Milvus - Mem0 — ContextQMD

Milvus 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

npm install @zilliz/milvus2-sdk-node

<CodeGroup> ```python Python 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
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>