feat(vector-stores): add Weaviate adapter to TypeScript OSS SDK (#5800)

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

docs/components/vectordbs/dbs/weaviate.mdx

@@ -4,14 +4,21 @@ description: "Use Weaviate as an open-source vector search engine in Mem0 for st
 ---
 [Weaviate](https://weaviate.io/) is an open-source vector search engine. It allows efficient storage and retrieval of high-dimensional vector embeddings, enabling powerful search and retrieval capabilities.
 
-
 ### Installation
-```bash
+
+<CodeGroup>
+```bash Python
 pip install weaviate-client
 ```
 
+```bash TypeScript
+npm install weaviate-client
+```
+</CodeGroup>
+
 ### Usage
 
+<CodeGroup>
 ```python Python
 import os
 from mem0 import Memory
@@ -33,20 +40,73 @@ 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": "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: "weaviate",
+    config: {
+      collectionName: "test",
+      embeddingModelDims: 1536,
+      clusterUrl: "http://localhost:8080",
+    },
+  },
+};
+
+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 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.",
+  },
+];
+
+await memory.add(messages, {
+  userId: "alice",
+  metadata: {
+    category: "movies",
+  },
+});
+```
+</CodeGroup>
+
+The TypeScript SDK picks the connection mode from the config you pass:
+
+- `clusterUrl` pointing at `localhost` connects to a local instance.
+- `clusterUrl` plus `apiKey` connects to a Weaviate Cloud cluster (for example `https://my-cluster.weaviate.cloud`).
+- Any other `clusterUrl` without an `apiKey` connects to a custom deployment, using the host and port from the URL.
+
+You can also pass a pre-configured `client` (a `WeaviateClient` instance) to reuse an existing connection.
+
 ### Config
 
 Here are the parameters available for configuring Weaviate:
 
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `collection_name` | The name of the collection to store the vectors | `mem0` |
-| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
-| `cluster_url` | URL for the Weaviate server | `None` |
-| `auth_client_secret` | API key for Weaviate authentication | `None` |
-| `additional_headers` | Additional headers to include in requests (`Dict[str, str]`) | `None` |
+| Python | TypeScript | Description | Default Value |
+| --- | --- | --- | --- |
+| `collection_name` | `collectionName` | The name of the collection to store the vectors | `mem0` |
+| `embedding_model_dims` | `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `cluster_url` | `clusterUrl` | URL for the Weaviate server | `None` |
+| `auth_client_secret` | `apiKey` | API key for Weaviate authentication | `None` |
+| `additional_headers` | `additionalHeaders` | Additional headers to include in requests | `None` |