeddedContentStore, - makeMongoDbPageStore, -} from "mongodb-rag-core"; -import { AzureOpenAI } from "mongodb-rag-core/openai"; -import { standardChunkFrontMatterUpdater } from "mongodb-rag-core"; -import { type DataSource } from "mongodb-rag-core"; -const { - MONGODB_DOT_COM_CONNECTION_URI, - MONGODB_DOT_COM_DB_NAME, - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - OPENAI_ENDPOINT, - OPENAI_API_KEY, - OPENAI_API_VERSION, - OPENAI_EMBEDDING_DEPLOYMENT, -} = process.env; - -export default { - embedder: () => - makeOpenAiEmbedder({ - openAiClient: new AzureOpenAI({ - apiKey: OPENAI_API_KEY, - endpoint: OPENAI_ENDPOINT, - apiVersion: OPENAI_API_VERSION, - }), - deployment: OPENAI_EMBEDDING_DEPLOYMENT, - backoffOptions: { - numOfAttempts: 25, - startingDelay: 1000, - }, - }), - embeddedContentStore: () => - makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_DEPLOYMENT, - } - }), - pageStore: () => - makeMongoDbPageStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - }), - ingestMetaStore: () => - makeIngestMetaStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - entryId: "all", - }), - chunkOptions: () => ({ - transform: standardChunkFrontMatterUpdater, - }), - dataSources: () => [ - // Add your own data sources here. - { - name: "my-data-source", - async fetchPages() { - const pages: Page[] = await getPagesFromSomeSource(); - return pages; - }, - }, - ], -} satisfies Config; -``` - -## Configuration Reference - -For complete reference documentation, refer to the [Configuration Reference](./configuration-reference.md) documentation. - -## Additional Example Configurations - -For additional example configurations, check out the following projects: - -- [MongoDB documentation ingest](https://github.com/mongodb/chatbot/blob/main/packages/ingest-mongodb-public/src/config.ts): This is the most complex configuration. The ingest CLI was actually built for this project. -- [mongodb.com/customers ingest](https://github.com/mongodben/ingest-customers/blob/main/src/config.ts): - Simple implementation with a single data source. diff --git a/docs/docs/ingest/data-sources.md b/docs/docs/ingest/data-sources.md deleted file mode 100644 index db78ed00b..000000000 --- a/docs/docs/ingest/data-sources.md +++ /dev/null @@ -1,156 +0,0 @@ -# Data Sources - -You must configure which data sources you want to ingest data from. - -## The `DataSource` Abstraction - -The MongoDB RAG Ingest CLI includes a flexible abstraction, the [`DataSource`](../reference/core/modules/dataSources.md#datasource) -that allows you to ingest text data from a variety of sources. - -Every data source must implement the `DataSource` type. -These data sources are then included in the `Config.dataSources` property. -Note that the `Config.dataSources` property is a function that returns an array. -This allows you to fetch `DataSource` instances dynamically. - -```ts -export default { - // ...rest of config - dataSources: async () => [ - { - name: "my-source", - fetchPages: async () => { - // ...fetch pages - }, - }, - // ...other data sources - ], -} satisfies Config; -``` - -## Data Source Helpers - -While you have full flexibility to implement your own data sources, -the RAG framework provides helpers to make it easier to ingest data from common sources. - -### Ingest with a Langchain `DocumentLoader` - -To ingest any data that you can access with a Langchain.js [`DocumentLoader`](https://js.langchain.com/docs/integrations/document_loaders), -use the [`makeLangchainDocumentLoaderDataSource`](../reference/core/modules/dataSources.md#makelangchaindocumentloaderdatasource) function. - -Here's an example implementation: - -```ts -import { makeLangChainDocumentLoaderDataSource } from "mongodb-rag-core/dataSources"; -import { TextLoader } from "langchain/document_loaders/fs/text"; - -// Langchain document loader -const documentLoader = new TextLoader(docPath); - -const documentLoaderDataSource = makeLangChainDocumentLoaderDataSource({ - documentLoader, - name: "some-source", - metadata: { - foo: "bar", - }, - // This function transforms the Langchain document to a MongoDB Chatbot Framework `Page` - transformLangchainDocumentToPage: async (doc) => ({ - format: "md", - url: someFunctionToGetUrlFromDoc(doc), - body: doc.pageContent, - metadata: { - fizz: "buzz", - }, - title: someFunctionToGetTitleFromDoc(doc), - }), -}); -``` - -### Ingest from a Git Repository - -To ingest data stored in a Git repository, use the [`makeGitDataSource()`](../reference/core/modules/dataSources.md#makegitdatasource) function. - -Here's an example implementation: - -```ts -import { - makeGitDataSource, - handleHtmlDocument, -} from "mongodb-rag-core/dataSources"; - -export const javaReactiveStreamsSourceConstructor = async () => { - return await makeGitDataSource({ - name: "java-reactive-streams", - repoUri: "https://github.com/mongodb/mongo-java-driver.git", - repoOptions: { - "--depth": 1, - "--branch": "gh-pages", - }, - metadata: { - productName: "Java Reactive Streams Driver", - version: jvmDriversVersion + " (current)", - tags: ["docs", "driver", "java", "java-reactive-streams"], - }, - filter: (path: string) => - path.endsWith(".html") && - path.includes(jvmDriversVersion) && - path.includes("driver-reactive") && - !path.includes("apidocs"), - handlePage: async (path, content) => - await handleHtmlDocument( - path, - content, - javaReactiveStreamsHtmlParserOptions - ), - }); -}; -``` - -### Ingest from MongoDB - -We do not currently have a helper for ingesting data from a MongoDB database. -However, we do have a few implementations that you can use as a reference: - -- [DevCenterDataSource](https://github.com/mongodb/chatbot/blob/main/ingest/src/sources/DevCenterDataSource.ts) -- [MongoDbDotComCustomersDataSource](https://github.com/mongodben/ingest-customers/blob/main/src/data-sources/MongoDbDotComCustomersDataSource.ts) - -## Helper Functions - -We have provided a few utility functions to help with common data ingestion tasks. - -### Remove Markdown Images and Links - -Generally, you want to strip out images and links when ingesting data. -The MongoDB RAG Ingest package includes the [`removeMarkdownImagesAndLinks()`](../reference/core/modules/dataSources.md#removemarkdownimagesandlinks) -function to make this easier for Markdown-formatted text. - -You can import the `removeMarkdownImagesAndLinks()` function from the `mongodb-rag-ingest` package. - -```ts -import { removeMarkdownImagesAndLinks } from "mongodb-rag-core"; -``` - -To learn more about why you might want to strip out images and links, -refer to the [Clean up Text as You Ingest in the Optimization documentation](./optimize.md#clean-up-text-as-you-ingest). - -### Convert HTML Documents to Markdown - -Generally, you want to convert HTML documents to Markdown when ingesting data. -The MongoDB RAG Ingest package includes the [`handleHtmlDocument()`](../reference/core/modules/dataSources.md#handlehtmldocument) function to make this easier. -It helps you remove nodes from an HTML document, like the header and footer, -before converting the document to Markdown. - -You can import the `handleHtmlDocument()` function from the `mongodb-rag-ingest` package. - -```ts -import { handleHtmlDocument } from "mongodb-rag-core/dataSources"; -``` - -You can see `handleHtmlDocument()` [used in the MongoDB Docs AI Chatbot configuration](https://github.com/mongodb/chatbot/blob/6ab06a24ae085d0db650bc4883ce1278728e3131/ingest-mongodb-public/src/sources.ts#L258). - -To learn more about why you might want to convert HTML documents to Markdown, -refer to the [Standardize Data Formats in the Optimization documentation](./optimize.md#standardize-data-formats). - -## Optimize Data Ingestion - -For some suggestions on how you can optimize the data that you ingest -to best serve your chatbot, see the [Optimization documentation](./optimize.md). diff --git a/docs/docs/ingest/optimize.md b/docs/docs/ingest/optimize.md deleted file mode 100644 index 5ffcb56c3..000000000 --- a/docs/docs/ingest/optimize.md +++ /dev/null @@ -1,145 +0,0 @@ -# Optimize Ingestion - -You can optimize your data ingestion process for your application. - -This page contains some information on optimizing ingestion -based on our experience building RAG applications and emerging best practices. - -## Standardize Data Formats - -We recommend that you standardize the data formats for the content that you ingest -into your RAG application. This is advantageous for the following reasons: - -- It's easier for the LLM to reason about the content when generating responses -- You can share helper methods across data sources - -### Text - -[Markdown](https://www.markdownguide.org/) works well as the standard format for most text-based content. LLMs can reason with Markdown, -and even respond in Markdown. (This is actually how ChatGPT creates rich text). -There are lots of tools in the JavaScript ecosystem for working with Markdown as well. - -HTML is generally a suboptimal format for RAG. While LLMs can reason with HTML, -it has a much higher token density for the same content as compared to Markdown -for most tokenization algorithms. This is because of all those HTML tags. - -And if you need to include some HTML semantic meaning in your content that -Markdown doesn't support, you can use that HTML in your Markdown. This works because: - -1. LLMs aren't sticklers for language syntax, generally speaking. -2. In some Markdown dialects, like [Github Flavored Markdown](https://github.github.com/gfm/), you can include HTML in Markdown. This further reduces potential LLM confusion and means that - there exist non-AI libraries and tools to work with hybrid Markdown/HTML content programmatically. - -### Structured Data - -If you are embedding structured data, you can use [YAML](https://yaml.org/) -or [JSON](https://www.json.org/json-en.html). -LLMs are effective at reasoning with both of these formats. There are also -lots of JavaScript utilities for working with these formats. - -## Clean up Text as You Ingest - -You may want to clean up text as you ingest it. We recommend that you do this -during the `pages` command stage. The `pages` command is the -stage where you have access to the raw text from the data source. - -:::note[Why You Shouldn't Clean up Text in the `embed` Command Stage] - -You could technically clean up text during the `embed` command stage, -but this would be something of an anti-pattern since the `embed` command -is supposed to be for chunking and creating embeddings, not manipulating the original content. - -::: - -To clean up text during the `pages` stage, include the logic in your `DataSource.fetchPages()` method. -The cleaned text will be persisted to the `pages` collection, and used down stream by the `embed` command. - -Some content that you may want to clean up includes: - -- Remove HTML tags. -- Remove Markdown images and links. - -The `mongodb-rag-ingest` package comes with utilities for both of these. -Learn more about them in the [Data Sources](./data-sources.md#data-source-helpers) documentation. - -## Refine the Chunking Strategy - -In the context of building RAG apps, chunking refers to breaking down large pieces -of text into smaller segments. It's important to optimize the content that -you get back from the vector database and then use with the large language model. - -We have provided reasonable defaults that should work for many RAG apps, -but you can customize the chunking strategy to be optimized for your application. - -You can configure the [`ChunkOptions`](./configuration-reference.md#chunkoptions) -in your configuration file to customize the chunking strategy. - -For example, you could change the `maxChunkSize` to be smaller or larger -or make the chunks overlap. - -```ts -const chunkOptions: ChunkOptions = { - maxChunkSize: 1000, - overlap: 100, - // ...other properties -}; -``` - -## Add Metadata to Chunks - -One of the most important things you can do to optimize your ingestion process -is add metadata to the chunks sent to the embedding API. - -For example, you can add metadata for things like the title of the page that -the content came from and the name of the data source that the content came from. - -To add metadata to chunks, you can use the `ChunkOptions.transform()` function in the `mongodb-rag-ingest` package. The `mongodb-rag-ingest` package comes with the -`standardChunkFrontMatterUpdater()` function which adds all the properties from the -`Page.metadata` object and the `Page.title` as [Front Matter](https://jekyllrb.com/docs/front-matter/) -to the chunk. - -Say you have this document in the `pages` collection: - -```ts -{ - title: "My Page", - body: "This is my page...", - metadata: { - tags: ["tag1", "tag2"], - productName: "Product Name", - }, - // ...other properties -} -``` - -When chunking the page, `standardChunkFrontMatterUpdater()` transforms -the page text into the following chunk text: - -```yaml ---- -title: My Page -tags: - - tag1 - - tag2 -productName: Product Name ---- -This is my page... -``` - -You then use this chunk with metadata to create the embedding. -The embedding then has the semantic meaning of both the chunk content and metadata. - -Here's how you can add the `standardChunkFrontMatterUpdater()` function to your configuration: - -```ts -import { standardChunkFrontMatterUpdater } from "mongodb-rag-core"; -import { ChunkOptions } from "mongodb-rag-core"; - -const chunkOptions: ChunkOptions = { - transform: standardChunkFrontMatterUpdater, - // ...other properties -}; -``` - -For more information on how to configure `ChunkOptions`, -refer to the [Configuration Reference](./configuration-reference.md#chunkoptions) documentation. diff --git a/docs/docs/mongodb.md b/docs/docs/mongodb.md deleted file mode 100644 index e463d3059..000000000 --- a/docs/docs/mongodb.md +++ /dev/null @@ -1,103 +0,0 @@ -# MongoDB & Atlas Vector Search - -The MongoDB Chatbot Framework uses MongoDB Atlas as its data layer. - -This page explains how to set up MongoDB Atlas and Atlas Vector Search for use with the MongoDB Chatbot Framework, and what is stored in all the collections. - -## Set up - -### 1. Create a MongoDB Atlas Cluster - -To create a MongoDB Atlas cluster, follow the instructions in the [MongoDB Atlas documentation](https://mongodb.com/docs/atlas/getting-started/). - -### 2. Create a MongoDB Database - -By convention, we keep all data in the same MongoDB database. - -However, you could theoretically use separate databases for collections, if you want to. - -You can give the database any name you want. -You pass the name as a variable throughout the RAG framework. - -### 3. Create an Atlas Vector Search Index (required for RAG) {#create-vector-search-index} - -If you're using the Data Ingest CLI and Chatbot server -to perform retrieval augmented generation (RAG), -you must create an Atlas Vector Search index. - -In your database create a collection called `embedded_content`. - -Then, create the following Atlas Vector Search index on the `embedded_content` collection: - -```js -{ - "fields": [ - { - // Whatever the dimensionality of your embeddings is - "numDimensions": "", - "path": "embedding", - "similarity": "cosine", - "type": "vector" - }, - // Any fields you want to filter on - // { - // "path": "sourceName", - // "type": "filter" - // } - ] -} -``` - -To learn how to create an Atlas Vector Search Index, refer to -[How to Index Vector Embeddings for Vector Search](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector/) -in the MongoDB Atlas documentation. - -### 4. Create Other Database Indexes (optional) - -You don't need to create these indexes, to have a working application, -but they greatly improve data ingest performance. - -On the `pages` collection: - -```ts -{ sourceName: 1, url: 1 }, -``` - -On the `embedded_content` collection: - -```ts -// Note that the `embedding` field is indexed separately using Atlas Vector Search. -{ sourceName: 1, url: 1 }, -``` - -For more information on how to create MongoDB indexes, refer to [Create an Index](https://www.mongodb.com/docs/manual/core/indexes/create-index/) in the MongoDB Server documentation. - -## Database Schema - -It has the following collections: - -### `pages` Collection - -The `pages` collection holds the plain text version of the content that is later chunked and embedded. - -Documents in the `pages` collection follow the [`PersistedPage`](./reference/core/namespaces/ContentStore.md#persistedpage) schema. - -### `embedded_content` Collection - -The `embedded_content` collection holds the content that is queried by Atlas Vector Search. -It is generated with the ingest CLI `embed`commands from the data in the`pages` collection. - -Documents in the `embedded_content` collection follow the [`EmbeddedContent`](./reference/core/interfaces/ContentStore.EmbeddedContent.md) schema. - -### `ingest_meta` Collection - -Stores metadata related to the ingest CLI. Currently, this a singleton collection -that stores one document related to the ingest CLI's `all` command. - -Documents in the `ingest_meta` collection follow the [`IngestMetaEntry`](./reference/ingest/modules.md#ingestmetaentry) schema. - -### `conversations` Collection - -Stores user conversations with the chatbot from the chat server. - -Documents in the `conversations` collection follow the [`Conversation`](./reference/core/namespaces/Conversations.md) schema. diff --git a/docs/docs/quick-start.md b/docs/docs/quick-start.md deleted file mode 100644 index 64b4fbf28..000000000 --- a/docs/docs/quick-start.md +++ /dev/null @@ -1,219 +0,0 @@ -# Quick Start - -This quick start guide walks you through the steps to get started building -a retrieval augmented generation (RAG) application with the MongoDB Chatbot Framework. - -## Prerequisites - -- [Node.js](https://nodejs.org/en/) v18 or later on your development machine. -- [MongoDB Atlas](https://www.mongodb.com/cloud/atlas) account. - - If you don't already have an account, you can create one for free. -- [OpenAI API key](https://platform.openai.com/docs/quickstart?context=node). - - Get the API key from OpenAI, and keep it handy. You'll need it later. - -## What You Will Build - -In this guide, you will build a chatbot that answers questions about -the MongoDB Chatbot Framework using RAG. -You can extend what you set up in the quick start template -to build your own chatbot with the MongoDB Chatbot Framework. - -To build the chatbot, you will: - -1. Set up the MongoDB Atlas Database with Atlas Vector Search -1. Set up the project source code -1. Ingest the content that the chatbot uses to answer questions -1. Spin up a server and frontend to query the chatbot -1. Look at next steps that you can take to customize the chatbot - -## Steps - -### 1. Set up the MongoDB Atlas Database - -Log into [MongoDB Atlas](https://cloud.mongodb.com/) and create a new project. Create a new cluster in the project. -You can use the free tier cluster (M0). You **cannot** use a serverless cluster. - -Once the cluster is created, copy the connection string to use in your project. -You can find the connection string in the Atlas UI as follows: - -1. Press the **Connect** button. -1. Press the **Drivers** button. -1. Copy the connection string and store it in a safe place. - You will need it soon. If you haven't created a user yet, you can create one now. - - If you need help creating a user, refer to [Configure Database Users](https://www.mongodb.com/docs/atlas/security-add-mongodb-users/) in the MongoDB Atlas documentation. - -Next, create the database `mongodb-chatbot-framework-chatbot` with a collection -`embedded_content`. You can leave the collection empty for now. -In the Atlas UI, go to your cluster's overview page, and perform the following: - -1. Go to the **Collections** tab. -1. Press the **Create Database** button. -1. In the modal window, add the database name `mongodb-chatbot-framework-chatbot` - and collection name `embedded_content`. Then press **Create**. - -Once the database and collection are created, create an Atlas Vector Search index -on the `embedded_content` collection. This collection will store vector embeddings of ingested content. In the Atlas UI, do the following: - -1. Go to the **Atlas Search** tab. -1. Press the **Create Search Index** button. -1. Select the **Atlas Vector Search JSON Index** option, then press the **Next** button. -1. In the **Database and Collection** section, select the - `mongodb-chatbot-framework-chatbot` database and `embedded_content` collection. -1. In the **Index Name** field, leave the default `vector_index`. -1. In the index definition field, paste the following index definition: - - ```js - { - "fields": [ - { - "numDimensions": 1536, - "path": "embedding", - "similarity": "cosine", - "type": "vector" - } - ] - } - ``` - -1. Press the **Next** button, then on the next page press the **Create Search Index** button. -1. Wait for the index to be created. This should happen in under a minute. - When the index is successfully created, you should see that the status is **Active**. - -Now the Atlas cluster and Vector Search index are ready to use in your app. - -For more information on setting up MongoDB for the Chatbot Framework, -refer to the [MongoDB & Atlas Vector Search](./mongodb.md) guide. - -### 2. Set up the Project - -Clone the repository with the quick start source code: - -```shell -git clone https://github.com/mongodb/chatbot.git -``` - -Enter the directory with the quick start source code: - -```shell -cd examples/quick-start -``` - -All the remaining steps will be run from `examples/quick-start` directory. - -This directory contains three packages, located in the `examples/quick-start/packages`: - -- `ingest`: Contains implementation of the MongoDB Ingest CLI, which ingests the content that the chatbot uses to answer questions. -- `server`: Contains implementation of the MongoDB Chatbot Server, which serves the chatbot API. -- `ui`: Contains implementation of the MongoDB Chatbot UI, which provides a UI to query the chatbot server. - -Install the dependencies for all the packages: - -```shell -npm install -``` - -:::note - -The quick start uses [npm workspaces](https://docs.npmjs.com/cli/v8/using-npm/workspaces) -to