Overview
bRAG-langchain is a practical Retrieval-Augmented Generation (RAG) repository maintained by BragAI. It provides a collection of executable Jupyter notebooks that guide users through data loading and chunking, embedding generation, vector store setup (e.g. ChromaDB, Pinecone), multi-query retrieval, reranking, and assembling RAG pipelines.
Key Features
- Structured notebooks from introductory to advanced topics for step-by-step reproducible experiments.
- Demonstrations of multi-query retrieval, Reciprocal Rank Fusion (RRF), and reranking strategies to improve retrieval quality.
- Support examples for common embedding providers and vector stores, making it easy to adapt to different deployment environments.
Use Cases
- Education and training: hands-on resource for learning RAG architecture and implementation.
- Rapid prototyping: build and test RAG-based QA systems and retrieval services using provided notebooks.
- Retrieval strategy research: compare retrieval, reranking, and multi-model setups for downstream generation quality.
Technical Highlights
- Executable Jupyter notebooks emphasizing reproducibility and experiment reporting.
- Integrations with OpenAI, Cohere, Pinecone, Chroma demonstrated via environment-configured examples.
- Multiple retrieval and reranking methods included as references for benchmarking and productionization.