About Semantic Kernel

This repository contains the semantic-kernel, a powerful tool designed to facilitate seamless integration and interaction with various services. The API is built to be robust, efficient, and easy to use, providing developers with the necessary tools to build and deploy applications quickly.

Features

• High Performance, capable of handling large volumes of requests with minimal latency.

• Scalability: Easily scale your applications to meet growing demands without compromising performance.

• Security: Built-in security features to protect your data and ensure safe transactions.

• Flexibility: Compatible with various platforms and can be customized to meet specific project requirements.

Getting Started

To start using the semantic-kernel, follow these steps:

1. Installation: Clone the repository and install the necessary dependencies.

   git clone https://github.com/Bryan-Roe/semantic-kernel.git
   cd semantic-kernel
   npm install

2. Configuration: Configure the API settings by editing the config.json file to match your environment and requirements.

3. Running the API: Start the API server using the following command:

   npm start

4. Testing

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Project Details

emoji: 🚀

colorFrom: indigo

colorTo: blue

sdk: docker

pinned: false

app_port: 3000

suggested_hardware: a10g-small

license: other

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference license: apache-2.0 datasets:

• Bryan-Roe/Dataset language:
• en metrics:
• accuracy
• code_eval base_model:
• Bryan-Roe/semantic-kernel new_version: Bryan-Roe/semantic-kernel

---

Model Card for Model ID

runme

id: 01J0BYQX0015D3BH4FX0NPA9QQ

version: v3

---

Semantic Kernel

Status

• Python
  
• .NET

Overview

Semantic Kernel is a set of libraries for easily integrating AI into applications and services implemented with C#, Java, and Python.

It provides:

• abstractions for AI services (such as chat, text to images, audio to text, etc.) and memory stores
• implementations of those abstractions for services from OpenAI, Azure OpenAI, Hugging Face, local models, and more, and for a multitude of vector databases, such as those from Chroma, Qdrant, Milvus, and Azure
• a common representation for plugins, which can then be orchestrated automatically by AI
• the ability to create such plugins from a multitude of sources, including from OpenAPI specifications, prompts, and arbitrary code written in the target language
• extensible support for prompt management and rendering, including built-in handling of common formats like Handlebars and Liquid
• and a wealth of functionality layered on top of these abstractions, such as filters for responsible AI, dependency injection integration, and more. Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code.

What makes Semantic Kernel special, however, is its ability to automatically orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

Please star the repo to show your support for this project!

It provides:

Key Features and Functionality

• abstractions for AI services (such as chat, text to images, audio to text, etc.) and memory stores
• implementations of those abstractions for services from OpenAI, Azure OpenAI, Hugging Face, local models, and more, and for a multitude of vector databases, such as those from Chroma, Qdrant, Milvus, and Azure
• a common representation for plugins, which can then be orchestrated automatically by AI
• the ability to create such plugins from a multitude of sources, including from OpenAPI specifications, prompts, and arbitrary code written in the target language
• extensible support for prompt management and rendering, including built-in handling of common formats like Handlebars and Liquid
• and a wealth of functionality layered on top of these abstractions, such as filters for responsible AI, dependency injection integration, and more.

Semantic Kernel is utilized by enterprises due to its flexibility, modularity and observability. Backed with security enhancing capabilities like telemetry support, and hooks and filters so you’ll feel confident you’re delivering responsible AI solutions at scale. Semantic Kernel was designed to be future proof, easily connecting your code to the latest AI models evolving with the technology as it advances. When new models are released, you’ll simply swap them out without needing to rewrite your entire codebase.

Please star the repo to show your support for this project

Getting started with Semantic Kernel

The Semantic Kernel SDK is available in C#, Python, and Java. To get started, choose your preferred language below. See the Feature Matrix for a breakdown of feature parity between our currently supported languages.

Using Semantic Kernel in C#	Using Semantic Kernel in Python	Using Semantic Kernel in Java

The quickest way to get started with the basics is to get an API key from either OpenAI or Azure OpenAI and to run one of the C#, Python, and Java console applications/scripts below.

For C

1. Go to the Quick start page here and follow the steps to dive in.
2. After Installing the SDK, we advise you follow the steps and code detailed to write your first console app.

For Python

1. Go to the Quick start page here and follow the steps to dive in.
2. You'll need to ensure that you toggle to Python in the Choose a programming language table at the top of the page.

For Java

The Java code is in the semantic-kernel-java repository. See semantic-kernel-java build for instructions on how to build and run the Java code.

