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feat: Stage 4 — entity extraction + knowledge graph + KG-expanded RAG
KevinIsInCoding +1: Claude Sonnet 4.6Entity extraction pipeline: - extraction/normalizer.py: HGNC alias table (TDP-43→TARDBP, BIIB067→tofersen, etc.), compound/mechanism alias maps, CanonicalRegistry for persistent canonical_ids.json - extraction/extractor.py: Claude Sonnet NER, 10 papers/batch, uses full_text over abstract when available, resumable via .progress.json, rate-limit retry - scripts/extract_entities.py: CLI with --max for test runs, --reset to restart Knowledge graph: - graph/builder.py: NetworkX DiGraph, seed ALS entities (genes/compounds/mechanisms), upsert nodes+edges with running-average confidence, trial→target links - graph/query.py: expand_query_entities() (BFS, confidence-filtered), find_trials_for_entities() (KG-linked trial lookup) - graph/serializer.py: pickle (fast) + JSON (human-readable) save/load - scripts/build_graph.py: CLI runner Agent upgrade (KG+RAG): - agents/research_agent.py: step 1 = KG expansion of query entities before RAG; "tofersen" now surfaces SOD1 papers even if not …42fb3af -
feat: initial project scaffold and Stage 2 ingestion pipeline
KevinIsInCoding +1: Claude Sonnet 4.6- Project foundation: config, models, tools, llm, logging, prompts - Tool schemas: extract_entities.json, search_landscape.json - Stage 2 ingestion: - ingestion/pubmed.py: PubMed Entrez client (batch fetch, rate-limit aware) - ingestion/pmc.py: PMC XML full-text fetcher (JATS section parser) - ingestion/semantic_scholar.py: citation count enrichment via S2 batch API - ingestion/clinicaltrials.py: ClinicalTrials.gov v2 ALS trials client - scripts/ingest_papers.py: orchestrates PubMed + PMC + citations - scripts/ingest_trials.py: fetches recruiting ALS trials - Stub modules for Stage 3-5 (rag/, graph/, agents/, app.py, main.py) - MIT License, .gitignore (excludes data/, .env, logs/) - CLAUDE.md architecture guide Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>0ccfe4a