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Semantic Kernel , a powerful tool for integrating large language models into your applications, now supports streaming responses. In this blog post, we’ll explore how to leverage this feature to obtain streamed results from LLMs like AzureOpenAI and OpenAI. Why Streamed Responses Matter When working with language models, especially in conversational scenarios, streaming responses offer several advantages: Real-time Interaction: Streaming allows you to receive partial responses as they become available, enabling more interactive and dynamic conversations. Reduced Latency: Instead of waiting for the entire response, you can start processing and displaying content incrementally, reducing overall latency. Efficient Resource Usage: Streaming conserves memory and resources by handling data in smaller chunks. How to Use Streaming Responses I've published a complete video on how to generate this using Python and that can be found on my YouTube channel named Shweta Lodha . Here, I'm jus

If you want to explore Azure OpenAI but not sure how to get started, then you are at the right place. I've created a video, which explains everything about get your journey started. Have a look:

This article talks about how we can use dependency injection in Azure Functions. Dependency injection is a very well known design pattern that is used to implement IoC as provides you a facility to segregate object creation logic from its usage.  Prerequisites And Tools Used In order to implement dependency injection in Azure Function App, you need an active Azure subscription, Visual Studio (I’m using Visual Studio 2019), and working knowledge of the C# language.  Create Function App in Visual Studio The first step is to create a new project in Visual Studio of type Azure Functions: and select Http Trigger as shown below:  Add Classes For Dependency Injection Next, we need to add the service (classes) which we want to inject. For simplicity, we will create an interface and a class implementing that interface. This interface will have only one method named GetCurrentTime() and it will provide current time to the caller. Here are the definitions: public interface ICurrentTimeProvider {

This article talks about an end-to-end flow, wherein an email content having specific subject line will be read, email body would be analyzed using Azure Cognitive Services (Sentiment analysis), analysis results would be saved in Azure Table Storage and finally chart would be drawn in Excel. All these steps include no coding at all. You can watch out the complete recording of this flow on my YouTube channel named Shweta Lodha.

This article explains the basics of NER (Named Entity Recognition) - PII (Personal Identifiable Information) and how it can be used to redact the sensitive/confidential information before passing it to next stage. It also includes code walk through and the Azure Text Analytics instance creation. Watch out this complete flow on my YouTube channel named Shweta Lodha.

In this article, I’m going to write about another interesting Azure-based service named  Translator , which falls under the umbrella of  Azure Cognitive Services . This service helps us to translate documents from one language to another and at the same time, it retains the formatting and the structure of the source document. So, let’s say, if any text in the source document is in italics, then the newly translated document, will also have the text in italics. Key Features of Translator Service Let’s have a look at a few of the key features, of the  Translator  service, Auto-detection of the language of the source document Translates large files Translates multiple files in a shot Preserves formatting of the source document Supports custom translations Supports custom glossaries Supported document types – pdf, csv, html/htm, doc/docx, msg, rtf, txt, etc. Implementation can be done using C#/Python as SDKs are available. Suppo

Today I'll be mentioning one of the useful functions of Azure Text Analytics - Sentiment Analysis. Azure text analytics is a cloud-based offering from Microsoft and it provides Natural Language Processing over raw text.  Use Case Described In this article, I will explain how to use customer-provided product reviews to understand the market insight and how one can take a call on manufacturing the products in the future. Here is the pictorial representation of this use case.   Here are the high-level steps of how we can achieve this entire flow: Step 1 This entire process starts with the data collection part and for this, I'm using a CSV file with customer-provided reviews. Here is the gist of it: Step 2 Once data is collected, we need to import the data and for that, I'm using Jupyter Notebook inside Visual Studio Code. Here is the Python code to read and extract data from CSV file: import csv feedbacks = [ ] counter = 0 with open ( 'Feedback.csv' ,