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AI-Plugins-Planners-Persona-SemanticKernel.pptx
The document outlines the features and functionalities of the Semantic Kernel, an open-source SDK designed for integrating and orchestrating AI models from various platforms. It details key components such as AI agent creation, prompt engineering, plugin development, and automation through planners, while emphasizing community support and responsible AI practices. The document also provides guidance on getting started with Semantic Kernel using different programming languages and includes resources for developers.
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AI-Plugins-Planners-Persona-SemanticKernel.pptx
- 1. Semantic Kernel Udaiappa Ramachandran( Udai ) https://udai.io
- 2. About me • UdaiappaRamachandran ( Udai ) • CTO/CSO-Akumina, Inc. • Microsoft Azure MVP • Cloud Expert • Microsoft Azure, Amazon Web Services, and Google • New Hampshire Cloud User Group (http://www.meetup.com/nashuaug ) • https://udai.io
- 3. Agenda • Introduction toSemantic Kernel • Getting Started • Plugins • Planner • Persona • Co-Pilot • Demo…Demo…Demo…
- 4. AI Application Terminology •Plugins • Planner • Persona • Co-Pilot • Vector Embedding • Prompt Engineering • Semantic Kernel
- 5. Overview of SemanticKernel • Open-Source SDK: Semantic Kernel is an open-source Software Development Kit designed to streamline the integration and orchestration of various AI models. • AI Model Integration: It enables seamless integration with AI models from prominent platforms like OpenAI, Azure OpenAI, and Hugging Face. • Enhanced AI Agent Development: The SDK focuses on facilitating the development of sophisticated AI agents, providing tools and frameworks for effective implementation. • Versatility and Flexibility: Semantic Kernel is designed to be versatile, catering to a wide range of applications and user requirements in AI development. • Community and Support: It offers robust community support, including tutorials, forums, and resources for developers to collaborate and enhance their AI projects.. https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 6. Key Features ofSemantic Kernel • AI Agent Creation: Guidelines for developing AI agents tailored to specific needs. • Prompt Engineering: Techniques for effective AI interaction prompts. • AI Services Integration: Utilizing various AI services and plugins for enhanced functionality. • Automation with Planners: Leveraging planners for improved automation capabilities. • AI Memories Management: Utilizing vector databases for contextual information storage. • Responsible AI Practices: Adherence to ethical standards in AI development. https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 7. Building AI agentswith Semantic Kernel • Initialization: Start by initializing Semantic Kernel, setting up the basic framework for the AI agent. • Model Integration: Integrate various AI models from platforms like OpenAI and Hugging Face, customizing the agent to specific tasks. • Plugin Utilization: Enhance the agent's capabilities by incorporating plugins that offer additional functionalities and integrations. • Memory and Context Management: Implement AI memory features for context retention, ensuring the agent maintains a coherent conversation history or task memory. • Customization and Testing: Customize the AI agent based on specific use cases and perform thorough testing to ensure optimal performance and reliability. https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 8. Semantic Kernel Plugins •Extensibility: Plugins extend the core capabilities of Semantic Kernel, enabling custom features tailored to specific needs. • Integration: They allow for the integration of external services and APIs, enhancing the AI agent's functionality and data access. • Flexibility: Plugins provide the flexibility to adapt the AI agent to various domains and applications. • Custom Development: Developers can create their own plugins to incorporate unique features or connect to proprietary systems. • Community Contributions: Leverage plugins developed by the community for a wide range of functionalities, fostering collaborative improvements and innovation. https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 9. Semantic Kernel Planners •Task Automation: Planners in Semantic Kernel automate complex tasks, streamlining workflows and reducing manual intervention. • Efficiency Improvement: They enhance efficiency by orchestrating various components and processes within the AI agent. • Customizable Workflows: Planners allow for the customization of workflows to suit specific automation needs. • Adaptability: They are adaptable to different scenarios, ensuring optimal task execution under varying conditions. • Integration of AI Models: Planners effectively integrate different AI models to handle complex decision-making processes.