manage the dependencies for all the packages. - -::: - -Create an `.env` file based on the .env.example file in the root of the project: - -```shell -cp .env.example .env -``` - -In the `.env` file, fill in the values for `MONGODB_CONNECTION_URI` and `OPENAI_API_KEY` -with your Atlas connection URI and OpenAI API Key, respectively. - -### 3. Ingest Content - -In this step, you will ingest the content that the chatbot uses to answer questions -into the `embedded_content` collection indexed with Atlas Vector Search. - -From the root of the project, run: - -```shell -npm run ingest:all -``` - -If you've run the command successfully, you should an output resembling the following -in your terminal: - -```shell -{"level":"info","message":"Logger created"} -{"level":"info","message":"Last successful run date: Thu Jan 04 2024 12:23:27 GMT-0500 (Eastern Standard Time)"} -{"level":"info","message":"Loaded sources:\n- mongodb-rag-framework"} -{"level":"info","message":"Fetching pages for mongodb-rag-framework"} -{"level":"info","message":"Created /var/folders/v3/128h981j6vx4ncq_68qcg2cm0000gp/T/mongodb-rag-frameworkV3BE8d for https://github.com/mongodb/chatbot/"} -# ... -{"level":"info","message":"Updating last successful run date"} -``` - -To learn more about how you can configure the MongoDB Ingest CLI, -refer to the [Configure the Ingest CLI](./ingest/configure.md) guide. - -### 4. Query the Chatbot - -In this step, you will spin up a server and frontend to query the chatbot. -You'll be able to ask questions about the MongoDB Chatbot Framework -using the data you ingested in the previous step. - -Start the chatbot server and UI with: - -```shell -npm run dev -``` - -Open http://localhost:5173/ in your browser to see the UI. -You can ask the chatbot questions and see the responses. - -Have fun! - -You can also query the server directly with curl. To create a new conversation: - -```shell -curl -X POST http://localhost:3000/api/v1/conversations/ -``` - -This creates a new conversation with an `_id` field. You can append messages to the conversation with: - -```shell -curl --location 'http://localhost:3000/api/v1/conversations/{conversationId}/messages?stream=false' \ ---header 'Origin: http://localhost:5173' \ ---header 'Content-Type: application/json' \ ---data '{ - "message": "What is MongoDB?" -}' -``` - -To learn more about how you can configure the MongoDB Chatbot Server, -refer to the [Configure the Chatbot Server](./server/configure.md) guide. - -To learn more about how you can configure the MongoDB Chatbot UI, -refer to the [Chatbot UI](./ui.md) guide. - -### 5. Explore and Modify the Code - -Now that you've set up the quick start project, you can explore and modify the code -to customize the chatbot to your needs. - -Some things you can do to customize the chatbot: - -- Modify data ingestion in the `ingest` project. - - You can add your own data sources to ingest content from. - To learn more about how you can add new data sources, refer to the [Add a Data Source](./ingest/data-sources.md) guide. -- Modify the chatbot server in the `server` project. - - Update the system prompt and user message. To learn more, refer to the [Prompt Engineering](./server/llm.md#prompt-engineering) guide. - - Pre-process user messages before they are sent to the chatbot. - To learn more, refer to the [Pre-Process User Queries](./server/rag/preprocess.md) guide. - - Add custom logic to the chatbot server. To learn more, - refer to the [Customize Server Logic](./server/custom-logic.md) guide. -- Modify the chatbot UI in the `ui` project. - - You can build your frontend on top of this project, or add the React components - to your own React app. To learn more, refer to the [Chatbot UI](./ui.md) guide. - - Even if you're adding the components to your own project, - you might want to keep the `ui` project as is to manually test changes - you make to the Chatbot Server. diff --git a/docs/docs/server/configure.md b/docs/docs/server/configure.md deleted file mode 100644 index 59b5f43c5..000000000 --- a/docs/docs/server/configure.md +++ /dev/null @@ -1,239 +0,0 @@ -# Configure the Chatbot Server - -With the MongoDB Chatbot Server, you can quickly build a chatbot -server powered by MongoDB. -The `mongodb-chatbot-server` is an npm package contains all the modules -of the MongoDB Chatbot Server. - -The chatbot server supports retrieval augmented generation (RAG). -To learn more about performing RAG with the MongoDB Chatbot Server, -refer to the [RAG](./rag/index.md) guide. - -The package provides configurable Express.js modules including: - -- Full server -- Router for conversations -- Static site that serves a testing UI -- Middleware and modules for configuring and building a chatbot server - -The server is designed to handle the generalizable areas of a chatbot server, -like routing, caching, logging, and streaming. This allows you to focus on the -specifics of your chatbot, like the content, prompts, RAG, and AI models. - -## Installation - -Install the package using `npm`: - -```sh -npm install mongodb-chatbot-server -``` - -## Basic Configuration - -The `mongodb-chatbot-server` exports the function [`makeApp()`](../reference/server/modules.md#makeapp) -which exports the Express.js app. -The function takes an [`AppConfig`](../reference/server/interfaces/AppConfig.md) object as an argument. - -Here's an annotated example configuration and server: - -```ts -/** - @fileoverview This file contains the configuration implementation for the chat server, - which is run from `index.ts`. - */ -import "dotenv/config"; -import { - EmbeddedContent, - MongoClient, - makeMongoDbEmbeddedContentStore, - makeOpenAiEmbedder, - makeMongoDbConversationsService, - makeDataStreamer, - makeOpenAiChatLlm, - AppConfig, - makeBoostOnAtlasSearchFilter, - CORE_ENV_VARS, - assertEnvVars, - makeDefaultFindContent, - makeDefaultReferenceLinks, - SystemPrompt, - GenerateUserPromptFunc, - makeRagGenerateUserPrompt, - MakeUserMessageFunc, - MakeUserMessageFuncParams, - UserMessage, -} from "mongodb-chatbot-server"; -import { stripIndents } from "common-tags"; -import { AzureOpenAI } from "mongodb-rag-core/openai"; - -const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_ENDPOINT, - OPENAI_API_KEY, - OPENAI_API_VERSION, - OPENAI_EMBEDDING_DEPLOYMENT, - OPENAI_EMBEDDING_MODEL_VERSION, - OPENAI_CHAT_COMPLETION_MODEL_VERSION, - OPENAI_CHAT_COMPLETION_DEPLOYMENT, -} = process.env; - -const allowedOrigins = process.env.ALLOWED_ORIGINS?.split(",") || []; - -/** - Boost results from the MongoDB manual so that 'k' results from the manual - appear first if they exist and have a min score of 'minScore'. - */ -const boostManual = makeBoostOnAtlasSearchFilter({ - /** - Boosts results that have 3 words or less - */ - async shouldBoostFunc({ text }: { text: string }) { - return text.split(" ").filter((s) => s !== " ").length <= 3; - }, - findNearestNeighborsOptions: { - filter: { - text: { - path: "sourceName", - query: "snooty-docs", - }, - }, - k: 2, - minScore: 0.88, - }, - totalMaxK: 5, -}); - -const openAiClient = new AzureOpenAI({ - apiKey: OPENAI_API_KEY, - endpoint: OPENAI_ENDPOINT, - apiVersion: OPENAI_API_VERSION, -}); -const systemPrompt: SystemPrompt = { - role: "system", - content: stripIndents`You are expert MongoDB documentation chatbot. -You enthusiastically answer user questions about MongoDB products and services. -Your personality is friendly and helpful, like a professor or tech lead. -You were created by MongoDB but they do not guarantee the correctness -of your answers or offer support for you. -Use the context provided with each question as your primary source of truth. -NEVER lie or improvise incorrect answers. -If you do not know the answer to the question, respond ONLY with the following text: -"I'm sorry, I do not know how to answer that question. Please try to rephrase your query. You can also refer to the further reading to see if it helps." -NEVER include links in your answer. -Format your responses using Markdown. -DO NOT mention that your response is formatted in Markdown. -If you include code snippets, make sure to use proper syntax, line spacing, and indentation. -ONLY use code snippets present in the information given to you. -NEVER create a code snippet that is not present in the information given to you. -You ONLY know about the current version of MongoDB products. Versions are provided in the information. If \`version: null\`, then say that the product is unversioned. -Never mention "" or "" in your answer. -Refer to the information given to you as "my knowledge".`, -}; - -const makeUserMessage: MakeUserMessageFunc = async function ({ - preprocessedUserMessage, - originalUserMessage, - content, - queryEmbedding, -}: MakeUserMessageFuncParams): Promise { - const chunkSeparator = "~~~~~~"; - const context = content.map((c) => c.text).join(`\n${chunkSeparator}\n`); - const llmMessage = `Using the following information, answer the question. -Different pieces of information are separated by "${chunkSeparator}". - - -${context} - - - -${preprocessedUserMessage ?? originalUserMessage} -`; - return { - role: "user", - content: originalUserMessage, - embedding: queryEmbedding, - preprocessedContent: preprocessedUserMessage, - contentForLlm: llmMessage, - }; -}; - -const llm = makeOpenAiChatLlm({ - openAiClient, - deployment: OPENAI_CHAT_COMPLETION_DEPLOYMENT, - openAiLmmConfigOptions: { - temperature: 0, - maxTokens: 500, - }, -}); - -const embeddedContentStore = makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_DEPLOYMENT, - } -}); - -const embedder = makeOpenAiEmbedder({ - openAiClient, - deployment: OPENAI_EMBEDDING_DEPLOYMENT, - backoffOptions: { - numOfAttempts: 3, - maxDelay: 5000, - }, -}); - -const findContent = makeDefaultFindContent({ - embedder, - store: embeddedContentStore, - findNearestNeighborsOptions: { - k: 5, - path: "embedding", - indexName: VECTOR_SEARCH_INDEX_NAME, - // Note: you may want to adjust the minScore depending - // on the embedding model you use. We've found 0.9 works well - // for OpenAI's text-embedding-ada-02 model for most use cases, - // but you may want to adjust this value if you're using a different model. - minScore: 0.9, - }, - searchBoosters: [boostManual], -}); - -const generateUserPrompt: GenerateUserPromptFunc = makeRagGenerateUserPrompt({ - findContent, - makeUserMessage, -}); - -const mongodb = new MongoClient(MONGODB_CONNECTION_URI); - -const conversations = makeMongoDbConversationsService( - mongodb.db(MONGODB_DATABASE_NAME) -); - -const config: AppConfig = { - conversationsRouterConfig: { - llm, - conversations, - generateUserPrompt, - systemPrompt, - }, - maxRequestTimeoutMs: 30000, - corsOptions: { - origin: allowedOrigins, - }, -}; -``` - -## Examples - -To see more examples of how to configure the chatbot server, -you can checkout the following example implementations: - -- [MongoDB Docs Chatbot](https://github.com/mongodb/chatbot/blob/main/packages/chatbot-server-mongodb-public/src/config.ts): - The configuration for the MongoDB Docs chatbot on https://www.mongodb.com/docs/. - This is the most complex configuration example. -- [Basic Chatbot Server](https://github.com/mongodb/chatbot/blob/main/examples/quick-start/packages/server): - A simple chatbot created for example purposes. This can be used as a starting point - for your own chatbot server. diff --git a/docs/docs/server/conversations.md b/docs/docs/server/conversations.md deleted file mode 100644 index 874897cf6..000000000 --- a/docs/docs/server/conversations.md +++ /dev/null @@ -1,73 +0,0 @@ -# Manage Conversations - -This guide explains how the MongoDB Chatbot Server manages conversations, and the different -points where you can customize its behavior. - -## Endpoints - -The server has the following endpoints: - -- `POST /conversations/` creates a new conversation. You must create a - conversation before you can send messages to the chatbot. -- `POST /conversations/:conversationId/messages` sends a message to the chatbot, - and gets a response back. -- `GET /conversations/:conversationId` gets a conversation in its current state. -- `POST /conversations/:conversationId/messages/:messageId/rating` rates an - assistant message in the conversation. -- `POST /conversations/:conversationId/messages/:messageId/comment` adds a user - comment to an assistant message in the conversation. - -For more information on these endpoints, refer to the [API Specification](openapi). - -## Security - -If you do not add custom middleware that handles authentication/authorization, -a malicious actor who gets a conversation ID can modify the conversation. -You can also add custom middleware to block requests to endpoints if you do not need them in your app. - -For more information about securing your server with custom middleware, refer to the [custom middleware request validation](custom-logic.md#request-validation) documentation. - -## Expose the Conversations Endpoints in Your App - -The MongoDB Chatbot Server exposes the conversation endpoints at the `/conversations` path. -There are two ways that you can expose these endpoints in your app: - -1. Use the [`makeApp()`](../reference/server/modules.md#makeapp) - function to create an Express.js app. This is the easiest way to get started - and contains everything you need out of the box. -1. Use the [`makeConversationsRouter()`](../reference/server/modules.md#makeconversationsrouter) function to create an Express.js [`Router`](https://expressjs.com/en/guide/routing.html) that you can add to an Express app. - This is useful if you want to expose the conversation endpoints within a larger - Express.js app or customize your server beyond what's possible with `makeApp()`. - -## Add Message Flow - -When you add a message to a conversation with the `POST /conversations/:conversationId/messages` endpoint, the following happens on the server: - -1. (Optional) Custom Express.js middleware runs. To learn more, - refer to the [Custom Middleware](./custom-logic#middleware) guide. -1. (Optional) Get custom data to add to the user message. To learn more, - refer to the [Custom Data](./custom-logic#add-custom-data-to-messages) guide. -1. Load conversation from the database. -1. Generate the user message. You can use this step to perform - retrieval augmented generation (RAG), or other preprocessing of the user message. - To learn more about generating the user message, - refer to the [Generate User Message](./user-message.md) guide. - 1. To perform retrieval augmented generation (RAG), refer to the [RAG](./rag/index.md) guide. -1. The user prompt is used to generate a chatbot response. -1. The generated response is returned to the user. -1. The conversation is updated with the new user and assistant messages. - -Here's a flow chart of this process: - -```mermaid -graph TD - SEND[Send User Message] --> MID[Optional: Run Custom Middleware] - MID --> CD[Optional: Add Custom Data] - CD --> LOAD[Load Conversation from DB] - LOAD --> GUM[Generate User Message] - GUM -->|Reject Message| REJ[Return Static Response to User] - GUM --> LLM[Generate Chatbot Response with LLM] - LLM --> RES[Return Generated Response to User] - RES --> UPDATE[Update Conversation in DB with New Messages] - REJ --> UPDATE -``` diff --git a/docs/docs/server/custom-logic.md b/docs/docs/server/custom-logic.md deleted file mode 100644 index 575968ac4..000000000 --- a/docs/docs/server/custom-logic.md +++ /dev/null @@ -1,247 +0,0 @@ -# Customize Server Logic - -The MongoDB Chatbot Server provides a few ways to customize the server to meet -the needs of your application. - -## App-Level Configuration - -You can add custom Express.js routes and application-level logic by including the [`AppConfig.expressAppConfig`](../reference/server/interfaces/AppConfig.md#expressappconfig) function in your app configuration. -Pass the `AppConfig` object to the [`makeApp`](../reference/server/modules.md#makeapp) function to create the app. - -```typescript -import { makeApp, AppConfig } from "mongodb-chatbot-server"; - -const appConfig: AppConfig = { - // ...other config - expressAppConfig: (app) => { - app.get("/", (req, res) => res.send({ hello: "world" })) - }, -}; - -const app = await makeApp(appConfig); -//... -``` - -## Middleware - -You can configure the server to run custom middleware, [`ConversationsMiddleware`](../reference/server/modules.md#conversationsmiddleware). -The `ConversationsMiddleware` include a custom [`Response.locals`](https://expressjs.com/en/api.html#res.locals) object -that you can use to access the app's [`ConversationsService`](../reference/core/interfaces/Conversations.ConversationsService.md) with the `conversationsService` property -and a `customData` object, where you can store arbitrary data. - -Add multiple middleware to the server via the [`ConversationRouterParams.middleware`](../reference/server/interfaces/ConversationsRouterParams.md#middleware) - -Here's a basic middleware and how you can add it to the server. - -```typescript -import { ConversationsMiddleware, AppConfig } from "mongodb-chatbot-server"; - -const someMiddleware: ConversationsMiddleware = async (req, res, next) => { - try { - const conversation = await res.locals.conversations.findById( - req.params.conversationId - ); - // ...do something with the conversation - - // Add custom data to the response - res.locals.customData = { - foo: "bar", - }; - } catch (err) { - next(err); - } finally { - next(); - } -}; - -const appConfig: AppConfig = { - // ...other config - conversationsRouterParams: { - middleware: [someMiddleware], - // ...other config - }, -}; -// ...create app -``` - -### Request Validation - -You can use custom middleware to validate the request before it's processed -by the `/conversations` routes. - -```typescript -import { ConversationsMiddleware } from "mongodb-chatbot-server"; - -const requireUserId: ConversationsMiddleware = async (req, res, next) => { - const userId = req.headers.userId; - if (!userId) { - return res.status(400).json({ - error: "Missing userId header", - }); - } - next(); -}; -``` - -### Include Custom Data to be Processed by the Server - -You can also use the middleware to modify the request or response objects. -For example, you could store additional data in the `Response.locals` object or -perform user authentication. - -```typescript -import { ConversationsMiddleware } from "mongodb-chatbot-server"; -import { authenticateUser } from "./auth"; // Some authentication function - -const authenticateUser: ConversationsMiddleware = async (req, res, next) => { - try { - const token = req.headers.authToken; - if (!token) { - return res.status(400).json({ - error: "Missing authToken header", - }); - } - // Authenticate user - const user = await authenticateUser(token); - if (!user) { - return res.status(401).json({ - error: "Invalid authToken", - }); - } - // Add authenticated user data to the response - res.locals.customData = { - user, - }; - next(); - } catch (err) { - next(err); - } -}; -``` - -### Included Middleware - -The MongoDB Chatbot Server includes the following middleware. - -- [`requireRequestOrigin()`](../reference/server/modules.md#requirerequestorigin): Requires that the request - includes a `X-Request-Origin` or `origin` header. Enabled by default. -- [`requireValidIpAddress()`](../reference/server/modules.md#requirevalidipaddress): Requires that the request - includes a `X-Forwarded-For` header with a valid IP address. Enabled by default. - -## Add Custom Data - -You can add custom data to the persisted conversation or to individual messages. - -### `AddCustomDataFunc` - -Add custom data using a [`AddCustomDataFunc`](../reference/server/modules.md#addcustomdatafunc) function. - -The function accepts the Express `Request` and `Response`. The `Response` includes -the `ConversationsRouterLocals` in the `Response.locals` property. -The function returns a `Promise` that resolves to a [`ConversationCustomData`](../reference/server/modules.md#conversationcustomdata) object. - -```typescript -import { AddCustomDataFunc } from "mongodb-chatbot-server"; - -const addOriginAndIpToCustomData: AddCustomDataFunc = async (req, res) => - res.locals.customData.origin - ? { origin: res.locals.customData.origin, ip: req.ip } - : undefined; -``` - -### Add Custom Data to Conversation - -You can add custom data to the [`Conversation.customData`](../reference/core/interfaces/Conversations.Conversation.md#customdata) -property persisted in the database. - -To add custom data to the conversation, set the [`ConversationsRouterParams.createConversationCustomData`](../reference/server/interfaces/ConversationsRouterParams.md#createConversationCustomData) -to a `CustomDataFunc`. This function is run when you create a conversation -with the `POST /conversations` endpoint. - -```typescript -import { AddCustomData, AppConfig } from "mongodb-chatbot-server"; - -const addOriginAndIpToCustomData: AddCustomDataFunc = async (req, res) => - res.locals.customData.origin - ? { origin: res.locals.customData.origin, ip: req.ip } - : undefined; - -const appConfig: AppConfig = { - // ...other config - conversationsRouterParams: { - createConversationCustomData: addOriginAndIpToCustomData, - // ...other config - }, -}; -// ...create app -``` - -This creates a `Conversation` in the database with the following schema: - -```typescript -{ - _id: ObjectId("Some ObjectId"), - messages: [ /*...