This repository provides examples of integrating Chroma, a vector database, with LangChain, a framework for building LLM applications. It targets developers looking to implement document question-answering and persistent vector storage within their LLM projects.

How It Works

The examples demonstrate how to leverage Chroma's capabilities for efficient similarity search and data storage, enabling natural language querying over custom document sets. This integration allows for the creation of sophisticated RAG (Retrieval Augmented Generation) systems.

Quick Start & Requirements

• Install via pip: pip install chromadb langchain
• Requires Python 3.7+
• See notebook examples for detailed usage.

Highlighted Details

• Demonstrates Document Question-Answering with Chroma and LangChain.
• Provides examples for using a persistent Chroma database with LangChain.

Maintenance & Community

This repository appears to be a collection of examples rather than an actively maintained project with a dedicated community.

Licensing & Compatibility

The repository does not explicitly state a license. Users should assume all rights are reserved or consult the underlying libraries (Chroma and LangChain) for their respective licenses.

Limitations & Caveats

This repository serves as a collection of illustrative examples and does not represent a standalone library or framework. Users will need to integrate these patterns into their own LangChain projects.