> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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:

<CodeGroup>
  ```python Python theme={null}
  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 TypeScript theme={null}
  // 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" } });
  ```
</CodeGroup>

<Note>
  The in-memory vector database is only supported in the TypeScript implementation.
</Note>

## 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:

<Tabs>
  <Tab title="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)   |
  </Tab>

  <Tab title="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                        |
  </Tab>
</Tabs>

## 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.