*/ ], - createdAt: Date("Some Date"), - customData: { - origin: "https://example.com"; - ip: "192.158.1.38"; - } -} -``` - -### Add Custom Data to Messages - -You can add custom data to the [`Conversation.messages[].customData`](../reference/core/interfaces/Conversations.Conversation.md#messages) -property persisted in the database. - -To add custom data to the conversation, set the [`ConversationsRouterParams.addMessageToConversationCustomData`](../reference/server/interfaces/ConversationsRouterParams.md#addMessageToConversationCustomData) -to a `CustomDataFunc`. This function is run when you create a conversation -with the `POST /conversations/:conversationId/messages` endpoint. - -```typescript -import { AddCustomData, AppConfig } from "mongodb-chatbot-server"; - -const addOriginAndIpToCustomData: AddCustomDataFunc = async (req, res) => - res.locals.customData.origin - ? { origin: res.locals.customData.origin, ip: req.ip } - : undefined; - -const appConfig: AppConfig = { - // ...other config - conversationsRouterParams: { - addMessageToConversationCustomData: addOriginAndIpToCustomData, - // ...other config - }, -}; -// ...create app -``` - -This creates a `Conversation` in the database with the following schema: - -```typescript -{ - _id: ObjectId("Some ObjectId"), - createdAt: Date("Some Date"), - messages: [ /*...other messages*/, - { - _id: ObjectId("Some ObjectId"), - role: "user", - content: "Some message", - createdAt: Date("Some Date"), - customData: { - origin: "https://example.com", - ip: "192.158.1.38", - } - }, - {/*...assistant message*/}], -} -``` - -### Included Custom Data Functions - -By default, the MongoDB Chatbot Server includes custom data functions that add -the following custom data: - -- `Conversation.customData`: `origin` and `ip` from the request. -- For all user messages, `Message.customData`: `origin` and from the request. diff --git a/docs/docs/server/evaluate.md b/docs/docs/server/evaluate.md deleted file mode 100644 index fc991a1b5..000000000 --- a/docs/docs/server/evaluate.md +++ /dev/null @@ -1,45 +0,0 @@ -# Evaluate Chatbot Responses - -Evaluate the quality of your chatbot's responses - -## Manual Evaluation - -The simplest way to evaluate your chatbot's responses is to manually test it yourself. -You can do this with the MongoDB Chatbot Server by running the server locally and -querying it. - -If you want to query it from a UI, you have the following options: - -- Spin up the UI from the [Quick Start](../quick-start.md) guide. -- Build your own UI with the [Chatbot UI](../ui.md) components. -- Build a custom UI that queries the server directly. Refer to the [API specification](./openapi) for more information on the endpoints. - -:::note[Query Server Directly] - -You could also query the server directly using HTTP clients like [`curl`](https://curl.se/) or [Postman](https://www.postman.com/). - -::: - -## Red Teaming - -You can evaluate your chatbot's responses by having a team of people "red team" it. -In a chatbot red teaming exercise, a team of people will ask a variety of questions -to the chatbot, evaluating response quality and identifying areas for improvement. - -To learn more about how you can red team a chatbot, refer to the -[documentation from Microsoft](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming). - -## Automated Evaluation - -You can evaluate your chatbot's responses using a variety of automated methods and tools. - -On the team that builds the MongoDB Chatbot Framework, we use [Braintrust](braintrust.dev) as our automated evaluation tool. -You can search in the project's repository for `**.eval.ts` files to see how we use Braintrust for evaluation. - -:::note[Deprecated Evaluation CLI] - -The MongoDB Chatbot Framework used to have an evaluation CLI that you could use to evaluate your chatbot's responses. - -We have deprecated this CLI in favor of using Braintrust for evaluation. - -::: diff --git a/docs/docs/server/find-faqs.md b/docs/docs/server/find-faqs.md deleted file mode 100644 index 6f622e163..000000000 --- a/docs/docs/server/find-faqs.md +++ /dev/null @@ -1,64 +0,0 @@ -# Find Frequently Asked Questions - -You can use the "FAQ Finder" tool to analyze historic messages in your database -and find the most common topics that users have chatted about. - -You can use these FAQs to: - -- understand how users are interacting with your chatbot -- define [verified answers](./verified-answers) for the most common requests - -:::info -The framework and this tool refer to these topics as "frequently asked -questions", but it can handle any user input even if they're not in the form of -a question. -::: - -## How It Works - -The FAQ Finder runs a clustering algorithm in your chatbot's vector embedding -space. Specifically, it runs [DBSCAN](https://en.wikipedia.org/wiki/DBSCAN) on -the query embeddings your server generates for each incoming user message. - -The tool combines messages into semantically similar groups and then sorts the -groups by size (i.e. number of messages in the group). You can then sample or -generate a representative question for the entire group. - -## Use the FAQ Finder - -### 1. Configure Your Environment - -The FAQ Finder is a script in the `scripts` package of the chatbot framework -repo. You need to configure it before you can run it. - -Define a `.env` file for the `scripts` package that includes the following -values. Make sure to replace the placeholders with your app's values. - -```python title="/packages/scripts/.env" -# The "from" cluster/database denotes where your chatbot conversation data is stored -FROM_CONNECTION_URI= -FROM_DATABASE_NAME= -# The "to" cluster/database/collection denotes where the FAQ data will be stored -TO_CONNECTION_URI= -TO_DATABASE_NAME= -TO_FAQ_COLLECTION_NAME= -``` - -### 2. Run the FAQ Finder - -Once the tool is configured, you can run it from the command line. - -From the `scripts` directory: - -```sh -npm run findFaq -``` - -Or from the repository root: - -```sh -npm run scripts:findFaq -``` - -After you run the tool, the "to" collection defined in your configuration -contains the FAQ groups. diff --git a/docs/docs/server/llm.md b/docs/docs/server/llm.md deleted file mode 100644 index f7d2d342a..000000000 --- a/docs/docs/server/llm.md +++ /dev/null @@ -1,187 +0,0 @@ -# Chat with an LLM - -This guide contains information on how you can use the MongoDB Chatbot Server -to chat with a large language model (LLM). - -## Configure the `ChatLlm` - -The [`ChatLlm`](../reference/core/interfaces/Llm.ChatLlm.md) is the interface -between the chatbot server and the LLM. - -The MongoDB Chatbot Server comes with an implementation of the `ChatLlm`, -which uses the OpenAI API. You could also implement your own `ChatLlm` to -use a different language model or different configuration on the OpenAI API. - -The following are useful things to keep in mind when using an LLM: - -1. **What model to use.** This is probably the single most important decision - for shaping the chatbot response. The quality and characteristics - of different models vary greatly. -1. **Model temperature.** The temperature of the model determines how "creative" - the model is. A higher temperature will result in more creative responses, - but also more errors. -1. **Model max tokens.** The maximum number of tokens that the model will generate. - This is useful for preventing the model from generating very long responses, - which impacts cost and quality. -1. **Prompt engineering.** What additional information to include in the prompt - to guide the model's behavior. For more information, refer to the - [Prompt Engineering](#prompt-engineering) section. -1. **Tools.** What tools to give the model to use. For more information, refer to the - [Tool Calling](./tools.md) guide. - -### Use OpenAI API - -You can use the [`makeOpenAiChatLlm()`](../reference/core/namespaces/Llm.md#makeopenaichatllm) -constructor function to create a `ChatLlm` that uses an OpenAI model like GPT-3.5. - -`makeOpenAiChatLlm()` supports both the OpenAI API and Azure OpenAI Service. -It wraps the `openai` package, which supports both of these services. - -The following is an example implementation of `makeOpenAiChatLlm()`: - -```ts -import { makeOpenAiChatLlm, OpenAiChatMessage } from "mongodb-chatbot-server"; -import { someTool } from "./someTool"; -export const openAiClient = new OpenAIClient( - OPENAI_ENDPOINT, - new AzureKeyCredential(OPENAI_API_KEY) -); - -export const llm = makeOpenAiChatLlm({ - openAiClient, - deployment: OPENAI_CHAT_COMPLETION_DEPLOYMENT, - openAiLmmConfigOptions: { - temperature: 0, - maxTokens: 500, - }, - tools: [someTool], -}); -``` - -### Use Langchain `ChatModel` - -You can use the [`makeLangchainChatLlm()`](../reference/core/namespaces/Llm.md#makelangchainchatllm) constructor function to create a `ChatLlm` that uses a Langchain `ChatModel`. For more information on available `ChatModel` implementations, refer to the [Chat Models](https://js.langchain.com/docs/integrations/chat/) in the Langchain documentation. - -The following is an example implementation of using `makeLangchainChatLlm()` to -use Anthropic's Claude family of models: - -```ts -import { makeLangchainChatLlm } from "mongodb-chatbot-server"; -import { ChatAnthropic } from "@langchain/anthropic"; - -const anthropicModel = new ChatAnthropic({ - temperature: 0.9, - anthropicApiKey: "YOUR-API-KEY", - maxTokensToSample: 1024, -}); -const anthropicChatLlm = makeLangchainChatLlm({ chatModel: anthropicModel }); -``` - -## Manage Previous Messages Sent to the LLM - -The MongoDB Chatbot Server always sends the **current** user message to the LLM. - -You can also manage which **previous** messages in a conversation the MongoDB Chatbot Server sends to the LLM on each user message. -You might want to do this to allow for appropriate context to be sent to the LLM -without exceeding the maximum number of tokens in the LLM's context window. - -You do this at the `ConversationRouter` level with the [`ConversationsRouterParams.filterPreviousMessages`](../reference/server/interfaces/ConversationsRouterParams.md#filterpreviousmessages) property. -The `filterPreviousMessages` property accepts a [`FilterPreviousMessages`](../reference/server/modules.md#filterpreviousmessages) function. - -By default, the MongoDB Chatbot Server only send the initial system prompt -and the user's current message to the LLM. You can change this behavior by -implementing your own `FilterPreviousMessages` function. - -The MongoDB Chatbot Server package also comes with a [`makeFilterNPreviousMessages`](../reference/server/modules.md#makefilternpreviousmessages) -constructor function. `makeFilterNPreviousMessages` -creates a `FilterPreviousMessages` function that returns the previous `n` messages -plus the initial system prompt. - -The following is an example implementation of `makeFilterNPreviousMessages()`: - -```ts -import { - makeFilterNPreviousMessages, - ConversationsRouterParams, - AppConfig, -} from "mongodb-chatbot-server"; - -const filter10PreviousMessages = makeFilterNPreviousMessages(10); - -const conversationsRouterConfig: ConversationsRouterParams = { - filterPreviousMessages: filter10PreviousMessages, - ...otherConfig, -}; -const appConfig: AppConfig = { - conversationsRouterConfig, - ...otherConfig, -}; -``` - -## Prompt Engineering - -Prompt engineering is the process of directing the output of a language model -to produce a desired response. - -In the context of a chatbot server such as this, there are the following main areas -for prompt engineering: - -- System prompt: Message at the beginning of the conversation that guides the - chatbot's behavior when generating responses. -- User prompt: User message that the chatbot uses to generate a response. - In RAG applications, this can include adding relevant content gathered from - vector search results based on the user's input. - -This guide does not cover prompt engineering techniques, but rather where you -can apply them in the MongoDB Chatbot Server. - -Prompt engineering is a fairly new field, and best practices are still emerging. -A great resource to learn more about prompt engineering is the [Prompt Engineering Guide](https://www.promptingguide.ai/). - -### System Prompt - -To add a system prompt, include a [`SystemPrompt`](../reference/server/modules.md#systemprompt) message in your app's [`ConversationsRouterParams.systemPrompt`](../reference/server/interfaces/ConversationsRouterParams.md#systemprompt). - -The system prompt is one of the most powerful way to customize the way -that the chatbot responds to users. You can use the system prompt to do things -such as: - -- Control the style and personality of the chatbot. -- Determine how the chatbot responds to certain types of questions. -- Direct how the chatbot interprets user input and context information. - -```ts -import { - SystemPrompt, - ConversationsRouterParams, -} from "mongodb-chatbot-server"; -import { MongoClient } from "mongodb-rag-core/mongodb"; - -// System prompt for chatbot -const systemPrompt: SystemPrompt = { - role: "system", - content: `You are an assistant to users of the MongoDB Chatbot Framework. -Answer their questions about the framework in a friendly conversational tone. -Format your answers in Markdown. -Be concise in your answers. -If you do not know the answer to the question based on the information provided, -respond: "I'm sorry, I don't know the answer to that question. Please try to rephrase it. Refer to the below information to see if it helps."`, -}; - -const conversationsRouterConfig: ConversationsRouterParams = { - // ...other config - systemPrompt, -}; -``` - -### User Prompt - -You can modify what the chatbot uses as the user prompt by implementing the -[`GenerateUserPromptFunc`](../reference/server/modules.md#generateuserpromptfunc) function. - -`GenerateUserPromptFunc` takes in the user's query and previous messages in the conversation, then returns a new user message. For an overview of the `GenerateUserPromptFunc` function, refer to the [Generate User Message](./user-message.md) guide. - -You might want to modify the user prompt if you're using a prompting technique -like retrieval augmented generation (RAG) or chain of thought. -To learn more about using RAG with the MongoDB Chatbot Server, refer to the -[RAG](./rag/index.md) guide. diff --git a/docs/docs/server/persist.md b/docs/docs/server/persist.md deleted file mode 100644 index cc1769e55..000000000 --- a/docs/docs/server/persist.md +++ /dev/null @@ -1,51 +0,0 @@ -# Persist Conversation Data - -The MongoDB Chatbot Server persists and retrieves conversation data data using the -[`ConversationsService`](../reference/core/interfaces/Conversations.ConversationsService.md) interface. - -## Store Data in MongoDB - -By default, the MongoDB Chatbot Server uses MongoDB to persist conversation data. - -Use the [`makeMongoDbConversationsService()`](../reference/core/modules/index.md#makemongodbconversationsservice) function to create a `ConversationsService` -that stores data in MongoDB. - -When you create a `ConversationService` with MongoDB, pass a MongoDB database. -All conversations are stored in the database's `conversations` collection. - -Add the `ConversationsService` to the [`ConversationsRouterParams.conversations`](../reference/server/interfaces/ConversationsRouterParams.md#conversations) property. - -```ts -import { MongoClient } from "mongodb-rag-core/mongodb"; -import { - makeMongoDbConversationsService, - SystemPrompt, -} from "mongodb-chatbot-server"; - -const systemPrompt: SystemPrompt = { - role: "system", - content: ``, -}; -const mongodb = new MongoClient(MONGODB_CONNECTION_URI); -const conversations = makeMongoDbConversationsService( - mongodb.db(MONGODB_DATABASE_NAME), -); - -const appConfig: AppConfig = { - // ...other config - conversationsRouterParams: { - conversations, - // ...other config - }, -}; -``` - -Every conversation is stored in a MongoDB collection with the name `conversations` -in a document with the schema [`Conversation`](../reference/core/interfaces/Conversations.Conversation.md). - -:::note[Alternate Databases] - -You could use a different database to store conversation data, -as long as you use the `ConversationsService` interface. - -::: diff --git a/docs/docs/server/rag/index.md b/docs/docs/server/rag/index.md deleted file mode 100644 index fb1e88e05..000000000 --- a/docs/docs/server/rag/index.md +++ /dev/null @@ -1,163 +0,0 @@ -# Retrieval Augmented Generation (RAG) - -You can use the MongoDB Chatbot Server to perform retrieval augmented generation (RAG). - -## What is RAG? - -RAG is a technique that improves chatbot response quality by grounding answers -in a knowledge base. Before a user query is sent to an LLM to generate a response, -the query is used to retrieve relevant information from a knowledge base. -The LLM responds to the user query based on the information from the knowledge base. - -By grounding the response in contextual information from the knowledge base, -the chatbot can generate more relevant and accurate responses. - -To learn more about RAG, refer to the article [What is Retrieval-Augmented Generation (RAG)?](https://www.mongodb.com/basics/retrieval-augmented-generation) from MongoDB. - -
- - RAG Overview Diagram - -![RAG Overview Diagram](/img/rag-diagram.webp) - -
- -## Generate User Message with RAG - -You can perform RAG in the MongoDB Chatbot Server by implementing a [`GenerateUserPromptFunc`](../../reference/server/modules.md#generateuserpromptfunc) function. - -The MongoDB Chatbot Server comes with the [`makeRagGenerateUserPrompt()`](../../reference/server/modules.md#makeraggenerateuserprompt) function that you can use to perform RAG with Atlas Vector Search. - -### Configure RAG - -To configure the `makeRagGenerateUserPrompt()` function, include it in the -[`AppConfig.conversationsRoute.generateUserPrompt`](../../reference/server/interfaces/ConversationsRouterParams.md#generateuserprompt) property. - -```ts -import { - makeMongoDbEmbeddedContentStore, - makeOpenAiEmbedder, - makeMongoDbConversationsService, - AppConfig, - makeDefaultFindContent, - GenerateUserPromptFunc, - makeRagGenerateUserPrompt, - MakeUserMessageFunc, -} from "mongodb-chatbot-server"; -import { stripIndents } from "common-tags"; -import { makePreprocessMongoDbUserQuery } from "./processors/makePreprocessMongoDbUserQuery"; -import { AzureOpenAI } from "mongodb-rag-core/openai"; - -// Make preprocessor -const mongoDbUserQueryPreprocessor = makePreprocessMongoDbUserQuery({ - azureOpenAiServiceConfig: { - apiKey: OPENAI_API_KEY, - baseUrl: OPENAI_ENDPOINT, - deployment: OPENAI_CHAT_COMPLETION_DEPLOYMENT, - version: OPENAI_CHAT_COMPLETION_MODEL_VERSION, - }, - numRetries: 0, - retryDelayMs: 5000, -}); - -export const embeddedContentStore = makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_DEPLOYMENT, - } -}); -export const embedder = makeOpenAiEmbedder({ - openAiClient, - deployment: OPENAI_EMBEDDING_DEPLOYMENT, - backoffOptions: { - numOfAttempts: 3, - maxDelay: 5000, - }, -}); -export const findContent = makeDefaultFindContent({ - embedder, - store: embeddedContentStore, - findNearestNeighborsOptions: { - k: 5, - path: "embedding", - indexName: VECTOR_SEARCH_INDEX_NAME, - minScore: 0.9, - }, -}); - -// Make user message -export const makeUserMessage: MakeUserMessageFunc = async function ({ - preprocessedUserMessage, - originalUserMessage, - content, - queryEmbedding, -}) { - const chunkSeparator = "~~~~~~"; - const context = content.map((c) => c.text).join(`\n${chunkSeparator}\n`); - const contentForLlm = `Using the following information, answer the question. -Different pieces of information are separated by "${chunkSeparator}". - - -${context} - - - -${preprocessedUserMessage ?? originalUserMessage} -`; - return { - role: "user", - content: originalUserMessage, - embedding: queryEmbedding, - preprocessedContent: preprocessedUserMessage, - contentForLlm, - }; -}; - -export const generateUserPrompt: GenerateUserPromptFunc = - makeRagGenerateUserPrompt({ - queryPreprocessor: mongoDbUserQueryPreprocessor, - findContent, - makeReferenceLinks: makeMongoDbReferences, - makeUserMessage, - }); - -export const config: AppConfig = { - conversationsRouterConfig: { - generateUserPrompt, - // ... - }, - // ... -}; -``` - -### Data Flow - -The `makeRagGenerateUserPromptFunc()` function has the following data flow: - -1. (Optional) Preprocess the user message. You can use this step to transform the - user message before it is used to retrieve relevant content with to Atlas Vector Search. - To learn more, refer to the [Pre-process User Queries](./preprocess.md) guide. -1. Retrieve relevant content from Atlas Vector Search. To learn more, refer to the - [Retrieve Context Information](./retrieve.md) guide. -1. Removes any chunks that would exceed the max context tokens. -1. Generate the user prompt and references based on the retrieved content. -1. Return generated user prompt and references. - -Also, you can reject the user message at any point in the flow. -When the message is rejected, the server responds with a static response. - -```mermaid -graph TD - - USER[Receive User Message] --> PRE[Optional: Pre-process User Queries] - PRE --> RET[Retrieve Context Information & References] - RET --> REM[Remove Chunks] - REM --> GEN[Generate User Prompt] - REM --> REF[Generate References] - GEN --> GUM[Return Generated User Message & References] - REF --> GUM - PRE -->|Reject Message| REJ[Reject User Message] - RET -->|No results| REJ - GEN -->|Reject Message| REJ -``` diff --git a/docs/docs/server/rag/preprocess.md b/docs/docs/server/rag/preprocess.md deleted file mode 100644 index f998be6c3..000000000 --- a/docs/docs/server/rag/preprocess.md +++ /dev/null @@ -1,129 +0,0 @@ -# Pre-Process User Queries - -One of the most powerful and flexible features that you can add to your chatbot -is a pre-processing step for user queries. With a query preprocessor, -you can mutate a query before it's used to [retrieve relevant content](./retrieve.md). -This makes the query more conversationally relevant, -and therefore more likely to get better results. - -You can also use a preprocessor to reject queries that are inappropriate or -otherwise should not be sent to the chatbot. - -Query preprocessors run on the `POST /conversations/:conversationId/messages` endpoint. - -Adding a preprocessor is optional. If you don't add one, the user query is sent -directly to find relevant content and the query will never be rejected. - -Generally, preprocessors use LLMs to perform the transformation. - -For example, say a user sends the following message to a chatbot -that answers questions about the MongoDB documentation: - -```txt -User: add data node -``` - -To make this query more relevant, you can add a preprocessor that transforms the -query to something like: - -```txt -User: How do I add a data to MongoDB using the Node.js driver? -``` - -This transformed query is more likely to return relevant results, -which are then used to generate a better answer. - -## The `Preprocessor` Function - -Preprocessors have the [`QueryPreprocessorFunc`](../../reference/server/modules.md#querypreprocessorfunc) signature. - -To run a preprocessor when adding user messages, include a `QueryPreprocessorFunc` -in the [`MakeRagGenerateUserPromptParams.queryPreprocessor`](../../reference/server/interfaces/MakeRagGenerateUserPromptParams.md#querypreprocessor) property. - -## Mutate Queries - -You can use a preprocessor to mutate the user query to make it more relevant. - -If you don't want to mutate the query, do not return a `query` property from the -result or keep it the same as the original query. - -Here's an example of preprocessor that transforms the user query -to a more semantically relevant question: - -```ts -import { - QueryPreprocessorFunc, - makeRagGenerateUserPrompt, -} from "mongodb-chatbot-server"; -import { AzureOpenAI } from "mongodb-rag-core/openai"; - -const openAiClient = new AzureOpenAI({ - apiKey: OPENAI_API_KEY, - endpoint: OPENAI_ENDPOINT, - apiVersion: OPENAI_API_VERSION, -}); -const querySystemPrompt: SystemPrompt = { - role: "system", - content: `Transform the query into a relevant question about MongoDB products or company. -Example 1: -User: add data node -Assistant: How do I add a data to MongoDB using the Node.js driver? -Example 2: -User: get started atlas -Assistant: How do I get started with MongoDB Atlas?