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).
