Configure Embedding Models on Azure AI Foundry with Open Web UI
Introduction Let’s take a closer look at an exciting development in the AI space. Embedding models are the key to transforming complex data into usable insights, driving innovations like smarter chatbots and tailored recommendations. With Azure AI Foundry, Microsoft’s powerful platform, you’ve got the tools to build and scale these models effortlessly. Add in Open Web UI, a intuitive interface for engaging with AI systems, and you’ve got a winning combo that’s hard to beat. In this article, we’ll explore how embedding models on Azure AI Foundry, paired with Open Web UI, are paving the way for accessible and impactful AI solutions for developers and businesses. Let’s dive in! To proceed with configuring the embedding model from Azure AI Foundry on Open Web UI, please firstly configure the requirements below. Requirements: Setup Azure AI Foundry Hub/Projects Deploy Open Web UI – refer to my previous article on how you can deploy Open Web UI on Azure VM. Optional: Deploy LiteLLM with Azure AI Foundry models to work on Open Web UI - refer to my previous article on how you can do this as well. Deploying Embedding Models on Azure AI Foundry Navigate to the Azure AI Foundry site and deploy an embedding model from the “Model + Endpoint” section. For the purpose of this demonstration, we will deploy the “text-embedding-3-large” model by OpenAI. You should be receiving a URL endpoint and API Key to the embedding model deployed just now. Take note of that credential because we will be using it in Open Web UI. Configuring the embedding models on Open Web UI Now head to the Open Web UI Admin Setting Page > Documents and Select Azure Open AI as the Embedding Model Engine. Copy and Paste the Base URL, API Key, the Embedding Model deployed on Azure AI Foundry and the API version (not the model version) into the fields below: Click “Save” to reflect the changes. Expected Output Now let us look into the scenario for when the embedding model configured on Open Web UI and when it is not. Without Embedding Models configured. With Azure Open AI Embedding models configured. Conclusion And there you have it! Embedding models on Azure AI Foundry, combined with the seamless interaction offered by Open Web UI, are truly revolutionizing how we approach AI solutions. This powerful duo not only simplifies the process of building and deploying intelligent systems but also makes cutting-edge technology more accessible to developers and businesses of all sizes. As we move forward, it’s clear that such integrations will continue to drive innovation, breaking down barriers and unlocking new possibilities in the AI landscape. So, whether you’re a seasoned developer or just stepping into this exciting field, now’s the time to explore what Azure AI Foundry and Open Web UI can do for you. Let’s keep pushing the boundaries of what’s possible!Building a Basic Chatbot with Azure OpenAI
Overview In this turorial, we'll build a simple chatbot that uses Azure OpenAI to generate responses to user queries. To create a basic chatbot, we need to set up a language model resource that enables conversation capabilities. In this tutorial, we will: Set up the Azure OpenAI resource using the Azure AI Foundry portal. Retrieve the API key needed to connect the resource to your chatbot application. Once the API key is configured in your code, you will be able to integrate the language model into your chatbot and enable it to generate responses. By the end of this tutorial, you'll have a working chatbot that can generate responses using the Azure OpenAI model. Signing In and Setting Up Your Azure AI Foundry Workspace Signing In to Azure AI Foundry Open the Azure AI Foundry page in your web browser. Login to your Azure account. If you don't have an account, you can sign up. Setting Up Your Azure AI Foundry Workspace Select + Create project to create a new project. Perform the following tasks: Enter Project name. It must be a unique value. Select Hub you'd like to use (create a new one if needed). Select Create. Setting Up the Azure OpenAI Resource in Azure AI Foundry In this step, you'll learn how to set up the Azure OpenAI resource in Azure AI Foundry. Azure OpenAI is a pre-trained language model that can generate responses to user queries. We'll be using it in our chatbot. Select Models + endpoints from the left side menu. On this page, you can deploy language models and set up Azure AI resources. In this step, we will deploy the Azure OpenAI GPT-4 language model. Select + Deploy model. Select Deploy base model. In this tutorial, we will deploy the GPT-4o model. Select GPT-4o. Select Confirm. Select Deploy. The model will be deployed. Once the deployment is complete, you will see the model listed on the Models + endpoints page. Now that the model is deployed, you can retrieve the API key needed to connect the model to your chatbot application. Select the model you deployed on the Models + endpoints page. ` On the model details page, you can view information about the model, including the API key. We will come back this page later to add the required information into the environment variables. Setting Up the Project and Install the Libraries Now, you will create a folder to work in and set up a virtual environment to develop a program. Creating a Folder to Work Inside It Open a terminal window and type the following command to create a folder named basic-chatbot in the default path. mkdir basic-chatbot Type the following command inside your terminal to navigate to the basic-chatbot folder you created. cd basic-chatbot Creating a Virtual Environment Type the following command inside your terminal to create a virtual environment named .venv. python -m venv .venv Type the following command inside your terminal to activate the virtual environment. .venv\Scripts\activate.bat NOTE If it worked, you should see (.venv) before the command prompt. Installing the Required Packages Type the following commands inside your terminal to install the required packages. openai: A Python library that provides integration with the Azure OpenAI API. python-dotenv: A Python library for managing environment variables stored in an .env file. pip install openai python-dotenv Setting up the Project in Visual Studio Code To create a basic chatbot program, you will need two files: example.py: This file