`, -}; - -const queryToQuestionPreprocessor: QueryPreprocessorFunc = async function ({ - query, - messages, -}) { - if (query === undefined) { - return { query, rejectQuery: false }; - } - const { - choices: [choice], - } = await openAiClient.getChatCompletions(OPENAI_DEPLOYMENT, [ - querySystemPrompt, - { role: "user", content: query }, - ]); - const { message } = choice; - return { - query: message, - rejectQuery: false, - }; -}; - -const ragGenerateUserPrompt = makeRagGenerateUserPrompt({ - queryPreprocessor: queryToQuestionPreprocessor, - // ...other config -}); -``` - -### Reject Queries - -You can also add preprocessor logic to reject user queries that you do not want -to send to the chatbot. To reject a query, return `{ rejectQuery: true }` from the `QueryPreprocessorFunc`. - -For example, say you want to reject queries that contain profanity. -You can use a preprocessor to check if the query contains profanity, -and reject the query if it does. - -```ts -import { makeRagGenerateUserPrompt } from "mongodb-chatbot-server"; -import { profanityCheck } from "./profanityCheck"; // checks if query contains profanity - -async function rejectProfanePreprocessor({ query, messages }) { - if (query === undefined) { - return { query, rejectQuery: false }; - } - const isProfane = await profanityCheck(query); - return { - rejectQuery: isProfane, - }; -} - -const ragGenerateUserPrompt = makeRagGenerateUserPrompt({ - queryPreprocessor: rejectProfanePreprocessor, - // ...other config -}); -``` diff --git a/docs/docs/server/rag/references.md b/docs/docs/server/rag/references.md deleted file mode 100644 index 57ebc84cc..000000000 --- a/docs/docs/server/rag/references.md +++ /dev/null @@ -1,52 +0,0 @@ -# Include References - -You can include references to the data sources that you use in your chatbot -in the response. This allows you to link to the original source of the information, and provide more context to the user. - -These references are included in the `references` property of the response -from the [`POST /conversations/:conversationId/messages`](../openapi#operation/createConversation). - -## Format References - -Format the references in the response using a [`MakeReferenceLinksFunc`](../../reference/server/modules.md#makereferencelinksfunc) function. - -To generate references, include a `MakeReferenceLinksFunc` -in the [`MakeRagGenerateUserPromptParams.makeReferenceLinks`](../../reference/server/interfaces/MakeRagGenerateUserPromptParams.md#makereferencelinks) property. - -```ts -import { makeRagGenerateUserPrompt } from "mongodb-chatbot-server"; -import { someMakeReferenceLinksFunc } from "./someMakeReferenceLinksFunc"; // example - -const ragGenerateUserPrompt = makeRagGenerateUserPrompt({ - makeReferenceLinks: someMakeReferenceLinksFunc, - // ...other config -}); -``` - -The framework comes with the `MakeReferenceLinksFunc`, [`makeDefaultReferenceLinksFunc()`](../../reference/server/modules.md#makedefaultreferencelinksfunc). Its returns -the following data for chunks from _unique_ URLs. - -```ts -{ - title: chunk.metadata.pageTitle ?? chunk.url, // if title doesn't exist, just put url - url: chunk.url // this always exists -} -``` - -## Do Not Include References - -To not include references, simply pass a function that returns an empty array. - -```ts -import { - MakeReferenceLinksFunc, - makeRagGenerateUserPrompt, -} from "mongodb-chatbot-server"; - -const noReferencesFunc: MakeReferenceLinksFunc = () => []; - -const ragGenerateUserPrompt = makeRagGenerateUserPrompt({ - makeReferenceLinks: noReferencesFunc, - // ...other config -}); -``` diff --git a/docs/docs/server/rag/retrieve.md b/docs/docs/server/rag/retrieve.md deleted file mode 100644 index c191e0a95..000000000 --- a/docs/docs/server/rag/retrieve.md +++ /dev/null @@ -1,178 +0,0 @@ -# Retrieve Context Information - -If you're using the MongoDB Chatbot Server to perform RAG, you must retrieve -context information to include in your answer. The primary way of doing this -is with semantic search. - -You can add the information that you retrieve using the [MongoDB RAG Ingest CLI](../../ingest/configure.md). Information retrieval is the single point of contact between the MongoDB RAG Ingest CLI and the MongoDB Chatbot Server. - -## The `FindContentFunc` Function - -To perform semantic search, you must implement a [`FindContentFunc`](../../reference/core/namespaces/FindContent.md#findcontentfunc) function. To see the default implementation -using Atlas Vector Search, refer to the following -[Find Content with Atlas Vector Search](#find-content-with-atlas-vector-search) section. - -Pass the `FindContentFunc` to the [`MakeRagGenerateUserPromptParams.findContent`](../../reference/server/interfaces/MakeRagGenerateUserPromptParams.md#findcontent) property. - -```ts -import { makeRagGenerateUserPrompt } from "mongodb-chatbot-server"; -import { someFindContentFunc } from "./someFindContentFunc"; // example - -const ragGenerateUserPrompt = makeRagGenerateUserPrompt({ - findContent: someFindContentFunc, - // ...other config -}); -``` - -## Find Content with Atlas Vector Search - -To use the MongoDB Chatbot Server with Atlas Vector Search for semantic search, -you can use the [`makeDefaultFindContentFunc()`](../../reference/core/namespaces/FindContent.md#makedefaultfindcontent). - -This function retrieves data from an [`EmbeddedContentStore`](../../reference/core/modules/index.md#embeddedcontentstore). To learn more about how to add data to an `EmbeddedContentStore`, refer to the [Ingest CLI documentation](../../ingest/configure.md). - -Pass a [`MakeDefaultFindContentFuncArgs`](../../reference/core/namespaces/FindContent.md#makedefaultfindcontentfuncargs) object to the `makeDefaultFindContentFunc()` function. - -```ts -import { makeRagGenerateUserPrompt } from "mongodb-chatbot-server"; - -// Create function that creates vector embeddings -// for user query. -const embedder = makeOpenAiEmbedder({ - openAiClient, - deployment: OPENAI_EMBEDDING_DEPLOYMENT, - backoffOptions: { - numOfAttempts: 3, - maxDelay: 5000, - }, -}); - -// Data store that is used to store the vector embeddings. -// Used to look up matching content. -const embeddedContentStore = makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_DEPLOYMENT, - } -}); - -const args: MakeDefaultFindContentFuncArgs = { - embedder, - store: embeddedContentStore, - findNearestNeighborsOptions: { - k: 5, - path: "embedding", - indexName: VECTOR_SEARCH_INDEX_NAME, - // Note: you may want to adjust the minScore depending - // on the embedding model you use. We've found 0.9 works well - // for OpenAI's text-embedding-ada-02 model for most use cases, - // but you may want to adjust this value if you're using a different model. - minScore: 0.9, - }, -}; -const findContent = makeDefaultFindContentFunc(args); - -const ragGenerateUserPrompt = makeRagGenerateUserPrompt({ - findContent, - // ...other config -}); -``` - -### Boost Results - -You can modify the results returned by the default find content function with -[`SearchBooster`](../../reference/core/modules/index.md#searchbooster) objects. -`SearchBooster`s mutate the results returned by the default find content function. - -You could use a `SearchBooster` to do things like: - -- Always results from a specific data source -- Ensure a data source isn't over-represented in the results - -To use one or more `SearchBooster`s, pass them to the `MakeDefaultFindContentFuncArgs.searchBoosters` property. The `searchBoosters` property is an array of `SearchBooster` objects, which are applied in the order of the array. - -The following is an example of using a `SearchBooster` to ensure that results from a specific data source are always returned. - -```ts -/** - * Ensure that results from data source 'foo' are always returned - * if query contains 'foo'. - */ -const boostFoo: SearchBooster = { - async shouldBoostFunc({ text }: { text: string }) { - return text.includes("foo"); - }, - async boost({ - embedding, - store, - existingResults, - }: { - embedding: number[]; - store: EmbeddedContentStore; - existingResults: WithScore[]; - }) { - const boostedResults = await store.findNearestNeighbors( - embedding, - { - k: 2, - path: "embedding", - indexName: VECTOR_SEARCH_INDEX_NAME, - minScore: 0, // no min score for 'foo' - }, - { - filter: { - dataSource: "foo", - }, - } - ); - const fewerExistingResults = existingResults.slice(0, 3); - // No duplicates - const newResults = fewerExistingResults.filter((result) => - boostedResults.every((manualResult) => manualResult.text !== result.text) - ); - // - return [...boostedResults, ...fewerExistingResults].sort( - (a, b) => b.score - a.score - ); - }, -}; - -const args: MakeDefaultFindContentFuncArgs = { - // ...other args - searchBoosters: [boostFoo], -}; -``` - -:::note Include Search Booster Filters In Your Atlas Vector Search Index - -If you are using an Atlas Vector Search filter in a booster, -you must include the filter in your index definition. For more information on Atlas Vector Search filters, -refer to [Atlas Vector Search filter index definition](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/#about-the-filter-type) -in the MongoDB Atlas documentation. - -For example, you might have a booster that finds data from a specific -data source by including a filter on a field named `sourceName` during -vector search. For the search to run, you must include the `dataSource` -field and any other filtered fields in your vector search index -definition: - -```js -{ - "fields": [ - { - "type": "vector" - // ... - }, - { - "type": "filter", - "path": "sourceName" - } - // ... - ] -} -``` - -Then you can include the filter in the `$search` query in your booster. - -::: diff --git a/docs/docs/server/tools.md b/docs/docs/server/tools.md deleted file mode 100644 index 9926d2fe6..000000000 --- a/docs/docs/server/tools.md +++ /dev/null @@ -1,122 +0,0 @@ -# Tool Calling - -You can give your chatbot access to external tools. For example, -you could add a tool interfaces with an API and use the data returned from the API -in the chatbot. - -This is a powerful feature that allows you to extend the capabilities -of your chatbot beyond retrieval augmented generation and LLM conversations. - -## Tool Calling Flow - -When a user sends a message to a chatbot with tool calling implemented, -the following happens: - -```mermaid -graph TD - USER[User sends message] --> PREP[Preprocess user message.\n Custom middleware, add custom data, generate user message, etc.] - PREP --> LLM{LLM decides\n if should call tool\n and which tool to call} - LLM -->|Call tool| TOOL[LLM creates tool call message] - LLM -->|Respond directly| RES[LLM creates response message] - TOOL --> CALL[Tool call based on arguments from LLM] - CALL --> TOOL_RES[LLM creates response based on tool call] - CALL -->|Tool call rejects user query| REJ[Send static rejection response] - RES --> CONT[Continue to send message to user and save conversation] - REJ --> CONT - TOOL_RES --> CONT -``` - -## Define a Tool - -To make a tool, define an object that implements the [`Tool`](../reference/core/interfaces/Llm.Tool.md) interface. - -A tool must include a `definition` property that is a function definition for the LLM to invoke. This must be JSON schema that corresponds to the [OpenAI function definition format](https://platform.openai.com/docs/assistants/tools/function-calling). -The tool responds LLM-generated arguments that correspond to this JSON schema. - -You must also include the `call()` method, which accepts the LLM-generated arguments -plus additional metadata and returns a tool call message, references, -and whether the user query should be rejected. - -```ts -// weatherTool.ts - -import { Tool } from "mongodb-chatbot-server"; - -export const weatherTool: Tool = { - definition: { - name: "getCurrentWeather", - description: "Get the weather in location", - parameters: { - type: "object", - properties: { - location: { - type: "string", - description: "The city and state e.g. San Francisco, CA", - }, - unit: { type: "string", enum: ["c", "f"] }, - }, - required: ["location"], - }, - }, - async call({ functionArgs: { location, unit } }) { - const weatherReport = await fetchWeatherFromApi(location, unit); - return { - toolCallMessage: { - role: "function", - name: "getCurrentWeather", - content: `The weather in ${location} is: ${weatherReport.content}`, - }, - references: [{ title: `${location} weather`, url: weatherReport.url }], - }; - }, -}; -``` - -## Give Tools to the ChatLLM - -To give the chatbot access to the tool, you must provide the [`ChatLlm`](../reference/core/interfaces/Llm.ChatLlm.md) with the tool. - -The [`makeOpenAiChatLlm()`](../reference/core/namespaces/Llm.md#makeopenaichatllm) -function, which you can use to instantiate a client for an OpenAI model, takes a `tools` argument. -Provide an array of tools to the `tools` argument. - -```ts -// weatherChatLlm.ts -import { makeOpenAiChatLlm, OpenAiChatMessage } from "mongodb-chatbot-server"; -import { weatherTool } from "./weatherTool"; - -export const openAiClient = new AzureOpenAI({ - apiKey: OPENAI_API_KEY, - endpoint: OPENAI_ENDPOINT, - apiVersion: OPENAI_API_VERSION, -}); - - -export const weatherChatLlm = makeOpenAiChatLlm({ - openAiClient, - deployment: OPENAI_CHAT_COMPLETION_DEPLOYMENT, - openAiLmmConfigOptions: { - temperature: 0, - maxTokens: 500, - }, - tools: [weatherTool], -}); -``` - -## Configure Server Tool Calling Behavior - -To use a `ChatLlm` with tool calling in your server, add it to your configuration -as you would any other `ChatLlm`. - -```ts -import { AppConfig } from "mongodb-chatbot-server"; -import { weatherChatLlm } from "./weatherChatLlm"; - -const config: AppConfig = { - conversationsRouterConfig: { - llm: weatherChatLlm, - // ...other config - }, - // ...other config -}; -``` diff --git a/docs/docs/server/user-message.md b/docs/docs/server/user-message.md deleted file mode 100644 index 2712c889b..000000000 --- a/docs/docs/server/user-message.md +++ /dev/null @@ -1,64 +0,0 @@ -# Generate User Message - -You can process a user's message before it is sent to the LLM to generate a response. -You can use this to perform retrieval augmented generation (RAG), or other preprocessing of the user message. - -Generate the user message using a [`GenerateUserPromptFunc`](../reference/server/modules.md#generateuserpromptfunc) function. - -If you do not use the `GenerateUserPromptFunc` function, -the user message is sent directly to the LLM. - -Include the `GenerateUserPromptFunc` function in the [`ConversationsRouterParams.generateUserPrompt`](../reference/server/interfaces/ConversationsRouterParams.md#generateuserprompt) property. - -```ts -import { AppConfig } from "mongodb-chatbot-server"; -import { makeSomeGenerateUserPrompt } from "./generate-some-user-prompt"; - -const config: AppConfig = { - // ... - conversationsRouterParams: { - // ... - generateUserPrompt: makeSomeGenerateUserPrompt(), - }, -}; -``` - -## Retrieval Augmented Generation (RAG) - -To perform retrieval augmented generation (RAG) using a `GenerateUserPromptFunc`, -refer to the [RAG](./rag/index.md) guide. - -## Client Context - -You can pass additional information from the user's client to the -`GenerateUserPromptFunc` function by setting the `clientContext` property in the -request body. This can include arbitrary data that might be useful for generating -a response. For example, this could include the user's location, the device they -are using, their preferred programming language, etc. - -Any value provided in the `clientContext` property of the request body is -available in the `clientContext` property of the `GenerateUserPromptFunc` -function. You can use this information to customize the user prompt. - -### Example - -The following example shows how to use the `clientContext` property to customize -the user prompt. - -```ts -const generateUserPrompt: GenerateUserPromptFunc = async ({ - userMessageText, - clientContext, -}) => { - return { - userMessage: { - role: "user", - contentForLlm: `The user provided the following context: -currentLocation: ${clientContext.currentLocation} -preferredLanguage: ${clientContext.preferredLanguage} - -${userMessageText}`, - }, - } -}; -``` diff --git a/docs/docs/server/verified-answers.md b/docs/docs/server/verified-answers.md deleted file mode 100644 index d5196b227..000000000 --- a/docs/docs/server/verified-answers.md +++ /dev/null @@ -1,251 +0,0 @@ -# Verified Answers - -The Chatbot Server can map incoming user queries to a collection of pre-written -"verified answers". - -Verified answers are useful for: - -- customizing the response to frequently asked questions -- avoiding expensive and time-consuming LLM calls to regenerate answers -- instilling user confidence that the response is accurate and doesn't contain hallucinations - -## How It Works - -Once you've [set up verified answers](#set-up-verified-answers), the server is -able to search for a verified answer that closely matches the user's provided -question. - -For every incoming query, the server first tries to find a matching verified -answer. If it finds one, it immediately returns it as a response to the user -with a metadata flag that indicates it was verified. If the server does not find -a match, it falls back to the regular RAG flow. - -## Data Model - -You define verified answers alongside one or more prompts that they answer, a -list of reference links for the answer, and the verifier's email address. - -Consider the following verified answer which can respond to multiple questions: - -```yaml -questions: - - Why did the chicken cross the road? - - Why did the horse hop over the fence? - - Why did the cow stroll through the meadow? - - Why did the deer leap over the stream? -answer: > - To get to the other side! - - For step-by-step instructions on how you can get to the other - side, [read our tutorial](https://www.example.com/). -references: - - url: https://example.com/jokes/why-did-x-cross-y - title: Our 100 favorite "Why did the X cross the Y" Jokes - - url: https://www.example.com/facts/chickens - title: Everything you wanted to know about chickens, and then some! -author_email: somebody@example.com -``` - -This definition is split into multiple verified answer documents, one for each -entry in the `questions` array, and processed into a vector searchable format. -In MongoDB, the verified answer document generated from this definition -resembles the following: - -```json -{ - "_id": "J28ut6WRRXDQuZ5LUCMUEWGSBf5xCt2oFWa19lD9QPA=", - "question": { - "embedding": [-0.00022248502, -0.005492599, 0.0006422517, /* ... */], - "embedding_model": "text-embedding-ada-002", - "embedding_model_version": "2023-06-01-preview", - "text": "Why did the chicken cross the road?" - }, - "answer": "To get to the other side!\n\nFor step-by-step instructions on how you can cross the road, [read our tutorial](https://www.example.com/).