## Baidu VectorDB

Baidu VectorDB 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

Python

```
pip install pymochow
```

TypeScript

```
npm install @mochow/mochow-sdk-node
```

### Usage

```
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"})
```

```
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.