Semantic Kernel (SK) is a lightweight foundation that lets you easily mix conventional programming languages with the latest in Semantic Kernel (SK) is a lightweight SDK that lets you easily mix conventional programming languages with the latest in Large Language Model (LLM) AI "prompts" with templating, chaining, and planning capabilities out-of-the-box.

Semantic Kernel is a lightweight foundation that lets you easily mix conventional programming languages with the latest in Large Language Model (LLM) AI "prompts" with templating, chaining, and planning capabilities out-of-the-box. To learn more about Microsoft Semantic Kernel, visit the Microsoft Semantic Kernel documentation.

The Microsoft Semantic Kernel for Java is a library that implements the key concepts and foundations of Microsoft Semantic Kernel. It is designed to be used in Java applications in both client (desktop, mobile, CLIs) and server environments in an idiomatic way, and to be easily integrated with other Java libraries

The Semantic Kernel for Java is an SDK that implements the key concepts of the Semantic Kernel in Java. It is designed to be used in Java applications and services in an idiomatic way, and to be easily integrated with other Java libraries and frameworks.

The Semantic Kernel for Java is an SDK that implements the key concepts of the Semantic Kernel in Java. It is designed to be used in Java applications and services in an idiomatic way, and to be easily integrated with other Java libraries and frameworks.

To run the LLM prompts and semantic functions in this kernel, make sure you have an Open AI API Key

To get an idea of how to use the Semantic Kernel for Java, you can check the syntax-examples folder for examples of common AI-enabled scenarios.

To run the LLM prompts and semantic functions in this kernel, make sure you have an Open AI API Key or Azure Open AI service key.

To run the LLM prompts and semantic functions in this kernel, make sure you have an Open AI API Key or Azure Open AI service key.

The library is organized in a set of dependencies published to Maven Central. For a list of the Maven dependencies and how to use each of them, see PACKAGES.md.

Alternatively, check the samples folder for examples of common AI-enabled scenarios implemented with Semantic Kernel for Java.

The documentation is hosted on the Microsoft Learn platform. Visit learn.microsoft.com/semantic-kernel/overview/?tabs=Java.

Join the Microsoft Semantic Kernel Discord community to discuss the Semantic Kernel and get help from the community. We have a #java channel for Java-specific questions.

To build the Semantic Kernel for Java, you will need:

an Open AI API Key or Azure Open AI service key.

To build the Semantic Kernel, you will need:

• Required:
  • OpenJDK 17 or newer
  • JDK 17 or newer

1. Clone this repository

   git clone -b java-development https://github.com/microsoft/semantic-kernel/
   

2. Build the project with the Maven Wrapper git clone -b experimental-java https://github.com/microsoft/semantic-kernel/

3. Build the Semantic Kernel

    git clone -b java-v1 https://github.com/microsoft/semantic-kernel/
   

4. Build the project with the Maven Wrapper

    cd semantic-kernel/java
    ./mvnw install
   

5. (Optional) To run a FULL build including static analysis and end-to-end tests that might require a valid OpenAI key, run the following command:

    ./mvnw clean install -Prelease,bug-check,with-samples
   

The library is organized in a set of dependencies published to Maven Central. For a list of the Maven dependencies and how to use each of them, see PACKAGES.md.

Alternatively, check the samples folder for examples of common AI-enabled scenarios implemented with Semantic Kernel for Java.

Join the Microsoft Semantic Kernel Discord community to discuss the Semantic Kernel and get help from the community. We have a #java channel for Java-specific questions.

The project may contain end-to-end tests that require an OpenAI key to run. To run these tests locally, you will need to set the following environment variable:

• CLIENT_KEY - the OpenAI API key.

If you are using Azure OpenAI, you will also need to set the following environment variables:

• AZURE_OPENAI_ENDPOINT - the Azure OpenAI endpoint found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_API_KEY - the Azure OpenAI API key found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_ENDPOINT - the Azure OpenAI endpoint found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_API_KEY - the Azure OpenAI API key found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_ENDPOINT - the Azure OpenAI endpoint found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_API_KEY - the Azure OpenAI API key found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_API_KEY - the Azure OpoenAI API key found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_ENDPOINT - the Azure OpenAI endpoint found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_API_KEY - the Azure OpenAI API key found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_OPENAI_DEPLOYMENT_NAME - the custom name you chose for your deployment when you deployed a model. It can be

• CLIENT_ENDPOINT - the Azure OpenAI endpoint found in Keys * Endpoint section of the Azure OpenAI service.

• AZURE_CLIENT_KEY - the Azure OpenAI API key found in Keys * Endpoint section of the Azure OpenAI service.

• MODEL_ID - the custom name you chose for your deployment when you deployed a model. It can be found under Resource Management > Deployments in the Azure Portal.

For more information, see the Azure OpenAI documentation on how to get your Azure OpenAI credentials.

To run the unit tests only, run the following command:

./mvnw package

To run all tests, including integration tests that require an OpenAI key, run the following command:

./mvnw verify -Prelease,bug-check,with-samples

Before submitting a pull request, please make sure that you have run the project with the command:

./mvnw clean package -Pbug-check

The bug-check profile will detect some static analysis issues that will prevent merging as well as apply formatting requirements to the code base.

Also ensure that:

• All new code is covered by unit tests
• All new code is covered by integration tests

Once your proposal is ready, submit a pull request to the java-development branch. The pull request will be reviewed by the Once your proposal is ready, submit a pull request to the main branch. The pull request will be reviewed by the Once your proposal is ready, submit a pull request to the java-v1 branch. The pull request will be reviewed by the project maintainers. Once your proposal is ready, submit a pull request to the java-v1 branch. The pull request will be reviewed by the project maintainers.

Make sure your pull request has an objective title and a clear description explaining the problem and solution.

This project is licensed under the MIT License.

This project has adopted the Microsoft Open Source Code of Conduct.

The Semantic Kernel for Java code has moved to semantic-kernel-java, please make code changes and submit issues to that repository. This move is purely to ease the development. The various Semantic Kernel languages are all still aligned in their development.

Project coordination is still performed within this Project Board.

To enhance the existing documentation, we have added more detailed explanations and examples to help users understand how to use the various features of the repository. These explanations and examples are included in the relevant sections of the documentation files such as README.md and java/README.md.

We have included more code snippets and usage examples in the documentation to provide practical guidance on how to use the repository's features. These code snippets and examples are designed to help users quickly grasp the concepts and apply them in their own projects.

To help users navigate the repository, we have added a section that explains the structure of the repository and the purpose of each directory and file. This section provides an overview of the repository's organization and helps users understand where to find specific components and resources.