LangChain Handbook

Overview

A collection of Jupyter Notebooks that teach LangChain usage with Pinecone vector databases. It provides hands-on examples for building retrieval-augmented generation (RAG) applications. The handbook is maintained by the Pinecone community and has over 3,000 GitHub stars.

Best for

Best for
Developers learning LangChain and Pinecone for RAG applications

Use cases

• Learning LangChain fundamentals through interactive notebooks
• Building RAG pipelines with Pinecone as the vector store
• Prototyping LLM applications with step-by-step code examples

Notes

A collection of Jupyter Notebooks that teach LangChain usage with Pinecone vector databases. It provides hands-on examples for building retrieval-augmented generation (RAG) applications. The handbook is maintained by the Pinecone community and has over 3,000 GitHub stars.

3,024 stars on GitHub. Last updated 2026-05-21. Licensed MIT.

Use cases

• Learning LangChain fundamentals through interactive notebooks
• Building RAG pipelines with Pinecone as the vector store
• Prototyping LLM applications with step-by-step code examples

Pros

• Free and open-source with practical, runnable examples
• Well-structured for beginners to intermediate developers
• High community trust indicated by 3,024 stars

Cons

• Tied exclusively to Pinecone vector database
• Jupyter Notebook format not directly deployable to production
• Requires local setup and API keys for full use

Indexed from awesome-langchain and enriched against its public facts.