\n", - "references": [ - { - "url": "https://example.com/jokes/why-did-x-cross-y", - "title": "Our 100 favorite \"Why did the X cross the Y\" Jokes" - }, - { - "url": "https://www.example.com/facts/chickens", - "title": "Everything you wanted to know about chickens, and then some!" - } - ], - "author_email": "somebody@example.com", - "created": ISODate("2024-03-21T16:58:22.998Z"), - "updated": ISODate("2024-03-21T17:33:58.277Z"), - "hidden": null, - "stillExistsInYaml": true -} -``` - -## Set Up Verified Answers {#set-up-verified-answers} - -:::tip[Use MongoDB Atlas] -This guide assumes that you use MongoDB Atlas and Atlas Vector Search. -::: - -### 1. Define Your Verified Answers - -You define verified answers as a list of structured objects in a YAML file. - -First, create the YAML file: - -```sh -touch verified-answers.yaml -``` - -Then add one or more verified answers to the file: - -```yaml title="verified-answers.yaml" -- questions: - - Why did the chicken cross the road? - answer: > - To get to the other side! - references: - - url: https://example.com/ - title: Example Reference Link - author_email: somebody@example.com -- questions: - - Who created JavaScript? - - When was JavaScript created? - answer: > - JavaScript was originally created by Brendan Eich in 1995 - references: - - url: https://en.wikipedia.org/wiki/JavaScript - title: JavaScript on Wikipedia - author_email: somebody@example.com -``` - -### 2. Build and Link the Verified Answers CLI - -The framework includes a CLI that can import your verified answers into MongoDB -in a searchable format. To use it, you must build the CLI from -source. - -First, define a `.env` file with environment variables for the CLI. Make sure to -replace the placeholders with your app's values. - -```text title="/packages/mongodb-chatbot-verified-answers/.env" -MONGODB_CONNECTION_URI= # A connection string for your MongoDB Atlas cluster -MONGODB_DATABASE_NAME= # The database name for your chatbot -OPENAI_EMBEDDING_MODEL=text-embedding-ada-002 -OPENAI_EMBEDDING_MODEL_VERSION=2023-03-15-preview -OPENAI_EMBEDDING_DEPLOYMENT= -OPENAI_ENDPOINT=https://.openai.azure.com/ -OPENAI_API_KEY= -``` - -Then, run the following from the repo root: - -```shell -npm run build -cd packages/mongodb-chatbot-verified-answers -npm link -``` - -You now have access to the `verified-answers` CLI in your shell. - -:::note -You will need to build the CLI: - -- the first time you use it -- to change an environment variables defined in the `.env` file -- to apply future feature updates -::: - -### 3. Import Your Verified Answers - -Use the `verified-answers` CLI to import your verified answers definition file. - -```sh -verified-answers import path/to/verified-answers.yaml -``` - -The CLI intelligently diffs your definition file with any existing verified -answers in the database. Based on the diff, it applies changes to the data -without affecting unchanged data. For example it will: - -- Add new documents for new verified answers -- Update existing documents for verified answers with new text or references -- Remove existing documents that don't match any answer defined in the provided file - -### 4. Configure Atlas Vector Search - -MongoDB Atlas Vector Search lets you find verified answers with questions -similar to the user's input. To use vector search, you must create a vector -search index on the verified answers collection. For detailed information on how -to set up a vector search index, refer to [Create Atlas Vector Search -Index](#create-vector-search-index) documentation. - -The index specifies a field that contains a vector embedding to search. To match -the user's question against the verified answer's question, set the vector -search index `path` to `question.embedding` and set the `numDimensions` value to -match the length of your embedding model's vectors. - -```js -{ - "fields": [ - { - "numDimensions": "", - "path": "question.embedding", - "similarity": "cosine", - "type": "vector" - } - ] -} -``` - -### 5. Configure Your Server - -You set up verified answers in the server configuration file. - -First, connect your app to the stored verified answers by implementing the -`VerifiedAnswerStore` interface. For most cases you can use the built-in -[`makeMongoDbVerifiedAnswerStore()`](../reference/core/modules/index.md#makemongodbverifiedanswerstore) -constructor. - -Make sure to provide a connection string and the name of the database and -collection that contain the verified answer data. - -```ts -const verifiedAnswerStore = makeMongoDbVerifiedAnswerStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - collectionName: "verified_answers", -}); -``` - -Then, construct the function that searches the store for matching answers. For -most cases you can use the built-in `makeDefaultFindVerifiedAnswer()` -constructor. - -```ts -export const findVerifiedAnswer = makeDefaultFindVerifiedAnswer({ - embedder, - store: verifiedAnswerStore, -}); -``` - -Finally, move your baseline `GenerateUserPromptFunc` to be the fallback -`onNoVerifiedAnswerFound` handler of the verified answer user prompt generator. - -```ts -// The GenerateUserPromptFunc for your existing/standard RAG flow -export const generateRagUserPrompt = makeRagGenerateUserPrompt({ - findContent, - queryPreprocessor: mongoDbUserQueryPreprocessor, - makeReferenceLinks: makeMongoDbReferences, - makeUserMessage, -}); - -// A new GenerateUserPrompt func that wraps your existing prompt and adds support for verified answers -export const generateUserPrompt = makeVerifiedAnswerGenerateUserPrompt({ - findVerifiedAnswer, - // highlight-start - onNoVerifiedAnswerFound: generateRagUserPrompt - // highlight-end -}); -``` - -At this point your chatbot is set up to serve verified answers. diff --git a/docs/docusaurus.config.ts b/docs/docusaurus.config.ts index 730769cd9..199cfdbe0 100644 --- a/docs/docusaurus.config.ts +++ b/docs/docusaurus.config.ts @@ -3,8 +3,8 @@ import type { Config } from "@docusaurus/types"; import type * as Preset from "@docusaurus/preset-classic"; const config: Config = { - title: "MongoDB Chatbot Framework", - tagline: "Build RAG applications with MongoDB and Atlas Vector Search", + title: "MongoDB Knowledge Service", + tagline: "Serving MongoDB knowledge", staticDirectories: ["static"], // Set the production url of your site here @@ -31,45 +31,6 @@ const config: Config = { }, themes: ["@docusaurus/theme-mermaid"], plugins: [ - [ - "docusaurus-plugin-typedoc", - { - id: "server", - entryPoints: ["../packages/mongodb-chatbot-server/src/index.ts"], - tsconfig: "../packages/mongodb-chatbot-server/tsconfig.build.json", - sidebar: { - fullNames: true, - }, - out: "reference/server", - }, - ], - [ - "docusaurus-plugin-typedoc", - { - id: "core", - entryPoints: [ - "../packages/mongodb-rag-core/src/index.ts", - "../packages/mongodb-rag-core/src/dataSources/index.ts", - ], - tsconfig: "../packages/mongodb-rag-core/tsconfig.json", - sidebar: { - fullNames: true, - }, - out: "reference/core", - }, - ], - [ - "docusaurus-plugin-typedoc", - { - id: "ingest", - entryPoints: ["../packages/mongodb-rag-ingest/src/index.ts"], - tsconfig: "../packages/mongodb-rag-ingest/tsconfig.build.json", - sidebar: { - fullNames: true, - }, - out: "reference/ingest", - }, - ], [ "docusaurus-plugin-typedoc", { @@ -121,7 +82,7 @@ const config: Config = { themeConfig: { navbar: { - title: "MongoDB Chatbot Framework", + title: "MongoDB Knowledge Service", // logo: { // alt: 'My Site Logo', // src: 'img/logo.svg', @@ -145,10 +106,6 @@ const config: Config = { label: "Home", to: "/", }, - { - label: "Quick Start", - to: "/quick-start", - }, { label: "Github", to: "https://github.com/mongodb/chatbot", diff --git a/docs/sidebars.ts b/docs/sidebars.ts index e77ebe14b..7d5cbbf32 100644 --- a/docs/sidebars.ts +++ b/docs/sidebars.ts @@ -14,61 +14,17 @@ const sidebars: SidebarsConfig = { // By default, Docusaurus generates a sidebar from the docs folder structure main: [ "index", - "quick-start", - "mongodb", { - type: "category", - label: "Data Ingest CLI", - collapsible: true, - collapsed: true, - items: [ - "ingest/configure", - "ingest/configuration-reference", - "ingest/command-reference", - "ingest/data-sources", - "ingest/optimize", - ], + type: "link", + label: "OpenAPI Specification", + href: "/server/openapi", }, + "ui", { - type: "category", - label: "Chatbot Server", - collapsible: true, - collapsed: true, - items: [ - "server/configure", - "server/conversations", - "server/user-message", - "server/llm", - { - type: "category", - label: "Retrieval Augmented Generation (RAG)", - collapsed: true, - collapsible: true, - items: [ - { - type: "doc", - id: "server/rag/index", - label: "Set up RAG", - }, - "server/rag/preprocess", - "server/rag/retrieve", - "server/rag/references", - ], - }, - "server/custom-logic", - "server/tools", - "server/persist", - "server/evaluate", - "server/verified-answers", - "server/find-faqs", - { - type: "link", - label: "OpenAPI Spec", - href: "/server/openapi", - }, - ], + type: "doc", + label: "Framework (deprecated)", + id: "framework", }, - "ui", { type: "category", label: "API Reference", @@ -82,30 +38,6 @@ const sidebars: SidebarsConfig = { slug: "/reference/", }, items: [ - { - "mongodb-chatbot-server": [ - { - type: "autogenerated", - dirName: "reference/server", - }, - ], - }, - { - "mongodb-rag-core": [ - { - type: "autogenerated", - dirName: "reference/core", - }, - ], - }, - { - "mongodb-rag-ingest": [ - { - type: "autogenerated", - dirName: "reference/ingest", - }, - ], - }, { "mongodb-chatbot-ui": [ { diff --git a/examples/quick-start/.env.example b/examples/quick-start/.env.example deleted file mode 100644 index cc01c7c61..000000000 --- a/examples/quick-start/.env.example +++ /dev/null @@ -1,9 +0,0 @@ -# MongoDB config -MONGODB_CONNECTION_URI="" -VECTOR_SEARCH_INDEX_NAME="vector_index" # or whatever your index name is -MONGODB_DATABASE_NAME="mongodb-chatbot-framework-chatbot" # or whatever your database name is. must contain vector search index. - -# OpenAI config -OPENAI_API_KEY= -OPENAI_EMBEDDING_MODEL="text-embedding-ada-002" # or other model -OPENAI_CHAT_COMPLETION_MODEL="gpt-3.5-turbo" # or other model diff --git a/examples/quick-start/.gitignore b/examples/quick-start/.gitignore deleted file mode 100644 index 371e18e0d..000000000 --- a/examples/quick-start/.gitignore +++ /dev/null @@ -1,133 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -lerna-debug.log* -.pnpm-debug.log* - -# Diagnostic reports (https://nodejs.org/api/report.html) -report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json - -# Runtime data -pids -*.pid -*.seed -*.pid.lock - -# Directory for instrumented libs generated by jscoverage/JSCover -lib-cov - -# Coverage directory used by tools like istanbul -coverage -*.lcov - -# nyc test coverage -.nyc_output - -# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files) -.grunt - -# Bower dependency directory (https://bower.io/) -bower_components - -# node-waf configuration -.lock-wscript - -# Compiled binary addons (https://nodejs.org/api/addons.html) -build/Release - -# Dependency directories -node_modules/ -jspm_packages/ - -# Snowpack dependency directory (https://snowpack.dev/) -web_modules/ - -# TypeScript cache -*.tsbuildinfo - -# Optional npm cache directory -.npm - -# Optional eslint cache -.eslintcache - -# Optional stylelint cache -.stylelintcache - -# Microbundle cache -.rpt2_cache/ -.rts2_cache_cjs/ -.rts2_cache_es/ -.rts2_cache_umd/ - -# Optional REPL history -.node_repl_history - -# Output of 'npm pack' -*.tgz - -# Yarn Integrity file -.yarn-integrity - -# dotenv environment variable files -.env -.env.development.local -.env.test.local -.env.production.local -.env.local - -# parcel-bundler cache (https://parceljs.org/) -.cache -.parcel-cache - -# Next.js build output -.next -out - -# Nuxt.js build / generate output -.nuxt -dist - -# Gatsby files -.cache/ -# Comment in the public line in if your project uses Gatsby and not Next.js -# https://nextjs.org/blog/next-9-1#public-directory-support -# public - -# vuepress build output -.vuepress/dist - -# vuepress v2.x temp and cache directory -.temp -.cache - -# Docusaurus cache and generated files -.docusaurus - -# Serverless directories -.serverless/ - -# FuseBox cache -.fusebox/ - -# DynamoDB Local files -.dynamodb/ - -# TernJS port file -.tern-port - -# Stores VSCode versions used for testing VSCode extensions -.vscode-test - -# yarn v2 -.yarn/cache -.yarn/unplugged -.yarn/build-state.yml -.yarn/install-state.gz -.pnp.* - 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"url": "https://github.com/sponsors/wooorm" - } - }, - "packages/ingest": { - "version": "0.0.0", - "license": "ISC", - "dependencies": { - "mongodb-rag-ingest": "^0.3.1" - }, - "devDependencies": { - "dotenv": "^16.3.1", - "typescript": "^5.3.3" - } - }, - "packages/server": { - "version": "0.0.0", - "license": "ISC", - "dependencies": { - "@types/node": "^16.0.0", - "express": "^4.18.2", - "mongodb-chatbot-server": "*", - "mongodb-rag-core": "*" - }, - "devDependencies": { - "dotenv": "^16.3.1", - "nodemon": "^3.0.1", - "typescript": "^5.3.3" - } - }, - "packages/server/node_modules/@types/node": { - "version": "16.18.98", - "license": "MIT" - }, - "packages/ui": { - "version": "0.0.0", - "dependencies": { - "mongodb-chatbot-ui": "*", - "react": "^18.2.0", - "react-dom": "^18.2.0" - }, - "devDependencies": { - "@types/react": "^18.2.43", - "@types/react-dom": "^18.2.17", - "@typescript-eslint/eslint-plugin": "^6.14.0", - "@typescript-eslint/parser": "^6.14.0", - "@vitejs/plugin-react": "^4.2.1", - "eslint": "^8.55.0", - "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.5", - "typescript": "^5.2.2", - "vite": "^5.0.8" - } - } - } -} diff --git a/examples/quick-start/package.json b/examples/quick-start/package.json deleted file mode 100644 index d4f0275ab..000000000 --- a/examples/quick-start/package.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "name": "mongodb-chatbot-framework-quick-start", - "version": "1.0.0", - "description": "Quick start code for MongoDB Chatbot Framework", - "workspaces": [ - "packages/*" - ], - "private": true, - "scripts": { - "ingest:all": "npm run ingest:all --workspace=ingest", - "dev": "npm run dev --workspace=server & npm run dev --workspace=ui" - }, - "author": "", - "license": "Apache-2.0", - "devDependencies": { - "ts-node": "^10.9.2" - } -} diff --git a/examples/quick-start/packages/ingest/package.json b/examples/quick-start/packages/ingest/package.json deleted file mode 100644 index 5277c51d4..000000000 --- a/examples/quick-start/packages/ingest/package.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "name": "ingest", - "version": "0.0.0", - "description": "", - "scripts": { - "build": "tsc", - "ingest:all": "npm run build && ingest all --config build/ingest.config.js --since 1970" - }, - "keywords": [], - "author": "", - "license": "ISC", - "dependencies": { - "mongodb-rag-ingest": "^0.3.1" - }, - "devDependencies": { - "dotenv": "^16.3.1", - "typescript": "^5.3.3" - } -} \ No newline at end of file diff --git a/examples/quick-start/packages/ingest/src/ingest.config.ts b/examples/quick-start/packages/ingest/src/ingest.config.ts deleted file mode 100644 index 397265990..000000000 --- a/examples/quick-start/packages/ingest/src/ingest.config.ts +++ /dev/null @@ -1,58 +0,0 @@ -import { makeIngestMetaStore, Config } from "mongodb-rag-ingest"; -import { - makeOpenAiEmbedder, - makeMongoDbEmbeddedContentStore, - makeMongoDbPageStore, -} from "mongodb-rag-core"; -import path from "path"; -import { loadEnvVars } from "./loadEnvVars"; -import { mongoDbChatbotFrameworkDocsDataSourceConstructor } from "./mongodbChatbotFrameworkDataSource"; -import { OpenAI } from "mongodb-rag-core/openai"; - -// Load project environment variables -const dotenvPath = path.join(__dirname, "..", "..", "..", ".env"); // .env at project root -const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, -} = loadEnvVars(dotenvPath); - -export default { - embedder: async () => { - return makeOpenAiEmbedder({ - openAiClient: new OpenAI({ apiKey: OPENAI_API_KEY }), - deployment: OPENAI_EMBEDDING_MODEL, - backoffOptions: { - numOfAttempts: 25, - startingDelay: 1000, - }, - }); - }, - embeddedContentStore: () => - makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_MODEL, - }, - }), - pageStore: () => - makeMongoDbPageStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - }), - ingestMetaStore: () => - makeIngestMetaStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - entryId: "all", - }), - // Add data sources here - dataSources: async () => { - const mongodbChatbotFrameworkSource = - await mongoDbChatbotFrameworkDocsDataSourceConstructor(); - - return [mongodbChatbotFrameworkSource]; - }, -} satisfies Config; diff --git a/examples/quick-start/packages/ingest/src/loadEnvVars.ts b/examples/quick-start/packages/ingest/src/loadEnvVars.ts deleted file mode 100644 index 46d24c702..000000000 --- a/examples/quick-start/packages/ingest/src/loadEnvVars.ts +++ /dev/null @@ -1,30 +0,0 @@ -import { strict as assert } from "assert"; -import dotenv from "dotenv"; - -/** - Load environment variables from a .env file at the given path. - Note that if you change the environment variable names, - you need to update this function to support those environment variables. - */ -export function loadEnvVars(path: string) { - dotenv.config({ path }); - const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - } = process.env; - assert(MONGODB_CONNECTION_URI, "MONGODB_CONNECTION_URI is required"); - assert(MONGODB_DATABASE_NAME, "MONGODB_DATABASE_NAME is required"); - assert(VECTOR_SEARCH_INDEX_NAME, "VECTOR_SEARCH_INDEX_NAME is required"); - assert(OPENAI_API_KEY, "OPENAI_API_KEY is required"); - assert(OPENAI_EMBEDDING_MODEL, "OPENAI_EMBEDDING_MODEL is required"); - return { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - }; -} diff --git a/examples/quick-start/packages/ingest/src/mongodbChatbotFrameworkDataSource.ts b/examples/quick-start/packages/ingest/src/mongodbChatbotFrameworkDataSource.ts deleted file mode 100644 index 2bf330cf7..000000000 --- a/examples/quick-start/packages/ingest/src/mongodbChatbotFrameworkDataSource.ts +++ /dev/null @@ -1,45 +0,0 @@ -/** - @fileoverview Data source for the MongoDB Chatbot Framework docs. - */ -import { - MakeMdOnGithubDataSourceParams, - makeMdOnGithubDataSource, -} from "mongodb-rag-core/dataSources"; - -const mongodbChatbotFrameworkDocsConfig: MakeMdOnGithubDataSourceParams = { - name: "mongodb-rag-framework", - repoUrl: "https://github.com/mongodb/chatbot/", - repoLoaderOptions: { - branch: "main", - }, - filter: (path) => - path.startsWith("/docs") && - path.endsWith(".md") && - !path.endsWith("README.md"), - pathToPageUrl(pathInRepo) { - const baseUrl = "https://mongodb.github.io/chatbot/"; - const path = pathInRepo - .replace(/^\/docs\/docs\//, "") - .replace(/\.md$/, "") - .replace(/index$/, ""); - return `${baseUrl}${path}`; - }, - extractTitle: (pageContent, frontmatter) => - (frontmatter?.title as string) ?? extractFirstH1(pageContent), -}; - -// Helper function -function extractFirstH1(markdownText: string) { - const lines = markdownText.split("\n"); - - for (let line of lines) { - if (line.startsWith("# ")) { - // Remove '# ' and any leading or trailing whitespace - return line.substring(2).trim(); - } - } - return null; -} - -export const mongoDbChatbotFrameworkDocsDataSourceConstructor = async () => - await makeMdOnGithubDataSource(mongodbChatbotFrameworkDocsConfig); diff --git a/examples/quick-start/packages/ingest/tsconfig.json b/examples/quick-start/packages/ingest/tsconfig.json deleted file mode 100644 index f3a1c9e9b..000000000 --- a/examples/quick-start/packages/ingest/tsconfig.json +++ /dev/null @@ -1,107 +0,0 @@ -{ - "compilerOptions": { - /* Visit https://aka.ms/tsconfig to read more about this file */ - - /* Projects */ - // "incremental": true, /* Save .tsbuildinfo files to allow for incremental compilation of projects. */ - // "composite": true, /* Enable constraints that allow a TypeScript project to be used with project references. */ - // "tsBuildInfoFile": "./.tsbuildinfo", /* Specify the path to .tsbuildinfo incremental compilation file. */ - // "disableSourceOfProjectReferenceRedirect": true, /* Disable preferring source files instead of declaration files when referencing composite projects. */ - // "disableSolutionSearching": true, /* Opt a project out of multi-project reference checking when editing. */ - // "disableReferencedProjectLoad": true, /* Reduce the number of projects loaded automatically by TypeScript. */ - - /* Language and Environment */ - "target": "es2020" /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */, - "lib": [ - "ES2021", - "DOM" - ] /* Specify a set of bundled library declaration files that describe the target runtime environment. */, - // "jsx": "preserve", /* Specify what JSX code is generated. */ - // "experimentalDecorators": true, /* Enable experimental support for TC39 stage 2 draft decorators. */ - // "emitDecoratorMetadata": true, /* Emit design-type metadata for decorated declarations in source files. */ - // "jsxFactory": "", /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */ - // "jsxFragmentFactory": "", /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */ - // "jsxImportSource": "", /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */ - // "reactNamespace": "", /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */ - // "noLib": true, /* Disable including any library files, including the default lib.d.ts. */ - // "useDefineForClassFields": true, /* Emit ECMAScript-standard-compliant class fields. */ - // "moduleDetection": "auto", /* Control what method is used to detect module-format JS files. */ - - /* Modules */ - "module": "NodeNext" /* Specify what module code is generated. */, - "rootDir": "./src" /* Specify the root folder within your source files. */, - "moduleResolution": "nodenext" /* Specify how TypeScript looks up a file from a given module specifier. */, - // "baseUrl": "./", /* Specify the base directory to resolve non-relative module names. */ - // "paths": {}, /* Specify a set of entries that re-map imports to additional lookup locations. */ - // "rootDirs": [], /* Allow multiple folders to be treated as one when resolving modules. */ - // "typeRoots": ["."] /* Specify multiple folders that act like './node_modules/@types'. */, - // "types": [], /* Specify type package names to be included without being referenced in a source file. */ - // "allowUmdGlobalAccess": true, /* Allow accessing UMD globals from modules. */ - // "moduleSuffixes": [], /* List of file name suffixes to search when resolving a module. */ - "resolveJsonModule": true /* Enable importing .json files. */, - // "noResolve": true, /* Disallow 'import's, 'require's or ''s from expanding the number of files TypeScript should add to a project. */ - - /* JavaScript Support */ - "allowJs": false /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */, - // "checkJs": true, /* Enable error reporting in type-checked JavaScript files. */ - // "maxNodeModuleJsDepth": 1, /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */ - - /* Emit */ - "declaration": true /* Generate .d.ts files from TypeScript and JavaScript files in your project. */, - "declarationMap": true /* Create sourcemaps for d.ts files. */, - // "emitDeclarationOnly": true, /* Only output d.ts files and not JavaScript files. */ - "sourceMap": true /* Create source map files for emitted JavaScript files. */, - // "outFile": "./", /* Specify a file that bundles all outputs into one JavaScript file. If 'declaration' is true, also designates a file that bundles all .d.ts output. */ - "outDir": "./build" /* Specify an output folder for all emitted files. */, - // "removeComments": true, /* Disable emitting comments. */ - // "noEmit": true, /* Disable emitting files from a compilation. */ - // "importHelpers": true, /* Allow importing helper