> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Microsoft integrations > Integrate with Microsoft using LangChain Python. This page covers all LangChain integrations with [Microsoft Azure](https://portal.azure.com) and other [Microsoft](https://www.microsoft.com) products. **Recommended: Azure OpenAI** We recommend using [Azure OpenAI](https://reference.langchain.com/python/langchain-openai/llms/azure/AzureOpenAI) across [chat models](#chat-models), [LLMs](#llms), and [embedding models](#embedding-models). With the [v1 API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/api-version-lifecycle?tabs=python) (Generally Available as of August 2025), you can use your Azure endpoint and API keys directly with the [`langchain-openai`](https://reference.langchain.com/python/langchain-openai/) package to call any model deployed in [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/) (including OpenAI, Llama, DeepSeek, Mistral, and Phi) through a single interface. You also get native support for Microsoft Entra ID authentication and access to the latest features including the [Responses API](#responses-api) and [reasoning models](/oss/python/integrations/chat/azure_chat_openai). [Get started here](#azure-openai). **Samples and tutorials:** * [Azure-Samples/langchain-azure-openai-starter](https://github.com/Azure-Samples/langchain-azure-openai-starter): Start with a production-ready LangChain and Azure OpenAI app template that lets you deploy directly to Azure with a single `azd` command. * [microsoft/langchain-for-beginners](https://github.com/microsoft/langchain-for-beginners): A hands-on course introducing LangChain with Azure OpenAI. * [Azure-Samples/langchain-agent-python](https://github.com/Azure-Samples/langchain-agent-python): Build and deploy LangChain agents on Azure. **Claude on Azure** Microsoft Foundry also offers access to all [Anthropic Claude models](https://learn.microsoft.com/en-us/azure/foundry/foundry-models/how-to/use-foundry-models-claude), including Opus, Sonnet, and Haiku. Claude models are served through a dedicated Anthropic-native endpoint rather than the Azure OpenAI v1 API. Use [`langchain-anthropic`](/oss/python/integrations/chat/anthropic) pointed at your Foundry Anthropic endpoint. ## Chat models Microsoft offers three main options for accessing chat models through Azure: 1. **[Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/)** (recommended) — Access any model deployed in Microsoft Foundry (including OpenAI, Llama, DeepSeek, Mistral, and Phi) through a single interface, with enterprise features such as keyless authentication through [Microsoft Entra ID](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/managed-identity), regional data residency, and private networking. Use [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) on the v1 API, or [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI) for traditional deployments. Azure OpenAI also supports the [Responses API](#responses-api), which gives you access to server-side tools like code interpreter, image generation, and file search directly from your chat model. 2. **[Azure AI](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models)** — Recommended for accessing tools, storage, and custom middleware from the broader Azure ecosystem alongside your chat model. 3. **[Azure ML](https://learn.microsoft.com/en-us/azure/machine-learning/)** — Allows deployment and management of custom or fine-tuned open-source models with Azure Machine Learning. ### Azure OpenAI To get started with Azure OpenAI, [create an Azure deployment](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource) and install the `langchain-openai` package: ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-openai ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-openai ``` On the v1 API, use [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) directly against your Azure endpoint—no `api_version` required: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install azure-identity ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from azure.identity import DefaultAzureCredential, get_bearer_token_provider from langchain_openai import ChatOpenAI token_provider = get_bearer_token_provider( DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default", ) llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure deployment name base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key=token_provider, # callable that handles token refresh ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure deployment name base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key="your-azure-api-key", ) ``` For traditional Azure OpenAI API versions, use [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI): ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import AzureChatOpenAI ``` See the [Azure ChatOpenAI integration page](/oss/python/integrations/chat/azure_chat_openai) for end-to-end setup, Entra ID authentication, tool calling, and reasoning examples. #### Responses API Azure OpenAI supports the [Responses API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/responses), which provides stateful conversations, built-in tools (web search, file search, code interpreter), and structured reasoning summaries. [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) automatically routes to the Responses API when you set the `reasoning` parameter, or you can opt in explicitly with `use_responses_api=True`: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from