> ## 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.

# Milvus

> Use Milvus as an open-source vector database in Mem0, scalable from local development to production workloads.

[Milvus](https://milvus.io/) is an open-source vector database that suits AI applications of every size, from running a demo chatbot in a Jupyter notebook to building web-scale search that serves billions of users.

### Usage

The TypeScript SDK loads the Milvus client lazily. Install it alongside `mem0ai` when you use this provider:

```bash theme={null}
npm install @zilliz/milvus2-sdk-node
```

<CodeGroup>
  ```python Python theme={null}
  import os
  from mem0 import Memory

  config = {
      "vector_store": {
          "provider": "milvus",
          "config": {
              "collection_name": "test",
              "embedding_model_dims": 1536,
              "url": "127.0.0.1",
              "token": "8e4b8ca8cf2c67",
              "db_name": "my_database",
          }
      }
  }

  m = Memory.from_config(config)
  messages = [
      {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
      {"role": "assistant", "content": "How about thriller movies? 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 TypeScript theme={null}
  import { Memory } from 'mem0ai/oss';

  const config = {
    vectorStore: {
      provider: 'milvus',
      config: {
        collectionName: 'test',
        embeddingModelDims: 1536,
        url: 'http://localhost:19530',
        token: '8e4b8ca8cf2c67',
        dbName: 'my_database',
      },
    },
  };

  const memory = new Memory(config);
  const messages = [
    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
    { role: "assistant", content: "How about thriller movies? 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." },
  ];
  await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
  ```
</CodeGroup>

### Config

Here are the parameters available for configuring Milvus:

<Tabs>
  <Tab title="Python">
    | Parameter | Description | Default Value |
    | - | - | - |
    | `url` | Full URL/Uri for Milvus/Zilliz server | `http://localhost:19530` |
    | `token` | Token for Zilliz server / for local setup defaults to None. | `None` |
    | `collection_name` | The name of the collection | `mem0` |
    | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
    | `metric_type` | Metric type for similarity search | `L2` |
    | `db_name` | Name of the database | `""` |
  </Tab>

  <Tab title="TypeScript">
    | Parameter | Description | Default Value |
    | - | - | - |
    | `url` | Full URL/Uri for Milvus/Zilliz server | `http://localhost:19530` |
    | `token` | Token for Zilliz Cloud (optional for a local setup) | `undefined` |
    | `collectionName` | The name of the collection | `mem0` |
    | `embeddingModelDims` | Dimensions of the embedding model | `1536` |
    | `metricType` | Metric type for similarity search (`L2`, `IP`, `COSINE`, `HAMMING`, `JACCARD`) | `L2` |
    | `dbName` | Name of the database | `undefined` |
  </Tab>
</Tabs>


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