Please file Java Semantic Kernel specific issues in the semantic-kernel-java repository.

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Technical Model Description

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Technical Model Details

Model Description

1. Clone the repository: git clone https://github.com/microsoft/semantic-kernel.git

   1. To access the latest Java code, clone and checkout the Java development branch: git clone -b java-development https://github.com/microsoft/semantic-kernel.git

2. Follow the instructions here

Learning how to use Semantic Kernel

To learn how to use the Semantic Kernel, you can explore various resources and tutorials available in the repository. The Semantic Kernel is a versatile tool designed to facilitate the integration of semantic understanding into applications. It provides a range of functionalities that enable developers to build more intelligent and context-aware systems.

Key Features

• **Semantic

• Getting Started with C# notebook

• Getting Started with Python notebook

• Getting Started with C# notebook

• Getting Started with Python notebook

• Developed by: [More Information Needed]

• Funded by [optional]: [More Information Needed]

• Shared by [optional]: [More Information Needed]

• Model type: [More Information Needed]

• Language(s) (NLP): [More Information Needed]

• License: [More Information Needed]

• Finetuned from model [optional]: [More Information Needed]

1. 📖 Getting Started
2. 🔌 Detailed Samples
3. 💡 Concepts

• Getting Started with C# notebook

• Getting Started with Python notebook

• Developed by: [More Information Needed]

• Funded by [optional]: [More Information Needed]

• Shared by [optional]: [More Information Needed]

• Model type: [More Information Needed]

• Language(s) (NLP): [More Information Needed]

• License: [More Information Needed]

• Finetuned from model [optional]: [More Information Needed]

1. 📖 Getting Started
2. 🔌 Detailed Samples
3. 💡 Concepts
4. 📊 Integrating with external data sources

Model Sources [optional]

1. 📖 Overview of the kernel
2. 🔌 Understanding AI plugins
3. 👄 Creating semantic functions
4. 💽 Creating native functions
5. ⛓️ Chaining functions together
6. 🤖 Auto create plans with planner
7. 💡 Create and run a ChatGPT plugin

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Model Uses and Applications

Direct Model Use

[More Information Needed]

Downstream Applications [optional]

[More Information Needed]

Out-of-Scope Applications and Limitations

[More Information Needed]

Known Limitations and Risks

[More Information Needed]

Implementation Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Getting Started with Model Implementation

Use the code below to get started with the model.

[More Information Needed]

Model Training

Model Training Data

[More Information Needed]

Model Training Procedure

Data Preprocessing [optional]

[More Information Needed]

Model Training Parameters

• Training regime: [More Information Needed]

Performance Metrics [optional]

[More Information Needed]

Model Evaluation Results

Testing Data, Factors & Metrics

Test Dataset Analysis

[More Information Needed]

Evaluation Factors

[More Information Needed]

Performance Metrics

[More Information Needed]

Evaluation Results

[More Information Needed]

Results Summary

Model Examination Results [optional]

[More Information Needed]

Environmental Impact Assessment

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Test Data, Metrics & Evaluation Factors

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• C# API reference
• Python API reference
• Java API reference (coming soon)

Visual Studio Code extension: design semantic functions with ease

The Semantic Kernel extension for Visual Studio Code makes it easy to design and test semantic functions. The extension provides an interface for designing semantic functions and allows you to test them with the push of a button with your existing models and data.

Join the community

We welcome your contributions and suggestions to SK community! One of the easiest ways to participate is to engage in discussions in the GitHub repository. Bug reports and fixes are welcome!

For new features, components, or extensions, please open an issue and discuss with us before sending a PR. This is to avoid rejection as we might be taking the core in a different direction, but also to consider the impact on the larger ecosystem.

To learn more and get started:

• Read the documentation

• Learn how to contribute to the project

• Ask questions in the GitHub discussions

• Ask questions in the Discord community

• Attend regular office hours and SK community events

• Follow the team on our blog

Contributor Wall of Fame

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

Copyright (c) Microsoft Corporation. All rights reserved.

Licensed under the MIT license.

Enhancing Documentation

Detailed Explanations and Examples

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.

Code Snippets and Usage Examples

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.

Repository Structure Explanation

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.

Security Fixes

This repository uses several tools to automatically identify and fix security vulnerabilities:

CodeQL

CodeQL is used to analyze the code for security vulnerabilities and errors. The results are shown as code scanning alerts in GitHub. The repository includes multiple CodeQL workflows such as .github/workflows/codeql-adv.yml, .github/workflows/codeql-analysis.yml, and .github/workflows/codeql.yml. These workflows are configured to run on push, pull request, and scheduled events. CodeQL analyzes various languages including C/C++, C#, Java, JavaScript, TypeScript, Python, and Ruby.

