Allora RAG Information Bot

We're building an Agentic RAG Chatbot powered by GPT-4 to answer questions about Allora Labs using content from our documentation, research papers, and codebase. The chatbot is accessible via Slack, Discord, and our documentation website.

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Documention needs to be updated (4/23/25)

Description

Our Q&A chat project uses a LlamaCloud vector database called allora_production. Data is converted into 3072-dimensional vectors using the OpenAI text-embedding-3-large model. Our LlamaIndex connects this database with GPT-4o. When a user submits a question via a FastAPI POST endpoint, the following happens:

1. Receive Question: The user's question is received as plain text.
2. Convert to Vector: The question is embedded into a vector.
3. Generate Response: GPT-4o, informed by context retrieved from LlamaCloud, generates a helpful answer.

This workflow effectively bridges the user's query with GPT-4o by gathering relevant background information from our stored context.

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Training Data (Updated 2/20/25)

Major Research Papers

• Allora White Paper
• Merit-based Sortition in Decentralized Systems
• Optimizing Decentralized Online Learning for Supervised Regression and Classification Problems

Major Repos/Readmes

• allora-chain
• allora-offchain-node
• coin prediction worker
• coin prediction reputer
• autogluon-prediction
• allora-docs

Other Documentation

• CometBFT Docs
• Cosmos Docs

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Updating the Knowledge Context

To add new documentation, you must add additional data embeddings to the allora_production Llamacloud index. Follow these steps:

1. Split & Vectorize:
   Use the LangChain library (or another method that adheres to 3072 dimensions and uses text-embedding-3-large) to split and vectorize your data.

2. Store in Pinecone:
   Ensure the vectorized data is stored in Pinecone.

3. Automatic Update:
   Any new embeddings are automatically included in responses. Verify insertion by searching for specific keys (e.g., an ID or source) in Pinecone.

Note: Adjust chunk_size and chunk_overlap to control the granularity of your data representation.

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Example: Splitting and Vectorizing PDF

Process Overview:

• Load PDF:
  Uses PyMuPDFLoader to load a PDF from a local path.

• Split Document:
  Uses RecursiveCharacterTextSplitter to break the document into chunks.

• Generate Embeddings & Vector Store:
  Creates text embeddings with OpenAI and stores them in a Pinecone vector store.

Example: Testing the Allora Chatbot Server

Process Overview

• Define the Server URL:
  Set the endpoint URL for your chatbot.

• Prepare the Request Payload:
  Create a JSON object with your question.

• Send the Request:
  POST the payload using Python's requests library.

• Handle the Response:
  Print the chatbot's response and its sources.

import requests

# URL of the chatbot endpoint. Replace with your actual server URL.
url = "https://your-chatbot-endpoint.com/api/chat"

# The payload containing the message/question for the chatbot.
payload = {
    "message": "What makes Allora's reward distribution different than others?"
}

try:
    # Send a POST request to the server with the JSON payload.
    response = requests.post(url, json=payload)
    
    # Raise an error if the request was unsuccessful.
    response.raise_for_status()
    
    # Parse the JSON response.
    data = response.json()
    
    # Output the chatbot response and its sources.
    print("Response:")
    print("Message:", data.get("response"))
    print("Sources:", data.get("sources"))

except requests.exceptions.HTTPError as http_err:
    print(f"HTTP error occurred: {http_err}")
except Exception as err:
    print(f"Other error occurred: {err}")

For this particular example, you should expect an output similar to:

Response:
Message: Allora's reward distribution is differentiated and based on a carefully designed incentive mechanism that aligns with the interests of the network and allows for continual learning and improvement.
Sources: ['/markdown_files4/pages/devs/reference/module-accounts.mdx', '/markdown_files4/pages/home/overview.mdx']

Future Updates

When new data is added, update this document to keep track of changes and ensure the knowledge context remains current.