Hello Java AI developers!<\/p>\n
We are announcing the release of Semantic Kernel for Java v1.2.0! Since our release of v1.0 this past May, we have been working to improve and expand the capabilities we provide. Today, we are thrilled to share the latest updates, new features, and experimental features with you.<\/p>\n
To get greater awareness of the Java version of Semantic Kernel, we’ve created a separate Java repository for it. Several folks had difficulty finding updates to the Java project, so it’s now much easier to find at github.com\/microsoft\/semantic-kernel-java<\/a>.<\/p>\n With this new repo, our goal is to make it easier for Java developers to contribute and collaborate. We still continue to track the work for the Java library in the Semantic Kernel project to keep alignment with the other languages. See the full backlog and the planned work in this sprint here: Current Sprint \u00b7 Semantic Kernel for Java.<\/a><\/p>\n We have addressed several issues reported to use by the community. Here are some of the key fixes included in this release:<\/p>\n These features are currently in beta. We encourage the community to try these features and to help us improve them through feedback or pull requests.<\/p>\n Hugging Face<\/a> is a machine learning platform with models for text and natural language processing. The source for the Hugging Face implementation is in the aiservices\/huggingface<\/a> module.\nSee the sample code here.<\/a><\/p>\n Vector store databases allow you to store business data in vector embeddings that are used as long-term, semantic memory for AI applications. Essentially, now you can now store and retrieve data based on similarity specific to your application\u2019s needs. This semantic memory can be used to generate content based on the stored information.<\/p>\n The Semantic Kernel for Java presents an abstract framework that allows any vector store database to be used within your application. In this release, we have two experimental implementations, one for Azure AI Search<\/a> and another for Redis Vector Search<\/a>. The code can be found in the semantickernel-experimental<\/a> module. Note that this feature is still incomplete, we have plans to also include a JDBC implementation module.<\/em><\/p>\n Azure AI Search<\/a> is a text and vector search engine that provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.<\/p>\n To manage information using Azure AI Search, start by creating a model to <\/a>represent the records stored in the vector database. This model should include a key field to uniquely <\/a>identify each record, data fields for the information you want to store, and vector fields for any associated vector embeddings. For example:<\/p>\n Next, create an AzureAISearchVectorStore to manage the collections (which correspond to indexes in Azure AI Search) in your database. With each collection, you can upsert, retrieve, and <\/a>delete records individually or in batches.<\/p>\n Find a more complete example here<\/a>. Let us know your experience with the new feature in our #java Discord channel<\/a>.<\/p>\n Redis<\/a> is an in-memory data structure store that functions as a versatile key-value, vector, and document database. It provides fast, secure, and scalable data retrieval, making it ideal for various applications, including real-time analytics, AI-driven search, and managing vector embeddings and documents.<\/p>\n Similarly, for Redis, you need to define your record model. Once defined, you can use Redis to upsert, get, and delete records, managing data efficiently, including vector embeddings, in a manner similar to Azure AI Search.<\/p>\n Find the example here<\/a>.<\/p>\n Join our Discord channel at aka.ms\/java-sk-discord<\/a> to interact with other developers, ask questions, share insights, and be a part of this growing community. You can also get more information about the project\u2019s roadmap and upcoming features.<\/p>\n We encourage you to try out the library, provide feedback, and report any issues you <\/a>encounter. Your input is invaluable to us in refining the <\/a>API and its features.<\/p>\n Happy coding and AI experimenting!<\/p>\n The Microsoft Semantic Kernel for Java Team<\/p>\n","protected":false},"excerpt":{"rendered":" Hello Java AI developers! We are announcing the release of Semantic Kernel for Java v1.2.0! Since our release of v1.0 this past May, we have been working to improve and expand the capabilities we provide. Today, we are thrilled to share the latest updates, new features, and experimental features with you. What\u2019s New in Semantic […]<\/p>\n","protected":false},"author":9388,"featured_media":2364,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[47,17,24,1],"tags":[82,20,63,81,9],"class_list":["post-2994","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-announcement","category-announcements","category-java","category-semantic-kernel","tag-announcement","tag-java","tag-microsoft-semantic-kernel","tag-release","tag-semantic-kernel"],"acf":[],"blog_post_summary":" Hello Java AI developers! We are announcing the release of Semantic Kernel for Java v1.2.0! Since our release of v1.0 this past May, we have been working to improve and expand the capabilities we provide. Today, we are thrilled to share the latest updates, new features, and experimental features with you. What\u2019s New in Semantic […]<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/posts\/2994","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/users\/9388"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/comments?post=2994"}],"version-history":[{"count":0,"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/posts\/2994\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/media\/2364"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/media?parent=2994"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/categories?post=2994"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/agent-framework\/wp-json\/wp\/v2\/tags?post=2994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Fixes<\/h3>\n
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New Features<\/h3>\n
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Experimental Features<\/h3>\n
Hugging Face<\/h4>\n
Vector store databases:<\/h4>\n
Using Semantic Kernel Memory (Experimental)<\/h2>\n
Azure AI Search (experimental)<\/h3>\n
class GitHubFile {\r\n @VectorStoreRecordKeyAttribute()\r\n private final String id;\r\n @VectorStoreRecordDataAttribute(hasEmbedding = true, embeddingFieldName = \"embedding\")\r\n private final String description;\r\n @VectorStoreRecordDataAttribute\r\n private final String link;\r\n @VectorStoreRecordVectorAttribute(dimensions = 1536)\r\n private final List<Float> embedding;\r\n}<\/code><\/pre>\n\/\/ Initialize the Azure AI Search client\r\nvar searchClient = new SearchIndexClientBuilder()\r\n .endpoint(AZURE_AI_SEARCH_ENDPOINT)\r\n .credential(new AzureKeyCredential(AZURE_AISEARCH_KEY))\r\n .buildAsyncClient();\r\n\r\n\/\/ Create a new Azure AI Search vector store\r\nvar vectorStore = AzureAISearchVectorStore.builder()\r\n .withClient(searchClient)\r\n .withOptions(new AzureAISearchVectorStoreOptions())\r\n .build();\r\n\r\nString collectionName = \"skgithubfiles\";\r\nvar collection = vectorStore.getCollection(collectionName, GitHubFile.class, null);\r\n\r\n\/\/ Generate embeddings for the description and store data\r\nvar description = \"README: Installation, getting started with Semantic Kernel, and how to contribute\";\r\nvar descriptionEmbedding = embeddingGeneration.generateEmbeddingsAsync(Collections.singletonList(description)).block().get(0);\r\ncollection.upsertAsync(new GitHubFile(\r\n \"readme\",\r\n description,\r\n \"https:\/\/github.com\/microsoft\/semantic-kernel-java\/blob\/main\/README.md\",\r\n descriptionEmbedding.getVector()), null).block();\r\n\r\n\/\/ Retrieve records from the collection\r\nvar readme = collection.getAsync(\"readme\", null).block();\r\n\r\n\/\/ Query the Azure AI Search client for semantic results\r\nvar result = searchClient.getSearchAsyncClient(collectionName)\r\n .search(\"How to get started with the Semantic Kernel?\")\r\n .blockFirst();\r\n<\/code><\/pre>\nRedis Vector Store (experimental)<\/h3>\n
Join the Community<\/h2>\n