Added azure mysql for mem0 (#3531)

docs/components/vectordbs/dbs/azure_mysql.mdx

@@ -0,0 +1,128 @@
+---
+title: Azure MySQL
+---
+
+[Azure Database for MySQL](https://azure.microsoft.com/products/mysql) is a fully managed relational database service that provides enterprise-grade reliability and security. It supports JSON-based vector storage for semantic search capabilities in AI applications.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "azure_mysql",
+        "config": {
+            "host": "your-server.mysql.database.azure.com",
+            "port": 3306,
+            "user": "your_username",
+            "password": "your_password",
+            "database": "mem0_db",
+            "collection_name": "memories",
+        }
+    }
+}
+
+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"})
+```
+
+#### Using Azure Managed Identity
+
+For production deployments, use Azure Managed Identity instead of passwords:
+
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_mysql",
+        "config": {
+            "host": "your-server.mysql.database.azure.com",
+            "user": "your_username",
+            "database": "mem0_db",
+            "collection_name": "memories",
+            "use_azure_credential": True,  # Uses DefaultAzureCredential
+            "ssl_disabled": False
+        }
+    }
+}
+```
+
+<Note>
+When `use_azure_credential` is enabled, the password is obtained via Azure DefaultAzureCredential (supports Managed Identity, Azure CLI, etc.)
+</Note>
+
+### Config
+
+Here are the parameters available for configuring Azure MySQL:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `host` | MySQL server hostname | Required |
+| `port` | MySQL server port | `3306` |
+| `user` | Database user | Required |
+| `password` | Database password (optional with Azure credential) | `None` |
+| `database` | Database name | Required |
+| `collection_name` | Table name for storing vectors | `"mem0"` |
+| `embedding_model_dims` | Dimensions of embedding vectors | `1536` |
+| `use_azure_credential` | Use Azure DefaultAzureCredential | `False` |
+| `ssl_ca` | Path to SSL CA certificate | `None` |
+| `ssl_disabled` | Disable SSL (not recommended) | `False` |
+| `minconn` | Minimum connections in pool | `1` |
+| `maxconn` | Maximum connections in pool | `5` |
+
+### Setup
+
+#### Create MySQL Flexible Server using Azure CLI:
+
+```bash
+# Create resource group
+az group create --name mem0-rg --location eastus
+
+# Create MySQL Flexible Server
+az mysql flexible-server create \
+    --resource-group mem0-rg \
+    --name mem0-mysql-server \
+    --location eastus \
+    --admin-user myadmin \
+    --admin-password <YourPassword> \
+    --version 8.0.21
+
+# Create database
+az mysql flexible-server db create \
+    --resource-group mem0-rg \
+    --server-name mem0-mysql-server \
+    --database-name mem0_db
+
+# Configure firewall
+az mysql flexible-server firewall-rule create \
+    --resource-group mem0-rg \
+    --name mem0-mysql-server \
+    --rule-name AllowMyIP \
+    --start-ip-address <YourIP> \
+    --end-ip-address <YourIP>
+```
+
+#### Enable Azure AD Authentication:
+
+1. In Azure Portal, navigate to your MySQL Flexible Server
+2. Go to **Security** > **Authentication** and enable Azure AD
+3. Add your application's managed identity as a MySQL user:
+
+```sql
+CREATE AADUSER 'your-app-identity' IDENTIFIED BY 'your-client-id';
+GRANT ALL PRIVILEGES ON mem0_db.* TO 'your-app-identity'@'%';
+FLUSH PRIVILEGES;
+```
+
+<Tip>
+For production, use [Managed Identity](https://learn.microsoft.com/azure/active-directory/managed-identities-azure-resources/) to eliminate password management.
+</Tip>

docs/docs.json

@@ -156,6 +156,7 @@
 															"components/vectordbs/dbs/pinecone",
 															"components/vectordbs/dbs/mongodb",
 															"components/vectordbs/dbs/azure",
+															"components/vectordbs/dbs/azure_mysql",
 															"components/vectordbs/dbs/redis",
 															"components/vectordbs/dbs/valkey",
 															"components/vectordbs/dbs/elasticsearch",
@@ -496,6 +497,7 @@
 															"v0x/components/vectordbs/dbs/pinecone",
 															"v0x/components/vectordbs/dbs/mongodb",
 															"v0x/components/vectordbs/dbs/azure",
+															"v0x/components/vectordbs/dbs/azure_mysql",
 															"v0x/components/vectordbs/dbs/redis",
 															"v0x/components/vectordbs/dbs/valkey",
 															"v0x/components/vectordbs/dbs/elasticsearch",

