Configurations

Reference for embedder configuration options in Mem0, including provider selection and model settings.

Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.

How to define configurations?

The config is defined as an object (or dictionary) with two main keys:

• embedder: Specifies the embedder provider and its configuration
  • provider: The name of the embedder (e.g., “openai”, “ollama”)
  • config: A nested object or dictionary containing provider-specific settings

How to use configurations?

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 = {
    "embedder": {
        "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"})

import { Memory } from 'mem0ai/oss';

const config = {
  embedder: {
    provider: 'openai',
    config: {
      apiKey: process.env.OPENAI_API_KEY || '',
      model: 'text-embedding-3-small',
      // Provider-specific settings go here
    },
  },
};

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

Why is Config Needed?

Config is essential for:

1. Specifying which embedding model to use.
2. Providing necessary connection details (e.g., model, api_key, embedding_dims).
3. Ensuring proper initialization and connection to your chosen embedder.

Master List of All Params in Config

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

Python

Parameter	Description	Provider
model	Embedding model to use	All
api_key	API key of the provider	All
embedding_dims	Dimensions of the embedding model	All
http_client_proxies	Allow proxy server settings	All
ollama_base_url	Base URL for the Ollama embedding model	Ollama
model_kwargs	Key-Value arguments for the Huggingface embedding model	Huggingface
azure_kwargs	Key-Value arguments for the AzureOpenAI embedding model	Azure OpenAI
openai_base_url	Base URL for OpenAI API	OpenAI
vertex_credentials_json	Path to the Google Cloud credentials JSON file for VertexAI	VertexAI
memory_add_embedding_type	The type of embedding to use for the add memory action	VertexAI
memory_update_embedding_type	The type of embedding to use for the update memory action	VertexAI
memory_search_embedding_type	The type of embedding to use for the search memory action	VertexAI
lmstudio_base_url	Base URL for LM Studio API	LM Studio

TypeScript

Parameter	Description	Provider
model	Embedding model to use	All
apiKey	API key of the provider	All
embeddingDims	Dimensions of the embedding model	All

Supported Embedding Models

For detailed information on configuring specific embedders, please visit the Embedding Models section. There you’ll find information for each supported embedder with provider-specific usage examples and configuration details.