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Add hybrid retrieval (TF-IDF + embeddings), OCR, A/B eval, and answer trust/UX
Aissugur Kurganbekov +1: Claude Opus 4.8Smarter retrieval: - Hybrid dense+lexical retrieval: fuse TF-IDF with fastembed vectors via Reciprocal Rank Fusion (retrieval.hybrid_rank), wired into answering. The abstention gate still runs on TF-IDF so grounding stays conservative. Graceful fallback to pure TF-IDF when fastembed/the model is unavailable. - Store chunk vectors on Chunk.embedding (new column); idempotent ensure_schema() migration adds it to existing SQLite DBs on startup. - OCR fallback for scanned/image PDFs (PyMuPDF + pytesseract), graceful when the Tesseract binary is absent; tesseract-ocr added to the Docker image. - A/B eval harness (app.ab_eval / eval_cli --ab) comparing TF-IDF vs hybrid. Answer trust / UX: - answer_question now returns answer metadata (confidence, question_type, top_score, term coverage, chunk counts, latency); AskResponse.metadata and Citation.match_text added. - Frontend: confidence meter, exact source-span highlighting, and a printable one-page evidence report. Verified: 4…f65a6b3
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FinanceFlow AI: source-grounded RAG Q&A over finance documents
Aissugur Kurganbekov +1: Claude Opus 4.8 (1M context)Full-stack MVP: FastAPI + SQLite backend, React/Vite frontend. - Upload PDF/TXT -> chunk -> TF-IDF retrieval -> focused extractive answer - Two abstention gates + citations for anti-hallucination - Golden-dataset evaluation (UI + CLI) with coverage/abstention/evidence metrics - One-click demo mode, sample-question chips, clean B2B dashboard - Logging, global exception handler, validated/typed API with Swagger docs - 19 pytest tests + GitHub Actions CI - Dockerfiles, docker-compose, Makefile Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>fc5156b