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feat(trials): replace hardcoded entity regex with LLM extraction
KevinIsInCoding +1: Claude Sonnet 4.6- Remove _KNOWN_TARGETS regex list — cannot handle novel compounds like AMX0114 - Add extract_target_entities_llm(): batches 10 trials per Claude Haiku call, extracts biological target (gene/protein) not drug name, canonicalizes via extraction/normalizer. AMX0114 now correctly maps to TARDBP. - Add data/tools/extract_trial_targets.json tool schema - Add TRIAL_EXTRACTION_TOOLS export to tools.py - Widen default status filter from RECRUITING-only to also include NOT_YET_RECRUITING and ACTIVE_NOT_RECRUITING - ingest_trials.py: --status now accepts multiple values; creates Anthropic client and passes it to fetch_als_trials() Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>0569d05
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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: Stage 3 — RAG pipeline + working end-to-end query
KevinIsInCoding +1: Claude Sonnet 4.6- rag/indexer.py: ChromaDB collection builder with section-aware chunking (one chunk per PMC XML section, falls back to abstract) - rag/retriever.py: semantic search + entity search with citation-weighted re-ranking (score = similarity * log(citation_count + 2)) - agents/research_agent.py: streaming synthesis agent (mirrors beacon pattern) uses search_research_landscape tool → RAG + trial match → Claude synthesis - scripts/build_index.py: CLI to build ChromaDB index from papers.jsonl - main.py: Rich CLI interface with streaming output - Add openai dependency (required by llm.py provider abstraction) Verified: 510 chunks indexed from 499 papers, end-to-end query returns structured research landscape with PMIDs, mechanisms, and trial links. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>56fa10c -
fix(pmc): add rich progress bar to get_pmcids (499 calls, ~3 min)
KevinIsInCoding +1: Claude Sonnet 4.6Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>83263a0 -
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