Posts

Semantic Kernel: Creating Plugins to access the business data

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In the modern of software application development, every single application wants an AI capability to be integrated for providing results to their end-users based on their questions/prompt. The challenge in such scenario is to integrate the AI capability to an existing application with seamless integration with the smooth data sharing. This is where we can make use of the Semantic Kernel .  What is Semantic Kernel? In a very simple words, Semantic Kernel is Microsoft’s open-source SDK that is Designed to integrate large language models (LLMs) like GPT with traditional programming logic. It supports multiple languages like C#, Python, and Java, making it accessible for a range of developers. It provides hybrid AI orchestration that combines AI capabilities like summarizing , answering , and generating with conventional app code and workflows. It offers Plugin-friendly architecture to easily integrates external APIs and functions as "skills" that the AI can call. We can use Se...
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Azure Open AI: Generating SQL Statement from the Prompt and executing it

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In this article, we will implement the process of generating SQL Statements from the simple English language prompt.  AI Prompt is an input send to the AI model to guide its response. In a simple language we can define Prompt as a beginning of the conversation so that the appropriate response can be received. The accuracy of the response is depending on the appropriate prompt input to the model. The AI model is capable enough to understand and compile the prompt and generate response. In different context the prompt can be set in different format, e.g. Technical Prompt for expecting technical answers, Creative Prompt for expecting a story or a poem, and the Simple Prompt for simple translation. OfCourse for the accurate response the prompt must be accurate.  In the Line-of-Business (LOB) applications we have the data mostly stored in the Relational Databases. To extract information from various table from database, we need to write complex queries with joins across tables (if ...
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Azure AI Search: Indexers and Indexes using C# Code

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In this article, we will implement the Azure AI Search Service Data Source, Index, and Indexer creation using the C# code. We will implement the process using the ASP.NET Core 9 APIs. The application targets not only to configure Azure AI Search but also the application exposes API endpoints to perform search operations on the Data Source configured with Azure AI Search.  Formally known as Azure Cognitive Search, the Azure AI Search is an enterprise-ready information retrieval system. We can build the heterogeneous contents information ingestion into search index e.g. Documents, Relational Data, etc to query so that the data can be retrieved. Teh Azure AI Service is designed for high-performance and scalability. Some of the features of Azure AI Search are as follows: Rich Indexing capabilities with content transformation, data chunking, vectorization for RAG. Vector and full-text search for data retrieval with efficiency. Advanced Query syntax Integration with other Azure Cloud hos...
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