69c971708ab850f2422177a8d1fbb76ded36037f
Day 4: - R7 fixed: overlap-density ranking. p06-firmware-interface now passes (was the last memory-ranking failure). Harness 16/18→17/18. - R9 fixed: LLM extractor checks project registry before trusting model-supplied project. Hallucinated projects fall back to interaction's known scope. Registry lookup via load_project_registry(), matched by project_id. Host-side script mirrors this via GET /projects at startup. Day 5: - R8 addressed: 5 integration tests in test_extraction_pipeline.py covering the full LLM extract → persist as candidate → promote/ reject flow, project fallback, failure handling, and dedup behavior. Uses mocked subprocess to avoid real claude -p calls. Harness: 17/18 (only p06-tailscale remains — chunk bleed from source content, not a memory/ranking issue). Tests: 280 → 286 (+6). Batch complete. Before/after for this batch: R1: fixed (extraction pipeline operational on Dalidou) R5: fixed (batch endpoint + host-side script) R7: fixed (overlap-density ranking) R9: fixed (project trust-preservation via registry check) R8: addressed (5 integration tests) Harness: 16/18 → 17/18 Active memories: 36 → 41 Nightly pipeline: backup → cleanup → rsync → extract → auto-triage Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
AtoCore
Personal context engine that enriches LLM interactions with durable memory, structured context, and project knowledge.
Quick Start
pip install -e .
uvicorn src.atocore.main:app --port 8100
Usage
# Ingest markdown files
curl -X POST http://localhost:8100/ingest \
-H "Content-Type: application/json" \
-d '{"path": "/path/to/notes"}'
# Build enriched context for a prompt
curl -X POST http://localhost:8100/context/build \
-H "Content-Type: application/json" \
-d '{"prompt": "What is the project status?", "project": "myproject"}'
# CLI ingestion
python scripts/ingest_folder.py --path /path/to/notes
# Live operator client
python scripts/atocore_client.py health
python scripts/atocore_client.py audit-query "gigabit" 5
API Endpoints
| Method | Path | Description |
|---|---|---|
| POST | /ingest | Ingest markdown file or folder |
| POST | /query | Retrieve relevant chunks |
| POST | /context/build | Build full context pack |
| GET | /health | Health check |
| GET | /debug/context | Inspect last context pack |
Architecture
FastAPI (port 8100)
|- Ingestion: markdown -> parse -> chunk -> embed -> store
|- Retrieval: query -> embed -> vector search -> rank
|- Context Builder: retrieve -> boost -> budget -> format
|- SQLite (documents, chunks, memories, projects, interactions)
'- ChromaDB (vector embeddings)
Configuration
Set via environment variables (prefix ATOCORE_):
| Variable | Default | Description |
|---|---|---|
| ATOCORE_DEBUG | false | Enable debug logging |
| ATOCORE_PORT | 8100 | Server port |
| ATOCORE_CHUNK_MAX_SIZE | 800 | Max chunk size (chars) |
| ATOCORE_CONTEXT_BUDGET | 3000 | Context pack budget (chars) |
| ATOCORE_EMBEDDING_MODEL | paraphrase-multilingual-MiniLM-L12-v2 | Embedding model |
Testing
pip install -e ".[dev]"
pytest
Operations
scripts/atocore_client.pyprovides a live API client for project refresh, project-state inspection, and retrieval-quality audits.docs/operations.mdcaptures the current operational priority order: retrieval quality, Wave 2 trusted-operational ingestion, AtoDrive scoping, and restore validation.
Architecture Notes
Implementation-facing architecture notes live under docs/architecture/.
Current additions:
docs/architecture/engineering-knowledge-hybrid-architecture.md— 5-layer hybrid modeldocs/architecture/engineering-ontology-v1.md— V1 object and relationship inventorydocs/architecture/engineering-query-catalog.md— 20 v1-required queriesdocs/architecture/memory-vs-entities.md— canonical home splitdocs/architecture/promotion-rules.md— Layer 0 to Layer 2 pipelinedocs/architecture/conflict-model.md— contradictory facts detection and resolution
Description
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