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BM25

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Headlines · 3

  1. Building Domain Agents from RAG to Agentic Research

    This tutorial presents a progression from basic RAG to domain-specific agents and deeper research workflows. It covers document ingestion, chunking, embedding, hybrid retrieval, reranking, dynamic routing, ReAct loops, query planning, evidence gathering, memory, and answer verification, with a travel-policy example throughout. The article also compares RAG with fine-tuning and frames agentic search, integrated research, and full-stack AI research as progressively higher-complexity deliverables.

    掘金 · 人工智能 · 🔥 3
  2. kb replaces default RRF fusion with weighted hybrid retrieval

    The kb project developer says relevant cross-language documents were being retrieved by vector search but then eliminated when BGE-M3 and BM25 results were merged with Reciprocal Rank Fusion. The default hybrid path was changed to weighted_fuse, which normalizes each list and divides by the number of retrieval methods that actually returned a document; relevance thresholds, larger candidate pools, and regression tests were also added. The developer retained RRF for comparisons and explicitly noted that no large-scale benchmark was run.

    掘金 · 人工智能 · 🔥 2
  3. ETF Agent Maps the RAG Evidence-Delivery Chain

    An ETF Agent engineering report organizes RAG into four interacting chains: knowledge construction, online retrieval, evidence consumption and delivery, and evaluation and governance. In the checked private knowledge path, lexical retrieval can use BM25 when the PostgreSQL pg_search extension, a BM25 index, and configuration are available; otherwise it uses PostgreSQL full-text search. Code support alone does not show that any particular query used BM25. The public Open ETF Agent repository offers a smaller teaching implementation with restricted lexical retrieval, original-page readback, and source-linked reporting on synthetic documents, rather than the full private architecture. An RQ-08 evaluation fixed three source versions to one candidate set, compared the ranking of 306 real indexed chunks, and observed actual Knowledge calls. However, the chunks lacked complete manual relevance labels, and some reads confirmed only that a location was found. The results therefore do not establish improved real Recall or end-to-end financial-answer accuracy.

    掘金 · 人工智能 · 🔥 0

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xerj• Pinned

xerj: A local-first search engine and MCP server for AI agents.

What problem it solves Coding agents often need to read many files into their context to locate relevant code and documentation. Projects also need a shared way to search code, structured data, logs, and documents, retain agent memory, and expose those capabilities through familiar APIs and MCP clients. Key features Automatic folder indexing — Point the CLI at a folder and XERJ detects file types,…

github.comhttps://github.com/xerj-org/xerj