Persistent Memory: Stores user interactions and preferences
Contextual Responses: Uses stored memories to provide personalized responses
Memory Search: Search through stored memories
Memory Management: View and clear user memories

Usage

Basic Chat with Memory

from api.chat_service import ChatService

# Initialize chat service
chat_service = ChatService("user_id")
chat_service.initialize()

# Send a message
result = chat_service.chat("My name is Alice and I love sci-fi movies")
print(result["response"])

Memory Operations

# Get all memories for a user
memories = chat_service.get_user_memories()

# Search memories
search_results = chat_service.search_memories("movies")

# Clear all memories
chat_service.clear_user_memories()

Configuration

The Mem0 configuration is defined in config/config.py:

MEM0_CONFIG = {
    "vector_store": {
        "provider": "milvus",
        "config": {
            "embedding_model_dims": 2048,
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "api_key": OPENAI_API_KEY_FROM_CONFIG,
            "model": "doubao-seed-1-6-250615",
            "openai_base_url": OPENAI_API_BASE_URL_CONFIG
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "api_key": OPENAI_EMBEDDING_KEY,
            "model": "doubao-embedding-large-text-250515",
            "openai_base_url": OPENAI_EMBEDDING_BASE
        }
    },
}

How It Works

Memory Retrieval: When a user sends a message, the system searches for relevant memories about the user
Enhanced Prompt: The retrieved memories are formatted and included in the prompt to the LLM
Response Generation: The LLM generates a response considering the user's memories
Memory Storage: The conversation is automatically stored as new memories

API Endpoints

The main API endpoints remain the same:

POST /chat - Send a message and get a response
GET /health - Health check

Additional memory management endpoints can be added to the main API if needed.