This repository curates a collection of resources and examples for building Retrieval-Augmented Generation (RAG) systems. It targets developers and researchers looking to implement RAG pipelines using popular frameworks like Langchain and LlamaIndex, with a focus on vector databases and embedding models. The project aims to provide practical, runnable examples for various RAG use cases.

How It Works

The project showcases RAG implementations through a variety of notebooks and code examples, demonstrating different architectural choices and library integrations. It covers core RAG concepts such as document retrieval, embedding generation, and response synthesis, often leveraging vector stores like Qdrant. The examples highlight techniques like hybrid search, multimodal RAG, and agentic RAG, often utilizing Langchain Expression Language (LCEL) for pipeline construction.

Quick Start & Requirements

Examples are provided as notebooks, with many offering direct Colab links for easy execution. Specific requirements vary per notebook but generally include Python, and libraries like Langchain, LlamaIndex, HuggingFace Transformers, and Qdrant. Some advanced examples may require specific LLM API keys or hardware configurations.

Highlighted Details

• Demonstrates RAG with open-source LLMs and frameworks like Langchain and HuggingFace.
• Features advanced techniques including hybrid search (miniCOIL), multimodal RAG (Gemini), and agentic RAG.
• Includes examples for specific use cases like chatting with websites, PDFs, and patent documents.
• Covers observability and evaluation of RAG systems using tools like BeyondLLM.

Maintenance & Community

The project lists several libraries and frameworks used, including Langchain, LlamaIndex, Qdrant, HuggingFace, and Gemini. Community discussion is encouraged via a provided link.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README snippet. Compatibility for commercial use or closed-source linking would depend on the licenses of the individual libraries and models used in the examples.

Limitations & Caveats

Some examples mention deprecated frameworks (e.g., GenAI Stack). The breadth of examples suggests varying levels of maturity and maintenance across different RAG implementations within the repository.