bRAG-langchain and complex-RAG-guide
One tool is a popular RAG application builder with Langchain integration, while the other is a nascent guide for building complex, production-ready RAG systems, making them **complements** where the guide could leverage or inform the use of the builder for its implementations.
About bRAG-langchain
bragai/bRAG-langchain
Everything you need to know to build your own RAG application
Structured as progressive Jupyter notebooks using LangChain, covering foundational vector storage with ChromaDB/Pinecone, multi-query retrieval, semantic routing, and advanced techniques like RAPTOR and ColBERT token-level indexing. Demonstrates end-to-end optimization strategies including reciprocal rank fusion, Cohere re-ranking, and self-RAG approaches, with integration points for OpenAI embeddings, LangSmith tracing, and metadata-filtered vector stores.
About complex-RAG-guide
Megaboy12346/complex-RAG-guide
Build a robust, production-ready RAG system with effective data preparation, anonymization, and LLM integration. Explore best practices and metrics. 🐙📦
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