Anto01
7d8d599030
Day 1 (labeled corpus):
- scripts/eval_data/interactions_snapshot_2026-04-11.json — frozen
snapshot of 64 real claude-code interactions pulled from live
Dalidou (test-client captures filtered out). This is the stable
corpus the whole mini-phase labels against, independent of future
captures.
- scripts/eval_data/extractor_labels_2026-04-11.json — 20 hand-labeled
interactions drawn by length-stratified random sample. Positives:
5/20 = ~25%, total expected candidates: 7. Plan deviation: Codex's
plan asked for 30 (10/10/10 buckets); the real corpus is heavily
skewed toward instructional/status content, so honest labeling of
20 already crosses the fail-early threshold of "at least 5 plausible
positives" without padding.
Day 2 (baseline measurement):
- scripts/extractor_eval.py — file-based eval runner that loads the
snapshot + labels, runs extract_candidates_from_interaction on each,
and reports yield / recall / precision / miss-class breakdown.
Returns exit 1 on any false positive or false negative.
Current rule extractor against the labeled set:
labeled=20 exact_match=15 positive_expected=5
yield=0.0 recall=0.0 precision=0.0
false_negatives=5 false_positives=0
miss_classes:
recommendation_prose
architectural_change_summary
spec_update_announcement
layered_recommendation
alignment_assertion
Interpretation: the rule-based extractor matches exactly zero of the
5 plausible positive interactions in the labeled set, and the misses
are spread across 5 distinct cue classes with no single dominant
pattern. This is the Day 4 hard-stop signal landing on Day 2 — a
single rule expansion cannot close a 5-way miss, and widening rules
blindly will collapse precision. The right move is to go straight to
the Day 4 decision gate and consider LLM-assisted extraction.
Escalating to DEV-LEDGER.md as R5 for human ratification before
continuing. Not skipping Day 3 silently.
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