BCEmbedding: Bilingual and Crosslingual Embedding for RAG 最新、最详细的bce embedding base v1相关信息，请移步（The latest "Updates" should be checked in）： GitHub 主要特点(Key Features)： 中英双语，以及中英跨语种能力(Bilingual and Crosslingual capability in English and Chinese)； RAG优化，适配更多真实业务场景(RAG adaptation for more domains, including Education, Law, Finance, Medical, Literature, FAQ, Textbook, Wikipedia, etc.)； 方便集成进langchain和llamaindex(Easy integrations for langchain and llamaindex in BCEmbedding )。 EmbeddingModel 不需要“精心设计”instruction，尽可能召回有用片段。 (No need for "instruction") 最佳实践（Best practice） ：embedding召回top50 100片段，reranker对这50 100片段精排，最后取top5 10片段。（1. Get top 50 100 passages with bce embedding base v1 for " recall "； 2. Rerank passages with bce reranker base v1 and get top 5 10 for " precision " finally. ） News: BCEmbedding 技术博客（ Technical Blog ）: 为RAG而生 BCEmbedding技术报告 Related link for RerankerModel : bce reranker base v1 Third party Examples: RAG applications: QAnything, ragflow, HuixiangDou, ChatPDF. Efficient inference framework: ChatLLM.cpp, Xinference, mindnlp (Huawei GPU, 华为GPU). Click to Open Contents 🌐 Bilingual and Crosslingual Superiority 💡 Key Features 🚀 Latest Updates 🍎 Model List 📖 Manual In…
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