feat(ts-sdk): add Milvus vector store provider (closes #5774) (#5889)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

docs/components/vectordbs/dbs/milvus.mdx

@@ -6,7 +6,14 @@ description: "Use Milvus as an open-source vector database in Mem0, scalable fro
 
 ### Usage
 
-```python
+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
 
@@ -33,10 +40,39 @@ messages = [
 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` |
@@ -45,3 +81,15 @@ Here are the parameters available for configuring Milvus:
 | `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>

docs/components/vectordbs/overview.mdx

@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.
 
 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, and an in-memory store.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, and an in-memory store.
 </Note>
 
 <CardGroup cols={3}>