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Oracle Backend for Microservices and AI allows developers to build microservices in Helidon and/or Spring Boot and provisions a "backend as a service" with Oracle Database and other infrastructure components that operate on multiple clouds. Oracle Backend for Microservices and AI vastly simplifies the task of building, testing, and operating microservices platforms for reliable, secure, and scalable enterprise applications.
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{{< hint type=[warning] icon=gdoc_fire title="Version 1.3.1 (production) released October, 2024" >}}
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Oracle Backend for Microservices and AI Version 1.3.0 is now generally available and suitable for production use. This version supports and recommends Helidon 4.1.1, Spring Boot 3.3.x, 3.2.x, Spring 6.1 and Spring Cloud 2023.0.x, with limited backwards compatibility for Spring Boot 2.7.x.
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{{< hint type=[warning] icon=gdoc_fire title="Version 1.4.0 (production) released February 2025" >}}
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Oracle Backend for Microservices and AI Version 1.4.0 is now generally available and suitable for production use. This version supports and recommends Helidon 4.1.1, Spring Boot 3.3.x, 3.2.x, Spring 6.1 and Spring Cloud 2023.0.x, with limited backwards compatibility for Spring Boot 2.7.x.
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{{< /hint >}}
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If you are building Spring Boot applications with Oracle Database, you should also check out [Spring Cloud Oracle](https://github.com/oracle/spring-cloud-oracle) which is the home of a number of the components used in Oracle Backend for Microservices and AI, and which you can also use in your own applications!
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## New Developer Preview Feature - Oracle AI Microservices Sandbox
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Oracle Backend for Microservices and AI Version 1.3.1 introduces [Oracle AI Microservices Sandbox](https://github.com/oracle-samples/oaim-sandbox) as a *Developer Preview* feature.
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The Oracle AI Microservices Sandbox provides a streamlined environment where developers and data scientists can explore the potential of Generative Artificial Intelligence (GenAI) combined with Retrieval-Augmented Generation (RAG) capabilities. By integrating Oracle Database 23ai AI Vector Search, the Sandbox enables users to enhance existing Large Language Models (LLMs) through RAG.
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It provides a "sandbox" for experimentation and iteration, allowing you to easily optimize a chatbot/RAG use case by exploring different models, chunking strategies, vector similarity algorithms, prompts, model parameters, memory, re-ranking, and evaluation.
This feature is provided as a "developer preview" meaning it is provided for developers to experiment with, without any formal support, but with the expectation that it may become a formal feature in a future release. You may open issues in GitHub and best-effort assistance will be provided. Please be aware that this feature is under development, and not yet considered stable. Functionality, interfaces, etc., may change in non-backwards compatible ways.
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For more information about this developer preview feature, please visit:
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- the [GitHub repo](https://github.com/oracle-samples/oaim-sandbox)
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- the [documentation](https://oracle-samples.github.io/oaim-sandbox/)
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- this OCI Cloud Coaching session titled [Building a real chatbot with Oracle Database 23ai](https://www.youtube.com/watch?v=oG9MPCpwUlU), (video) which includes discussion of the background concepts and a demonstration of this feature
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## Try it out with CloudBank AI
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To learn more about deploying and using Oracle Backend for Microservices and AI, we recommend our
@@ -95,8 +78,6 @@ Developers also have access to development or build time services and libraries
keywords: "support backend oracle ai sandbox microservices database"
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## Developer Preview Feature - Oracle AI Microservices Sandbox
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Oracle Backend for Microservices and AI Version 1.4.0 introduces [Oracle AI Microservices Sandbox](https://github.com/oracle-samples/oaim-sandbox) as a *Developer Preview* feature.
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The Oracle AI Microservices Sandbox provides a streamlined environment where developers and data scientists can explore the potential of Generative Artificial Intelligence (GenAI) combined with Retrieval-Augmented Generation (RAG) capabilities. By integrating Oracle Database 23ai AI Vector Search, the Sandbox enables users to enhance existing Large Language Models (LLMs) through RAG.
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It provides a "sandbox" for experimentation and iteration, allowing you to easily optimize a chatbot/RAG use case by exploring different models, chunking strategies, vector similarity algorithms, prompts, model parameters, memory, re-ranking, and evaluation.
This feature is provided as a "developer preview" meaning it is provided for developers to experiment with, without any formal support, but with the expectation that it may become a formal feature in a future release. You may open issues in GitHub and best-effort assistance will be provided. Please be aware that this feature is under development, and not yet considered stable. Functionality, interfaces, etc., may change in non-backwards compatible ways.
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For more information about this developer preview feature, please visit:
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- the [GitHub repo](https://github.com/oracle-samples/oaim-sandbox)
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- the [Documentation](https://oracle-samples.github.io/oaim-sandbox/)
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- this OCI Cloud Coaching session titled [Building a real chatbot with Oracle Database 23ai](https://www.youtube.com/watch?v=oG9MPCpwUlU), (video) which includes discussion of the background concepts and a demonstration of this feature.
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