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: 
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"})

​
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:
ParameterDescriptionProvider
modelEmbedding model to useAll
api_keyAPI key of the providerAll
embedding_dimsDimensions of the embedding modelAll
http_client_proxiesAllow proxy server settingsAll
ollama_base_urlBase URL for the Ollama embedding modelOllama
model_kwargsKey-Value arguments for the Huggingface embedding modelHuggingface
azure_kwargsKey-Value arguments for the AzureOpenAI embedding modelAzure OpenAI
openai_base_urlBase URL for OpenAI APIOpenAI
vertex_credentials_jsonPath to the Google Cloud credentials JSON file for VertexAIVertexAI
memory_add_embedding_typeThe type of embedding to use for the add memory actionVertexAI
memory_update_embedding_typeThe type of embedding to use for the update memory actionVertexAI
memory_search_embedding_typeThe type of embedding to use for the search memory actionVertexAI
lmstudio_base_urlBase URL for LM Studio APILM Studio

​
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.