Langchain Semantic Search

Overview

A Jupyter Notebook project that indexes Google Drive files and enables semantic search using GPT-3 and LangChain. It processes documents into embeddings and retrieves relevant content via natural language queries.

Best for

Best for
Developers exploring semantic search on personal Google Drive files with LangChain and GPT-3

Use cases

• Searching personal or team Google Drive documents with natural language
• Building a proof-of-concept semantic search over local file collections
• Experimenting with LangChain and GPT-3 for document retrieval workflows

Notes

A Jupyter Notebook project that indexes Google Drive files and enables semantic search using GPT-3 and LangChain. It processes documents into embeddings and retrieves relevant content via natural language queries.

44 stars on GitHub. Last updated 2023-02-07.

Use cases

• Searching personal or team Google Drive documents with natural language
• Building a proof-of-concept semantic search over local file collections
• Experimenting with LangChain and GPT-3 for document retrieval workflows

Pros

• Simple, focused implementation for Google Drive integration
• Leverages LangChain’s modular components for quick prototyping
• Open source with a clear, readable notebook format

Cons

• Limited to Google Drive as a data source
• Requires GPT-3 API access and associated costs
• Not production-ready; lacks error handling and scalability features

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