docs: Add SurrealDB to storage documentation (#3163)

Author: 1sarthakbhardwaj

docs/mintlify/key_modules/storages.mdx

@@ -61,9 +61,13 @@ The <b>Storage</b> module in CAMEL-AI gives you a **unified interface for saving
     - Schema-based, semantic search, hybrid queries
 
     **ChromaStorage**
-    - For [ChromaDB](https://www.trychroma.com/) (AI-native open-source embedding database)  
+    - For [ChromaDB](https://www.trychroma.com/) (AI-native open-source embedding database)
     - Simple API, scales from notebook to production
 
+    **SurrealStorage**
+    - For [SurrealDB](https://surrealdb.com/) (scalable, distributed database with WebSocket support)
+    - Efficient vector storage and similarity search with real-time updates
+
     **PgVectorStorage**
     - For [PostgreSQL with pgvector](https://github.com/pgvector/pgvector) (open-source vector engine)
     - Leverages PostgreSQL for vector search
@@ -298,6 +302,56 @@ Here are practical usage patterns for each storage type—pick the ones you need
 
 ---
 
+<Card title="SurrealDB Vector Storage" icon="database">
+  <b>Use for:</b> Scalable, distributed vector storage with WebSocket support.
+  <b>Perfect for:</b> Real-time vector search with distributed deployments and SQL-like querying.
+
+  <Tabs>
+    <Tab title="Python Example">
+      ```python
+      import os
+      from camel.storages import SurrealStorage, VectorDBQuery, VectorRecord
+
+      # Set environment variables for SurrealDB connection
+      os.environ["SURREAL_URL"] = "ws://localhost:8000/rpc"
+      os.environ["SURREAL_PASSWORD"] = "your_password"
+
+      # Create SurrealStorage instance with WebSocket connection
+      surreal_storage = SurrealStorage(
+          url=os.getenv("SURREAL_URL"),
+          table="camel_vectors",
+          namespace="ns",
+          database="db",
+          user="root",
+          password=os.getenv("SURREAL_PASSWORD"),
+          vector_dim=4,
+      )
+
+      # Add vector records
+      surreal_storage.add([
+          VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
+          VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
+      ])
+
+      # Query similar vectors
+      query_results = surreal_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
+      for result in query_results:
+          print(result.record.payload, result.similarity)
+
+      # Clear all vectors
+      surreal_storage.clear()
+      ```
+    </Tab>
+    <Tab title="Output">
+      ```markdown
+      >>> {'key2': 'value2'} 0.5669467095138407
+      ```
+    </Tab>
+  </Tabs>
+</Card>
+
+---
+
 <Card title="OceanBase Vector Storage" icon="earth-oceania">
   <b>Use for:</b> Massive vector storage with advanced analytics.  
   <b>Perfect for:</b> Batch operations, cloud or on-prem setups, and high-throughput search.