docs/v0x/components/vectordbs/dbs/azure_mysql.mdx

@@ -0,0 +1,128 @@
+---
+title: Azure MySQL
+---
+
+[Azure Database for MySQL](https://azure.microsoft.com/products/mysql) is a fully managed relational database service that provides enterprise-grade reliability and security. It supports JSON-based vector storage for semantic search capabilities in AI applications.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "azure_mysql",
+        "config": {
+            "host": "your-server.mysql.database.azure.com",
+            "port": 3306,
+            "user": "your_username",
+            "password": "your_password",
+            "database": "mem0_db",
+            "collection_name": "memories",
+        }
+    }
+}
+
+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"})
+```
+
+#### Using Azure Managed Identity
+
+For production deployments, use Azure Managed Identity instead of passwords:
+
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_mysql",
+        "config": {
+            "host": "your-server.mysql.database.azure.com",
+            "user": "your_username",
+            "database": "mem0_db",
+            "collection_name": "memories",
+            "use_azure_credential": True,  # Uses DefaultAzureCredential
+            "ssl_disabled": False
+        }
+    }
+}
+```
+
+<Note>
+When `use_azure_credential` is enabled, the password is obtained via Azure DefaultAzureCredential (supports Managed Identity, Azure CLI, etc.)
+</Note>
+
+### Config
+
+Here are the parameters available for configuring Azure MySQL:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `host` | MySQL server hostname | Required |
+| `port` | MySQL server port | `3306` |
+| `user` | Database user | Required |
+| `password` | Database password (optional with Azure credential) | `None` |
+| `database` | Database name | Required |
+| `collection_name` | Table name for storing vectors | `"mem0"` |
+| `embedding_model_dims` | Dimensions of embedding vectors | `1536` |
+| `use_azure_credential` | Use Azure DefaultAzureCredential | `False` |
+| `ssl_ca` | Path to SSL CA certificate | `None` |
+| `ssl_disabled` | Disable SSL (not recommended) | `False` |
+| `minconn` | Minimum connections in pool | `1` |
+| `maxconn` | Maximum connections in pool | `5` |
+
+### Setup
+
+#### Create MySQL Flexible Server using Azure CLI:
+
+```bash
+# Create resource group
+az group create --name mem0-rg --location eastus
+
+# Create MySQL Flexible Server
+az mysql flexible-server create \
+    --resource-group mem0-rg \
+    --name mem0-mysql-server \
+    --location eastus \
+    --admin-user myadmin \
+    --admin-password <YourPassword> \
+    --version 8.0.21
+
+# Create database
+az mysql flexible-server db create \
+    --resource-group mem0-rg \
+    --server-name mem0-mysql-server \
+    --database-name mem0_db
+
+# Configure firewall
+az mysql flexible-server firewall-rule create \
+    --resource-group mem0-rg \
+    --name mem0-mysql-server \
+    --rule-name AllowMyIP \
+    --start-ip-address <YourIP> \
+    --end-ip-address <YourIP>
+```
+
+#### Enable Azure AD Authentication:
+
+1. In Azure Portal, navigate to your MySQL Flexible Server
+2. Go to **Security** > **Authentication** and enable Azure AD
+3. Add your application's managed identity as a MySQL user:
+
+```sql
+CREATE AADUSER 'your-app-identity' IDENTIFIED BY 'your-client-id';
+GRANT ALL PRIVILEGES ON mem0_db.* TO 'your-app-identity'@'%';
+FLUSH PRIVILEGES;
+```
+
+<Tip>
+For production, use [Managed Identity](https://learn.microsoft.com/azure/active-directory/managed-identities-azure-resources/) to eliminate password management.
+</Tip>

