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7c18866cc2 Initial proposal for Java Kernel (#1081 )

### Motivation and Context
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### Description
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### Contribution Checklist
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- [ ] The code builds clean without any errors or warnings
- [ ] The PR follows SK Contribution Guidelines
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- [ ] The code follows the .NET coding conventions
(https://learn.microsoft.com/dotnet/csharp/fundamentals/coding-style/coding-conventions)
verified with `dotnet format`
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

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Semantic Kernel for Java

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.

To learn more about Microsoft Semantic Kernel, visit the Microsoft Semantic Kernel documentation.

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.

Get Started

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.

Requirements

To build the semantic kernel, you will need to have:

Required:
- JDK 8 or newer to build the Semantic Kernel library
- JDK 17 or newer to run samples

Build the semantic kernel

Clone this repository

 git clone [email protected]:microsoft/semantic-kernel.git

Build the semantic kernel
```
 cd semantic-kernel/java
 mvn install
```

Using the semantic kernel

Check the samples folder for examples of how to use the semantic kernel.

Discord community

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.

Contributing

Testing locally

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

OPENAI_API_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 OpoenAI 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 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:

mvn package

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

mvn verify

Submitting a pull request

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 main 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.

License

This project is licensed under the MIT License.

Code of Conduct

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

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