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Engram

Knowledge corpus service with vector search - your AI's long-term memory.

Overview

Engram is a FastAPI-based service providing semantic search over a knowledge corpus using PostgreSQL with pgvector. It runs on GPU-enabled infrastructure for fast embedding generation with sentence-transformers.

Requirements

  • Python: 3.11+ (3.12+ recommended for production)
  • GPU: NVIDIA GPU with CUDA support
  • Base Image: NVIDIA PyTorch 25.12 (for GPU deployment)
  • Database: PostgreSQL 16 with pgvector extension

Deployment Architecture

Internet/Tailscale → Caddy (TLS termination) → Engram (port 8800) → PostgreSQL + pgvector

Components

  • Caddy: Reverse proxy with automatic TLS via Step CA ACME

    • Terminates TLS at engram.your-domain.example.com
    • Proxies to Engram on localhost:8800

  • Engram App: FastAPI service

    • GPU-accelerated embedding generation
    • Runs in NVIDIA PyTorch container
    • Uses BAAI/bge-large-en-v1.5 model (1024-dim embeddings)

  • PostgreSQL: Vector database backend

    • pgvector extension for similarity search
    • Separate container on internal network

Certificate Management

TLS certificates are automatically managed via:

  • Step CA: Internal ACME server at your-ca.example.com:9000
  • Auto-renewal: Caddy handles certificate lifecycle
  • Zero-trust: Mesh-only access via Tailscale

Quick Start

See deploy/README.md for deployment instructions.

Development

# Install dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run locally
python -m engram.cli

License

MIT