mem0/configs/vector_stores/azure_mysql.py

@@ -0,0 +1,84 @@
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class AzureMySQLConfig(BaseModel):
+    """Configuration for Azure MySQL vector database."""
+
+    host: str = Field(..., description="MySQL server host (e.g., myserver.mysql.database.azure.com)")
+    port: int = Field(3306, description="MySQL server port")
+    user: str = Field(..., description="Database user")
+    password: Optional[str] = Field(None, description="Database password (not required if using Azure credential)")
+    database: str = Field(..., description="Database name")
+    collection_name: str = Field("mem0", description="Collection/table name")
+    embedding_model_dims: int = Field(1536, description="Dimensions of the embedding model")
+    use_azure_credential: bool = Field(
+        False,
+        description="Use Azure DefaultAzureCredential for authentication instead of password"
+    )
+    ssl_ca: Optional[str] = Field(None, description="Path to SSL CA certificate")
+    ssl_disabled: bool = Field(False, description="Disable SSL connection (not recommended for production)")
+    minconn: int = Field(1, description="Minimum number of connections in the pool")
+    maxconn: int = Field(5, description="Maximum number of connections in the pool")
+    connection_pool: Optional[Any] = Field(
+        None,
+        description="Pre-configured connection pool object (overrides other connection parameters)"
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_auth(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Validate authentication parameters."""
+        # If connection_pool is provided, skip validation
+        if values.get("connection_pool") is not None:
+            return values
+
+        use_azure_credential = values.get("use_azure_credential", False)
+        password = values.get("password")
+
+        # Either password or Azure credential must be provided
+        if not use_azure_credential and not password:
+            raise ValueError(
+                "Either 'password' must be provided or 'use_azure_credential' must be set to True"
+            )
+
+        return values
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_required_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Validate required fields."""
+        # If connection_pool is provided, skip validation of individual parameters
+        if values.get("connection_pool") is not None:
+            return values
+
+        required_fields = ["host", "user", "database"]
+        missing_fields = [field for field in required_fields if not values.get(field)]
+
+        if missing_fields:
+            raise ValueError(
+                f"Missing required fields: {', '.join(missing_fields)}. "
+                f"These fields are required when not using a pre-configured connection_pool."
+            )
+
+        return values
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Validate that no extra fields are provided."""
+        allowed_fields = set(cls.model_fields.keys())
+        input_fields = set(values.keys())
+        extra_fields = input_fields - allowed_fields
+
+        if extra_fields:
+            raise ValueError(
+                f"Extra fields not allowed: {', '.join(extra_fields)}. "
+                f"Please input only the following fields: {', '.join(allowed_fields)}"
+            )
+
+        return values
+
+    class Config:
+        arbitrary_types_allowed = True

mem0/utils/factory.py

@@ -162,6 +162,7 @@ class VectorStoreFactory:
         "milvus": "mem0.vector_stores.milvus.MilvusDB",
         "upstash_vector": "mem0.vector_stores.upstash_vector.UpstashVector",
         "azure_ai_search": "mem0.vector_stores.azure_ai_search.AzureAISearch",
+        "azure_mysql": "mem0.vector_stores.azure_mysql.AzureMySQL",
         "pinecone": "mem0.vector_stores.pinecone.PineconeDB",
         "mongodb": "mem0.vector_stores.mongodb.MongoDB",
         "redis": "mem0.vector_stores.redis.RedisDB",

