ATOCore/README.md

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

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

Architecture Notes

Implementation-facing architecture notes live under docs/architecture/.

Current additions:

• docs/architecture/engineering-knowledge-hybrid-architecture.md
• docs/architecture/engineering-ontology-v1.md