- 10. Semantic Kernel Prompts •Importance of Prompts: Prompts are crucial for directing AI model behavior, serving as inputs or queries that elicit specific responses. • Prompt Engineering: This emerging field requires creativity and attention to detail in selecting words, phrases, and formats to guide AI model output generation. • Experimentation with Prompts: The document emphasizes experimenting with different prompts and parameters to achieve desired outcomes. • Examples of Prompts: It includes examples showing how varying prompt structures lead to different AI responses. • Tips for Effective Prompt Engineering: The document provides tips and strategies for mastering prompt engineering, highlighting its significance in AI model manipulation. https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 11. Semantic Kernel Memoryand Embeddings • Context Retention: AI Memory in Semantic Kernel is crucial for maintaining conversation context, enhancing the continuity and relevance of interactions. • Embedding Storage: The system stores embeddings, which are numerical representations of data, to facilitate quick and accurate retrieval of information. • Improved Understanding: These embeddings help in better understanding and processing of user queries and interactions. • Dynamic Learning: AI Memory enables dynamic learning from interactions, adapting to new information and user preferences. • Enhanced Performance: The combination of AI Memory and embeddings significantly enhances the overall performance and responsiveness of AI agents. https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 12. Copilot feature inSemantic Kernel • Real-time Assistance: Chat Copilot provides real-time assistance by integrating AI- driven responses into conversations. • Customization: It offers extensive customization options to tailor the AI's responses according to specific user needs or scenarios. • Enhanced User Interaction: This feature enhances user interactions, making them more engaging and informative. • Seamless Integration: Chat Copilot seamlessly integrates with existing systems, ensuring a smooth user experience. • User Feedback Incorporation: It has capabilities to learn from user feedback, continually improving the relevance and accuracy of its responses. https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 13. Semantic Kernel –Planner/Plugin https://learn.microsoft.com/en-us/semantic-kernel/overview/
- 14. Getting Started • SemanticKernel Supported Language: C#, Python and Java • Semantic Kernel SDK is available in C#, Python, and Java • Semantic Kernel : https://github.com/microsoft/semantic-kernel • Semantic Kernel Starters: https://github.com/microsoft/semantic-kernel-starters • Semantic Kernel in C# • https://github.com/microsoft/semantic-kernel/blob/main/dotnet/README.md • Using Semantic Kernel in Python • https://github.com/microsoft/semantic-kernel/blob/main/python/README.md • Using Semantic Kernel in Java • https://github.com/microsoft/semantic-kernel/blob/main/java/README.md
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- 22. Reference • https://learn.microsoft.com/en-us/semantic-kernel/ • SemanticKernel Starters: https://github.com/microsoft/semantic-kernel-starters • Semantic Kernel in C# • https://github.com/microsoft/semantic-kernel/blob/main/dotnet/README.md • Using Semantic Kernel in Python • https://github.com/microsoft/semantic-kernel/blob/main/python/README.md • Using Semantic Kernel in Java • https://github.com/microsoft/semantic-kernel/blob/main/java/README.md
- 23. Thanks for yourtime and trust! Boston Code Camp (BCC35)
Editor's Notes
- #4 how it actually work? planners --the magic that combines plugins together to accoplish a user's goal with planner you can build your own AI app apps (plugin extensibility | copilots) -- AI orchestration -- Foundation models | AI infrastructure At the core of every copilot is a planner -- planner tell a copilot what it should do next with the tools it has, SK has 3 OOTB planners => Action Planner (one action), Sequential planner (several action), Stepwise planner (multiple actions)
- #5 how it actually work? planners --the magic that combines plugins together to accoplish a user's goal with planner you can build your own AI app apps (plugin extensibility | copilots) -- AI orchestration -- Foundation models | AI infrastructure At the core of every copilot is a planner -- planner tell a copilot what it should do next with the tools it has, SK has 3 OOTB planners => Action Planner (one action), Sequential planner (several action), Stepwise planner (multiple actions)