LangChain 社区聚焦：LangChain 上的企业级 RAG 🔎📚 Soba Labs 的 Kacper Klusek 展示了如何在 LangChain 上构建企业级 RAG——在动模型之前先修好检索。在 Anthropic 公布的评测中，他推荐的检索链（上下文嵌入向量、BM25 和重排序器）将检索失败率降低了 67%。随后 LangSmith 在线评测能捕捉到文档变化后开始出现的性能衰减。 📖 阅读完整指南：https://sobalabs.ai/blog/enterprise-rag-on-langchain/
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/
来源：LangChain · x.com