For more information, see the CodeQL documentation.

Dependabot

Dependabot automatically checks for updates to dependencies and creates pull requests to update them. This helps in keeping the dependencies up-to-date and secure, reducing the risk of vulnerabilities in outdated packages. The repository includes a Dependabot configuration file .github/dependabot.yml. The configuration includes updates for nuget, npm, pip, and github-actions, and is set to run weekly on Monday.

For more information, see the Dependabot documentation.

Frogbot

Frogbot uses JFrog Xray to scan the project for security vulnerabilities. It automatically creates pull requests with fixes for vulnerable project dependencies. The repository includes Frogbot workflows such as .github/workflows/frogbot-scan-and-fix.yml and .github/workflows/frogbot-scan-pr.yml. These workflows are configured to run on push and pull request events, respectively. The necessary environment variables for JFrog Xray and GitHub token are set in the workflows.

For more information, see the Frogbot documentation.

Additional Security Linters

To further enhance security, the repository integrates additional security linters:

• ESLint: For JavaScript/TypeScript code, ESLint helps identify and report on patterns found in ECMAScript/JavaScript code. The repository includes an ESLint workflow in .github/workflows/eslint.yml.
• Bandit: For Python code, Bandit is a security linter designed to find common security issues. The repository includes a Bandit workflow in .github/workflows/bandit.yml.
• DevSkim: A security linter for various languages, DevSkim helps identify potential security issues early in the development process. The repository includes a DevSkim workflow in .github/workflows/devskim.yml.
• PHPMD: For PHP code, PHPMD is a tool that looks for several potential problems within the source code. The repository includes a PHPMD workflow in .github/workflows/phpmd.yml.
• rust-clippy: For Rust code, rust-clippy is a tool that runs a bunch of lints to catch common mistakes and help improve Rust code. The repository includes a rust-clippy workflow in .github/workflows/rust-clippy.yml.
• lintr: For R code, lintr provides static code analysis, checking for adherence to a given style, identifying syntax errors, and possible semantic issues. The repository includes a lintr workflow in .github/workflows/lintr.yml.

Automated Security Testing

The repository is set up with automated security testing workflows to ensure continuous security validation:

• EthicalCheck: For API security testing, the repository includes an EthicalCheck workflow in .github/workflows/ethicalcheck.yml.
• Mayhem for API: For API fuzz testing, the repository includes a Mayhem for API workflow in .github/workflows/mayhem-for-api.yml.
• OSSAR: For open source static analysis, the repository includes an OSSAR workflow in .github/workflows/ossar.yml.

Security Policies and Best Practices

The repository follows documented security policies and best practices to ensure the security of the project. These include guidelines for secure coding, regular security reviews, and mandatory security training for developers. The process for monitoring and responding to security alerts is also documented.

For more information, see the SECURITY.md file in the repository.

Ensuring Successful Completion of All GitHub Actions

To ensure that all GitHub Actions complete successfully, we have implemented a new workflow and script. This section provides information about the new workflow and how to use it.

New Workflow: ensure-success.yml

We have added a new workflow file .github/workflows/ensure-success.yml to ensure all GitHub Actions complete successfully. This workflow runs on push, pull_request, and schedule events. It checks the status of all other workflows and retries failed ones up to 3 times.

Updated Existing Workflows

We have updated existing workflows to include a step that triggers the new ensure-success.yml workflow upon completion. This ensures that the new workflow is executed after each workflow run.

New Script: check-workflow-status.sh

We have added a new script scripts/check-workflow-status.sh to check the status of all workflows and trigger retries if needed. This script is used by the new workflow to ensure successful completion of all GitHub Actions.

Instructions

To use the new workflow and script, follow these steps:

1. Ensure that the new workflow file .github/workflows/ensure-success.yml is present in your repository.
2. Ensure that the new script scripts/check-workflow-status.sh is present in your repository.
3. Update your existing workflows to include a step that triggers the new ensure-success.yml workflow upon completion.

By following these steps, you can ensure that all GitHub Actions complete successfully and that any failed workflows are retried automatically.