azure.identity import DefaultAzureCredential, get_bearer_token_provider from langchain_openai import ChatOpenAI token_provider = get_bearer_token_provider( DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default", ) llm = ChatOpenAI( model="gpt-5.4-mini", base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key=token_provider, use_responses_api=True, ) response = llm.invoke("Summarize the bitter lesson.") print(response.text) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key="your-azure-api-key", use_responses_api=True, ) response = llm.invoke("Summarize the bitter lesson.") print(response.text) ``` For a walkthrough of reasoning effort, reasoning summaries, and streaming with the Responses API, see the [Azure ChatOpenAI integration page](/oss/python/integrations/chat/azure_chat_openai). ### Azure AI > [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/developer/python/get-started) is the broader Azure AI platform. The `langchain-azure-ai` package lets you bring Azure-native tools, storage, and custom middleware into your LangChain app, and exposes chat models deployed in Foundry through the `AzureAIOpenAIApiChatModel` class. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-azure-ai ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-ai ``` See a [usage example](/oss/python/integrations/chat/azure_ai). ### Azure ML chat online endpoint ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-community ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-community ``` See the [Azure ML chat endpoint documentation](/oss/python/integrations/chat/azureml_chat_endpoint) for accessing chat models hosted with [Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning/). ## LLMs Microsoft offers two main options for accessing LLMs through Azure: 1. **[Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/)** (recommended) — Access any model deployed in Microsoft Foundry (including OpenAI, Llama, DeepSeek, Mistral, and Phi) as a completion LLM with [`AzureOpenAI`](https://reference.langchain.com/python/langchain-openai/llms/azure/AzureOpenAI). 2. **[Azure ML](https://learn.microsoft.com/en-us/azure/machine-learning/)** — Use custom or open-source models hosted on Azure Machine Learning online endpoints. ### Azure OpenAI See a [usage example](/oss/python/integrations/llms/azure_openai). ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-openai ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-openai ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from azure.identity import DefaultAzureCredential, get_bearer_token_provider from langchain_openai import AzureOpenAI token_provider = get_bearer_token_provider( DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default", ) llm = AzureOpenAI( azure_deployment="gpt-5.4-mini", # your Azure deployment name api_version="2025-04-01-preview", azure_ad_token_provider=token_provider, ) print(llm.invoke("Write a haiku about the ocean.")) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import AzureOpenAI llm = AzureOpenAI( azure_deployment="gpt-5.4-mini", # your Azure deployment name api_version="2025-04-01-preview", azure_endpoint="https://YOUR-RESOURCE-NAME.openai.azure.com/", api_key="your-azure-api-key", ) print(llm.invoke("Write a haiku about the ocean.")) ``` ### Azure ML ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-community ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-community ``` See a [usage example](/oss/python/integrations/llms/azure_ml). ## Embedding models Microsoft offers two main options for accessing embedding models through Azure: 1. **[Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/)** (recommended) — Use embedding models deployed in Microsoft Foundry (including OpenAI `text-embedding-3-small`, `text-embedding-3-large`, and Cohere) with [`AzureOpenAIEmbeddings`](https://reference.langchain.com/python/langchain-openai/embeddings/azure/AzureOpenAIEmbeddings). 2. **[Azure AI](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models)** — Recommended for accessing tools, storage, and custom middleware from the broader Azure ecosystem alongside your embedding model. ### Azure OpenAI See a [usage example](/oss/python/integrations/embeddings/azure_openai). ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-openai ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-openai ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from azure.identity import DefaultAzureCredential, get_bearer_token_provider from langchain_openai import AzureOpenAIEmbeddings token_provider = get_bearer_token_provider( DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default", ) embeddings = AzureOpenAIEmbeddings( azure_deployment="text-embedding-3-small", # your Azure deployment name api_version="2025-04-01-preview", azure_ad_token_provider=token_provider, ) vector = embeddings.embed_query("LangChain makes agents easy.") ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import AzureOpenAIEmbeddings embeddings = AzureOpenAIEmbeddings( azure_deployment="text-embedding-3-small", # your Azure deployment name api_version="2025-04-01-preview", azure_endpoint="https://YOUR-RESOURCE-NAME.openai.azure.com/", api_key="your-azure-api-key", ) vector = embeddings.embed_query("LangChain makes agents easy.") ``` ### Azure AI ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-azure-ai ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-ai ``` See a [usage example](/oss/python/integrations/providers/azure_ai#azure-ai-model-inference-for-embeddings). ## Middleware ### Azure AI Content Safety middleware > [Azure AI Content Safety](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/overview) provides guardrails you can apply to LangChain agents through middleware. The `langchain-azure-ai` package currently exports middleware for text moderation, image moderation, prompt injection detection, protected material detection, and groundedness evaluation. Install the middleware package: ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-azure-ai ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-ai ``` See the [Microsoft Foundry middleware guide](/oss/python/integrations/middleware/azure_ai). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_azure_ai.agents.middleware import AzureContentModerationMiddleware ``` ## Document loaders ### Azure AI data > [Azure AI Foundry (formerly Azure AI Studio](https://ai.azure.com/) provides the capability to upload data assets > to cloud storage and register existing data assets from the following sources: > > * `Microsoft OneLake` > * `Azure Blob Storage` > * `Azure Data Lake gen 2` First, you need to install several python packages. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install azureml-fsspec, azure-ai-generative ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add azureml-fsspec, azure-ai-generative ``` See a [usage example](/oss/python/integrations/document_loaders/azure_ai_data). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.document_loaders import AzureAIDataLoader ``` ### Azure AI document intelligence > [Azure AI Document Intelligence](https://aka.ms/doc-intelligence) (formerly known > as `Azure Form Recognizer`) is machine-learning > based service that extracts texts (including handwriting), tables, document structures, > and key-value-pairs > from digital or scanned PDFs, images, Office and HTML files. > > Document Intelligence supports `PDF`, `JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF`, `DOCX`, `XLSX`, `PPTX` and `HTML`. First, you need to install a python package. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install azure-ai-documentintelligence ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add azure-ai-documentintelligence ``` See a [usage example](/oss/python/integrations/document_loaders/azure_document_intelligence). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.document_loaders import AzureAIDocumentIntelligenceLoader ``` ### Azure Blob Storage > [Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data. `Azure Blob Storage` is designed for: * Serving images or documents directly to a browser. * Storing files for distributed access. * Streaming video and audio. * Writing to log files. * Storing data for backup and restore, disaster recovery, and archiving. * Storing data for analysis by an on-premises or Azure-hosted service. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-azure-storage ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-storage ``` See [usage examples for the Azure Blob Storage Loader](/oss/python/integrations/document_loaders/azure_blob_storage). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_azure_storage.document_loaders import AzureBlobStorageLoader ``` ### Microsoft OneDrive > [Microsoft OneDrive](https://en.wikipedia.org/wiki/OneDrive) (formerly `SkyDrive`) is a file-hosting service operated by Microsoft. First, you need to install a python package. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install o365 ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add o365 ``` See a [usage example](/oss/python/integrations/document_loaders/microsoft_onedrive). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders import OneDriveLoader ``` ### Microsoft OneDrive file > [Microsoft OneDrive](https://en.wikipedia.org/wiki/OneDrive) (formerly `SkyDrive`) is a file-hosting service operated by Microsoft. First, you need to install a python package. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install o365 ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add o365 ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders import OneDriveFileLoader ``` ### Microsoft word > [Microsoft Word](https://www.microsoft.com/en-us/microsoft-365/word) is a word processor developed by Microsoft. See a [usage example](/oss/python/integrations/document_loaders/microsoft_word). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders import UnstructuredWordDocumentLoader ``` ### Microsoft excel > [Microsoft Excel](https://en.wikipedia.org/wiki/Microsoft_Excel) is a spreadsheet editor developed by > Microsoft for Windows, macOS, Android, iOS and iPadOS. > It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming > language called Visual Basic for Applications (VBA). Excel forms part of the Microsoft 365 suite of software. The `UnstructuredExcelLoader` is used to load `Microsoft Excel` files. The loader works with both `.xlsx` and `.xls` files. The page content will be the raw text of the Excel file. If you use the loader in `"elements"` mode, an HTML representation of the Excel file will be available in the document metadata under the `text_as_html` key. See a [usage example](/oss/python/integrations/document_loaders/microsoft_excel). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders import UnstructuredExcelLoader ``` ### Microsoft SharePoint > [Microsoft SharePoint](https://en.wikipedia.org/wiki/SharePoint) is a website-based collaboration system > that uses workflow applications, “list” databases, and other web parts and security features to > empower business teams to work together developed by Microsoft. See a [usage example](/oss/python/integrations/document_loaders/microsoft_sharepoint). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders.sharepoint import SharePointLoader ``` ### Microsoft PowerPoint > [Microsoft PowerPoint](https://en.wikipedia.org/wiki/Microsoft_PowerPoint) is a presentation program by Microsoft. See a [usage example](/oss/python/integrations/document_loaders/microsoft_powerpoint). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders import UnstructuredPowerPointLoader ``` ### Microsoft OneNote First, let's install dependencies: ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install bs4 msal ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add bs4 msal ``` See a [usage example](/oss/python/integrations/document_loaders/microsoft_onenote). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders.onenote import OneNoteLoader ``` ### Playwright URL loader > [Playwright](https://github.com/microsoft/playwright) is an open-source automation tool > developed by `Microsoft` that allows you to programmatically control and automate > web browsers. It is designed for end-to-end testing, scraping, and automating > tasks across various web browsers such as `Chromium`, `Firefox`, and `WebKit`. First, let's install dependencies: ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install playwright unstructured ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add playwright unstructured ``` See a [usage example](/oss/python/integrations/document_loaders/url/#playwright-url-loader). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders.onenote import OneNoteLoader ``` ## Memory ### Azure cosmos DB chat message history > [Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/) provides chat message history storage for conversational AI applications, enabling you to persist and retrieve conversation history with low latency and high availability. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-azure-cosmosdb ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-cosmosdb ``` Configure your Azure Cosmos DB connection (sync or async, with access key or Microsoft Entra ID): ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_azure_cosmosdb import CosmosDBChatMessageHistory history = CosmosDBChatMessageHistory( cosmos_endpoint="https://.documents.azure.com:443/", cosmos_database="", cosmos_container="", session_id="", user_id="", credential="", ttl=3600, # optional: messages expire after 1 hour ) history.prepare_cosmos() history.add_user_message("Hello!") history.add_ai_message("Hi there!") ``` For async usage, import `AsyncCosmosDBChatMessageHistory` from the same package. ### Azure cosmos DB semantic cache > [`AzureCosmosDBNoSqlSemanticCache`](https://github.com/langchain-ai/langchain-azure/tree/main/libs/azure-cosmosdb) caches LLM responses in Azure Cosmos DB for NoSQL using vector similarity, returning cached results when a semantically similar prompt is seen again. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-azure-cosmosdb ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-cosmosdb ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from azure.cosmos import CosmosClient, PartitionKey from langchain_core.globals import set_llm_cache from langchain_azure_cosmosdb import AzureCosmosDBNoSqlSemanticCache cosmos_client = CosmosClient("", "") cache = AzureCosmosDBNoSqlSemanticCache( cosmos_client=cosmos_client, embedding=embedding, vector_embedding_policy=vector_embedding_policy, indexing_policy=indexing_policy, cosmos_container_properties={"partition_key": PartitionKey(path="/id")}, cosmos_database_properties={"id": "cache-db"}, vector_search_fields={"text_field": "text", "embedding_field": "embedding"}, database_name="cache-db", container_name="cache-container", ) set_llm_cache(cache) ``` For async usage, import `AsyncAzureCosmosDBNoSqlSemanticCache`. ## Vector stores ### Azure cosmos DB AI agents can rely on Azure Cosmos DB as a unified [memory system](https://learn.microsoft.com/en-us/azure/cosmos-db/ai-agents#memory-can-make-or-break-agents) solution, enjoying speed, scale, and simplicity. This service successfully [enabled OpenAI's ChatGPT service](https://www.youtube.com/watch?v=6IIUtEFKJec\&t) to scale dynamically with high reliability and low maintenance. Powered by an atom-record-sequence engine, it is the world's first globally distributed [NoSQL](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-nosql), [relational](https://learn.microsoft.com/en-us/azure/cosmos-db/distributed-relational), and [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) service that offers a serverless mode. Below are two available Azure Cosmos DB APIs that can provide vector store functionalities. #### Azure cosmos DB for MongoDB (vCore) > [Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/) makes it easy to create a database with full native MongoDB support. > You can apply your MongoDB experience and continue to use your favorite MongoDB drivers, SDKs, and tools by pointing your application to the API for MongoDB vCore account's connection string. > Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based applications with your data that's stored in Azure Cosmos DB. ##### Installation and setup See [detailed configuration instructions](/oss/python/integrations/vectorstores/azure_cosmos_db_mongo_vcore). We need to install `langchain-azure-ai` and `pymongo` python packages. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-azure-ai pymongo ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-ai pymongo ``` ##### Deploy Azure cosmos DB on Microsoft Azure Azure Cosmos DB for MongoDB vCore provides developers with a fully managed MongoDB-compatible database service for building modern applications with a familiar architecture. With Cosmos DB for MongoDB vCore, developers can enjoy the benefits of native Azure integrations, low total cost of ownership (TCO), and the familiar vCore architecture when migrating existing applications or building new ones. [Sign Up](https://azure.microsoft.com/en-us/free/) for free to get started today. See a [usage example](/oss/python/integrations/vectorstores/azure_cosmos_db_mongo_vcore). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_azure_ai.vectorstores import AzureCosmosDBMongoVCoreVectorSearch ``` #### Azure cosmos DB NoSQL > [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search) now offers vector indexing and search in preview. > This feature is designed to handle high-dimensional vectors, enabling efficient and accurate vector search at any scale. You can now store vectors > directly in the documents alongside your data. This means that each document in your database can contain not only traditional schema-free data, > but also high-dimensional vectors as other properties of the documents. This colocation of data and vectors allows for efficient indexing and searching, > as the vectors are stored in the same logical unit as the data they represent. This simplifies data management, AI application architectures, and the > efficiency of vector-based operations. ##### Installation and setup See [detail configuration instructions](/oss/python/integrations/vectorstores/azure_cosmos_db_no_sql). We need to install `langchain-azure-cosmosdb` and `azure-cosmos` python packages. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-azure-cosmosdb azure-cosmos ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-cosmosdb azure-cosmos ``` ##### Deploy Azure cosmos DB on Microsoft Azure Azure Cosmos DB offers a solution for modern apps and intelligent workloads by being very responsive with dynamic and elastic autoscale. It is available in every Azure region and can automatically replicate data closer to users. It has SLA guaranteed low-latency and high availability. [Sign Up](https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/quickstart-python?pivots=devcontainer-codespace) for free to get started today. See a [usage example](/oss/python/integrations/vectorstores/azure_cosmos_db_no_sql). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_azure_cosmosdb import AzureCosmosDBNoSqlVectorSearch ``` ### Azure Database for PostgreSQL > [Azure Database for PostgreSQL - Flexible Server](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/service-overview) is a relational database service based on the open-source Postgres database engine. It's a fully managed database-as-a-service that can handle mission-critical workloads with predictable performance, security, high availability, and dynamic scalability. See [set up instructions](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/quickstart-create-server-portal) for Azure Database for PostgreSQL. Simply use the [connection string](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/connect-python?tabs=cmd%2Cpassword#add-authentication-code) from your Azure Portal. Since Azure Database for PostgreSQL is open-source Postgres, you can use the [LangChain's Postgres support](/oss/python/integrations/vectorstores/pgvector/) to connect to Azure Database for PostgreSQL. ### Azure SQL Database > [Azure SQL Database](https://learn.microsoft.com/azure/azure-sql/database/sql-database-paas-overview?view=azuresql) is a robust service that combines scalability, security, and high availability, providing all the benefits of a modern database solution. It also provides a dedicated Vector data type & built-in functions that simplifies the storage and querying of vector embeddings directly within a relational database. This eliminates the need for separate vector databases and related integrations, increasing the security of your solutions while reducing the overall complexity. By leveraging your current SQL Server databases for vector search, you can enhance data capabilities while minimizing expenses and avoiding the challenges of transitioning to new systems. ##### Installation and setup See [detail configuration instructions](/oss/python/integrations/vectorstores/sqlserver). We need to install the `langchain-sqlserver` python package. ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} !pip install langchain-sqlserver==0.1.1 ``` ##### Deploy Azure SQL DB on Microsoft Azure [Sign Up](https://learn.microsoft.com/azure/azure-sql/database/free-offer?view=azuresql) for free to get started today. See a [usage example](/oss/python/integrations/vectorstores/sqlserver). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_sqlserver import SQLServer_VectorStore ``` ### Azure AI search [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) is a cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale. See the [Azure AI Search usage examples](/oss/python/integrations/vectorstores/azuresearch) for usage examples. