# Clone the repo
git clone https://github.com/noualit/llama-memory-local.git
cd llama-memory-local

# Create environment
conda create -n llama-memory python=3.11
conda activate llama-memory

# Install dependencies
pip install -e .

cp .env.example .env

# Database
DATABASE_URL="postgresql://postgres:yourpassword@localhost:5432/llamamem"

# Llama-server (LLM)
LLAMA_SERVER_BASE_URL="http://localhost:8080"

# Embedding model (nomic-embed-text via llama-server)
EMBEDDING_MODEL_URL="http://localhost:8081"

# Embedding model name (default: nomic-embed-text)
EMBEDDING_MODEL_NAME="nomic-embed-text"

# Service port
SERVICE_PORT=9001

psql -U postgres -c "CREATE DATABASE llamamem;"
alembic upgrade head

# Using the script
.\scripts\run_server.ps1

# Or directly
python -m uvicorn app.main:app --host 0.0.0.0 --port 9001

{
  "mcpServers": {
    "llama-memory": {
      "url": "http://YOUR_SERVER_IP:9001/mcp"
    }
  }
}

MEMORY WORKFLOW:
- At the start of each new conversation, call create_conversation with a short title.
- Use the conversation_id from create_conversation when calling save_memory.
- Before answering questions about past topics, call search_memories FIRST.
- When the user shares important information, save it with save_memory.
- If list_conversations has previous chats, check get_conversation_history for context.

curl http://localhost:9001/health

# Run tests
pytest tests/ -v

# Run with auto-reload
python -m uvicorn app.main:app --host 0.0.0.0 --port 9001 --reload