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‎docs-source/spring/content/_index.md‎

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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 1.4.0 (production) released March 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 type=[warning] icon=gdoc_fire title="Version 1.4.0 (production) released July 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 4.2.x, Spring Boot 3,4.x, 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!

‎docs-source/spring/content/platform/_index.md‎

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For more details, read about:
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- [API Gateway](./apigw)
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- [Service Discovery](./eureka)
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- [Configuration](./config)
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- [Workflow](./conductor)
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- [Spring Admin](./spring-admin)
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- [Using the CLI](../development/cli)
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- [Configuration](./config)
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- [Database Exporter](./dbexporter/)
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- [Database Operator](./dboperator/)
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- [Service Discovery](./eureka)
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- [External Secrets Operator](./extsecrets/)
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- [IntelliJ Plugin](./intellij-plugin)
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- [Apache Kafka](./kafka/)
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- [MicroTx](./microtx)
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- [ServiceOps Center](./soc/)
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- [Spring Admin](./spring-admin)
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- [VS Code Plugin](./vscode-plugin)
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- [IntelliJ Plugin](./intellij-plugin)
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- [Using the CLI for Oracle Backend for Microservices and AI](../development/cli)

‎docs-source/spring/content/platform/aiexpforapps/_index.md‎

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---
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title: "Oracle AI Explorer for Apps"
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description: "Oracle AI Explorer for Apps"
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keywords: "support backend oracle ai sandbox microservices database"
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title: "Oracle AI Optimizer and Toolkit"
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description: "Oracle AI Optimizer and Toolkit"
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keywords: "support backend oracle ai toolkit microservices oracle vector database"
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---
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## Developer Preview Feature - Oracle AI Explorer for Apps
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## Oracle AI Optimizer and Toolkit
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Oracle Backend for Microservices and AI Version 1.4.0 introduces [Oracle AI Explorer for Apps](https://github.com/oracle-samples/oaim-sandbox) as a *Developer Preview* feature.
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Oracle Backend for Microservices and AI Version 1.4.0 introduces [Oracle AI Optimizer and Toolkit](https://github.com/oracle-samples/ai-optimizer).
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The Oracle AI Explorer for Apps 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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The Oracle AI Optimizer and Toolkit 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 Oracle AI Optimizer and Toolkit 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.
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It provides a n environment 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.
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![AI Microservices Sandbox](./ai-microservices-sandbox.png)
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![Oracle AI Optimizer and Toolkit](./ai-microservices-sandbox.png)
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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 please visit:
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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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- the [GitHub repo](https://github.com/oracle-samples/ai-optimizer)
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- the [Documentation](https://oracle-samples.github.io/ai-optimizer/)
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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.

‎docs-source/spring/content/platform/dbexporter/_index.md‎

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title: "Unified Observability for Oracle Database"
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description: "Observability for the Oracle Database"
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keywords: "observability performance metrics oracle database"
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---
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This project aims to provide observability for the Oracle Database so that users can understand performance and diagnose issues easily across applications and database. Over time, this project will provide not just metrics, but also logging and tracing support, and integration into popular frameworks like Spring Boot. The project aims to deliver functionality to support both cloud and on-premises databases, including those running in Kubernetes and containers.
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## Main Features
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The main features of the exporter are:
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- Exports Oracle Database metrics in de facto standard Prometheus format
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- Works with many types of Oracle Database deployment including single instance and Oracle Autonomous Database
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- Supports wallet-based authentication
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- Supports both OCI and Azure Vault integration (for database username, password)
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- Multiple database support allows a single instance of the exporter to connect to, and export metrics from, - multiple databases
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- Can export the Alert Log in JSON format for easy ingest by log aggregators
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- Can run as a local binary, in a container, or in Kubernetes
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- Pre-buit AMD64 and ARM64 images provided
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- A set of standard metrics included "out of the box"
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- Easily define custom metrics
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- Define the scrape interval, down to a per-metric level
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- Define the query timeout
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- Control connection pool settings, can use with go-sql or Oracle Database connection pools, also works with Database Resident Connection Pools
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- Sample dashboard provided for Grafana
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Full documentation is [here](https://github.com/oracle/oracle-db-appdev-monitoring).
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### Oracle Database Dashboard:
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![Oracle Database Dashboard](images/exporter-running-against-basedb.png)
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### Transactional Event Queue Dashboard:
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![Oracle Database Dashboard](images/txeventq-dashboard-v2.png)

‎docs-source/spring/content/platform/dbexporter/images/exporter-running-against-basedb.png‎

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‎docs-source/spring/content/platform/dbexporter/images/txeventq-dashboard-v2.png‎

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‎docs-source/spring/content/platform/dboperator/_index.md‎

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title: "Oracle Database Operator for Kubernetes"
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description: "Database operator for kubernetes"
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keywords: "kubernetes operator oracle database"
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---
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The Oracle Database Operator for Kubernetes (OraOperator or the operator) extends the Kubernetes API with custom resources and controllers for automating the management of the Oracle Database lifecycle.
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Full documentation is [here](https://github.com/oracle/oracle-database-operator).

‎docs-source/spring/content/platform/extsecrets/_index.md‎

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title: "External Secrets Operator"
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description: "External Secrets Operator"
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keywords: "secretes operator oracle OCI oracle"
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---
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External Secrets Operator is a Kubernetes operator that integrates external secret management systems like OCI Vault, AWS Secrets Manager, HashiCorp Vault, Google Secrets Manager, Azure Key Vault and many more. The operator reads information from external APIs and automatically injects the values into a Kubernetes Secret.
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Full [documentation](https://external-secrets.io/latest/)
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![External Secrets Operator](images/diagrams-high-level-simple.png)

‎docs-source/spring/content/platform/extsecrets/images/diagrams-high-level-simple.png‎

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‎docs-source/spring/content/release-notes/_index.md‎

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keywords: "release notes additions updates deprecations removals springboot spring development microservices development oracle backend"
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## Version 1.4.0, March XX, 2025
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## Version 1.4.0, July, 2025
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This release adds:
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- Support for AWS deployment.
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This release removes the following components:
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- Prometheus Operator
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- Hashicorp Vault
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This release have an updated backend API and CLI. This means that the REST API and CLI calls are changed since the previous release.See the documentation.
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The following components were updated:
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| Component | New Version | Replaced Version |
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| Spring Boot 3.3.6 and Spring Cloud 2023.0.4 | | |
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In addition this release have:
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- Improved IntelliJ plugin and VSCode extension.
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- Updates to the ServiceOps Center
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TBD
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## Version 1.3.1, October 23rd, 2024
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