feat: add MySQLVectorStore initialization methods (#52)

Add constructor and classmethods for MySQLVectorStore.

integration.cloudbuild.yaml

@@ -43,7 +43,7 @@ availableSecrets:
       env: "DB_PASSWORD"
 
 substitutions:
-  _INSTANCE_ID: test-instance
+  _INSTANCE_ID: mysql-vector
   _REGION: us-central1
   _DB_NAME: test
   _VERSION: "3.8"

src/langchain_google_cloud_sql_mysql/__init__.py

@@ -13,14 +13,17 @@
 # limitations under the License.
 
 from .chat_message_history import MySQLChatMessageHistory
-from .engine import MySQLEngine
+from .engine import Column, MySQLEngine
 from .loader import MySQLDocumentSaver, MySQLLoader
+from .vectorstore import MySQLVectorStore
 from .version import __version__
 
 __all__ = [
+    "Column",
     "MySQLChatMessageHistory",
     "MySQLDocumentSaver",
     "MySQLEngine",
     "MySQLLoader",
+    "MySQLVectorStore",
     "__version__",
 ]

src/langchain_google_cloud_sql_mysql/chat_message_history.py

@@ -25,7 +25,7 @@ class MySQLChatMessageHistory(BaseChatMessageHistory):
     """Chat message history stored in a Cloud SQL MySQL database.
 
     Args:
-        engine (MySQLEngine): SQLAlchemy connection pool engine for managing
+        engine (MySQLEngine): Connection pool engine for managing
             connections to Cloud SQL for MySQL.
         session_id (str): Arbitrary key that is used to store the messages
             of a single chat session.

src/langchain_google_cloud_sql_mysql/engine.py

@@ -31,6 +31,21 @@
 
 USER_AGENT = "langchain-google-cloud-sql-mysql-python/" + __version__
 
+from dataclasses import dataclass
+
+
+@dataclass
+class Column:
+    name: str
+    data_type: str
+    nullable: bool = True
+
+    def __post_init__(self):
+        if not isinstance(self.name, str):
+            raise ValueError("Column name must be type string")
+        if not isinstance(self.data_type, str):
+            raise ValueError("Column data_type must be type string")
+
 
 def _get_iam_principal_email(
     credentials: google.auth.credentials.Credentials,
@@ -206,6 +221,20 @@ def connect(self) -> sqlalchemy.engine.Connection:
         """
         return self.engine.connect()
 
+    def _execute(self, query: str, params: Optional[dict] = None) -> None:
+        """Execute a SQL query."""
+        with self.engine.connect() as conn:
+            conn.execute(sqlalchemy.text(query), params)
+            conn.commit()
+
+    def _fetch(self, query: str, params: Optional[dict] = None):
+        """Fetch results from a SQL query."""
+        with self.engine.connect() as conn:
+            result = conn.execute(sqlalchemy.text(query), params)
+            result_map = result.mappings()
+            result_fetch = result_map.fetchall()
+            return result_fetch
+
     def init_chat_history_table(self, table_name: str) -> None:
         """Create table with schema required for MySQLChatMessageHistory class.
 
@@ -293,3 +322,51 @@ def _load_document_table(self, table_name: str) -> sqlalchemy.Table:
         metadata = sqlalchemy.MetaData()
         sqlalchemy.MetaData.reflect(metadata, bind=self.engine, only=[table_name])
         return metadata.tables[table_name]
+
+    def init_vectorstore_table(
+        self,
+        table_name: str,
+        vector_size: int,
+        content_column: str = "content",
+        embedding_column: str = "embedding",
+        metadata_columns: List[Column] = [],
+        metadata_json_column: str = "langchain_metadata",
+        id_column: str = "langchain_id",
+        overwrite_existing: bool = False,
+        store_metadata: bool = True,
+    ) -> None:
+        """
+        Create a table for saving of vectors to be used with MySQLVectorStore.
+
+        Args:
+            table_name (str): The MySQL database table name.
+            vector_size (int): Vector size for the embedding model to be used.
+            content_column (str): Name of the column to store document content.
+                Deafult: `page_content`.
+            embedding_column (str) : Name of the column to store vector embeddings.
+                Default: `embedding`.
+            metadata_columns (List[Column]): A list of Columns to create for custom
+                metadata. Default: []. Optional.
+            metadata_json_column (str): The column to store extra metadata in JSON format.
+                Default: `langchain_metadata`. Optional.
+            id_column (str):  Name of the column to store ids.
+                Default: `langchain_id`. Optional,
+            overwrite_existing (bool): Whether to drop existing table. Default: False.
+            store_metadata (bool): Whether to store metadata in the table.
+                Default: True.
+        """
+        query = f"""CREATE TABLE `{table_name}`(
+            `{id_column}` CHAR(36) PRIMARY KEY,
+            `{content_column}` TEXT NOT NULL,
+            `{embedding_column}` vector({vector_size}) USING VARBINARY NOT NULL"""
+        for column in metadata_columns:
+            nullable = "NOT NULL" if not column.nullable else ""
+            query += f",\n`{column.name}` {column.data_type} {nullable}"
+        if store_metadata:
+            query += f""",\n`{metadata_json_column}` JSON"""
+        query += "\n);"
+
+        with self.engine.connect() as conn:
+            if overwrite_existing:
+                conn.execute(sqlalchemy.text(f"DROP TABLE IF EXISTS `{table_name}`"))
+            conn.execute(sqlalchemy.text(query))

src/langchain_google_cloud_sql_mysql/indexes.py

@@ -0,0 +1,22 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import ABC
+from dataclasses import dataclass
+
+
+@dataclass
+class QueryOptions(ABC):
+    def to_string(self) -> str:
+        raise NotImplementedError("to_string method must be implemented by subclass")