mem0/vector_stores/azure_mysql.py

@@ -0,0 +1,463 @@
+import json
+import logging
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel
+
+try:
+    import pymysql
+    from pymysql.cursors import DictCursor
+    from dbutils.pooled_db import PooledDB
+except ImportError:
+    raise ImportError(
+        "Azure MySQL vector store requires PyMySQL and DBUtils. "
+        "Please install them using 'pip install pymysql dbutils'"
+    )
+
+try:
+    from azure.identity import DefaultAzureCredential
+    AZURE_IDENTITY_AVAILABLE = True
+except ImportError:
+    AZURE_IDENTITY_AVAILABLE = False
+
+from mem0.vector_stores.base import VectorStoreBase
+
+logger = logging.getLogger(__name__)
+
+
+class OutputData(BaseModel):
+    id: Optional[str]
+    score: Optional[float]
+    payload: Optional[dict]
+
+
+class AzureMySQL(VectorStoreBase):
+    def __init__(
+        self,
+        host: str,
+        port: int,
+        user: str,
+        password: Optional[str],
+        database: str,
+        collection_name: str,
+        embedding_model_dims: int,
+        use_azure_credential: bool = False,
+        ssl_ca: Optional[str] = None,
+        ssl_disabled: bool = False,
+        minconn: int = 1,
+        maxconn: int = 5,
+        connection_pool: Optional[Any] = None,
+    ):
+        """
+        Initialize the Azure MySQL vector store.
+
+        Args:
+            host (str): MySQL server host
+            port (int): MySQL server port
+            user (str): Database user
+            password (str, optional): Database password (not required if using Azure credential)
+            database (str): Database name
+            collection_name (str): Collection/table name
+            embedding_model_dims (int): Dimension of the embedding vector
+            use_azure_credential (bool): Use Azure DefaultAzureCredential for authentication
+            ssl_ca (str, optional): Path to SSL CA certificate
+            ssl_disabled (bool): Disable SSL connection
+            minconn (int): Minimum number of connections in the pool
+            maxconn (int): Maximum number of connections in the pool
+            connection_pool (Any, optional): Pre-configured connection pool
+        """
+        self.host = host
+        self.port = port
+        self.user = user
+        self.password = password
+        self.database = database
+        self.collection_name = collection_name
+        self.embedding_model_dims = embedding_model_dims
+        self.use_azure_credential = use_azure_credential
+        self.ssl_ca = ssl_ca
+        self.ssl_disabled = ssl_disabled
+        self.connection_pool = connection_pool
+
+        # Handle Azure authentication
+        if use_azure_credential:
+            if not AZURE_IDENTITY_AVAILABLE:
+                raise ImportError(
+                    "Azure Identity is required for Azure credential authentication. "
+                    "Please install it using 'pip install azure-identity'"
+                )
+            self._setup_azure_auth()
+
+        # Setup connection pool
+        if self.connection_pool is None:
+            self._setup_connection_pool(minconn, maxconn)
+
+        # Create collection if it doesn't exist
+        collections = self.list_cols()
+        if collection_name not in collections:
+            self.create_col(name=collection_name, vector_size=embedding_model_dims, distance="cosine")
+
+    def _setup_azure_auth(self):
+        """Setup Azure authentication using DefaultAzureCredential."""
+        try:
+            credential = DefaultAzureCredential()
+            # Get access token for Azure Database for MySQL
+            token = credential.get_token("https://ossrdbms-aad.database.windows.net/.default")
+            # Use token as password
+            self.password = token.token
+            logger.info("Successfully authenticated using Azure DefaultAzureCredential")
+        except Exception as e:
+            logger.error(f"Failed to authenticate with Azure: {e}")
+            raise
+
+    def _setup_connection_pool(self, minconn: int, maxconn: int):
+        """Setup MySQL connection pool."""
+        connect_kwargs = {
+            "host": self.host,
+            "port": self.port,
+            "user": self.user,
+            "password": self.password,
+            "database": self.database,
+            "charset": "utf8mb4",
+            "cursorclass": DictCursor,
+            "autocommit": False,
+        }
+
+        # SSL configuration
+        if not self.ssl_disabled:
+            ssl_config = {"ssl_verify_cert": True}
+            if self.ssl_ca:
+                ssl_config["ssl_ca"] = self.ssl_ca
+            connect_kwargs["ssl"] = ssl_config
+
+        try:
+            self.connection_pool = PooledDB(
+                creator=pymysql,
+                mincached=minconn,
+                maxcached=maxconn,
+                maxconnections=maxconn,
+                blocking=True,
+                **connect_kwargs
+            )
+            logger.info("Successfully created MySQL connection pool")
+        except Exception as e:
+            logger.error(f"Failed to create connection pool: {e}")
+            raise
+
+    @contextmanager
+    def _get_cursor(self, commit: bool = False):
+        """
+        Context manager to get a cursor from the connection pool.
+        Auto-commits or rolls back based on exception.
+        """
+        conn = self.connection_pool.connection()
+        cur = conn.cursor()
+        try:
+            yield cur
+            if commit:
+                conn.commit()
+        except Exception as exc:
+            conn.rollback()
+            logger.error(f"Database error: {exc}", exc_info=True)
