Great work on the pipeline refactoring! This is a solid Unix-style architecture with clean separation of concerns:
What you've built:
- ✅ Modular stages - Each pipeline script is independent and can be run separately
- ✅ Interface-based design - Storage, parsers, and embeddings all use ABC interfaces
- ✅ Swappable backends - SQLite for dev/testing, PostgreSQL+pgvector for production
- ✅ Clean data flow - Each stage reads/writes through well-defined interfaces
- ✅ Comprehensive docs - README and TESTING guide are clear and helpful
Architecture strengths:
- EntityCollectionInterface pattern you appreciated is used throughout
- Parser interfaces allow easy addition of new input formats (not just PMC XML)
- Storage abstraction means you can optimize per environment (in-memory for tests, Postgres for prod)
- Embedding interfaces separate generation from storage
Now that the refactoring is done, what's next?
Given your earlier motivation concerns (stop-start development, working in isolation, hard to measure progress), here are some concrete next steps:
1. Validation Run - Actually use it
# Run the full pipeline on a small dataset to validate everything works
python pipeline/ner_pipeline.py --storage sqlite --output-dir test_run
python pipeline/provenance_pipeline.py --storage sqlite --output-dir test_run
python pipeline/claims_pipeline.py --storage sqlite --output-dir test_run
python pipeline/evidence_pipeline.py --storage sqlite --output-dir test_run
# Then inspect the results
sqlite3 test_run/pipeline.db "SELECT COUNT(*) FROM entities"
sqlite3 test_run/pipeline.db "SELECT COUNT(*) FROM relationships"2. Add integration tests - Test the full pipeline end-to-end with fake data
3. Create example notebooks - Jupyter notebooks showing how to query the results would make progress visible
4. Build a simple query interface - A script that lets you ask questions of your graph
Which direction feels most valuable to you right now? Or is there a specific pain point in the current setup you want to address?