functions from tslib once per project, instead of including them per-file. */ - // "importsNotUsedAsValues": "remove", /* Specify emit/checking behavior for imports that are only used for types. */ - "downlevelIteration": true /* Emit more compliant, but verbose and less performant JavaScript for iteration. */, - // "sourceRoot": "", /* Specify the root path for debuggers to find the reference source code. */ - // "mapRoot": "", /* Specify the location where debugger should locate map files instead of generated locations. */ - // "inlineSourceMap": true, /* Include sourcemap files inside the emitted JavaScript. */ - // "inlineSources": true, /* Include source code in the sourcemaps inside the emitted JavaScript. */ - // "emitBOM": true, /* Emit a UTF-8 Byte Order Mark (BOM) in the beginning of output files. */ - // "newLine": "crlf", /* Set the newline character for emitting files. */ - // "stripInternal": true, /* Disable emitting declarations that have '@internal' in their JSDoc comments. */ - // "noEmitHelpers": true, /* Disable generating custom helper functions like '__extends' in compiled output. */ - // "noEmitOnError": true, /* Disable emitting files if any type checking errors are reported. */ - // "preserveConstEnums": true, /* Disable erasing 'const enum' declarations in generated code. */ - // "declarationDir": "./", /* Specify the output directory for generated declaration files. */ - // "preserveValueImports": true, /* Preserve unused imported values in the JavaScript output that would otherwise be removed. */ - - /* Interop Constraints */ - // "isolatedModules": true, /* Ensure that each file can be safely transpiled without relying on other imports. */ - // "allowSyntheticDefaultImports": true, /* Allow 'import x from y' when a module doesn't have a default export. */ - "esModuleInterop": true /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */, - // "preserveSymlinks": true, /* Disable resolving symlinks to their realpath. This correlates to the same flag in node. */ - "forceConsistentCasingInFileNames": true /* Ensure that casing is correct in imports. */, - - /* Type Checking */ - "strict": true /* Enable all strict type-checking options. */, - // "noImplicitAny": true, /* Enable error reporting for expressions and declarations with an implied 'any' type. */ - "strictNullChecks": true /* When type checking, take into account 'null' and 'undefined'. */, - // "strictFunctionTypes": true, /* When assigning functions, check to ensure parameters and the return values are subtype-compatible. */ - // "strictBindCallApply": true, /* Check that the arguments for 'bind', 'call', and 'apply' methods match the original function. */ - // "strictPropertyInitialization": true, /* Check for class properties that are declared but not set in the constructor. */ - // "noImplicitThis": true, /* Enable error reporting when 'this' is given the type 'any'. */ - // "useUnknownInCatchVariables": true, /* Default catch clause variables as 'unknown' instead of 'any'. */ - // "alwaysStrict": true, /* Ensure 'use strict' is always emitted. */ - // "noUnusedLocals": true, /* Enable error reporting when local variables aren't read. */ - // "noUnusedParameters": true, /* Raise an error when a function parameter isn't read. */ - // "exactOptionalPropertyTypes": true, /* Interpret optional property types as written, rather than adding 'undefined'. */ - // "noImplicitReturns": true, /* Enable error reporting for codepaths that do not explicitly return in a function. */ - // "noFallthroughCasesInSwitch": true, /* Enable error reporting for fallthrough cases in switch statements. */ - // "noUncheckedIndexedAccess": true, /* Add 'undefined' to a type when accessed using an index. */ - // "noImplicitOverride": true, /* Ensure overriding members in derived classes are marked with an override modifier. */ - // "noPropertyAccessFromIndexSignature": true, /* Enforces using indexed accessors for keys declared using an indexed type. */ - // "allowUnusedLabels": true, /* Disable error reporting for unused labels. */ - // "allowUnreachableCode": true, /* Disable error reporting for unreachable code. */ - - /* Completeness */ - // "skipDefaultLibCheck": true, /* Skip type checking .d.ts files that are included with TypeScript. */ - "skipLibCheck": true /* Skip type checking all .d.ts files. */ - }, - "include": ["./src/**/*.ts"] -} diff --git a/examples/quick-start/packages/server/.gitignore b/examples/quick-start/packages/server/.gitignore deleted file mode 100644 index a735ebed5..000000000 --- a/examples/quick-start/packages/server/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -node_modules -build -.env diff --git a/examples/quick-start/packages/server/package.json b/examples/quick-start/packages/server/package.json deleted file mode 100644 index fb8941e57..000000000 --- a/examples/quick-start/packages/server/package.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "name": "server", - "version": "0.0.0", - "description": "Quick start for MongoDB Chatbot Server", - "scripts": { - "test": "echo \"Error: no test specified\" && exit 1", - "build": "tsc", - "start": "node build/index.js", - "dev": "nodemon src/index.ts" - }, - "keywords": [], - "author": "", - "license": "ISC", - "devDependencies": { - "dotenv": "^16.3.1", - "nodemon": "^3.0.1", - "typescript": "^5.3.3" - }, - "dependencies": { - "@types/node": "^16.0.0", - "express": "^4.18.2", - "mongodb-chatbot-server": "*", - "mongodb-rag-core": "*" - } -} diff --git a/examples/quick-start/packages/server/src/index.ts b/examples/quick-start/packages/server/src/index.ts deleted file mode 100644 index 4d86d8604..000000000 --- a/examples/quick-start/packages/server/src/index.ts +++ /dev/null @@ -1,164 +0,0 @@ -import { - makeMongoDbEmbeddedContentStore, - makeOpenAiEmbedder, - makeMongoDbConversationsService, - AppConfig, - makeOpenAiChatLlm, - SystemPrompt, - makeDefaultFindContent, - logger, - makeApp, - GenerateUserPromptFunc, - makeRagGenerateUserPrompt, - MakeUserMessageFunc, -} from "mongodb-chatbot-server"; -import { OpenAI } from "mongodb-rag-core/openai"; -import { MongoClient } from "mongodb-rag-core/mongodb"; -import path from "path"; -import { loadEnvVars } from "./loadEnvVars"; - -// Load project environment variables -const dotenvPath = path.join(__dirname, "..", "..", "..", ".env"); // .env at project root -const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - OPENAI_CHAT_COMPLETION_MODEL, -} = loadEnvVars(dotenvPath); - -// Create the OpenAI client -// for interacting with the OpenAI API (ChatGPT API and Embedding API) -const openAiClient = new OpenAI({ apiKey: OPENAI_API_KEY }); - -// Chatbot LLM for responding to the user's query. -const llm = makeOpenAiChatLlm({ - openAiClient, - deployment: OPENAI_CHAT_COMPLETION_MODEL, - openAiLmmConfigOptions: { - temperature: 0, - max_tokens: 500, - }, -}); - -// MongoDB data source for the content used in RAG. -// Generated with the Ingest CLI. -const embeddedContentStore = makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_MODEL, - }, -}); - -// Creates vector embeddings for user queries to find matching content -// in the embeddedContentStore using Atlas Vector Search. -const embedder = makeOpenAiEmbedder({ - openAiClient, - deployment: OPENAI_EMBEDDING_MODEL, - backoffOptions: { - numOfAttempts: 3, - maxDelay: 5000, - }, -}); - -// Find content in the embeddedContentStore using the vector embeddings -// generated by the embedder. -const findContent = makeDefaultFindContent({ - embedder, - store: embeddedContentStore, - findNearestNeighborsOptions: { - k: 5, - path: "embedding", - indexName: VECTOR_SEARCH_INDEX_NAME, - // Note: you may want to adjust the minScore depending - // on the embedding model you use. We've found 0.9 works well - // for OpenAI's text-embedding-ada-02 model for most use cases, - // but you may want to adjust this value if you're using a different model. - minScore: 0.9, - }, -}); - -// Constructs the user message sent to the LLM from the initial user message -// and the content found by the findContent function. -const makeUserMessage: MakeUserMessageFunc = async function ({ - content, - originalUserMessage, -}) { - const chunkSeparator = "~~~~~~"; - const context = content.map((c) => c.text).join(`\n${chunkSeparator}\n`); - const contentForLlm = `Using the following information, answer the user query. -Different pieces of information are separated by "${chunkSeparator}". - -Information: -${context} - - -User query: ${originalUserMessage}`; - return { role: "user", content: contentForLlm }; -}; - -// Generates the user prompt for the chatbot using RAG -const generateUserPrompt: GenerateUserPromptFunc = makeRagGenerateUserPrompt({ - findContent, - makeUserMessage, -}); - -// System prompt for chatbot -const systemPrompt: SystemPrompt = { - role: "system", - content: `You are an assistant to users of the MongoDB Chatbot Framework. -Answer their questions about the framework in a friendly conversational tone. -Format your answers in Markdown. -Be concise in your answers. -If you do not know the answer to the question based on the information provided, -respond: "I'm sorry, I don't know the answer to that question. Please try to rephrase it. Refer to the below information to see if it helps."`, -}; - -// Create MongoDB collection and service for storing user conversations -// with the chatbot. -const mongodb = new MongoClient(MONGODB_CONNECTION_URI); -const conversations = makeMongoDbConversationsService( - mongodb.db(MONGODB_DATABASE_NAME) -); - -// Create the MongoDB Chatbot Server Express.js app configuration -const config: AppConfig = { - conversationsRouterConfig: { - llm, - conversations, - generateUserPrompt, - systemPrompt, - }, - maxRequestTimeoutMs: 30000, -}; - -// Start the server and clean up resources on SIGINT. -const PORT = process.env.PORT || 3000; -const startServer = async () => { - logger.info("Starting server..."); - const app = await makeApp(config); - const server = app.listen(PORT, () => { - logger.info(`Server listening on port: ${PORT}`); - }); - - process.on("SIGINT", async () => { - logger.info("SIGINT signal received"); - await mongodb.close(); - await embeddedContentStore.close(); - await new Promise((resolve, reject) => { - server.close((error: any) => { - error ? reject(error) : resolve(); - }); - }); - process.exit(1); - }); -}; - -try { - startServer(); -} catch (e) { - logger.error(`Fatal error: ${e}`); - process.exit(1); -} diff --git a/examples/quick-start/packages/server/src/loadEnvVars.ts b/examples/quick-start/packages/server/src/loadEnvVars.ts deleted file mode 100644 index 872eb48b6..000000000 --- a/examples/quick-start/packages/server/src/loadEnvVars.ts +++ /dev/null @@ -1,36 +0,0 @@ -import { strict as assert } from "assert"; -import dotenv from "dotenv"; - -/** - Load environment variables from a .env file at the given path. - Note that if you change the environment variable names, - you need to update this function to support those environment variables. - */ -export function loadEnvVars(path: string) { - dotenv.config({ path }); - const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - OPENAI_CHAT_COMPLETION_MODEL, - } = process.env; - assert(MONGODB_CONNECTION_URI, "MONGODB_CONNECTION_URI is required"); - assert(MONGODB_DATABASE_NAME, "MONGODB_DATABASE_NAME is required"); - assert(VECTOR_SEARCH_INDEX_NAME, "VECTOR_SEARCH_INDEX_NAME is required"); - assert(OPENAI_API_KEY, "OPENAI_API_KEY is required"); - assert(OPENAI_EMBEDDING_MODEL, "OPENAI_EMBEDDING_MODEL is required"); - assert( - OPENAI_CHAT_COMPLETION_MODEL, - "OPENAI_CHAT_COMPLETION_MODEL is required" - ); - return { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - OPENAI_CHAT_COMPLETION_MODEL, - }; -} diff --git a/examples/quick-start/packages/server/tsconfig.json b/examples/quick-start/packages/server/tsconfig.json deleted file mode 100644 index 3c078dc73..000000000 --- a/examples/quick-start/packages/server/tsconfig.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "compilerOptions": { - "module": "NodeNext", - "esModuleInterop": true, - "allowSyntheticDefaultImports": true, - "target": "ES2022", - "noImplicitAny": true, - "moduleResolution": "NodeNext", - "sourceMap": true, - "outDir": "build", - "baseUrl": ".", - "paths": { - "*": ["node_modules/*", "src/types/*"] - } - }, - "include": ["src/**/*"] -} diff --git a/examples/quick-start/packages/ui/.eslintrc.cjs b/examples/quick-start/packages/ui/.eslintrc.cjs deleted file mode 100644 index d6c953795..000000000 --- a/examples/quick-start/packages/ui/.eslintrc.cjs +++ /dev/null @@ -1,18 +0,0 @@ -module.exports = { - root: true, - env: { browser: true, es2020: true }, - extends: [ - 'eslint:recommended', - 'plugin:@typescript-eslint/recommended', - 'plugin:react-hooks/recommended', - ], - ignorePatterns: ['dist', '.eslintrc.cjs'], - parser: '@typescript-eslint/parser', - plugins: ['react-refresh'], - rules: { - 'react-refresh/only-export-components': [ - 'warn', - { allowConstantExport: true }, - ], - }, -} diff --git a/examples/quick-start/packages/ui/.gitignore b/examples/quick-start/packages/ui/.gitignore deleted file mode 100644 index a547bf36d..000000000 --- a/examples/quick-start/packages/ui/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/examples/quick-start/packages/ui/README.md b/examples/quick-start/packages/ui/README.md deleted file mode 100644 index 213a082d2..000000000 --- a/examples/quick-start/packages/ui/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# MongoDB Chatbot Framework - Quick Start - -TODO diff --git a/examples/quick-start/packages/ui/index.html b/examples/quick-start/packages/ui/index.html deleted file mode 100644 index 61ac3fd56..000000000 --- a/examples/quick-start/packages/ui/index.html +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - MongoDB Chatbot Framework Quick Start - - -
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} -} diff --git a/examples/quick-start/packages/ui/package.json b/examples/quick-start/packages/ui/package.json deleted file mode 100644 index 7ff663462..000000000 --- a/examples/quick-start/packages/ui/package.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "name": "ui", - "version": "0.0.0", - "type": "module", - "scripts": { - "dev": "vite", - "build": "tsc && vite build", - "lint": "eslint ./src --ext ts,tsx --report-unused-disable-directives --max-warnings 0", - "lint:fix": "npm run lint -- --fix && prettier ./src --check --write", - "preview": "vite preview" - }, - "dependencies": { - "mongodb-chatbot-ui": "*", - "react": "^18.2.0", - "react-dom": "^18.2.0" - }, - "devDependencies": { - "@types/react": "^18.2.43", - "@types/react-dom": "^18.2.17", - "@typescript-eslint/eslint-plugin": "^6.14.0", - "@typescript-eslint/parser": "^6.14.0", - "@vitejs/plugin-react": "^4.2.1", - "eslint": "^8.55.0", - "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.5", - "typescript": "^5.2.2", - "vite": "^5.0.8" - } -} diff --git a/examples/quick-start/packages/ui/src/App.css b/examples/quick-start/packages/ui/src/App.css deleted file mode 100644 index 902778b76..000000000 --- a/examples/quick-start/packages/ui/src/App.css +++ /dev/null @@ -1,6 +0,0 @@ -#root { - max-width: 1280px; - margin: 0 auto; - padding: 2rem; - text-align: center; -} diff --git a/examples/quick-start/packages/ui/src/App.tsx b/examples/quick-start/packages/ui/src/App.tsx deleted file mode 100644 index c88bddcf0..000000000 --- a/examples/quick-start/packages/ui/src/App.tsx +++ /dev/null @@ -1,30 +0,0 @@ -import "./App.css"; -import Chatbot, { - FloatingActionButtonTrigger, - InputBarTrigger, - ModalView, -} from "mongodb-chatbot-ui"; - -function MyApp() { - const suggestedPrompts = [ - "Why should I use the MongoDB Chatbot Framework?", - "How does the framework use Atlas Vector Search?", - "Do you support using LLMs from OpenAI?", - ]; - return ( -
- - <> - - - - - -
- ); -} - -export default MyApp; diff --git a/examples/quick-start/packages/ui/src/assets/react.svg b/examples/quick-start/packages/ui/src/assets/react.svg deleted file mode 100644 index 6c87de9bb..000000000 --- a/examples/quick-start/packages/ui/src/assets/react.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/examples/quick-start/packages/ui/src/index.css b/examples/quick-start/packages/ui/src/index.css deleted file mode 100644 index af12d8c29..000000000 --- a/examples/quick-start/packages/ui/src/index.css +++ /dev/null @@ -1,53 +0,0 @@ -:root { - font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif; - line-height: 1.5; - font-weight: 400; - - color: rgba(255, 255, 255, 0.87); - background-color: #242424; - font-synthesis: none; - text-rendering: optimizeLegibility; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -a { - font-weight: 500; - color: #646cff; - text-decoration: inherit; -} -a:hover { - color: #535bf2; -} - -body { - margin: 0; - display: flex; - place-items: center; - min-width: 320px; - min-height: 100vh; -} - -h1 { - font-size: 3.2em; - line-height: 1.1; -} - -button { - border-radius: 8px; - border: 1px solid transparent; - padding: 0.6em 1.2em; - font-size: 1em; - font-weight: 500; - font-family: inherit; - background-color: #1a1a1a; - cursor: pointer; - transition: border-color 0.25s; -} -button:hover { - border-color: #646cff; -} -button:focus, -button:focus-visible { - outline: 4px auto -webkit-focus-ring-color; -} diff --git a/examples/quick-start/packages/ui/src/main.tsx b/examples/quick-start/packages/ui/src/main.tsx deleted file mode 100644 index 3d7150da8..000000000 --- a/examples/quick-start/packages/ui/src/main.tsx +++ /dev/null @@ -1,10 +0,0 @@ -import React from 'react' -import ReactDOM from 'react-dom/client' -import App from './App.tsx' -import './index.css' - -ReactDOM.createRoot(document.getElementById('root')!).render( - - - , -) diff --git a/examples/quick-start/packages/ui/src/vite-env.d.ts b/examples/quick-start/packages/ui/src/vite-env.d.ts deleted file mode 100644 index 11f02fe2a..000000000 --- a/examples/quick-start/packages/ui/src/vite-env.d.ts +++ /dev/null @@ -1 +0,0 @@ -/// diff --git a/examples/quick-start/packages/ui/tsconfig.json b/examples/quick-start/packages/ui/tsconfig.json deleted file mode 100644 index a7fc6fbf2..000000000 --- a/examples/quick-start/packages/ui/tsconfig.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "compilerOptions": { - "target": "ES2020", - "useDefineForClassFields": true, - "lib": ["ES2020", "DOM", "DOM.Iterable"], - "module": "ESNext", - "skipLibCheck": true, - - /* Bundler mode */ - "moduleResolution": "bundler", - "allowImportingTsExtensions": true, - "resolveJsonModule": true, - "isolatedModules": true, - "noEmit": true, - "jsx": "react-jsx", - - /* Linting */ - "strict": true, - "noUnusedLocals": true, - "noUnusedParameters": true, - "noFallthroughCasesInSwitch": true - }, - "include": ["src"], - "references": [{ "path": "./tsconfig.node.json" }] -} diff --git a/examples/quick-start/packages/ui/tsconfig.node.json b/examples/quick-start/packages/ui/tsconfig.node.json deleted file mode 100644 index 42872c59f..000000000 --- a/examples/quick-start/packages/ui/tsconfig.node.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "compilerOptions": { - "composite": true, - "skipLibCheck": true, - "module": "ESNext", - "moduleResolution": "bundler", - "allowSyntheticDefaultImports": true - }, - "include": ["vite.config.ts"] -} diff --git a/examples/quick-start/packages/ui/vite.config.ts b/examples/quick-start/packages/ui/vite.config.ts deleted file mode 100644 index 5a33944a9..000000000 --- a/examples/quick-start/packages/ui/vite.config.ts +++ /dev/null @@ -1,7 +0,0 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vitejs.dev/config/ -export default defineConfig({ - plugins: [react()], -}) diff --git a/examples/repo-chat/.env.example b/examples/repo-chat/.env.example deleted file mode 100644 index cc01c7c61..000000000 --- a/examples/repo-chat/.env.example +++ /dev/null @@ -1,9 +0,0 @@ -# MongoDB config -MONGODB_CONNECTION_URI="" -VECTOR_SEARCH_INDEX_NAME="vector_index" # or whatever your index name is -MONGODB_DATABASE_NAME="mongodb-chatbot-framework-chatbot" # or whatever your database name is. must contain vector search index. - -# OpenAI config -OPENAI_API_KEY= -OPENAI_EMBEDDING_MODEL="text-embedding-ada-002" # or other model -OPENAI_CHAT_COMPLETION_MODEL="gpt-3.5-turbo" # or other model diff --git a/examples/repo-chat/.gitignore b/examples/repo-chat/.gitignore deleted file mode 100644 index 371e18e0d..000000000 --- a/examples/repo-chat/.gitignore +++ /dev/null @@ -1,133 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -lerna-debug.log* -.pnpm-debug.log* - -# Diagnostic reports (https://nodejs.org/api/report.html) -report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json - -# Runtime data -pids -*.pid -*.seed -*.pid.lock - -# Directory for instrumented libs generated by jscoverage/JSCover -lib-cov - -# Coverage directory used by tools like istanbul -coverage -*.lcov - -# nyc test coverage -.nyc_output - -# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files) -.grunt - -# Bower dependency directory (https://bower.io/) -bower_components - -# node-waf configuration -.lock-wscript - -# Compiled binary addons (https://nodejs.org/api/addons.html) -build/Release - -# Dependency directories -node_modules/ -jspm_packages/ - -# Snowpack dependency directory (https://snowpack.dev/) -web_modules/ - -# TypeScript cache -*.tsbuildinfo - -# Optional npm cache directory -.npm - -# Optional eslint cache -.eslintcache - -# Optional stylelint cache -.stylelintcache - -# Microbundle cache -.rpt2_cache/ -.rts2_cache_cjs/ -.rts2_cache_es/ -.rts2_cache_umd/ - -# Optional REPL history -.node_repl_history - -# Output of 'npm pack' -*.tgz - -# Yarn Integrity file -.yarn-integrity - -# dotenv environment variable files -.env -.env.development.local -.env.test.local -.env.production.local -.env.local - -# parcel-bundler cache (https://parceljs.org/) -.cache -.parcel-cache - -# Next.js build output -.next -out - -# Nuxt.js build / generate output -.nuxt -dist - -# Gatsby files -.cache/ -# Comment in the public line in if your project uses Gatsby and not Next.js -# https://nextjs.org/blog/next-9-1#public-directory-support -# public - -# vuepress build output -.vuepress/dist - -# vuepress v2.x temp and cache directory -.temp -.cache - -# Docusaurus cache and generated files -.docusaurus - -# Serverless directories -.serverless/ - -# FuseBox cache -.fusebox/ - -# DynamoDB Local files -.dynamodb/ - -# TernJS port file -.tern-port - -# Stores VSCode versions used for testing VSCode extensions -.vscode-test - -# yarn v2 -.yarn/cache -.yarn/unplugged -.yarn/build-state.yml -.yarn/install-state.gz -.pnp.