Replaces pure-dense top-8 retrieval with a three-stage pipeline:
- BM25 (tsvector + websearch_to_tsquery) and dense (pgvector) in parallel,
fused with Reciprocal Rank Fusion
- Optional type filter driven by classify_retrieval_intent() so questions
about prior conversations don't pull documents and vice versa
- Cross-encoder rerank (ms-marco-MiniLM-L-6-v2) over RRF candidates before
taking final top-N
Also adds scripts/reindex_docx_pptx.py — one-off re-ingest used to recover
table/header/text-box content in docx and pptx after the 93c0d89 extractor
upgrade — and scripts/test_retrieval.py to exercise the new pipeline against
representative queries.
Schema: requires GIN index on to_tsvector('english', document) (already
created out-of-band via psql since Apache AGE in shared_preload_libraries
blocks ALTER TABLE on this database).
