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‚ÄãSee detail configuration instructions.We need to install pymongo python package.pip install pymongoDeploy Azure Cosmos DB on Microsoft Azure‚ÄãAzure 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 for free to get started today.See a usage example.from langchain.vectorstores import AzureCosmosDBVectorSearchRetrievers‚ÄãAzure Cognitive Search‚ÄãAzure Cognitive Search (formerly known as Azure 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 contentRich indexing, with lexical analysis and optional AI enrichment for content extraction and transformationRich query syntax for text search, fuzzy search, autocomplete, geo-search and moreProgrammability through REST APIs and client libraries in Azure SDKsAzure integration at the data layer, machine learning layer, and AI (Cognitive Services)See set up instructions.See a usage example.from langchain.retrievers import AzureCognitiveSearchRetrieverPreviousGoogleNextOpenAILLMAzure OpenAIText Embedding ModelsAzure
All functionality related to Microsoft Azure
All functionality related to Microsoft Azure ->: 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‚ÄãSee detail configuration instructions.We need to install pymongo python package.pip install pymongoDeploy Azure Cosmos DB on Microsoft Azure‚ÄãAzure 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 for free to get started today.See a usage example.from langchain.vectorstores import AzureCosmosDBVectorSearchRetrievers‚ÄãAzure Cognitive Search‚ÄãAzure Cognitive Search (formerly known as Azure 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 contentRich indexing, with lexical analysis and optional AI enrichment for content extraction and transformationRich query syntax for text search, fuzzy search, autocomplete, geo-search and moreProgrammability through REST APIs and client libraries in Azure SDKsAzure integration at the data layer, machine learning layer, and AI (Cognitive Services)See set up instructions.See a usage example.from langchain.retrievers import AzureCognitiveSearchRetrieverPreviousGoogleNextOpenAILLMAzure OpenAIText Embedding ModelsAzure