+            raise
+        finally:
+            cur.close()
+            conn.close()
+
+    def create_col(self, name: str = None, vector_size: int = None, distance: str = "cosine"):
+        """
+        Create a new collection (table in MySQL).
+        Enables vector extension and creates appropriate indexes.
+
+        Args:
+            name (str, optional): Collection name (uses self.collection_name if not provided)
+            vector_size (int, optional): Vector dimension (uses self.embedding_model_dims if not provided)
+            distance (str): Distance metric (cosine, euclidean, dot_product)
+        """
+        table_name = name or self.collection_name
+        dims = vector_size or self.embedding_model_dims
+
+        with self._get_cursor(commit=True) as cur:
+            # Create table with vector column
+            cur.execute(f"""
+                CREATE TABLE IF NOT EXISTS `{table_name}` (
+                    id VARCHAR(255) PRIMARY KEY,
+                    vector JSON,
+                    payload JSON,
+                    INDEX idx_payload_keys ((CAST(payload AS CHAR(255)) ARRAY))
+                )
+            """)
+            logger.info(f"Created collection '{table_name}' with vector dimension {dims}")
+
+    def insert(self, vectors: List[List[float]], payloads: Optional[List[Dict]] = None, ids: Optional[List[str]] = None):
+        """
+        Insert vectors into the collection.
+
+        Args:
+            vectors (List[List[float]]): List of vectors to insert
+            payloads (List[Dict], optional): List of payloads corresponding to vectors
+            ids (List[str], optional): List of IDs corresponding to vectors
+        """
+        logger.info(f"Inserting {len(vectors)} vectors into collection {self.collection_name}")
+
+        if payloads is None:
+            payloads = [{}] * len(vectors)
+        if ids is None:
+            import uuid
+            ids = [str(uuid.uuid4()) for _ in range(len(vectors))]
+
+        data = []
+        for vector, payload, vec_id in zip(vectors, payloads, ids):
+            data.append((vec_id, json.dumps(vector), json.dumps(payload)))
+
+        with self._get_cursor(commit=True) as cur:
+            cur.executemany(
+                f"INSERT INTO `{self.collection_name}` (id, vector, payload) VALUES (%s, %s, %s) "
+                f"ON DUPLICATE KEY UPDATE vector = VALUES(vector), payload = VALUES(payload)",
+                data
+            )
+
+    def _cosine_distance(self, vec1_json: str, vec2: List[float]) -> str:
+        """Generate SQL for cosine distance calculation."""
+        # For MySQL, we need to calculate cosine similarity manually
+        # This is a simplified version - in production, you'd use stored procedures or UDFs
+        return """
+            1 - (
+                (SELECT SUM(a.val * b.val) /
+                (SQRT(SUM(a.val * a.val)) * SQRT(SUM(b.val * b.val))))
+                FROM (
+                    SELECT JSON_EXTRACT(vector, CONCAT('$[', idx, ']')) as val
+                    FROM (SELECT @row := @row + 1 as idx FROM (SELECT 0 UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3) t1, (SELECT 0 UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3) t2) indices
+                    WHERE idx < JSON_LENGTH(vector)
+                ) a,
+                (
+                    SELECT JSON_EXTRACT(%s, CONCAT('$[', idx, ']')) as val
+                    FROM (SELECT @row := @row + 1 as idx FROM (SELECT 0 UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3) t1, (SELECT 0 UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3) t2) indices
+                    WHERE idx < JSON_LENGTH(%s)
+                ) b
+                WHERE a.idx = b.idx
+            )
+        """
+
+    def search(
+        self,
+        query: str,
+        vectors: List[float],
+        limit: int = 5,
+        filters: Optional[Dict] = None,
+    ) -> List[OutputData]:
+        """
+        Search for similar vectors using cosine similarity.
+
+        Args:
+            query (str): Query string (not used in vector search)
+            vectors (List[float]): Query vector
+            limit (int): Number of results to return
+            filters (Dict, optional): Filters to apply to the search
+
+        Returns:
+            List[OutputData]: Search results
+        """
+        filter_conditions = []
+        filter_params = []
+
+        if filters:
+            for k, v in filters.items():
+                filter_conditions.append("JSON_EXTRACT(payload, %s) = %s")
+                filter_params.extend([f"$.{k}", json.dumps(v)])
+
+        filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else ""
+
+        # For simplicity, we'll compute cosine similarity in Python
+        # In production, you'd want to use MySQL stored procedures or UDFs
+        with self._get_cursor() as cur:
+            query_sql = f"""
+                SELECT id, vector, payload
+                FROM `{self.collection_name}`
+                {filter_clause}
+            """
+            cur.execute(query_sql, filter_params)
+            results = cur.fetchall()
+
+        # Calculate cosine similarity in Python
+        import numpy as np
+        query_vec = np.array(vectors)
+        scored_results = []
+
+        for row in results:
+            vec = np.array(json.loads(row['vector']))
+            # Cosine similarity
+            similarity = np.dot(query_vec, vec) / (np.linalg.norm(query_vec) * np.linalg.norm(vec))
+            distance = 1 - similarity
+            scored_results.append((row['id'], distance, row['payload']))
+
+        # Sort by distance and limit
