> ## 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.

# Baidu VectorDB (Mochow)

> Use Baidu Mochow as an enterprise vector database in Mem0 for high-performance vector storage and retrieval.

[Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.

### Installation

<CodeGroup>
  ```bash Python theme={null}
  pip install pymochow
  ```

  ```bash TypeScript theme={null}
  npm install @mochow/mochow-sdk-node
  ```
</CodeGroup>

### Usage

```python theme={null}
from mem0 import Memory

config = {
    "vector_store": {
        "provider": "baidu",
        "config": {
            "endpoint": "http://your-mochow-endpoint:8287",
            "account": "root",
            "api_key": "your-api-key",
            "database_name": "mem0",
            "table_name": "mem0_table",
            "embedding_model_dims": 1536,
            "metric_type": "COSINE"
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```

```typescript theme={null}
import { Memory } from "mem0ai/oss";

const memory = new Memory({
  embedder: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY || "",
      model: "text-embedding-3-small",
      embeddingDims: 1536,
    },
  },
  vectorStore: {
    provider: "baidu",
    config: {
      endpoint: process.env.BAIDU_ENDPOINT || "",
      account: process.env.BAIDU_ACCOUNT || "root",
      apiKey: process.env.BAIDU_API_KEY || "",
      databaseName: "mem0",
      tableName: "mem0_table",
      embeddingModelDims: 1536,
      metricType: "COSINE",
    },
  },
  llm: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY || "",
      model: "gpt-5-mini",
    },
  },
});
```

### Config

Here are the parameters available for configuring Baidu VectorDB:

| Parameter              | Description                                   | Default Value |
| ---------------------- | --------------------------------------------- | ------------- |
| `endpoint`             | Endpoint URL for your Baidu VectorDB instance | Required      |
| `account`              | Baidu VectorDB account name                   | `root`        |
| `api_key`              | API key for accessing Baidu VectorDB          | Required      |
| `database_name`        | Name of the database                          | `mem0`        |
| `table_name`           | Name of the table                             | `mem0`        |
| `embedding_model_dims` | Dimensions of the embedding model             | `1536`        |
| `metric_type`          | Distance metric for similarity search         | `L2`          |
| `client`               | Prebuilt Mochow client (TypeScript SDK only)  | `None`        |

For the TypeScript OSS SDK, use the camelCase equivalents:

* `databaseName`
* `tableName`
* `embeddingModelDims`
* `metricType`

For OSS TS usage, `endpoint`, `account`, `apiKey`, `databaseName`, `tableName`, and `embeddingModelDims` are required unless you inject a prebuilt client. `metricType` defaults to `L2`, matching the Python SDK.

### Distance Metrics

The following distance metrics are supported:

* `L2`: Euclidean distance (default)
* `IP`: Inner product
* `COSINE`: Cosine similarity

### Index Configuration

The vector index is automatically configured with the following HNSW parameters:

* `m`: 16 (number of connections per element)
* `efconstruction`: 200 (size of the dynamic candidate list)
* `auto_build`: true (automatically build index)
* `auto_build_index_policy`: Incremental build with 10000 rows increment

The TypeScript provider also creates a BM25 inverted index over a `textLemmatized` column so `keywordSearch()` runs against a real full-text index. Mem0 lemmatizes the query before it reaches the vector store, so only the lemmatized form of each memory is indexed. If you point `tableName` at a table created before this index existed, `keywordSearch()` returns `null` and search falls back to vector similarity alone; recreate the table to enable it.