Using Discussions for Problem-Solving

We encourage the use of GitHub Discussions to come up with solutions to problems. Discussions provide a platform for collaborative problem-solving and knowledge sharing within the community. Here are some guidelines for creating and participating in discussions:

Creating a Discussion

1. Navigate to the "Discussions" tab in the repository.
2. Click on the "New Discussion" button.
3. Choose an appropriate category for your discussion (e.g., Q&A, Ideas, General).
4. Provide a clear and concise title for your discussion.
5. Describe the problem or topic in detail, including any relevant context or background information.
6. Click on the "Start Discussion" button to create the discussion.

Participating in a Discussion

1. Browse the existing discussions to find topics of interest.
2. Click on a discussion to view the details and comments.
3. Add your comments, suggestions, or solutions to the discussion.
4. Be respectful and constructive in your responses.
5. Use reactions to show support or agreement with comments.

Examples of Using Discussions

• Problem-Solving: Use discussions to seek help with specific issues or challenges you are facing. Describe the problem, share any relevant code or error messages, and ask for suggestions or solutions from the community.
• Feature Requests: Use discussions to propose new features or enhancements. Describe the feature, explain its benefits, and gather feedback from the community.
• General Questions: Use discussions to ask general questions about the repository, its usage, or best practices. Share your knowledge and help others by answering their questions.

For more detailed guidelines on using discussions, refer to the DISCUSSIONS.md file in the root directory of the repository.

Contributing Guidelines

We welcome contributions from the community! To contribute to this project, please follow these guidelines:

1. Fork the repository: Create a fork of the repository to work on your changes.

2. Create a branch: Create a new branch for your changes.

   git checkout -b my-feature-branch

3. Make your changes: Implement your changes in the new branch.

4. Test your changes: Ensure that your changes do not break any existing functionality and pass all tests.

5. Commit your changes: Commit your changes with a descriptive commit message.

   git commit -m "Add new feature"

6. Push your changes: Push your changes to your forked repository.

   git push origin my-feature-branch

7. Create a pull request: Open a pull request to merge your changes into the main repository.

8. Review and feedback: Address any feedback or comments from the maintainers during the review process.

9. Merge: Once your pull request is approved, it will be merged into the main repository.

Thank you for your contributions!

CI/CD Pipeline Efficiency Improvements

To improve the efficiency of our CI/CD pipelines, we have implemented several enhancements:

Parallel Jobs

We have modified our workflows to run jobs in parallel where possible. This helps to reduce the overall build and deployment times. For example, in the .circleci/config.yml file, the test, build, and deploy jobs are now run in parallel.

Caching

We have implemented caching for dependencies to speed up the build process. By caching dependencies, we can avoid re-downloading them during each build, which significantly reduces build times. For example, in the .github/workflows/azure-webapps-php.yml workflow, we cache Composer dependencies.

Multi-Stage Builds

We have optimized our Docker builds by using multi-stage builds. This helps to reduce the size of the final Docker image by only including the necessary components. For example, in the .github/workflows/azure-container-webapp.yml workflow, we use multi-stage Docker builds to improve build times and reduce image size.

Automation of Issue Management

We have automated issue management using GitHub Actions. This includes auto-labeling issues based on their content, auto-assigning issues to specific team members based on predefined criteria, and auto-closing stale issues after a certain period of inactivity. For example, the .github/workflows/label-issues.yml workflow includes logic for auto-labeling and auto-assigning issues.

By implementing these improvements, we have enhanced the efficiency of our CI/CD pipelines and streamlined our development process.

New Workflow for Handling Server-Side Request Forgery Alerts

We have added a new workflow to specifically handle server-side request forgery (SSRF) alerts. This workflow is designed to detect and address SSRF vulnerabilities in the codebase.

Configuration

To configure the new workflow, follow these steps:

1. Create a new workflow file: Add a new workflow file named .github/workflows/ssrf-detection.yml to the repository.

2. Define the workflow: Add the following content to the workflow file:

   name: SSRF Detection
   
   on:
     push:
       branches: [ "main" ]
     pull_request:
       branches: [ "main" ]
     schedule:
       - cron: '0 0 * * 0'
   
   jobs:
     ssrf-detection:
       runs-on: ubuntu-latest
       steps:
         - name: Checkout code
           uses: actions/checkout@v4
   
         - name: Run SSRF detection
           run: |
             # Add your SSRF detection script or tool here
             echo "Running SSRF detection..."

3. Run the workflow: The workflow will automatically run on push, pull request, and scheduled events. It will detect and address any SSRF vulnerabilities in the codebase.

By following these steps, you can ensure that SSRF vulnerabilities are detected and addressed in your codebase, enhancing the security of your project.