+        scored_results.sort(key=lambda x: x[1])
+        scored_results = scored_results[:limit]
+
+        return [
+            OutputData(id=r[0], score=float(r[1]), payload=json.loads(r[2]) if isinstance(r[2], str) else r[2])
+            for r in scored_results
+        ]
+
+    def delete(self, vector_id: str):
+        """
+        Delete a vector by ID.
+
+        Args:
+            vector_id (str): ID of the vector to delete
+        """
+        with self._get_cursor(commit=True) as cur:
+            cur.execute(f"DELETE FROM `{self.collection_name}` WHERE id = %s", (vector_id,))
+
+    def update(
+        self,
+        vector_id: str,
+        vector: Optional[List[float]] = None,
+        payload: Optional[Dict] = None,
+    ):
+        """
+        Update a vector and its payload.
+
+        Args:
+            vector_id (str): ID of the vector to update
+            vector (List[float], optional): Updated vector
+            payload (Dict, optional): Updated payload
+        """
+        with self._get_cursor(commit=True) as cur:
+            if vector is not None:
+                cur.execute(
+                    f"UPDATE `{self.collection_name}` SET vector = %s WHERE id = %s",
+                    (json.dumps(vector), vector_id),
+                )
+            if payload is not None:
+                cur.execute(
+                    f"UPDATE `{self.collection_name}` SET payload = %s WHERE id = %s",
+                    (json.dumps(payload), vector_id),
+                )
+
+    def get(self, vector_id: str) -> Optional[OutputData]:
+        """
+        Retrieve a vector by ID.
+
+        Args:
+            vector_id (str): ID of the vector to retrieve
+
+        Returns:
+            OutputData: Retrieved vector or None if not found
+        """
+        with self._get_cursor() as cur:
+            cur.execute(
+                f"SELECT id, vector, payload FROM `{self.collection_name}` WHERE id = %s",
+                (vector_id,),
+            )
+            result = cur.fetchone()
+            if not result:
+                return None
+            return OutputData(
+                id=result['id'],
+                score=None,
+                payload=json.loads(result['payload']) if isinstance(result['payload'], str) else result['payload']
+            )
+
+    def list_cols(self) -> List[str]:
+        """
+        List all collections (tables).
+
+        Returns:
+            List[str]: List of collection names
+        """
+        with self._get_cursor() as cur:
+            cur.execute("SHOW TABLES")
+            return [row[f"Tables_in_{self.database}"] for row in cur.fetchall()]
+
+    def delete_col(self):
+        """Delete the collection (table)."""
+        with self._get_cursor(commit=True) as cur:
+            cur.execute(f"DROP TABLE IF EXISTS `{self.collection_name}`")
+        logger.info(f"Deleted collection '{self.collection_name}'")
+
+    def col_info(self) -> Dict[str, Any]:
+        """
+        Get information about the collection.
+
+        Returns:
+            Dict[str, Any]: Collection information
+        """
+        with self._get_cursor() as cur:
+            cur.execute("""
+                SELECT
+                    TABLE_NAME as name,
+                    TABLE_ROWS as count,
+                    ROUND(((DATA_LENGTH + INDEX_LENGTH) / 1024 / 1024), 2) as size_mb
+                FROM information_schema.TABLES
+                WHERE TABLE_SCHEMA = %s AND TABLE_NAME = %s
+            """, (self.database, self.collection_name))
+            result = cur.fetchone()
+
+        if result:
+            return {
+                "name": result['name'],
+                "count": result['count'],
+                "size": f"{result['size_mb']} MB"
+            }
+        return {}
+
+    def list(
+        self,
+        filters: Optional[Dict] = None,
+        limit: int = 100
+    ) -> List[List[OutputData]]:
+        """
+        List all vectors in the collection.
+
+        Args:
+            filters (Dict, optional): Filters to apply
+            limit (int): Number of vectors to return
+
+        Returns:
+            List[List[OutputData]]: List of vectors
+        """
+        filter_conditions = []
+        filter_params = []
+
+        if filters:
+            for k, v in filters.items():
+                filter_conditions.append("JSON_EXTRACT(payload, %s) = %s")
+                filter_params.extend([f"$.{k}", json.dumps(v)])
+
+        filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else ""
+
+        with self._get_cursor() as cur:
+            cur.execute(
+                f"""
+                SELECT id, vector, payload
+                FROM `{self.collection_name}`
+                {filter_clause}
+                LIMIT %s
+                """,
+                (*filter_params, limit)
+            )
+            results = cur.fetchall()
+
+        return [[
+            OutputData(
+                id=r['id'],
+                score=None,
+                payload=json.loads(r['payload']) if isinstance(r['payload'], str) else r['payload']
+            ) for r in results
+        ]]
+
+    def reset(self):
+        """Reset the collection by deleting and recreating it."""
+        logger.warning(f"Resetting collection {self.collection_name}...")
+        self.delete_col()
+        self.create_col(name=self.collection_name, vector_size=self.embedding_model_dims)
+
+    def __del__(self):
+        """Close the connection pool when the object is deleted."""
+        try:
+            if hasattr(self, 'connection_pool') and self.connection_pool:
+                self.connection_pool.close()
+        except Exception:
+            pass