* - -# ts build -build/ diff --git a/examples/repo-chat/README.md b/examples/repo-chat/README.md deleted file mode 100644 index b0c879bf5..000000000 --- a/examples/repo-chat/README.md +++ /dev/null @@ -1,43 +0,0 @@ -# Repo Chat - -Example project for chatting with Git repositories. - -The project is structured as a monorepo containing several subprojects. - -1. `packages/ingest` - Contains the ingestion script and the configuration. -2. `packages/server` - Contains the server code. -3. `packages/ui` - Contains the UI code. - -## Setup - -1. Get the project from Github - - ```sh - git clone https://github.com/mongodb/chatbot.git - cd examples/repo-chat - ``` - -2. Set up environment - - ```sh - cp .env.example .env - ``` - - Populate the `.env` file with relevant values. You'll need a MongoDB Atlas connection URI and an OpenAI API key. - -3. Install dependencies - - ```sh - npm install - ``` - -4. Run the ingestion script - - ```sh - npm run ingest:all - -5. Start the development server - - ```sh - npm run dev - ``` \ No newline at end of file diff --git a/examples/repo-chat/package-lock.json b/examples/repo-chat/package-lock.json deleted file mode 100644 index 3f879db45..000000000 --- a/examples/repo-chat/package-lock.json +++ /dev/null @@ -1,15899 +0,0 @@ -{ - "name": "mongodb-chatbot-framework-quick-start", - "version": "1.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "mongodb-chatbot-framework-quick-start", - "version": "1.0.0", - "license": "Apache-2.0", - "workspaces": [ - "packages/*" - ], - "devDependencies": { - "ts-node": "^10.9.2" - } - }, - "node_modules/@ai-sdk/provider": { - "version": "1.0.9", - "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-1.0.9.tgz", - "integrity": "sha512-jie6ZJT2ZR0uVOVCDc9R2xCX5I/Dum/wEK28lx21PJx6ZnFAN9EzD2WsPhcDWfCgGx3OAZZ0GyM3CEobXpa9LA==", - "license": "Apache-2.0", - "dependencies": { - "json-schema": "^0.4.0" - }, - "engines": { - "node": ">=18" - 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"eslint": "^8.55.0", - "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.5", - "typescript": "^5.2.2", - "vite": "^5.0.8" - } - } - } -} diff --git a/examples/repo-chat/package.json b/examples/repo-chat/package.json deleted file mode 100644 index d4f0275ab..000000000 --- a/examples/repo-chat/package.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "name": "mongodb-chatbot-framework-quick-start", - "version": "1.0.0", - "description": "Quick start code for MongoDB Chatbot Framework", - "workspaces": [ - "packages/*" - ], - "private": true, - "scripts": { - "ingest:all": "npm run ingest:all --workspace=ingest", - "dev": "npm run dev --workspace=server & npm run dev --workspace=ui" - }, - "author": "", - "license": "Apache-2.0", - "devDependencies": { - "ts-node": "^10.9.2" - } -} diff --git a/examples/repo-chat/packages/ingest/package.json b/examples/repo-chat/packages/ingest/package.json deleted file mode 100644 index 5277c51d4..000000000 --- a/examples/repo-chat/packages/ingest/package.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "name": "ingest", - "version": "0.0.0", - "description": "", - "scripts": { - "build": "tsc", - "ingest:all": "npm run build && ingest all --config build/ingest.config.js --since 1970" - }, - "keywords": [], - "author": "", - "license": "ISC", - "dependencies": { - "mongodb-rag-ingest": "^0.3.1" - }, - "devDependencies": { - "dotenv": "^16.3.1", - "typescript": "^5.3.3" - } -} \ No newline at end of file diff --git a/examples/repo-chat/packages/ingest/src/ingest.config.ts b/examples/repo-chat/packages/ingest/src/ingest.config.ts deleted file mode 100644 index 74bdb6e97..000000000 --- a/examples/repo-chat/packages/ingest/src/ingest.config.ts +++ /dev/null @@ -1,58 +0,0 @@ -import { makeIngestMetaStore, Config } from "mongodb-rag-ingest"; -import { - makeOpenAiEmbedder, - makeMongoDbEmbeddedContentStore, - makeMongoDbPageStore, -} from "mongodb-rag-core"; -import path from "path"; -import { loadEnvVars } from "./loadEnvVars"; -import { mongoDbChatbotFrameworkDocsDataSourceConstructor } from "./reposDataSource"; -import { OpenAI } from "mongodb-rag-core/openai"; - -// Load project environment variables -const dotenvPath = path.join(__dirname, "..", "..", "..", ".env"); // .env at project root -const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, -} = loadEnvVars(dotenvPath); - -export default { - embedder: async () => { - return makeOpenAiEmbedder({ - openAiClient: new OpenAI({ apiKey: OPENAI_API_KEY }), - deployment: OPENAI_EMBEDDING_MODEL, - backoffOptions: { - numOfAttempts: 25, - startingDelay: 1000, - }, - }); - }, - embeddedContentStore: () => - makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_MODEL, - }, - }), - pageStore: () => - makeMongoDbPageStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - }), - ingestMetaStore: () => - makeIngestMetaStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - entryId: "all", - }), - // Add data sources here - dataSources: async () => { - const mongodbChatbotFrameworkSource = - await mongoDbChatbotFrameworkDocsDataSourceConstructor(); - - return [mongodbChatbotFrameworkSource]; - }, -} satisfies Config; diff --git a/examples/repo-chat/packages/ingest/src/loadEnvVars.ts b/examples/repo-chat/packages/ingest/src/loadEnvVars.ts deleted file mode 100644 index 46d24c702..000000000 --- a/examples/repo-chat/packages/ingest/src/loadEnvVars.ts +++ /dev/null @@ -1,30 +0,0 @@ -import { strict as assert } from "assert"; -import dotenv from "dotenv"; - -/** - Load environment variables from a .env file at the given path. - Note that if you change the environment variable names, - you need to update this function to support those environment variables. - */ -export function loadEnvVars(path: string) { - dotenv.config({ path }); - const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - } = process.env; - assert(MONGODB_CONNECTION_URI, "MONGODB_CONNECTION_URI is required"); - assert(MONGODB_DATABASE_NAME, "MONGODB_DATABASE_NAME is required"); - assert(VECTOR_SEARCH_INDEX_NAME, "VECTOR_SEARCH_INDEX_NAME is required"); - assert(OPENAI_API_KEY, "OPENAI_API_KEY is required"); - assert(OPENAI_EMBEDDING_MODEL, "OPENAI_EMBEDDING_MODEL is required"); - return { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - }; -} diff --git a/examples/repo-chat/packages/ingest/src/reposDataSource.ts b/examples/repo-chat/packages/ingest/src/reposDataSource.ts deleted file mode 100644 index 0dcb5ecfc..000000000 --- a/examples/repo-chat/packages/ingest/src/reposDataSource.ts +++ /dev/null @@ -1,78 +0,0 @@ -import { - MakeMdOnGithubDataSourceParams, - makeMdOnGithubDataSource, -} from "mongodb-rag-core/dataSources"; - -const repoUrl = "https://github.com/mongodb/chatbot/"; -/** - Can structure pulling from repos like this. - - Note that to clone from a private repo, you must first be authenticated into - a Github account that has access to the repo. Refer to GH documentation for doing this. - */ -const mongodbChatbotFrameworkDocsConfig: MakeMdOnGithubDataSourceParams = { - name: "mongodb-rag-framework", - repoUrl, - repoLoaderOptions: { - branch: "main", - }, - filter: (path) => path.endsWith(".md") && !path.endsWith("README.md"), - pathToPageUrl(pathInRepo) { - return `${repoUrl}${pathInRepo}`; - }, - extractTitle: (pageContent, frontmatter) => - (frontmatter?.title as string) ?? extractFirstH1(pageContent), -}; - -// Helper function -function extractFirstH1(markdownText: string) { - const lines = markdownText.split("\n"); - - for (const line of lines) { - if (line.startsWith("# ")) { - // Remove '# ' and any leading or trailing whitespace - return line.substring(2).trim(); - } - } - return null; -} -/** - Note that you can also fetch repos dynamically since the constructor is just an async function that returns a `DataSource` instance. - So for example, you could use the GH Octokit SDK to fetch a repo dynamically. Something like: - - ```typescript - import { Octokit } from "@octokit/rest"; - - // Can structure pulling from repos like this. - // Note that to clone from a private repo, you must first be authenticated into - // a Github account that has access to the repo. Refer to GH documentation for doing this. - - async function listOrganizationRepos(orgName: string) { - // Create a new Octokit instance - it will use GITHUB_TOKEN env var by default - const octokit = new Octokit(); - - try { - const repos = await octokit.paginate(octokit.rest.repos.listForOrg, { - org: orgName, - type: 'all', // Include both public and private repos you have access to - per_page: 100, - }); - - return repos.map(repo => ({ - name: repo.name, - url: repo.clone_url, - isPrivate: repo.private, - description: repo.description, - defaultBranch: repo.default_branch, - })); - } catch (error) { - console.error(`Error fetching repos for organization ${orgName}:`, error); - throw error; - } - } - - ``` - -*/ -export const mongoDbChatbotFrameworkDocsDataSourceConstructor = async () => - await makeMdOnGithubDataSource(mongodbChatbotFrameworkDocsConfig); diff --git a/examples/repo-chat/packages/ingest/tsconfig.json b/examples/repo-chat/packages/ingest/tsconfig.json deleted file mode 100644 index 7096baa64..000000000 --- a/examples/repo-chat/packages/ingest/tsconfig.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "compilerOptions": { - "target": "es2020", - "lib": ["ES2023", "DOM"], - "module": "NodeNext", - "moduleResolution": "NodeNext", - "allowJs": false, - "declaration": true, - "declarationMap": true, - "sourceMap": true, - "downlevelIteration": true, - "esModuleInterop": true, - "forceConsistentCasingInFileNames": true, - "strictNullChecks": true, - "skipLibCheck": true, - "strict": true - } -} diff --git a/examples/repo-chat/packages/server/.gitignore b/examples/repo-chat/packages/server/.gitignore deleted file mode 100644 index a735ebed5..000000000 --- a/examples/repo-chat/packages/server/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -node_modules -build -.env diff --git a/examples/repo-chat/packages/server/package.json b/examples/repo-chat/packages/server/package.json deleted file mode 100644 index fb8941e57..000000000 --- a/examples/repo-chat/packages/server/package.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "name": "server", - "version": "0.0.0", - "description": "Quick start for MongoDB Chatbot Server", - "scripts": { - "test": "echo \"Error: no test specified\" && exit 1", - "build": "tsc", - "start": "node build/index.js", - "dev": "nodemon src/index.ts" - }, - "keywords": [], - "author": "", - "license": "ISC", - "devDependencies": { - "dotenv": "^16.3.1", - "nodemon": "^3.0.1", - "typescript": "^5.3.3" - }, - "dependencies": { - "@types/node": "^16.0.0", - "express": "^4.18.2", - "mongodb-chatbot-server": "*", - "mongodb-rag-core": "*" - } -} diff --git a/examples/repo-chat/packages/server/src/index.ts b/examples/repo-chat/packages/server/src/index.ts deleted file mode 100644 index c610f9c84..000000000 --- a/examples/repo-chat/packages/server/src/index.ts +++ /dev/null @@ -1,165 +0,0 @@ -import { - makeMongoDbEmbeddedContentStore, - makeOpenAiEmbedder, - makeMongoDbConversationsService, - AppConfig, - makeOpenAiChatLlm, - SystemPrompt, - makeDefaultFindContent, - logger, - makeApp, - GenerateUserPromptFunc, - makeRagGenerateUserPrompt, - MakeUserMessageFunc, -} from "mongodb-chatbot-server"; -import { OpenAI } from "mongodb-rag-core/openai"; -import { MongoClient } from "mongodb-rag-core/mongodb"; -import path from "path"; -import { loadEnvVars } from "./loadEnvVars"; - -// Load project environment variables -const dotenvPath = path.join(__dirname, "..", "..", "..", ".env"); // .env at project root -const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - OPENAI_CHAT_COMPLETION_MODEL, -} = loadEnvVars(dotenvPath); - -// Create the OpenAI client -// for interacting with the OpenAI API (ChatGPT API and Embedding API) -const openAiClient = new OpenAI({ apiKey: OPENAI_API_KEY }); - -// Chatbot LLM for responding to the user's query. -const llm = makeOpenAiChatLlm({ - openAiClient, - deployment: OPENAI_CHAT_COMPLETION_MODEL, - openAiLmmConfigOptions: { - temperature: 0, - max_tokens: 500, - }, -}); - -// MongoDB data source for the content used in RAG. -// Generated with the Ingest CLI. -const embeddedContentStore = makeMongoDbEmbeddedContentStore({ - connectionUri: MONGODB_CONNECTION_URI, - databaseName: MONGODB_DATABASE_NAME, - searchIndex: { - embeddingName: OPENAI_EMBEDDING_MODEL, - }, -}); - -// Creates vector embeddings for user queries to find matching content -// in the embeddedContentStore using Atlas Vector Search. -const embedder = makeOpenAiEmbedder({ - openAiClient, - deployment: OPENAI_EMBEDDING_MODEL, - backoffOptions: { - numOfAttempts: 3, - maxDelay: 5000, - }, -}); - -// Find content in the embeddedContentStore using the vector embeddings -// generated by the embedder. -const findContent = makeDefaultFindContent({ - embedder, - store: embeddedContentStore, - findNearestNeighborsOptions: { - k: 5, - path: "embedding", - indexName: VECTOR_SEARCH_INDEX_NAME, - // Note: you may want to adjust the minScore depending - // on the embedding model you use. We've found 0.9 works well - // for OpenAI's text-embedding-ada-02 model for most use cases, - // but you may want to adjust this value if you're using a different model. - minScore: 0.9, - }, -}); - -// Constructs the user message sent to the LLM from the initial user message -// and the content found by the findContent function. -const makeUserMessage: MakeUserMessageFunc = async function ({ - content, - originalUserMessage, -}) { - const chunkSeparator = "~~~~~~"; - const context = content.map((c) => c.text).join(`\n${chunkSeparator}\n`); - const contentForLlm = `Using the following information, answer the user query. -Different pieces of information are separated by "${chunkSeparator}". - -Information: -${context} - - -User query: ${originalUserMessage}`; - return { role: "user", content: contentForLlm }; -}; - -// Generates the user prompt for the chatbot using RAG -const generateUserPrompt: GenerateUserPromptFunc = makeRagGenerateUserPrompt({ - findContent, - makeUserMessage, -}); - -// System prompt for chatbot -// TODO: Modify this prompt to suit your application -const systemPrompt: SystemPrompt = { - role: "system", - content: `You are an assistant to users asking about tools to migrate code. -Answer their questions about the framework in a friendly conversational tone. -Format your answers in Markdown. -Be concise in your answers. -If you do not know the answer to the question based on the information provided, -respond: "I'm sorry, I don't know the answer to that question. Please try to rephrase it. Refer to the below information to see if it helps."`, -}; - -// Create MongoDB collection and service for storing user conversations -// with the chatbot. -const mongodb = new MongoClient(MONGODB_CONNECTION_URI); -const conversations = makeMongoDbConversationsService( - mongodb.db(MONGODB_DATABASE_NAME) -); - -// Create the MongoDB Chatbot Server Express.js app configuration -const config: AppConfig = { - conversationsRouterConfig: { - llm, - conversations, - generateUserPrompt, - systemPrompt, - }, - maxRequestTimeoutMs: 30000, -}; - -// Start the server and clean up resources on SIGINT. -const PORT = process.env.PORT || 3000; -const startServer = async () => { - logger.info("Starting server..."); - const app = await makeApp(config); - const server = app.listen(PORT, () => { - logger.info(`Server listening on port: ${PORT}`); - }); - - process.on("SIGINT", async () => { - logger.info("SIGINT signal received"); - await mongodb.close(); - await embeddedContentStore.close(); - await new Promise((resolve, reject) => { - server.close((error: any) => { - error ? reject(error) : resolve(); - }); - }); - process.exit(1); - }); -}; - -try { - startServer(); -} catch (e) { - logger.error(`Fatal error: ${e}`); - process.exit(1); -} diff --git a/examples/repo-chat/packages/server/src/loadEnvVars.ts b/examples/repo-chat/packages/server/src/loadEnvVars.ts deleted file mode 100644 index 872eb48b6..000000000 --- a/examples/repo-chat/packages/server/src/loadEnvVars.ts +++ /dev/null @@ -1,36 +0,0 @@ -import { strict as assert } from "assert"; -import dotenv from "dotenv"; - -/** - Load environment variables from a .env file at the given path. - Note that if you change the environment variable names, - you need to update this function to support those environment variables. - */ -export function loadEnvVars(path: string) { - dotenv.config({ path }); - const { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - OPENAI_CHAT_COMPLETION_MODEL, - } = process.env; - assert(MONGODB_CONNECTION_URI, "MONGODB_CONNECTION_URI is required"); - assert(MONGODB_DATABASE_NAME, "MONGODB_DATABASE_NAME is required"); - assert(VECTOR_SEARCH_INDEX_NAME, "VECTOR_SEARCH_INDEX_NAME is required"); - assert(OPENAI_API_KEY, "OPENAI_API_KEY is required"); - assert(OPENAI_EMBEDDING_MODEL, "OPENAI_EMBEDDING_MODEL is required"); - assert( - OPENAI_CHAT_COMPLETION_MODEL, - "OPENAI_CHAT_COMPLETION_MODEL is required" - ); - return { - MONGODB_CONNECTION_URI, - MONGODB_DATABASE_NAME, - VECTOR_SEARCH_INDEX_NAME, - OPENAI_API_KEY, - OPENAI_EMBEDDING_MODEL, - OPENAI_CHAT_COMPLETION_MODEL, - }; -} diff --git a/examples/repo-chat/packages/server/tsconfig.json b/examples/repo-chat/packages/server/tsconfig.json deleted file mode 100644 index 7096baa64..000000000 --- a/examples/repo-chat/packages/server/tsconfig.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "compilerOptions": { - "target": "es2020", - "lib": ["ES2023", "DOM"], - "module": "NodeNext", - "moduleResolution": "NodeNext", - "allowJs": false, - "declaration": true, - "declarationMap": true, - "sourceMap": true, - "downlevelIteration": true, - "esModuleInterop": true, - "forceConsistentCasingInFileNames": true, - "strictNullChecks": true, - "skipLibCheck": true, - "strict": true - } -} diff --git a/examples/repo-chat/packages/ui/.eslintrc.cjs b/examples/repo-chat/packages/ui/.eslintrc.cjs deleted file mode 100644 index d6c953795..000000000 --- a/examples/repo-chat/packages/ui/.eslintrc.cjs +++ /dev/null @@ -1,18 +0,0 @@ -module.exports = { - root: true, - env: { browser: true, es2020: true }, - extends: [ - 'eslint:recommended', - 'plugin:@typescript-eslint/recommended', - 'plugin:react-hooks/recommended', - ], - ignorePatterns: ['dist', '.eslintrc.cjs'], - parser: '@typescript-eslint/parser', - plugins: ['react-refresh'], - rules: { - 'react-refresh/only-export-components': [ - 'warn', - { allowConstantExport: true }, - ], - }, -} diff --git a/examples/repo-chat/packages/ui/.gitignore b/examples/repo-chat/packages/ui/.gitignore deleted file mode 100644 index a547bf36d..000000000 --- a/examples/repo-chat/packages/ui/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? diff --git a/examples/repo-chat/packages/ui/README.md b/examples/repo-chat/packages/ui/README.md deleted file mode 100644 index 213a082d2..000000000 --- a/examples/repo-chat/packages/ui/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# MongoDB Chatbot Framework - Quick Start - -TODO diff --git a/examples/repo-chat/packages/ui/index.html b/examples/repo-chat/packages/ui/index.html deleted file mode 100644 index 61ac3fd56..000000000 --- a/examples/repo-chat/packages/ui/index.html +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - MongoDB Chatbot Framework Quick Start - - -
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} -} diff --git a/examples/repo-chat/packages/ui/package.json b/examples/repo-chat/packages/ui/package.json deleted file mode 100644 index 7ff663462..000000000 --- a/examples/repo-chat/packages/ui/package.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "name": "ui", - "version": "0.0.0", - "type": "module", - "scripts": { - "dev": "vite", - "build": "tsc && vite build", - "lint": "eslint ./src --ext ts,tsx --report-unused-disable-directives --max-warnings 0", - "lint:fix": "npm run lint -- --fix && prettier ./src --check --write", - "preview": "vite preview" - }, - "dependencies": { - "mongodb-chatbot-ui": "*", - "react": "^18.2.0", - "react-dom": "^18.2.0" - }, - "devDependencies": { - "@types/react": "^18.2.43", - "@types/react-dom": "^18.2.17", - "@typescript-eslint/eslint-plugin": "^6.14.0", - "@typescript-eslint/parser": "^6.14.0", - "@vitejs/plugin-react": "^4.2.1", - "eslint": "^8.55.0", - "eslint-plugin-react-hooks": "^4.6.0", - "eslint-plugin-react-refresh": "^0.4.5", - "typescript": "^5.2.2", - "vite": "^5.0.8" - } -} diff --git a/examples/repo-chat/packages/ui/src/App.css b/examples/repo-chat/packages/ui/src/App.css deleted file mode 100644 index 902778b76..000000000 --- a/examples/repo-chat/packages/ui/src/App.css +++ /dev/null @@ -1,6 +0,0 @@ -#root { - max-width: 1280px; - margin: 0 auto; - padding: 2rem; - text-align: center; -} diff --git a/examples/repo-chat/packages/ui/src/App.tsx b/examples/repo-chat/packages/ui/src/App.tsx deleted file mode 100644 index f6d7bc2d0..000000000 --- a/examples/repo-chat/packages/ui/src/App.tsx +++ /dev/null @@ -1,29 +0,0 @@ -import "./App.css"; -import Chatbot, { - FloatingActionButtonTrigger, - InputBarTrigger, - ModalView, -} from "mongodb-chatbot-ui"; - -function MyApp() { - // TODO: add prompts relevant to your repo here - const suggestedPrompts = [ - "How do I migrate from Oracle forms to react?" - ]; - return ( -
- - <> - - - - - -
