ATOCore/README.md

# 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
```

## 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

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