This repository provides a straightforward example of implementing Retrieval-Augmented Generation (RAG) using the Langchain framework. It is designed for developers and researchers looking to quickly build RAG systems, particularly those leveraging OpenAI's models. The project simplifies the RAG pipeline, making it accessible for learning and rapid prototyping.

How It Works

The project demonstrates a RAG pipeline that involves loading documents (e.g., PDFs), splitting them into manageable chunks, and generating embeddings using an embedding model. These embeddings are then used to retrieve relevant information, which is subsequently fed into a language model (like OpenAI's) to generate contextually aware responses. This approach enhances the factual accuracy and relevance of LLM outputs by grounding them in specific data.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Run: python rag_chat.py
• Prerequisites: OpenAI API key.
• Links: Official Quick Start

Highlighted Details

• Focuses on a simple, step-by-step RAG implementation.
• Utilizes Langchain for orchestration of the RAG components.
• Demonstrates core RAG stages: loading, splitting, embedding, and retrieval.

Maintenance & Community

• This appears to be a personal project with limited community engagement signals in the README.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

The project is presented as a simple example and may lack the robustness, scalability, and advanced features required for production environments. The absence of a specified license could pose compatibility issues for commercial use.