This repository provides an updated tutorial for building Retrieval-Augmented Generation (RAG) systems using Langchain, specifically targeting developers and researchers who want to integrate local Large Language Models (LLMs) and databases. It offers a practical, hands-on approach to implementing a robust RAG pipeline with improved data handling and testing methodologies.

How It Works

The tutorial guides users through setting up a RAG pipeline that leverages local LLMs, avoiding reliance on external APIs. It emphasizes efficient data ingestion and retrieval from a vector database, demonstrating how to manage and update the knowledge base. The approach focuses on modularity and testability, enabling users to build and validate their RAG systems effectively.

Quick Start & Requirements

• Install via pip install -r requirements.txt.
• Requires Python 3.10+.
• Local LLM setup (e.g., Ollama) and a vector database (e.g., ChromaDB) are necessary.
• Official documentation: https://github.com/pixegami/rag-tutorial-v2

Highlighted Details

• Focus on local LLM integration for privacy and cost control.
• Enhanced database update mechanisms for dynamic knowledge bases.
• Inclusion of testing strategies for RAG pipeline validation.
• Practical examples for common RAG challenges.

Maintenance & Community

The project is maintained by Pixegami. Community interaction is encouraged via GitHub issues and discussions.

Licensing & Compatibility

The project is released under the MIT License, permitting commercial use and integration with closed-source applications.

Limitations & Caveats

The tutorial assumes a foundational understanding of Python and LLM concepts. While it focuses on local LLMs, performance will be dependent on the user's hardware capabilities.