Update the LangChain retrieval agent notebook to current SDKs

[nicoalba10]

Moves the LangChain handbook's retrieval agent notebook off pinecone-client==3.1.0, langchain==0.1.1, and langchain-community==0.0.13. It uses the same pins as the LangChain integration guide: pinecone==7.3.0 (since langchain-pinecone==0.2.13 requires pinecone<8), langchain-pinecone==0.2.13, langchain-openai==1.6.7, and langchain==1.4.3.

• RetrievalQA and initialize_agent are gone in LangChain 1.x, so the agent is now create_agent with a @tool that searches the vector store, plus an InMemorySaver checkpointer for conversational memory.
• Uses PineconeVectorStore from langchain-pinecone, text-embedding-3-small, and gpt-4o-mini.
• Loads SQuAD as rajpurkar/squad, which datasets 5.x requires.
• Points the Colab and nbviewer badges at main.

Ran end to end on Python 3.12: 18,891 vectors upserted, the agent answers the Notre Dame engineering question with the knowledge base, skips the tool for 2 * 7, and uses the conversation history for the follow-up summary.

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Note

Low Risk
Single documentation notebook with no production code; changes are dependency pins, API migrations, and re-run outputs only.

Overview
Modernizes the LangChain handbook retrieval-agent notebook (08-langchain-retrieval-agent.ipynb) so it runs on current Pinecone and LangChain 1.x stacks and matches the official integration pins.

Dependency installs move from legacy pinecone-client, langchain-community, and openai pins to pinecone==7.3.0, langchain==1.4.3, langchain-pinecone, langchain-openai, and datasets==5.1.0, with shared imports consolidated into the first setup cell (%pip).

The agent flow is rewritten: RetrievalQA plus initialize_agent / ConversationBufferWindowMemory are replaced by create_agent, a @tool that runs vectorstore.similarity_search, and InMemorySaver with a fixed thread_id. Demo calls go through a new chat() helper that prints tool use per turn.

Vector and model updates: PineconeVectorStore from langchain-pinecone, text-embedding-3-small, cosine metric, pc.has_index for index creation, SQuAD loaded as rajpurkar/squad, and the chat model set to gpt-4o-mini. Colab/nbviewer links and handbook URLs point at main and the updated learn-series paths; notebook outputs are refreshed from a Python 3.12 end-to-end run.

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