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6c9a545a06
Wire the FlashRank reranker subsystem from #1005 into Document-RAG: after vector retrieval, over-fetch a wider candidate pool, rerank with the cross-encoder, and keep the top doc_limit chunks for synthesis. Per maintainer review, the fetch and select sizes are two caller-controlled limits rather than one internal heuristic: - doc_limit: chunks selected into the synthesis prompt (unchanged meaning). - fetch_limit: candidate pool pulled from the vector store before reranking. 0 = derive (OVERFETCH_FACTOR x doc_limit); values below doc_limit are raised to it. Lets the caller control how hard the reranker has to work. Details: - schema: DocumentRagQuery.fetch_limit (additive, backward compatible). - document_rag.py / rag.py: fetch_limit resolved in the processor (mirrors doc_limit); the core applies the heuristic default and derives synthesis provenance from the chunk-selection focus when reranking ran. - provenance: tg:ChunkSelection focus stage (mirrors tg:EdgeSelection). - request translator + client SDKs + CLI: fetch-limit / --fetch-limit, threaded exactly like doc_limit and the GraphRAG limits. - tests: no-op identity, over-fetch/narrow, explicit fetch_limit, heuristic default, floor-at-doc_limit, provenance lineage, cross-repo topic wiring. Reranking is skipped byte-identically when no reranker role is wired. Requires the companion trustgraph-templates change wiring the reranker topics into the document-rag flow (mirrors #279 for GraphRAG). |
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| .. | ||
| __init__.py | ||
| agent.py | ||
| base.py | ||
| collection.py | ||
| config.py | ||
| diagnosis.py | ||
| document_loading.py | ||
| embeddings.py | ||
| embeddings_query.py | ||
| flow.py | ||
| iam.py | ||
| knowledge.py | ||
| library.py | ||
| metadata.py | ||
| nlp_query.py | ||
| primitives.py | ||
| prompt.py | ||
| reranker.py | ||
| retrieval.py | ||
| rows_query.py | ||
| sparql_query.py | ||
| structured_query.py | ||
| text_completion.py | ||
| tool.py | ||
| triples.py | ||