Configurations - Mem0

How to define configurations?

The config is defined as an object with two main keys:

vector_store: Specifies the vector database provider and its configuration
provider: The name of the vector database (e.g., “chroma”, “pgvector”, “qdrant”, “milvus”, “upstash_vector”, “azure_ai_search”, “vertex_ai_vector_search”, “valkey”)
- config: A nested dictionary containing provider-specific settings

How to Use Config

Here’s a general example of how to use the config with mem0:

Python

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "your_chosen_provider",
        "config": {
            # Provider-specific settings go here
        }
    }
}

m = Memory.from_config(config)
m.add("Your text here", user_id="user", metadata={"category": "example"})

TypeScript

// Example for in-memory vector database (Only supported in TypeScript)
import { Memory } from 'mem0ai/oss';

const configMemory = {
  vector_store: {
    provider: 'memory',
    config: {
      collectionName: 'memories',
      dimension: 1536,
    },
  },
};

const memory = new Memory(configMemory);
await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });

The in-memory vector database is only supported in the TypeScript implementation.

Why is Config Needed?

Config is essential for:

Specifying which vector database to use.
Providing necessary connection details (e.g., host, port, credentials).
Customizing database-specific settings (e.g., collection name, path).
Ensuring proper initialization and connection to your chosen vector store.

Master List of All Params in Config

Here’s a comprehensive list of all parameters that can be used across different vector databases:

Parameter	Description
`collection_name`	Name of the collection
`embedding_model_dims`	Dimensions of the embedding model
`client`	Custom client for the database
`path`	Path for the database
`host`	Host where the server is running
`port`	Port where the server is running
`user`	Username for database connection
`password`	Password for database connection
`dbname`	Name of the database
`url`	Full URL for the server
`api_key`	API key for the server
`on_disk`	Enable persistent storage
`endpoint_id`	Endpoint ID (vertex_ai_vector_search)
`index_id`	Index ID (vertex_ai_vector_search)
`deployment_index_id`	Deployment index ID (vertex_ai_vector_search)
`project_id`	Project ID (vertex_ai_vector_search)
`project_number`	Project number (vertex_ai_vector_search)
`vector_search_api_endpoint`	Vector search API endpoint (vertex_ai_vector_search)
`connection_string`	PostgreSQL connection string (for Supabase/PGVector)
`index_method`	Vector index method (for Supabase)
`index_measure`	Distance measure for similarity search (for Supabase)

Parameter	Description
`collectionName`	Name of the collection
`embeddingModelDims`	Dimensions of the embedding model
`dimension`	Dimensions of the embedding model (for memory provider)
`host`	Host where the server is running
`port`	Port where the server is running
`url`	URL for the server
`apiKey`	API key for the server
`path`	Path for the database
`onDisk`	Enable persistent storage
`redisUrl`	URL for the Redis server
`username`	Username for database connection
`password`	Password for database connection

Customizing Config

Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:

Identify the vector database you want to use from supported vector databases.
Refer to the Config section in the respective vector database’s documentation.
Include only the relevant parameters for your chosen database in the config dictionary.

Supported Vector Databases

For detailed information on configuring specific vector databases, please visit the Supported Vector Databases section. There you’ll find individual pages for each supported vector store with provider-specific usage examples and configuration details.