# AtoCore

Personal context engine that enriches LLM interactions with durable memory, structured context, and project knowledge.

## Quick Start

```bash
pip install -e .
uvicorn src.atocore.main:app --port 8100
```

## Usage

```bash
# 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

```text
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

```bash
pip install -e ".[dev]"
pytest
```

## Operations

- `scripts/atocore_client.py` provides a live API client for project refresh, project-state inspection, and retrieval-quality audits.
- `docs/operations.md` captures 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 model
- `docs/architecture/engineering-ontology-v1.md` — V1 object and relationship inventory
- `docs/architecture/engineering-query-catalog.md` — 20 v1-required queries
- `docs/architecture/memory-vs-entities.md` — canonical home split
- `docs/architecture/promotion-rules.md` — Layer 0 to Layer 2 pipeline
- `docs/architecture/conflict-model.md` — contradictory facts detection and resolution