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.vectorstores.azuresearch import AzureSearch ``` ## Retrievers ### Azure AI search > [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` or `Azure Cognitive Search` ) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications. > Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities: > > * A search engine for full text search over a search index containing user-owned content > * Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation > * Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more > * Programmability through REST APIs and client libraries in Azure SDKs > * Azure integration at the data layer, machine learning layer, and AI (AI Services) See [set up instructions](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal). See a [usage example](/oss/python/integrations/retrievers/azure_ai_search). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.retrievers import AzureAISearchRetriever ``` ## Vector store ### Azure Database for PostgreSQL > [Azure Database for PostgreSQL - Flexible Server](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/service-overview) is a relational database service based on the open-source Postgres database engine. It's a fully managed database-as-a-service that can handle mission-critical workloads with predictable performance, security, high availability, and dynamic scalability. See [set up instructions](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/quickstart-create-server-portal) for Azure Database for PostgreSQL. You need to [enable pgvector extension](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-use-pgvector) in your database to use Postgres as a vector store. Once you have the extension enabled, you can use the [PGVector in LangChain](/oss/python/integrations/vectorstores/pgvector/) to connect to Azure Database for PostgreSQL. See a [usage example](/oss/python/integrations/vectorstores/pgvector/). Simply use the [connection string](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/connect-python?tabs=cmd%2Cpassword#add-authentication-code) from your Azure Portal. ## Tools ### Azure Container apps dynamic sessions We need to get the `POOL_MANAGEMENT_ENDPOINT` environment variable from the Azure Container Apps service. See the [Azure dynamic sessions setup instructions](/oss/python/integrations/tools/azure_dynamic_sessions/#setup). We need to install a python package. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-azure-dynamic-sessions ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-azure-dynamic-sessions ``` See a [usage example](/oss/python/integrations/tools/azure_dynamic_sessions). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_azure_dynamic_sessions import SessionsPythonREPLTool ``` ### Bing search Follow the [Bing search tool documentation](/oss/python/integrations/tools/bing_search) to get a detailed explanations and instructions for this tool. The environment variable `BING_SUBSCRIPTION_KEY` and `BING_SEARCH_URL` are required from Bing Search resource. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.tools.bing_search import BingSearchResults from langchain_community.utilities import BingSearchAPIWrapper api_wrapper = BingSearchAPIWrapper() tool = BingSearchResults(api_wrapper=api_wrapper) ``` ## Toolkits ### Azure AI services We need to install several python packages. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install azure-ai-formrecognizer azure-cognitiveservices-speech azure-ai-vision-imageanalysis ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add azure-ai-formrecognizer azure-cognitiveservices-speech azure-ai-vision-imageanalysis ``` See a [usage example](/oss/python/integrations/tools/azure_ai_services). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.agent_toolkits import azure_ai_services ``` #### Azure AI services individual tools The `azure_ai_services` toolkit includes the following tools: * Image Analysis: [AzureAiServicesImageAnalysisTool](https://reference.langchain.com/python/langchain-community/tools/azure_ai_services/image_analysis/AzureAiServicesImageAnalysisTool) * Document Intelligence: [AzureAiServicesDocumentIntelligenceTool](https://reference.langchain.com/python/langchain-community/tools/azure_ai_services/document_intelligence/AzureAiServicesDocumentIntelligenceTool) * Speech to Text: [AzureAiServicesSpeechToTextTool](https://reference.langchain.com/python/langchain-community/tools/azure_ai_services/speech_to_text/AzureAiServicesSpeechToTextTool) * Text to Speech: [AzureAiServicesTextToSpeechTool](https://reference.langchain.com/python/langchain-community/tools/azure_ai_services/text_to_speech/AzureAiServicesTextToSpeechTool) * Text Analytics for Health: [AzureAiServicesTextAnalyticsForHealthTool](https://reference.langchain.com/python/langchain-community/tools/azure_ai_services/text_analytics_for_health/AzureAiServicesTextAnalyticsForHealthTool) ### Azure cognitive services We need to install several python packages. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install azure-ai-formrecognizer azure-cognitiveservices-speech azure-ai-vision-imageanalysis ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add azure-ai-formrecognizer azure-cognitiveservices-speech azure-ai-vision-imageanalysis ``` See a [usage example](/oss/python/integrations/tools/azure_cognitive_services). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.agent_toolkits import AzureCognitiveServicesToolkit ``` #### Azure AI services individual tools The `azure_ai_services` toolkit includes the tools that queries the `Azure Cognitive Services`: * `AzureCogsFormRecognizerTool`: Form Recognizer API * `AzureCogsImageAnalysisTool`: Image Analysis API * `AzureCogsSpeech2TextTool`: Speech2Text API * `AzureCogsText2SpeechTool`: Text2Speech API * `AzureCogsTextAnalyticsHealthTool`: Text Analytics for Health API ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.tools.azure_cognitive_services import ( AzureCogsFormRecognizerTool, AzureCogsImageAnalysisTool, AzureCogsSpeech2TextTool, AzureCogsText2SpeechTool, AzureCogsTextAnalyticsHealthTool, ) ``` ### Microsoft office 365 email and calendar We need to install `O365` python package. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install O365 ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add O365 ``` See a [usage example](/oss/python/integrations/tools/office365). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.agent_toolkits import O365Toolkit ``` #### Office 365 individual tools You can use individual tools from the Office 365 Toolkit: * `O365CreateDraftMessage`: creating a draft email in Office 365 * `O365SearchEmails`: searching email messages in Office 365 * `O365SearchEvents`: searching calendar events in Office 365 * `O365SendEvent`: sending calendar events in Office 365 * `O365SendMessage`: sending an email in Office 365 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.tools.office365 import O365CreateDraftMessage from langchain_community.tools.office365 import O365SearchEmails from langchain_community.tools.office365 import O365SearchEvents from langchain_community.tools.office365 import O365SendEvent from langchain_community.tools.office365 import O365SendMessage ``` ### Microsoft Azure PowerBI We need to install `azure-identity` python package. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install azure-identity ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add azure-identity ``` See a [usage example](/oss/python/integrations/tools/powerbi). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.agent_toolkits import PowerBIToolkit from langchain_community.utilities.powerbi import PowerBIDataset ``` #### PowerBI individual tools You can use individual tools from the Azure PowerBI Toolkit: * `InfoPowerBITool`: getting metadata about a PowerBI Dataset * `ListPowerBITool`: getting tables names * `QueryPowerBITool`: querying a PowerBI Dataset ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.tools.powerbi.tool import InfoPowerBITool from langchain_community.tools.powerbi.tool import ListPowerBITool from langchain_community.tools.powerbi.tool import QueryPowerBITool ``` ### PlayWright browser toolkit > [Playwright](https://github.com/microsoft/playwright) is an open-source automation tool > developed by `Microsoft` that allows you to programmatically control and automate > web browsers. It is designed for end-to-end testing, scraping, and automating > tasks across various web browsers such as `Chromium`, `Firefox`, and `WebKit`. We need to install several python packages. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install playwright lxml ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add playwright lxml ``` See a [usage example](/oss/python/integrations/tools/playwright). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.agent_toolkits import PlayWrightBrowserToolkit ``` #### PlayWright browser individual tools You can use individual tools from the PlayWright Browser Toolkit. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.tools.playwright import ClickTool from langchain_community.tools.playwright import CurrentWebPageTool from langchain_community.tools.playwright import ExtractHyperlinksTool from langchain_community.tools.playwright import ExtractTextTool from langchain_community.tools.playwright import GetElementsTool from langchain_community.tools.playwright import NavigateTool from langchain_community.tools.playwright import NavigateBackTool ``` ## Graphs ### Azure cosmos DB for apache gremlin We need to install a python package. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install gremlinpython ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add gremlinpython ``` See a [usage example](/oss/python/integrations/graphs/azure_cosmosdb_gremlin). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.graphs import GremlinGraph from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship ``` ## Utilities ### Bing search API > [Microsoft Bing](https://www.bing.com/), commonly referred to as `Bing` or `Bing Search`, > is a web search engine owned and operated by `Microsoft`. See a [usage example](/oss/python/integrations/tools/bing_search). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.utilities import BingSearchAPIWrapper ``` ## More ### Microsoft Presidio > [Presidio](https://microsoft.github.io/presidio/) (Origin from Latin praesidium ‘protection, garrison’) > helps to ensure sensitive data is properly managed and governed. It provides fast identification and > anonymization modules for private entities in text and images such as credit card numbers, names, > locations, social security numbers, bitcoin wallets, US phone numbers, financial data and more. First, you need to install several python packages and download a `SpaCy` model. ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker python -m spacy download en_core_web_lg ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker python -m spacy download en_core_web_lg ``` See [usage examples](https://python.langchain.com/v0.1/docs/guides/productionization/safety/presidio_data_anonymization). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_experimental.data_anonymizer import PresidioAnonymizer, PresidioReversibleAnonymizer ``` ***
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