Configurations

Reference for vector database configuration options in Mem0, including provider selection and connection settings.

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"})

// 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:

1. Specifying which vector database to use.
2. Providing necessary connection details (e.g., host, port, credentials).
3. Customizing database-specific settings (e.g., collection name, path).
4. 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:

Python

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)

TypeScript

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:

1. Identify the vector database you want to use from supported vector databases.
2. Refer to the Config section in the respective vector database’s documentation.
3. 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.