component-bm25-retriever
Retrieve and rank title-and-text documents with a field-aware BM25F implementation. Use for exact names, identifiers, quotations, rare terms, and queries with reliable lexical overlap.
SKILL.mdframework/skills/components/component-bm25-retriever/SKILL.md
GitHubBM25F Retriever Component
Provide the retriever capability for an Agentic RAG slot.
Interface
Input RetrievalRequest:
query: retrieval querydocuments: JSON-compatible documents containingidandtexttop_k: maximum returned documents- optional
k1andbfor saturation and body-length normalization - optional
title_boostandtitle_bfor title weighting and normalization
Defaults are top_k=10, k1=1.2, b=0.5, title_b=0.75, and
title_boost=3.0. These are the B3 parameters selected by the frozen retrieval
benchmark; callers may override every value.
Output RetrievalResult:
documents: ranked document dictionaries with BM25Fscore
Execution
Run scripts/component.py:run(inputs, context). Title and text term frequencies are
normalized against their own field lengths before being combined. This Component
performs retrieval only. Do not choose other Skills or generate an answer.