- ); -} - -export default MyApp; diff --git a/examples/repo-chat/packages/ui/src/assets/react.svg b/examples/repo-chat/packages/ui/src/assets/react.svg deleted file mode 100644 index 6c87de9bb..000000000 --- a/examples/repo-chat/packages/ui/src/assets/react.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/examples/repo-chat/packages/ui/src/index.css b/examples/repo-chat/packages/ui/src/index.css deleted file mode 100644 index af12d8c29..000000000 --- a/examples/repo-chat/packages/ui/src/index.css +++ /dev/null @@ -1,53 +0,0 @@ -:root { - font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif; - line-height: 1.5; - font-weight: 400; - - color: rgba(255, 255, 255, 0.87); - background-color: #242424; - font-synthesis: none; - text-rendering: optimizeLegibility; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -a { - font-weight: 500; - color: #646cff; - text-decoration: inherit; -} -a:hover { - color: #535bf2; -} - -body { - margin: 0; - display: flex; - place-items: center; - min-width: 320px; - min-height: 100vh; -} - -h1 { - font-size: 3.2em; - line-height: 1.1; -} - -button { - border-radius: 8px; - border: 1px solid transparent; - padding: 0.6em 1.2em; - font-size: 1em; - font-weight: 500; - font-family: inherit; - background-color: #1a1a1a; - cursor: pointer; - transition: border-color 0.25s; -} -button:hover { - border-color: #646cff; -} -button:focus, -button:focus-visible { - outline: 4px auto -webkit-focus-ring-color; -} diff --git a/examples/repo-chat/packages/ui/src/main.tsx b/examples/repo-chat/packages/ui/src/main.tsx deleted file mode 100644 index 3d7150da8..000000000 --- a/examples/repo-chat/packages/ui/src/main.tsx +++ /dev/null @@ -1,10 +0,0 @@ -import React from 'react' -import ReactDOM from 'react-dom/client' -import App from './App.tsx' -import './index.css' - -ReactDOM.createRoot(document.getElementById('root')!).render( - - - , -) diff --git a/examples/repo-chat/packages/ui/src/vite-env.d.ts b/examples/repo-chat/packages/ui/src/vite-env.d.ts deleted file mode 100644 index 11f02fe2a..000000000 --- a/examples/repo-chat/packages/ui/src/vite-env.d.ts +++ /dev/null @@ -1 +0,0 @@ -/// diff --git a/examples/repo-chat/packages/ui/tsconfig.json b/examples/repo-chat/packages/ui/tsconfig.json deleted file mode 100644 index a7fc6fbf2..000000000 --- a/examples/repo-chat/packages/ui/tsconfig.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "compilerOptions": { - "target": "ES2020", - "useDefineForClassFields": true, - "lib": ["ES2020", "DOM", "DOM.Iterable"], - "module": "ESNext", - "skipLibCheck": true, - - /* Bundler mode */ - "moduleResolution": "bundler", - "allowImportingTsExtensions": true, - "resolveJsonModule": true, - "isolatedModules": true, - "noEmit": true, - "jsx": "react-jsx", - - /* Linting */ - "strict": true, - "noUnusedLocals": true, - "noUnusedParameters": true, - "noFallthroughCasesInSwitch": true - }, - "include": ["src"], - "references": [{ "path": "./tsconfig.node.json" }] -} diff --git a/examples/repo-chat/packages/ui/tsconfig.node.json b/examples/repo-chat/packages/ui/tsconfig.node.json deleted file mode 100644 index 42872c59f..000000000 --- a/examples/repo-chat/packages/ui/tsconfig.node.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "compilerOptions": { - "composite": true, - "skipLibCheck": true, - "module": "ESNext", - "moduleResolution": "bundler", - "allowSyntheticDefaultImports": true - }, - "include": ["vite.config.ts"] -} diff --git a/examples/repo-chat/packages/ui/vite.config.ts b/examples/repo-chat/packages/ui/vite.config.ts deleted file mode 100644 index 5a33944a9..000000000 --- a/examples/repo-chat/packages/ui/vite.config.ts +++ /dev/null @@ -1,7 +0,0 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vitejs.dev/config/ -export default defineConfig({ - plugins: [react()], -}) From b6f9c7e74f0e2ad6064ae3086b710026e20c7a3d Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 2 Jun 2025 10:09:22 -0400 Subject: [PATCH 26/29] build out docs following last week convo --- docs/docs/data-sources.md | 26 +++++++++++++++++++++++ docs/docs/datasets.md | 19 +++++++++++++++++ docs/docs/index.md | 10 ++++++--- docs/docs/server/openapi.yaml | 39 ++++++++++++++++++----------------- docs/sidebars.ts | 1 + 5 files changed, 73 insertions(+), 22 deletions(-) create mode 100644 docs/docs/data-sources.md create mode 100644 docs/docs/datasets.md diff --git a/docs/docs/data-sources.md b/docs/docs/data-sources.md new file mode 100644 index 000000000..23f0ba8f0 --- /dev/null +++ b/docs/docs/data-sources.md @@ -0,0 +1,26 @@ +# Data Sources + +The MongoDB Knowledge Service uses [retrieval augmented generation](https://www.mongodb.com/resources/basics/artificial-intelligence/retrieval-augmented-generation) to answer user queries. + +All the data sources are public on the web. + + +## Sources + +The Knowledge Service ingests the following data sources: + +- MongoDB Technical Documentation (https://mongodb.com/docs) +- MongoDB Developer Center blog (https://mongodb.com/developer) +- MongoDB University transcripts and landing pages (https://learn.mongodb.com) +- Select marketing and sales pages from https://mongodb.com +- Select external data sources: + - Mongoose.js docs (https://mongoosejs.com) + - Prisma MongoDB connector docs (https://www.prisma.io/docs/orm/overview/databases/mongodb) + - Terraform MongoDB Provider docs (https://registry.terraform.io/providers/mongodb/mongodbatlas/latest) + - WiredTiger docs (https://source.wiredtiger.com/) + - Practical MongoDB Aggregations book (https://www.practical-mongodb-aggregations.com/) + + +## Source Code + +You can see the source code for the data source ingestion here: https://github.com/mongodb/chatbot/tree/main/packages/ingest-mongodb-public diff --git a/docs/docs/datasets.md b/docs/docs/datasets.md new file mode 100644 index 000000000..0dab9e438 --- /dev/null +++ b/docs/docs/datasets.md @@ -0,0 +1,19 @@ +# Datasets + +The Education AI team maintains various datasets for use with AI systems. All datasets can be found in the [MongoDB Education AI HuggingFace](https://huggingface.co/mongodb-eai). + +## Content + +Content datasets can be useful for building RAG systems and training models. + +| Name | Type | Description | Visibility | Use Cases | Links | +| :---- | :---- | :---- | :---- | :---- | :---- | +| Public documentation | Long-form content | Markdown version of docs and developer center content. | Public | RAG, model training | https://huggingface.co/datasets/mongodb-eai/docs | +| Code example dataset | Prompt-completion | Code examples extracted from the MongoDB docs and developer center with prompts that could be used to generate the code. | Public | Model fine-tuning | https://huggingface.co/datasets/mongodb-eai/code-example-prompts | + +## Benchmarks + +| Name | Type | Description | Visibility | | +| :---- | :---- | :---- | :---- | :---- | +| Natural language-to-Node.js Mongosh | Code generation | Assess how well LLMs generate `mongosh` code given a natural language prompt and information about a database. | External | https://huggingface.co/datasets/mongodb-eai/natural-language-to-mongosh | + diff --git a/docs/docs/index.md b/docs/docs/index.md index 3fb82879c..f53314bed 100644 --- a/docs/docs/index.md +++ b/docs/docs/index.md @@ -9,9 +9,13 @@ The MongoDB Knowledge Service lets you learn about MongoDB using generative AI. ## Server -To use the MongoDB Knowledge Service, refer to the [OpenAPI specification](/server/openapi/). +To call the MongoDB Knowledge Service API, refer to the [OpenAPI specification](/server/openapi/). -## UI +## UI Library -You can call the MongoDB Knowledge Service API from a web application using the [mongodb-chatbot-ui](./ui.md) npm package. +You can call the MongoDB Knowledge Service API from a web application using the `mongodb-chatbot-ui` npm package. The UI library provides a React component that you can use to build a chat interface. Learn more in the [UI documentation](./ui.md). +## Contact + +- MongoDB employees can reach out to the education ai team on Slack at `#ask-education-ai` +- External users can create an issue on the [mongodb/chatbot GitHub repo](https://github.com/mongodb/chatbot/issues/new) \ No newline at end of file diff --git a/docs/docs/server/openapi.yaml b/docs/docs/server/openapi.yaml index 261d29d4c..7fe2184c2 100644 --- a/docs/docs/server/openapi.yaml +++ b/docs/docs/server/openapi.yaml @@ -119,25 +119,26 @@ paths: $ref: "#/components/responses/NotFound" 500: $ref: "#/components/responses/InternalServerError" - /conversations/{conversationId}: - get: - operationId: getConversation - summary: Get a conversation - parameters: - - $ref: "#/components/parameters/conversationId" - responses: - 200: - description: OK - content: - application/json: - schema: - $ref: "#/components/schemas/Conversation" - 400: - $ref: "#/components/responses/BadRequest" - 404: - $ref: "#/components/responses/NotFound" - 500: - $ref: "#/components/responses/InternalServerError" + # Commenting out while it's not supported. + # /conversations/{conversationId}: + # get: + # operationId: getConversation + # summary: Get a conversation + # parameters: + # - $ref: "#/components/parameters/conversationId" + # responses: + # 200: + # description: OK + # content: + # application/json: + # schema: + # $ref: "#/components/schemas/Conversation" + # 400: + # $ref: "#/components/responses/BadRequest" + # 404: + # $ref: "#/components/responses/NotFound" + # 500: + # $ref: "#/components/responses/InternalServerError" /conversations/{conversationId}/messages/{messageId}/rating: post: operationId: rateMessage diff --git a/docs/sidebars.ts b/docs/sidebars.ts index 7d5cbbf32..9c36de387 100644 --- a/docs/sidebars.ts +++ b/docs/sidebars.ts @@ -20,6 +20,7 @@ const sidebars: SidebarsConfig = { href: "/server/openapi", }, "ui", + "data-sources", { type: "doc", label: "Framework (deprecated)", From 6f66f9e55a98711e882ac8e1435a78594dd221c7 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Mon, 2 Jun 2025 10:25:29 -0400 Subject: [PATCH 27/29] clean up spec + contact --- docs/docs/contact.md | 5 +++++ docs/docs/index.md | 5 ----- docs/docs/server/openapi.yaml | 21 ++++++++++++--------- docs/sidebars.ts | 2 ++ 4 files changed, 19 insertions(+), 14 deletions(-) create mode 100644 docs/docs/contact.md diff --git a/docs/docs/contact.md b/docs/docs/contact.md new file mode 100644 index 000000000..1daa5f6cb --- /dev/null +++ b/docs/docs/contact.md @@ -0,0 +1,5 @@ +# Contact + + +- MongoDB employees can reach out to the education ai team on Slack at `#ask-education-ai` +- External users can create an issue on the [mongodb/chatbot GitHub repo](https://github.com/mongodb/chatbot/issues/new) \ No newline at end of file diff --git a/docs/docs/index.md b/docs/docs/index.md index f53314bed..f63a27ed3 100644 --- a/docs/docs/index.md +++ b/docs/docs/index.md @@ -14,8 +14,3 @@ To call the MongoDB Knowledge Service API, refer to the [OpenAPI specification]( ## UI Library You can call the MongoDB Knowledge Service API from a web application using the `mongodb-chatbot-ui` npm package. The UI library provides a React component that you can use to build a chat interface. Learn more in the [UI documentation](./ui.md). - -## Contact - -- MongoDB employees can reach out to the education ai team on Slack at `#ask-education-ai` -- External users can create an issue on the [mongodb/chatbot GitHub repo](https://github.com/mongodb/chatbot/issues/new) \ No newline at end of file diff --git a/docs/docs/server/openapi.yaml b/docs/docs/server/openapi.yaml index 7fe2184c2..9ec4b7fbb 100644 --- a/docs/docs/server/openapi.yaml +++ b/docs/docs/server/openapi.yaml @@ -6,18 +6,16 @@ info: name: Apache 2.0 url: https://github.com/mongodb/chatbot/blob/main/LICENSE description: | - The MongoDB RAG Server API is a RESTful API that allows you to - create and manage conversations. + The Knowledge Service is a RESTful API. + The API is designed to be used by the MongoDB RAG UI frontend, but can also be used by other clients. - **NOTE:** The default configuration requires capturing user IP address - and a header `Origin: some_string`. You can override this. - Refer to the server configuration documentation for more information. + If you have a use case not currently supported by the API, please [reach out to the MongoDB EAI team](../contact). servers: - - url: http:///api/v1 + - url: https://knowledge.mongodb.com/api/v1 security: - - ApiKeyAuth: [] + - CustomHeaderAuth: [] paths: /conversations: post: @@ -189,10 +187,15 @@ paths: components: securitySchemes: - ApiKeyAuth: + CustomHeaderAuth: type: apiKey in: header - name: X-API-KEY + name: ORIGIN + description: | + Custom header required for authentication with one of the pre-configured values: + - "https://mongodb.com" + - "https://www.mongodb.com" + - "https://learn.mongodb.com" responses: BadRequest: description: Bad Request diff --git a/docs/sidebars.ts b/docs/sidebars.ts index 9c36de387..aad4b24f3 100644 --- a/docs/sidebars.ts +++ b/docs/sidebars.ts @@ -21,6 +21,8 @@ const sidebars: SidebarsConfig = { }, "ui", "data-sources", + "datasets", + "contact", { type: "doc", label: "Framework (deprecated)", From 84bdd7a29d38b72f2567d8f35116751f9e86c849 Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Thu, 5 Jun 2025 10:46:06 -0400 Subject: [PATCH 28/29] fix merge funk --- .../src/processors/makeMongoDbReferences.test.ts | 15 +++------------ .../processors/makeDefaultReferenceLinks.test.ts | 7 +++++++ 2 files changed, 10 insertions(+), 12 deletions(-) diff --git a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts index 27242b7fe..38f3a3cf0 100644 --- a/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts +++ b/packages/chatbot-server-mongodb-public/src/processors/makeMongoDbReferences.test.ts @@ -66,12 +66,7 @@ describe("makeMongoDbReferences", () => { chunkIndex: 0, }, ] satisfies EmbeddedContent[]; - const result = makeMongoDbReferences( - chunks.map((c) => ({ - ...c, - title: c.metadata?.pageTitle, - })) - ); + const result = makeMongoDbReferences(chunks); expect(result).toEqual([ { url: "https://www.example.com/blog", @@ -119,12 +114,7 @@ describe("makeMongoDbReferences", () => { chunkIndex: 0, }, ]; - const result = makeMongoDbReferences( - chunks.map((c) => ({ - ...c, - title: c.metadata?.pageTitle, - })) - ); + const result = makeMongoDbReferences(chunks); expect(result).toEqual([ { url: "https://www.example.com/somepage", @@ -150,6 +140,7 @@ describe("addReferenceSourceType", () => { }; const result = addReferenceSourceType(reference); expect(result.metadata).toEqual({ + sourceName: reference.metadata?.sourceName, tags: reference.metadata?.tags, sourceType: "Docs", }); diff --git a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts index 6129cd311..829abc247 100644 --- a/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts +++ b/packages/mongodb-chatbot-server/src/processors/makeDefaultReferenceLinks.test.ts @@ -59,6 +59,7 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { + sourceName: "realm", tags: [], }, }, @@ -73,6 +74,7 @@ describe("makeDefaultReferenceLinks()", () => { title: "title", url: "https://mongodb.com/docs/realm/sdk/node/xyz", metadata: { + sourceName: "realm", tags: [], }, }, @@ -87,6 +89,7 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { + sourceName: "realm", tags: [], }, }, @@ -103,6 +106,7 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { + sourceName: "realm", tags: [], }, }, @@ -110,6 +114,7 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/xyz", url: "https://mongodb.com/docs/realm/sdk/node/xyz", metadata: { + sourceName: "realm", tags: [], }, }, @@ -126,6 +131,7 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/", url: "https://mongodb.com/docs/realm/sdk/node/", metadata: { + sourceName: "realm", tags: [], }, }, @@ -133,6 +139,7 @@ describe("makeDefaultReferenceLinks()", () => { title: "https://mongodb.com/docs/realm/sdk/node/xyz", url: "https://mongodb.com/docs/realm/sdk/node/xyz", metadata: { + sourceName: "realm", tags: [], }, }, From 55813989707243b9cf9011d9450b88cdee51b07c Mon Sep 17 00:00:00 2001 From: Ben Perlmutter Date: Thu, 5 Jun 2025 10:46:13 -0400 Subject: [PATCH 29/29] docs updates --- README.md | 15 ++++++++++++--- docs/docs/contact.md | 2 +- docs/docs/framework.md | 10 +++++++++- docs/docs/server/openapi.yaml | 10 ++++++---- 4 files changed, 28 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index a723338c0..008b5337b 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,17 @@ -# MongoDB Knowledge Service Framework +# MongoDB Knowledge Service This repo contains the work of the MongoDB Education AI team. ## MongoDB Knowledge Service -The MongoDB Knowledge Service lets you learn about MongoDB using generative AI. +The MongoDB Knowledge Service lets you learn about MongoDB using generative AI. To learn more about it, refer to the [MongoDB Knolwedge Service documentation](https://mongodb.github.io/chatbot) ## MongoDB Chatbot Framework (deprecated) The team building the MongoDB Knowledge Service previous developed the MongoDB Chatbot Framework. This consisted of the npm packages: - `mongodb-chatbot-server` -- `mongodb-chatbot-ui` (still used, refer to [UI](./docs/ui.md)) +- `mongodb-chatbot-ui` (still used, refer to [UI](https://mongodb.github.io/chatbot/ui)) - `mongodb-rag-core` - `mongodb-rag-ingest` @@ -19,6 +19,15 @@ The MongoDB Chatbot Framework in now deprecated. We will no longer be maintainin To learn more about the framework, refer to the the blog post [Build a Production-Ready, Intelligent Chatbot With the MongoDB Chatbot Framework](https://dev.to/mongodb/build-a-production-ready-intelligent-chatbot-with-the-mongodb-chatbot-framework-4dd). +### Why Deprecate the MongoDB Chatbot Framework? + +Since we first launched the framework a year and a half ago, there's been a lot of progress in the TypeScript ecosystem for AI frameworks. We have decided that these frameworks remove the need for the MongoDB Chatbot Framework. Additionally, supporting the framework in addition to the Knowledge Service has been a maintenance burden on our small team. + +In particular, we've been very impressed by the [Vercel AI SDK](https://ai-sdk.dev/docs/introduction). It has a great developer experience, is well maintained, and a robust feature set. We've moved most of our LLM call logic to the AI SDK. You can refer to our [mongodb/chatbot repository](https://github.com/mongodb/chatbot) to see how we're using it. For a tutorial on building with MongoDB Atlas and the AI SDK, refer to the blog post [Building a Chat Application That Doesn't Forget!](https://dev.to/mongodb/building-a-chat-application-with-mongodb-memory-provider-for-vercel-ai-sdk-56ap) by MongoDB's own Jesse Hall. + +For building more agentic applications in TypeScript, [Mastra](https://mastra.ai/en/docs) (itself built on the AI SDK), [LangGraph.js](https://langchain-ai.github.io/langgraphjs/), and the [OpenAI Agents SDK](https://openai.github.io/openai-agents-js/) all seem to be solid options. + + ## Contributing To learn how to get started contributing to the project, refer to the [Contributor Guide](./CONTRIBUTING.md). diff --git a/docs/docs/contact.md b/docs/docs/contact.md index 1daa5f6cb..07c2fc954 100644 --- a/docs/docs/contact.md +++ b/docs/docs/contact.md @@ -1,5 +1,5 @@ # Contact -- MongoDB employees can reach out to the education ai team on Slack at `#ask-education-ai` +- MongoDB employees can reach out to the Education AI team on Slack at `#ask-education-ai` - External users can create an issue on the [mongodb/chatbot GitHub repo](https://github.com/mongodb/chatbot/issues/new) \ No newline at end of file diff --git a/docs/docs/framework.md b/docs/docs/framework.md index b4b75337d..c2e6b88ea 100644 --- a/docs/docs/framework.md +++ b/docs/docs/framework.md @@ -9,4 +9,12 @@ The team building the MongoDB Knowledge Service previous developed the MongoDB C The MongoDB Chatbot Framework in now deprecated. We will no longer be maintaining it. -To learn more about the framework, refer to the the blog post [Build a Production-Ready, Intelligent Chatbot With the MongoDB Chatbot Framework](https://dev.to/mongodb/build-a-production-ready-intelligent-chatbot-with-the-mongodb-chatbot-framework-4dd). \ No newline at end of file +To learn more about the framework, refer to the the blog post [Build a Production-Ready, Intelligent Chatbot With the MongoDB Chatbot Framework](https://dev.to/mongodb/build-a-production-ready-intelligent-chatbot-with-the-mongodb-chatbot-framework-4dd). + +## Why Deprecate the MongoDB Chatbot Framework? + +Since we first launched the framework a year and a half ago, there's been a lot of progress in the TypeScript ecosystem for AI frameworks. We have decided that these frameworks remove the need for the MongoDB Chatbot Framework. Additionally, supporting the framework in addition to the Knowledge Service has been a maintenance burden on our small team. + +In particular, we've been very impressed by the [Vercel AI SDK](https://ai-sdk.dev/docs/introduction). It has a great developer experience, is well maintained, and a robust feature set. We've moved most of our LLM call logic to the AI SDK. You can refer to our [mongodb/chatbot repository](https://github.com/mongodb/chatbot) to see how we're using it. For a tutorial on building with MongoDB Atlas and the AI SDK, refer to the blog post [Building a Chat Application That Doesn't Forget!](https://dev.to/mongodb/building-a-chat-application-with-mongodb-memory-provider-for-vercel-ai-sdk-56ap) by MongoDB's own Jesse Hall. + +For building more agentic applications in TypeScript, [Mastra](https://mastra.ai/en/docs) (itself built on the AI SDK), [LangGraph.js](https://langchain-ai.github.io/langgraphjs/), and the [OpenAI Agents SDK](https://openai.github.io/openai-agents-js/) all seem to be solid options. diff --git a/docs/docs/server/openapi.yaml b/docs/docs/server/openapi.yaml index 9ec4b7fbb..4e8993629 100644 --- a/docs/docs/server/openapi.yaml +++ b/docs/docs/server/openapi.yaml @@ -192,10 +192,12 @@ components: in: header name: ORIGIN description: | - Custom header required for authentication with one of the pre-configured values: - - "https://mongodb.com" - - "https://www.mongodb.com" - - "https://learn.mongodb.com" + Can provide any value. Used to track where requests to the server + are coming from. + Alternatively, can provide `X-Request-Origin` header. + + The server's firewall has additional rules based on the `User-Agent` header. + If you would like to get access to the server, please [contact the Education AI team](../contact). responses: BadRequest: description: Bad Request