will contain the code to interact with Azure resources. .env: This file will store the Azure credentials and configuration details. NOTE Purpose of the .env File The .env file is essential for storing the Azure information required to connect and use the resources you created. By keeping the Azure credentials in the .env file, you can ensure a secure and organized way to manage sensitive information. Setting Up example.py File Open Visual Studio Code. Select File from the menu bar. Select Open Folder. Select the basic-chatbot folder that you created, which is located at C:\Users\yourUserName\basic-chatbot. In the left pane of Visual Studio Code, right-click and select New File to create a new file named example.py. Add the following code to the example.py file to import the required libraries. from openai import AzureOpenAI from dotenv import load_dotenv import os # Load environment variables from the .env file load_dotenv() # Retrieve environment variables AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT") AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY") AZURE_OPENAI_MODEL_NAME = os.getenv("AZURE_OPENAI_MODEL_NAME") AZURE_OPENAI_CHAT_DEPLOYMENT_NAME = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME") AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION") # Initialize Azure OpenAI client client = AzureOpenAI( api_key=AZURE_OPENAI_API_KEY, api_version=AZURE_OPENAI_API_VERSION, base_url=f"{AZURE_OPENAI_ENDPOINT}/openai/deployments/{AZURE_OPENAI_CHAT_DEPLOYMENT_NAME}" ) print("Chatbot: Hello! How can I assist you today? Type 'exit' to end the conversation.") while True: user_input = input("You: ") if user_input.lower() == "exit": print("Chatbot: Ending the conversation. Have a great day!") break response = client.chat.completions.create( model=AZURE_OPENAI_MODEL_NAME, messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": user_input} ], max_tokens=200 ) print("Chatbot:", response.choices[0].message.content.strip()) Setting Up .env File To set up your development environment, we will create a .env file and store the necessary credentials directly. NOTE Complete folder structure: └── YourUserName . └── basic-chatbot . ├── example.py . └── .env In the left pane of Visual Studio Code, right-click and select New File to create a new file named .env. Add the following code to the .env file to include your Azure information. AZURE_OPENAI_API_KEY=your_azure_openai_api_key AZURE_OPENAI_ENDPOINT=https://your_azure_openai_endpoint AZURE_OPENAI_MODEL_NAME=your_model_name AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=your_deployment_name AZURE_OPENAI_API_VERSION=your_api_version Retrieving Environment Variables from Azure AI Foundry Now, you will retrieve the required information from Azure AI Foundry and update the .env file. Go to the Models + endpoints page and select your deployed model. On the Model Details page, copy the following information in to the .env file.: AZURE_OPENAI_API_KEY AZURE_OPENAI_ENDPOINT AZURE_OPENAI_MODEL_NAME AZURE_OPENAI_CHAT_DEPLOYMENT_NAME Paste this information into the .env file in the respective placeholders. Running the Chatbot Program Type the following command inside your terminal to run the program and see if it can answer questions. python example.py Interact with the chatbot by typing your questions or messages. The chatbot will generate responses based on the Azure OpenAI model you deployed. NOTE You can find the full example of this chatbot, including the code and .env template, in my GitHub repository: GitHub RepositoryAutomate Markdown and Image Translations Using Co-op Translator: Phi-3 Cookbook Case Study
Co-op Translator is an open source tool designed to automate the translation of Markdown files and images containing embedded text into multiple languages. Powered by Azure AI Services, it streamlines the traditionally time-consuming translation process, allowing you to make your projects globally accessible with minimal manual effort.A Beginner's Guide to Text Moderation and Prompt Shields for Large Language Model (LLM) Chatbots
Discover how to build an LLM chatbot using the Microsoft Azure OpenAI Service and Azure Content Safety. This guide walks you through integrating text moderation and prompt shields into your application to prevent 'jailbreaks' and output of harmful content.Enhancing Student Resumes: An Innovative Approach Using Azure OpenAI ChatGPT-4o
Discover the transformative power of AI in education with our innovative approach to enhancing student resumes. Leveraging Azure OpenAI ChatGPT-4o, we’ve automated the initial review and feedback process, ensuring meticulous examination of each resume. This not only saves time for career mentors but also significantly improves the quality of student resumes. Witness an increase in employment rates and a positive impact on our course reputation. Join us as we redefine educational and career support services, demonstrating the potential of AI in shaping the future of learning and career development.Build a chatbot service to ensure safe conversations: Using Azure Content Safety & Azure OpenAI
This tutorial is ideal for anyone who wants to build a chatbot service with strong content moderation capabilities. In this tutorial, you will learn how to build a chatbot service that interacts with users using Azure Cosmos DB, Azure Content Safety, and Azure OpenAI. This service provides the following features: 1. Analyze user messages for safety: Analyze messages entered by users using Azure Content Safety to evaluate them for hate, self-harm, sexual content, and violence. 2. Conversations with chatbot: Conduct conversations about safe messages using Azure OpenAI. 3. Manage conversation history: Store a user's conversation history in Azure Cosmos DB and load or clear the history as needed.Why should you migrate from OpenAI to Azure OpenAI?
As the field of AI continues to grow, developers are constantly seeking new and innovative ways to integrate it into their work. With the launch of Azure OpenAI Service, developers now have even more tools at their disposal to take advantage of this powerful technology. Azure OpenAI Service can be used to create chatbots, generate text, translate languages, and write different kinds of creative content. As the platform continues to evolve, developers will be able to use it to build even more powerful and sophisticated applications.
