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Soba Labs guide shows enterprise RAG on LangChain with retrieval fixes first

AIKacper Klusek of Soba Labs recommends fixing retrieval before changing the model when building enterprise RAG on LangChain. He points to a retrieval chain of contextual embeddings, BM25 and a reranker, which Anthropic's published evaluation credits with cutting failed retrievals by 67%.
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LangChain Community Spotlight: Enterprise RAG on LangChain 🔎📚

Kacper Klusek of Soba Labs shows how to build enterprise RAG on LangChain by fixing retrieval before you touch the model. In Anthropic's published evaluation, the retrieval chain he recommends (contextual embeddings, BM25 and a reranker) cut failed retrievals by 67%. LangSmith online evals then catch the decay that starts when your documents change.

📖 Read the full guide: https://sobalabs.ai/blog/enterprise-rag-on-langchain/

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LangChain · x.comPublished · added here

Original title: LangChain Community Spotlight: Enterprise RAG on LangChain 🔎📚

Topics

Deployment & engineeringTutorials & how-to

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#RAG