## What is Config?

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

## How to Define Config

The config is defined as a Python dictionary 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","azure_ai_search")
  - `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"})
```

## 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:

| 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 |

## 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](./dbs).
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](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.