bRAG-langchain

Introduction to bRAG-langchain

bRAG-langchain is a comprehensive repository designed to help developers build their own Retrieval-Augmented Generation (RAG) applications. This project provides a series of Jupyter notebooks that guide users through the process of setting up and experimenting with RAG, from basic concepts to advanced implementations.

Key Features:

• Hands-on Notebooks: Each notebook offers detailed instructions and code examples for various aspects of RAG.
• Multi-Query Techniques: Learn how to enhance response relevance through multi-querying methods.
• Customizable RAG Builds: Start with boilerplate code and customize it to fit specific needs.
• Environment Setup: Clear instructions for setting up the development environment across different operating systems (macOS, Linux, Windows).
• API Integration: Guidance on integrating with various APIs, including OpenAI and Pinecone.

Benefits:

• Comprehensive Learning: Ideal for both beginners and advanced users looking to deepen their understanding of RAG.
• Community Support: Open-source project with opportunities for collaboration and feedback.
• Real-World Applications: Practical examples and use cases to help users implement RAG in their projects.

Highlights:

• Focus on scalability and optimization of RAG systems.
• Upcoming notebooks for advanced integrations, such as MistralOCR.
• Encouragement for community contributions and feedback.