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A technical flowchart illustrating a hybrid retrieval system using dense (vector embeddings) and sparse (BM25) paths, combining results from both via an EnsembleRetriever to yield top-ranked documents.

A technical flowchart illustrating a hybrid retrieval system using dense (vector embeddings) and sparse (BM25) paths, combining results from both via an EnsembleRetriever to yield top-ranked documents.

根据这个画流程图用户输入 Query │ ├─────────────── 分成两条平行路径 ────────────────┐ │ │ Dense 路径 (向量) Sparse 路径 (BM25) │ │ 加载 Chroma (已预存 embedding) 读取原始 txt → clean → 重新切分 chunks │ │ Prefixed Embed Query BM25Retriever.from_documents(chunks) │ │ Chroma.as_retriever(k=8) bm25_retriever.k = 8 │ │ 返回 Top-8 Documents 返回 Top-8 Documents │ │ └─────────────── 合并 ────────────────┘ │ EnsembleRetriever (weights=[0.4 BM25, 0.6 Dense]) │ 返回融合后的 Top-8 │ 显示结果 Mehr sehen

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