feat(vector-store): add ClickZetta vector store support

docs/components/vectordbs/dbs/clickzetta.mdx

@@ -0,0 +1,66 @@
+[ClickZetta](https://www.singdata.com/) is a cloud-native data lakehouse platform developed by Singdata Technology, supporting vector storage and search capabilities.
+
+### Usage
+
+```python Python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "clickzetta",
+        "config": {
+            "collection_name": "mem0_memories",
+            "service": "your-service.clickzetta.com",
+            "instance": "your-instance",
+            "workspace": "your-workspace",
+            "schema": "public",
+            "username": "your-username",
+            "password": "your-password",
+            "vcluster": "default",
+            "protocol": "http",
+        }
+    }
+}
+
+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"})
+```
+
+### Config
+
+Let's see the available parameters for the `clickzetta` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection/table to store the vectors | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `service` | ClickZetta service endpoint | Required |
+| `instance` | ClickZetta instance name | Required |
+| `workspace` | ClickZetta workspace name | Required |
+| `schema` | Schema name for the table | Required |
+| `username` | Username for authentication | Required |
+| `password` | Password for authentication | Required |
+| `vcluster` | Virtual cluster name | Required |
+| `protocol` | Connection protocol (http/https) | `http` |
+| `distance_metric` | Distance metric for similarity search (cosine, euclidean, dot_product) | `cosine` |
+
+### Installation
+
+To use ClickZetta as a vector store, you need to install the ClickZetta connector:
+
+```bash
+pip install clickzetta-connector-python
+```
+
+Or install with all vector store dependencies:
+
+```bash
+pip install mem0ai[vector_stores]
+```

mem0/configs/vector_stores/clickzetta.py

@@ -0,0 +1,36 @@
+from typing import Any, Dict, Literal, Optional
+import warnings
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+# Suppress schema field name warning
+warnings.filterwarnings("ignore", message=".*Field name.*schema.*shadows.*")
+
+
+class ClickzettaConfig(BaseModel):
+    """ClickZetta Vector Store Configuration."""
+
+    model_config = ConfigDict(populate_by_name=True, protected_namespaces=())
+
+    collection_name: str = Field("mem0", description="Collection/table name")
+    embedding_model_dims: Optional[int] = Field(1536, description="Embedding vector dimensions")
+    service: str = Field(..., description="ClickZetta service address")
+    instance: str = Field(..., description="Instance name")
+    workspace: str = Field(..., description="Workspace name")
+    schema: str = Field(..., description="Schema name")
+    username: str = Field(..., description="Username")
+    password: str = Field(..., description="Password")
+    vcluster: str = Field(..., description="Virtual cluster name")
+    protocol: str = Field("http", description="Connection protocol (http/https)")
+    distance_metric: Literal["cosine", "euclidean", "dot_product"] = Field(
+        "cosine", description="Distance metric"
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_required_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        required_fields = ["service", "instance", "workspace", "schema", "username", "password", "vcluster"]
+        missing = [f for f in required_fields if not values.get(f)]
+        if missing:
+            raise ValueError(f"Missing required fields: {', '.join(missing)}")
+        return values

mem0/utils/factory.py

@@ -186,6 +186,7 @@ class VectorStoreFactory:
         "baidu": "mem0.vector_stores.baidu.BaiduDB",
         "cassandra": "mem0.vector_stores.cassandra.CassandraDB",
         "neptune": "mem0.vector_stores.neptune_analytics.NeptuneAnalyticsVector",
+        "clickzetta": "mem0.vector_stores.clickzetta.ClickZetta",
     }
 
     @classmethod