RAG pipeline now includes a cross-encoder rerank step between retrieval
and generation. The LLM re-sorts top-K results by relevance before
they become context. Falls back to original order if model output is
unparseable (~5% with 7B models). Also improved the generation prompt
to be domain-aware ("staffing database") and request specific citations.
Fixed 4 catalog manifests with bucket="data" (pre-federation leftover)
that poisoned the entire DataFusion query context on startup. The
"users", "lab_trials", "meta_runs", and "new_candidates" datasets
now correctly reference bucket="primary". This bug was surfaced by
the quality evaluation pipeline — wouldn't have been found by
structural tests alone.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
61 lines
1.4 KiB
JSON
61 lines
1.4 KiB
JSON
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"id": "5a35646f-b357-4636-b88f-203aba0d7435",
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"name": "people_test",
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"schema_fingerprint": "bba35b6cd1e399dc0504c760c767ab391f7ed81a41573c15b3bd4eb0e3bcc382",
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"objects": [
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{
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"bucket": "testing",
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"key": "datasets/people_test.parquet",
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"size_bytes": 1402,
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"created_at": "2026-04-16T13:47:32.669517398Z"
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}
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"created_at": "2026-04-16T13:47:32.669518210Z",
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"updated_at": "2026-04-16T13:47:32.669787451Z",
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"description": "",
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"owner": "",
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"sensitivity": "pii",
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"columns": [
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{
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"name": "person_id",
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"data_type": "Utf8",
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"is_pii": false
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{
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"name": "name",
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"sensitivity": "pii",
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"description": "",
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"is_pii": true
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{
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"lineage": {
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} |