bRAG-langchain and Learn_RAG_from_Scratch_LLM
These are complementary learning resources where the first provides a comprehensive production-ready RAG implementation framework, while the second offers a beginner-focused tutorial for understanding RAG fundamentals from scratch before applying them with the more advanced tooling.
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 Learn_RAG_from_Scratch_LLM
simranjeet97/Learn_RAG_from_Scratch_LLM
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
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