How to Build RAG Apps with BRAG LangChain Notebooks

BRAG LangChain provides five notebooks that walk from basic RAG setups to advanced multi-query, routing, indexing, and reranking techniques. This compact, hands-on guide helps developers learn and build real RAG apps without hunting through scattered examples. For a broader view of how retrieval systems fit into agentic workflows, see how to use Chroma Context-1 for local agentic search.

What It Is

BRAG LangChain is a curated collection of notebooks that demonstrate Retrieval-Augmented Generation workflows using LangChain building blocks. The repo escalates from a simple RAG demo to advanced topics like multi-query orchestration, custom indexing strategies, routing, and reranking, making it a useful study corpus for both beginners and practitioners.

Want to dive deeper? Here is another post you shouldnt miss: Awesome LLM Apps, Curated Open-Source LLM Projects and How to Use Activepieces for No-Code AI Agent Automation.

Available Notebooks

• Intro to RAG — Basic retrieval pipeline and single-document QA
• Multi-query — Send parallel queries and aggregate results
• Routing — Route queries to specialized indices or models
• Indexing — Custom index strategies for long documents
• Reranking — Re-rank candidate passages for higher precision

Run the notebooks on a small dataset first, then scale to representative documents. Compare simple embedding-based retrieval to the advanced routing and reranking notebooks to see the trade-offs. These notebooks are ideal for engineers who want to prototype RAG systems quickly and compare retrieval strategies side by side.

Why This Matters

This repository consolidates practical RAG patterns into one place and teaches comparison between retrieval strategies, not just embeddings. It helps bridge the gap between toy demos and production RAG systems. For another approach to agent-powered automation, see how to use Activepieces for no-code AI agent automation.

Try It Locally

1. Clone the repo and inspect the notebooks: git clone https://github.com/bragai/bRAG-langchain.git
2. Run them in Jupyter, Colab, or nteract, and adapt indexing settings to your data
3. Use the reranking and routing notebooks to evaluate against a baseline vector search

Project link:
https://github.com/bragai/bRAG-langchain