[[["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"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[[["\u003cp\u003eThis page introduces how to use LangChain, an LLM orchestration framework, to build generative AI applications or retrieval-augmented generation (RAG) workflows.\u003c/p\u003e\n"],["\u003cp\u003eBigtable offers LangChain interfaces for document loading, allowing for the saving, loading, and deleting of LangChain \u003ccode\u003eDocument\u003c/code\u003e objects, and chat message history, facilitating question-and-answer applications by storing and retrieving conversation messages.\u003c/p\u003e\n"],["\u003cp\u003eThe document loader procedure guide demonstrates how to install the integration package and LangChain, load documents from a table, add filters, customize connections and authentication, and manage \u003ccode\u003eBigtableSaver\u003c/code\u003e for storing and deleting documents.\u003c/p\u003e\n"],["\u003cp\u003eThe chat message history procedure guide explains how to install LangChain, authenticate to Google Cloud, initialize Bigtable schema, and use the \u003ccode\u003eBigtableChatMessageHistory\u003c/code\u003e class to add and delete conversation messages.\u003c/p\u003e\n"],["\u003cp\u003eLangChain is currently a Pre-GA feature, which means it is available "as is," may have limited support, and is subject to the "Pre-GA Offerings Terms" in the General Service Terms.\u003c/p\u003e\n"]]],[],null,["Build LLM-powered applications using LangChain\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\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 Bigtable\n\nBigtable offers the following LangChain interfaces:\n\n- [Document loader](#document-loader)\n- [Chat message history](#chat-message-history)\n\nLearn how to use LangChain with the\n[LangChain Quickstart for Bigtable](https://github.com/googleapis/langchain-google-bigtable-python).\nThis quickstart creates an application that accesses a Netflix Movie dataset so\nthat users can interact with movie data.\n\nDocument loader for Bigtable\n\nThe document loader saves, loads, and deletes a LangChain `Document` objects.\nFor example, you can load data for processing into embeddings and either store\nit in vector store or use it as a tool to provide specific context to chains.\n\nTo load documents from document loader in Bigtable, use the\n`BigtableLoader` class. `BigtableLoader` methods return one or more documents\nfrom a table. Use the `BigtableSaver` 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\nDocument loader procedure guide\n\nThe [Bigtable guide for document\nloader](https://github.com/googleapis/langchain-google-bigtable-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 `BigtableSaver` to store and delete documents\n\nChat message history for Bigtable\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 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\nBigtable extends this class with `BigtableChatMessageHistory`.\n\nChat message history procedure guide\n\nThe [Bigtable guide for chat message\nhistory](https://github.com/googleapis/langchain-google-bigtable-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- Initialize Bigtable schema\n- Initialize the `BigtableChatMessageHistory` class to add and delete messages\n- Use a client to customize the connection and authentication"]]
