feat: add Jina neural reranker — LoCoMo QA 53.2% → 57.3%#61
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- Add checkpoint/resume + parallelism + retry to ingestion script - Add LLMError and content filter handling to retryable errors - Add 60s timeout to Azure client to prevent silent hangs - Add eval_qa_rag_baseline.py with resume support - Add concurrency=5 + resume + content filter to eval_qa_accuracy.py - Add 10 regression tests for _parse_batch_result - Update .gitignore to exclude eval/checkpoints/ Results: pipeline 46.6%, RAG baseline 44.4% across all 10 LoCoMo convs Temporal gain: +31.5pp (pipeline vs RAG)
Results (n=1540, all 10 LoCoMo convs): - Pipeline v2: 46.6% overall (+2.2pp over RAG baseline 44.4%) - Temporal: +32.4pp over RAG (57.3% vs 24.9%) — dominant gain - Open-domain regression confirmed architectural, not two-pass related Changes: - Add per-category two-pass gate (category != 4) to eval_qa_accuracy.py - Add two_pass_used field to per-pair results for paper analysis - Update result.md with final all-10 benchmark numbers and SOTA comparison Next: BM25 + RRF, cross-encoder reranker, temporal date filtering
- Integrate Jina reranker for neural reranking after semantic search - Update LoCoMo benchmark results: 53.2% → 57.3% overall QA accuracy (n=1540, all 10 convs) - Update uv.lock with new dependencies - Add cl.md to .gitignore
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Summary
Test plan
make lintpassesmake testpasses