Anto01
4da81c9e4e
scripts/retrieval_eval.py walks a fixture file of project-hinted
questions, runs each against POST /context/build, and scores the
returned formatted_context against per-fixture expect_present and
expect_absent substring checklists. Exit 0 on all-pass, 1 on any
miss. Human-readable by default, --json for automation.
First live run against Dalidou at SHA 1161645: 4/6 pass. The two
failures are real findings, not harness bugs:
- p05-configuration FAIL: "GigaBIT M1" appears in the p05 pack.
Cross-project bleed from a shared p05 doc that legitimately
mentions the p04 mirror under test. Fixture kept strict so
future ranker tuning can close the gap.
- p05-vendor-signal FAIL: "Zygo" missing. The vendor memory exists
with confidence 0.9 but get_memories_for_context walks memories
in fixed order (effectively by updated_at / confidence), so lower-
ranked memories get pushed out of the per-project budget slice by
higher-confidence ones even when the query is specifically about
the lower-ranked content. Query-relevance ordering of memories is
the natural next fix.
Docs sync:
- master-plan-status.md: Phase 9 reflection entry now notes that
capture→reinforce runs automatically and project memories reach
the context pack, while extract remains batch/manual. First batch-
extract pass surfaced 1 candidate from 42 interactions — extractor
rule tuning is a known follow-up.
- next-steps.md: the 2026-04-11 retrieval quality review entry now
shows the project-memory-band work as DONE, and a new
"Reflection Loop Live Check" subsection records the extractor-
coverage finding from the first batch run.
- Both files now agree with the code; follow-up reviewers
(Codex, future Claude) should no longer see narrative drift.
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