mem0/vector_stores/configs.py

@@ -21,6 +21,7 @@ class VectorStoreConfig(BaseModel):
         "neptune": "NeptuneAnalyticsConfig",
         "upstash_vector": "UpstashVectorConfig",
         "azure_ai_search": "AzureAISearchConfig",
+        "azure_mysql": "AzureMySQLConfig",
         "redis": "RedisDBConfig",
         "valkey": "ValkeyConfig",
         "databricks": "DatabricksConfig",

pyproject.toml

@@ -27,6 +27,7 @@ dependencies = [
 graph = [
     "langchain-neo4j>=0.4.0",
     "langchain-aws>=0.2.23",
+    "langchain-memgraph>=0.1.0",
     "neo4j>=5.23.1",
     "rank-bm25>=0.2.2",
     "kuzu>=0.11.0",
@@ -44,6 +45,8 @@ vector_stores = [
     "psycopg-pool>=3.2.6,<4.0.0",
     "pymongo>=4.13.2",
     "pymochow>=2.2.9",
+    "pymysql>=1.1.0",
+    "dbutils>=3.0.3",
     "valkey>=6.0.0",
     "databricks-sdk>=0.63.0",
     "azure-identity>=1.24.0",
@@ -69,7 +72,6 @@ extras = [
     "sentence-transformers>=5.0.0",
     "elasticsearch>=8.0.0,<9.0.0",
     "opensearch-py>=2.0.0",
-    "langchain-memgraph>=0.1.0",
 ]
 test = [
     "pytest>=8.2.2",

