Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
This commit is contained in:
@@ -6,7 +6,14 @@ description: "Use Milvus as an open-source vector database in Mem0, scalable fro
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### Usage
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```python
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The TypeScript SDK loads the Milvus client lazily. Install it alongside `mem0ai` when you use this provider:
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```bash
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npm install @zilliz/milvus2-sdk-node
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```
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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@@ -33,10 +40,39 @@ messages = [
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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```typescript TypeScript
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import { Memory } from 'mem0ai/oss';
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const config = {
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vectorStore: {
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provider: 'milvus',
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config: {
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collectionName: 'test',
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embeddingModelDims: 1536,
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url: 'http://localhost:19530',
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token: '8e4b8ca8cf2c67',
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dbName: 'my_database',
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},
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},
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};
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const memory = new Memory(config);
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const messages = [
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{ role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
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{ role: "assistant", content: "How about thriller movies? They can be quite engaging." },
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{ role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
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{ role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
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];
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await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
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```
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</CodeGroup>
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### Config
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Here are the parameters available for configuring Milvus:
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<Tabs>
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<Tab title="Python">
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `url` | Full URL/Uri for Milvus/Zilliz server | `http://localhost:19530` |
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@@ -45,3 +81,15 @@ Here are the parameters available for configuring Milvus:
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| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
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| `metric_type` | Metric type for similarity search | `L2` |
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| `db_name` | Name of the database | `""` |
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</Tab>
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<Tab title="TypeScript">
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `url` | Full URL/Uri for Milvus/Zilliz server | `http://localhost:19530` |
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| `token` | Token for Zilliz Cloud (optional for a local setup) | `undefined` |
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| `collectionName` | The name of the collection | `mem0` |
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| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
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| `metricType` | Metric type for similarity search (`L2`, `IP`, `COSINE`, `HAMMING`, `JACCARD`) | `L2` |
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| `dbName` | Name of the database | `undefined` |
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</Tab>
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</Tabs>
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