[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["必要な情報がない","missingTheInformationINeed","thumb-down"],["複雑すぎる / 手順が多すぎる","tooComplicatedTooManySteps","thumb-down"],["最新ではない","outOfDate","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["サンプル / コードに問題がある","samplesCodeIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-08-30 UTC。"],[],[],null,["# Build LLM-powered applications using LangChain\n\n\u003cbr /\u003e\n\nMySQL \\| [PostgreSQL](/sql/docs/postgres/langchain \"View this page for the PostgreSQL database engine\") \\| [SQL Server](/sql/docs/sqlserver/langchain \"View this page for the SQL Server database engine\")\n\n\u003cbr /\u003e\n\n|\n| **Preview\n| --- LangChain**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\nThis page introduces how to build LLM-powered applications using\n[LangChain](https://www.langchain.com/). The overviews on this\npage link to procedure guides in GitHub.\n\nWhat is LangChain?\n------------------\n\nLangChain is an LLM orchestration framework that helps developers build\ngenerative AI applications or retrieval-augmented generation (RAG) workflows. It\nprovides the structure, tools, and components to streamline complex LLM\nworkflows.\n\nFor more information about LangChain, see the [Google\nLangChain](https://python.langchain.com/docs/integrations/platforms/google)\npage. For more information about the LangChain framework, see the [LangChain](https://python.langchain.com/docs/get_started/introduction)\nproduct documentation.\n\nLangChain components for Cloud SQL for MySQL\n--------------------------------------------\n\nCloud SQL for MySQL offers the following LangChain interfaces:\n\n- [Vector store](#vector-store)\n- [Document loader](#document-loader)\n- [Chat message history](#chat-message-history)\n\nLearn how to use LangChain with the\n[LangChain Quickstart for Cloud SQL for MySQL](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/samples/langchain_quick_start.ipynb).\n\nVector store for Cloud SQL for MySQL\n------------------------------------\n\nVector store retrieves and stores documents and metadata from a vector database.\nVector store gives an application the ability to perform semantic searches that\ninterpret the meaning of a user query. This type of search is a called a\nvector search, and it can find topics that match the query conceptually. At\nquery time, vector store retrieves the embedding vectors that are\nmost similar to the embedding of the search request. In LangChain, a vector\nstore takes care of storing embedded data and performing the vector search\nfor you.\n\nTo work with vector store in Cloud SQL for MySQL, use the\n`MySQLVectorStore` class.\n\nFor more information, see the [LangChain Vector\nStores](https://python.langchain.com/docs/modules/data_connection/vectorstores/)\nproduct documentation.\n\n### Vector store procedure guide\n\nThe [Cloud SQL for MySQL guide for vector\nstore](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/docs/vector_store.ipynb)\nshows you how to do the following:\n\n- Install the integration package and LangChain\n- Create a `MySQLEngine` object and configure a connection pool to your Cloud SQL for MySQL database\n- Initialize a table\n- Create an embedding object using `VertexAIEmbeddings`\n- Initialize a default `MySQLVectorStore`\n- Add texts\n- Delete texts\n- Search for documents\n- Search for documents by vector\n- Add an index to accelerate vector search queries\n- Remove an index\n- Create a custom vector store\n- Search for documents with a metadata filter\n\nDocument loader for Cloud SQL for MySQL\n---------------------------------------\n\nThe document loader saves, loads, and deletes a LangChain `Document`\nobjects. For example, you can load data for processing into embeddings and\neither store it in vector store or use it as a tool to provide specific context\nto chains.\n\nTo load documents from document loader in Cloud SQL for MySQL, use the\n`MySQLLoader` class. `MySQLLoader` methods return one or more documents from a\ntable. Use the `MySQLDocumentSaver` class to save and delete documents.\n\nFor more information, see the [LangChain Document\nloaders](https://python.langchain.com/docs/modules/data_connection/document_loaders/) topic.\n\n### Document loader procedure guide\n\nThe [Cloud SQL for MySQL guide for document\nloader](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/docs/document_loader.ipynb) shows you how to do the following:\n\n- Install the integration package and LangChain\n- Load documents from a table\n- Add a filter to the loader\n- Customize the connection and authentication\n- Customize Document construction by specifying customer content and metadata\n- How to use and customize a `MySQLDocumentSaver` to store and delete documents\n\nChat message history for Cloud SQL for MySQL\n--------------------------------------------\n\nQuestion and answer applications require a history of the things said in the\nconversation to give the application context for answering further questions\nfrom the user. The LangChain `ChatMessageHistory` class lets the application\nsave messages to a database and retrieve them when needed to formulate further\nanswers. A message can be a question, an answer, a statement, a greeting or any\nother piece of text that the user or application gives during the conversation.\n`ChatMessageHistory` stores each message and chains messages together for each\nconversation.\n\nCloud SQL for MySQL extends this class with `MySQLChatMessageHistory`.\n\n### Chat message history procedure guide\n\nThe [Cloud SQL for MySQL guide for chat message\nhistory](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/blob/main/docs/chat_message_history.ipynb) shows you how to do the following:\n\n- Install LangChain and authenticate to Google Cloud\n- Create a `MySQLEngine` object and configure a connection pool to your Cloud SQL for MySQL database\n- Initialize a table\n- Initialize the `MySQLChatMessageHistory` class to add and delete messages\n- Create a chain for message history using the LangChain Expression Language (LCEL) and Google's Vertex AI chat models"]]