tests/vector_stores/test_azure_mysql.py

@@ -0,0 +1,269 @@
+import json
+import pytest
+from unittest.mock import Mock, patch
+
+from mem0.vector_stores.azure_mysql import AzureMySQL, OutputData
+
+
+@pytest.fixture
+def mock_connection_pool():
+    """Create a mock connection pool."""
+    pool = Mock()
+    conn = Mock()
+    cursor = Mock()
+
+    # Setup cursor mock
+    cursor.fetchall = Mock(return_value=[])
+    cursor.fetchone = Mock(return_value=None)
+    cursor.execute = Mock()
+    cursor.executemany = Mock()
+    cursor.close = Mock()
+
+    # Setup connection mock
+    conn.cursor = Mock(return_value=cursor)
+    conn.commit = Mock()
+    conn.rollback = Mock()
+    conn.close = Mock()
+
+    # Setup pool mock
+    pool.connection = Mock(return_value=conn)
+    pool.close = Mock()
+
+    return pool
+
+
+@pytest.fixture
+def azure_mysql_instance(mock_connection_pool):
+    """Create an AzureMySQL instance with mocked connection pool."""
+    with patch('mem0.vector_stores.azure_mysql.PooledDB') as mock_pooled_db:
+        mock_pooled_db.return_value = mock_connection_pool
+
+        instance = AzureMySQL(
+            host="test-server.mysql.database.azure.com",
+            port=3306,
+            user="testuser",
+            password="testpass",
+            database="testdb",
+            collection_name="test_collection",
+            embedding_model_dims=128,
+            use_azure_credential=False,
+            ssl_disabled=True,
+        )
+        instance.connection_pool = mock_connection_pool
+        return instance
+
+
+def test_azure_mysql_init(mock_connection_pool):
+    """Test AzureMySQL initialization."""
+    with patch('mem0.vector_stores.azure_mysql.PooledDB') as mock_pooled_db:
+        mock_pooled_db.return_value = mock_connection_pool
+
+        instance = AzureMySQL(
+            host="test-server.mysql.database.azure.com",
+            port=3306,
+            user="testuser",
+            password="testpass",
+            database="testdb",
+            collection_name="test_collection",
+            embedding_model_dims=128,
+        )
+
+        assert instance.host == "test-server.mysql.database.azure.com"
+        assert instance.port == 3306
+        assert instance.user == "testuser"
+        assert instance.database == "testdb"
+        assert instance.collection_name == "test_collection"
+        assert instance.embedding_model_dims == 128
+
+
+def test_create_col(azure_mysql_instance):
+    """Test collection creation."""
+    azure_mysql_instance.create_col(name="new_collection", vector_size=256)
+
+    # Verify that execute was called (table creation)
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    assert cursor.execute.called
+
+
+def test_insert(azure_mysql_instance):
+    """Test vector insertion."""
+    vectors = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+    payloads = [{"text": "test1"}, {"text": "test2"}]
+    ids = ["id1", "id2"]
+
+    azure_mysql_instance.insert(vectors=vectors, payloads=payloads, ids=ids)
+
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    assert cursor.executemany.called
+
+
+def test_search(azure_mysql_instance):
+    """Test vector search."""
+    # Mock the database response
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    cursor.fetchall = Mock(return_value=[
+        {
+            'id': 'id1',
+            'vector': json.dumps([0.1, 0.2, 0.3]),
+            'payload': json.dumps({"text": "test1"})
+        },
+        {
+            'id': 'id2',
+            'vector': json.dumps([0.4, 0.5, 0.6]),
+            'payload': json.dumps({"text": "test2"})
+        }
+    ])
+
+    query_vector = [0.2, 0.3, 0.4]
+    results = azure_mysql_instance.search(query="test", vectors=query_vector, limit=5)
+
+    assert isinstance(results, list)
+    assert cursor.execute.called
+
+
+def test_delete(azure_mysql_instance):
+    """Test vector deletion."""
+    azure_mysql_instance.delete(vector_id="test_id")
+
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    assert cursor.execute.called
+
+
+def test_update(azure_mysql_instance):
+    """Test vector update."""
+    new_vector = [0.7, 0.8, 0.9]
+    new_payload = {"text": "updated"}
+
+    azure_mysql_instance.update(vector_id="test_id", vector=new_vector, payload=new_payload)
+
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    assert cursor.execute.called
+
+
+def test_get(azure_mysql_instance):
+    """Test retrieving a vector by ID."""
+    # Mock the database response
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    cursor.fetchone = Mock(return_value={
+        'id': 'test_id',
+        'vector': json.dumps([0.1, 0.2, 0.3]),
+        'payload': json.dumps({"text": "test"})
+    })
+
+    result = azure_mysql_instance.get(vector_id="test_id")
+
+    assert result is not None
+    assert isinstance(result, OutputData)
+    assert result.id == "test_id"
+
+
+def test_list_cols(azure_mysql_instance):
+    """Test listing collections."""
+    # Mock the database response
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    cursor.fetchall = Mock(return_value=[
+        {"Tables_in_testdb": "collection1"},
+        {"Tables_in_testdb": "collection2"}
+    ])
+
+    collections = azure_mysql_instance.list_cols()
+
+    assert isinstance(collections, list)
+    assert len(collections) == 2
+
+
+def test_delete_col(azure_mysql_instance):
+    """Test collection deletion."""
+    azure_mysql_instance.delete_col()
+
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    assert cursor.execute.called
+
+
+def test_col_info(azure_mysql_instance):
+    """Test getting collection information."""
+    # Mock the database response
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    cursor.fetchone = Mock(return_value={
+        'name': 'test_collection',
+        'count': 100,
+        'size_mb': 1.5
+    })
+
+    info = azure_mysql_instance.col_info()
+
+    assert isinstance(info, dict)
+    assert cursor.execute.called
+
+
+def test_list(azure_mysql_instance):
+    """Test listing vectors."""
+    # Mock the database response
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    cursor.fetchall = Mock(return_value=[
+        {
+            'id': 'id1',
+            'vector': json.dumps([0.1, 0.2, 0.3]),
+            'payload': json.dumps({"text": "test1"})
+        }
+    ])
+
+    results = azure_mysql_instance.list(limit=10)
+
+    assert isinstance(results, list)
+    assert len(results) > 0
+
+
+def test_reset(azure_mysql_instance):
+    """Test resetting the collection."""
+    azure_mysql_instance.reset()
+
+    conn = azure_mysql_instance.connection_pool.connection()
+    cursor = conn.cursor()
+    # Should call execute at least twice (drop and create)
+    assert cursor.execute.call_count >= 2
+
+
+@pytest.mark.skipif(True, reason="Requires Azure credentials")
+def test_azure_credential_authentication():
+    """Test Azure DefaultAzureCredential authentication."""
+    with patch('mem0.vector_stores.azure_mysql.DefaultAzureCredential') as mock_cred:
+        mock_token = Mock()
+        mock_token.token = "test_token"
+        mock_cred.return_value.get_token.return_value = mock_token
+
+        instance = AzureMySQL(
+            host="test-server.mysql.database.azure.com",
+            port=3306,
+            user="testuser",
+            password=None,
+            database="testdb",
+            collection_name="test_collection",
+            embedding_model_dims=128,
+            use_azure_credential=True,
+        )
+
+        assert instance.password == "test_token"
+
+
+def test_output_data_model():
+    """Test OutputData model."""
+    data = OutputData(
+        id="test_id",
+        score=0.95,
+        payload={"text": "test"}
+    )
+
+    assert data.id == "test_id"
+    assert data.score == 0.95
+    assert data.payload == {"text": "test"}