style(ruff): make files compliant.

Author: pabroux

langchain/data_extractor.py

@@ -1,12 +1,11 @@
 import os
 from typing import List, Optional
 
+from langchain.chat_models import init_chat_model
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.utils.function_calling import tool_example_to_messages
 from pydantic import BaseModel, Field
 
-from langchain.chat_models import init_chat_model
-
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 
 llm = init_chat_model("gpt-4o-mini", model_provider="openai")

langchain/simple_llm_application.py

@@ -1,11 +1,10 @@
 import os
 
+from langchain.chat_models import init_chat_model
 from langchain_core.messages import HumanMessage, SystemMessage
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 
-from langchain.chat_models import init_chat_model
-
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 
 

langchain/text_classifier.py

@@ -1,11 +1,10 @@
 import os
 
+from langchain.chat_models import init_chat_model
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 from pydantic import BaseModel, Field
 
-from langchain.chat_models import init_chat_model
-
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 
 llm = init_chat_model("gpt-4o-mini", model_provider="openai")

langgraph/agents/agent.py

@@ -1,14 +1,13 @@
 import os
 from typing import Annotated
 
-from langchain_tavily import TavilySearch
-from typing_extensions import TypedDict
-
 from langchain.chat_models import init_chat_model
+from langchain_tavily import TavilySearch
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import START, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
+from typing_extensions import TypedDict
 
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 os.environ["TAVILY_API_KEY"] = input("Tavily API key: ")
@@ -68,7 +67,7 @@ def chatbot(state: State):
 def stream_graph_updates(user_input: str):
     for event in graph.stream(
         {"messages": [{"role": "user", "content": user_input}]},
-        config, # setup the memory thread to use
+        config,  # setup the memory thread to use
     ):
         for value in event.values():
             print("Assistant:", value["messages"][-1].content)

langgraph/agents/agent_human_assistance.py

@@ -1,17 +1,16 @@
 import os
 from typing import Annotated
 
+from langchain.chat_models import init_chat_model
 from langchain_core.messages import ToolMessage
 from langchain_core.tools import InjectedToolCallId, tool
 from langchain_tavily import TavilySearch
-from typing_extensions import TypedDict
-
-from langchain.chat_models import init_chat_model
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import START, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
 from langgraph.types import Command, interrupt
+from typing_extensions import TypedDict
 
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 os.environ["TAVILY_API_KEY"] = input("Tavily API key: ")

langgraph/agents/agent_time_travel.py

@@ -1,14 +1,13 @@
 import os
 from typing import Annotated
 
-from langchain_tavily import TavilySearch
-from typing_extensions import TypedDict
-
 from langchain.chat_models import init_chat_model
+from langchain_tavily import TavilySearch
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import START, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
+from typing_extensions import TypedDict
 
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 os.environ["TAVILY_API_KEY"] = input("Tavily API key: ")

langgraph/rag/rag.py

@@ -2,20 +2,20 @@
 from typing import Annotated, List, Literal
 
 import bs4  # BeautifulSoup to parse HTML
+from langchain.chat_models import init_chat_model
 from langchain_community.document_loaders import WebBaseLoader
 from langchain_core.documents import Document
 from langchain_core.vectorstores import InMemoryVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langgraph.graph import START, StateGraph
 from typing_extensions import TypedDict
 
 # LangChain Hub is a centralized platform for uploading,
 # browsing, pulling, and managing prompts to help developers
 # discover and share polished prompt templates for various large
 # language models (LLMs)
 from langchain import hub
-from langchain.chat_models import init_chat_model
-from langgraph.graph import START, StateGraph
 
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 

langgraph/rag/rag_delegation.py

@@ -1,20 +1,20 @@
 import os
 from typing import List
 
-import bs4 # BeautifulSoup to parse HTML
+import bs4  # BeautifulSoup to parse HTML
+from langchain.chat_models import init_chat_model
 from langchain_community.document_loaders import WebBaseLoader
 from langchain_core.documents import Document
 from langchain_core.messages import SystemMessage
 from langchain_core.tools import tool
 from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from typing_extensions import TypedDict
-
-from langchain import hub
-from langchain.chat_models import init_chat_model
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, MessagesState, StateGraph
 from langgraph.prebuilt import ToolNode, create_react_agent, tools_condition
+from typing_extensions import TypedDict
+
+from langchain import hub
 
 os.environ["OPENAI_API_KEY"] = input("OpenAI API key: ")
 
@@ -56,7 +56,9 @@
 
 
 # Turn the retrieve step into a tool call option for the LLM
-@tool(response_format="content_and_artifact") # make the model see only the first output
+@tool(
+    response_format="content_and_artifact"
+)  # make the model see only the first output
 def retrieve(query: str):
     """Retrieve information related to a query."""
     retrieved_docs = vector_store.similarity_search(query, k=2)