{"data":{"slug":"azure-azure-search-vector-samples","name":"azure-search-vector-samples","tagline":"Code samples for vector search capabilities in Azure AI Search","github_url":"https://github.com/Azure/azure-search-vector-samples","owner":"Azure","repo":"azure-search-vector-samples","owner_avatar_url":"https://avatars.githubusercontent.com/u/6844498?v=4","primary_language":"Jupyter Notebook","stars":910,"forks":376,"topics":["azure","azurecognitivesearch","embeddings","vector","vector-search"],"archived":false,"github_pushed_at":"2026-09-19T01:56:49+00:00","maintenance_label":"Very active","stars_delta_30d":0,"url":"https://www.graphcanon.com/tools/azure-azure-search-vector-samples","markdown_url":"https://www.graphcanon.com/tools/azure-azure-search-vector-samples.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/azure-azure-search-vector-samples","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=azure-azure-search-vector-samples","description":"A repository of code samples for Vector search capabilities in Azure AI Search.","homepage_url":"https://azure.microsoft.com/products/search","license":"MIT","open_issues":68,"watchers":256,"ai_summary":"This repository offers Jupyter Notebook examples to demonstrate how to implement and utilize vector search within the context of Azure AI Search services.","readme_excerpt":"# Vector samples - Azure AI Search\n\nThis repository provides Python, C#, REST, and JavaScript code samples for [vector support](https://learn.microsoft.com/azure/search/vector-search-overview) in Azure AI Search.\n\nThere are breaking changes from REST API version 2023-07-01-Preview to newer API versions. These breaking changes also apply to the Azure SDK beta packages targeting that REST API version. See [Upgrade REST APIs](https://learn.microsoft.com/azure/search/search-api-migration) for migration guidance.\n\nPreview features are available under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). \n\n## demo-python samples\n\n| Sample | Description |\n| ------ | ------------|\n| [demo-python readme](demo-python/readme.md) |  A growing collection of notebooks that demonstrate aspects of vector search support, including data chunking and embedding of both text and image content and queries, using a variety of frameworks and techniques.|\n\n## demo-dotnet samples\n\n| Sample | Description |\n| ------ | ------------|\n| [DotNetVectorDemo](demo-dotnet/DotNetVectorDemo/readme.md) | A .NET console app that calls Azure OpenAI to vectorize data. It then calls Azure AI Search to create, load, and query vector data.|\n| [DotNetIntegratedVectorizationDemo](demo-dotnet/DotNetIntegratedVectorizationDemo/readme.md) | A .NET console app that calls Azure AI Search to create an index, indexer, data source, and skillset. An Azure Storage account provides the data. Azure OpenAI is called by the skillset during indexing, and again during query execution to vectorize text queries. |\n| [QuantizationAndStorageOptions](demo-dotnet/demo-dotnet/QuantizationAndStorageOptions/readme.md) | A .NET console app that demonstrates [narrow data types](https://learn.microsoft.com/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields) and built-in scalar quantization, reducing vector index size in memory and on disk. It also disables storage of vectors returned in query response, which you don't need if you're not returning vectors in a query.  |\n\n## demo-java samples\n\n| Sample | Description |\n| ------ | ------------|\n| [demo-vectors](demo-java/demo-vectors/readme.md) | A Java console app that calls Azure OpenAI to vectorize data. It then calls Azure AI Search to create, load, and query vector data.|\n| [demo-integrated-vectorization](demo-java/demo-integrated-vectorization/readme.md) | A Java console app that calls Azure AI Search to create an index, indexer, data source, and skillset. An Azure Storage account provides the data. Azure OpenAI is called by the skillset during indexing, and again during query execution to vectorize text queries. |\n\n## demo-javascript samples\n\n| Sample | Description |\n| ------ | ------------|\n| [JavaScriptVectorDemo](demo-javascript/readme.md) | A single folder contains three code samples. The `azure-search-vector-sample.js` script calls just Azure OpenAI and is used to generate embeddings for fields in an index. The `docs-text-openai-embeddings.js` program is an end-to-end code sample that calls Azure OpenAI for embeddings and Azure AI Seach to create, load, and query an index that contains vectors. The `query-text-openai-embeddings.js` script generates an embedding for a vector query. |\n\n## Other vector samples and tools\n\n- [azure-ai-search-lab](https://github.com/jelledruyts/azure-ai-search-lab) A learning and experimentation lab for trying out various AI-enabled search scenarios in Azure. It includes web application front-end which uses Azure AI Search and Azure OpenAI to execute searches with a variety of options - ranging from simple keyword search, to semantic ranking, vector and hybrid search, and using generative AI to answer search queries in various ways. This allows you to quickly understand what each option does, how it affects the search results, and how various approaches compare against each other.\n- [chat-with-your-data-solution-accelerator](https://github.com/","github_created_at":"2023-04-19T22:39:29+00:00","created_at":"2026-07-11T11:31:50.570965+00:00","updated_at":"2026-09-20T06:17:15.318576+00:00","categories":[{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"azure","name":"azure"},{"slug":"azurecognitivesearch","name":"azurecognitivesearch"},{"slug":"embeddings","name":"embeddings"},{"slug":"vector-search","name":"vector-search"}],"trust":{"provenance":{"is_fork":false,"github_id":630208579,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-20T06:17:13.588Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1,"last_release_at":null,"stars_delta_30d":0,"open_issues_delta_30d":8},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:31:52.098Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"mcp":{"source":"repo_scan","observed_at":"2026-09-20T06:17:14.652Z","server_manifest":false},"scan":{"source":"repo_scan","observed_at":"2026-09-20T06:17:14.652Z"},"languages":{"value":["jupyter notebook","javascript"],"source":"github.language+package.json","observed_at":"2026-09-20T06:17:14.652Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-09-20T06:17:14.652Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When developing applications that require advanced semantic search functionalities on unstructured data within the Microsoft ecosystem, as it integrates seamlessly with Azure resources","If you aim to exploit pre-built models from Azure Cognitive Services to create embeddings for your content and leverage them via vector search"],"when_not_to_use":["When working in non-Microsoft cloud environments due to its tight integration with Azure services","For users who require real-time processing capabilities, as Azure AI Search might not be optimized for low-latency queries compared to specialized vector databases"],"source":"enrich:decision_facts","observed_at":"2026-07-17T12:57:03.717Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"azure-search-vector-samples offers Jupyter Notebook examples for implementing vector search with Azure AI Search services"}]}}