Azure Logic App AI-Powered Monitoring Solution: Automate, Analyze, and Act on Your Azure Data
Introduction In today’s cloud-driven world, monitoring and analyzing application health is critical for business continuity and operational excellence. However, the sheer volume of monitoring data can make it challenging to extract actionable insights quickly. Enter the Azure Logic App AI-Powered Monitoring Solution—an intelligent, serverless pipeline that leverages Azure Logic Apps and Azure OpenAI to automate monitoring, analyze data, and deliver comprehensive reports right to your inbox. This solution is ideal for organizations seeking to modernize their monitoring workflows, reduce manual analysis, and empower teams with AI-driven insights for faster decision-making. What Does This Solution Accomplish? The Azure Logic App AI-Powered Monitoring Solution creates an automated pipeline that: Extracts monitoring data from Azure Log Analytics using KQL queries. Analyzes data with AI using the Azure OpenAI GPT-4o model. Generates intelligent reports and sends them via email. Runs automatically on a daily schedule. Uses managed identity for secure authentication across Azure services. Business Case Solved Automated Monitoring: No more manual log reviews—let AI do the heavy lifting. Actionable Insights: Receive daily, AI-generated summaries highlighting system health, key metrics, potential issues, and recommendations. Operational Efficiency: Reduce time-to-insight and empower teams to act faster on critical events. Secure and Scalable: Built on Azure’s serverless and identity-driven architecture. Key Features Serverless Architecture: Built on Azure Logic Apps Standard for scalability and cost efficiency. AI-Powered Insights: Uses Azure OpenAI for advanced data analysis and summarization. Infrastructure as Code: Deployable via Bicep templates for reproducibility and automation. Secure by Design: Managed identity and Azure RBAC ensure secure access. Cost Effective: Pay-per-execution model with optimized resource usage. Customizable: Easily modify KQL queries and AI prompts to fit your monitoring needs. Solution Architecture Technologies Involved Azure Logic Apps Standard: Orchestrates the workflow. Azure OpenAI Service (GPT-4o): Performs AI-powered data analysis and summarization. Azure Log Analytics: Source for monitoring data, queried via KQL. Application Insights: Monitors workflow execution and telemetry. Azure Storage Account: Stores Logic App runtime data. Managed Identity: Secures authentication across Azure services. Infrastructure as Code (Bicep): Enables automated, repeatable deployments. Office 365 Connector: Sends email notifications. Support Documentation: https://docs.microsoft.com/en-us/azure/logic-apps/ Issues: https://github.com/vinod-soni-microsoft/logicapp-ai-summarize/issues Star this repository if you find it helpful!
Azure AI Foundry/Azure AI Service - cannot access agents
I'm struggling with getting agents to work via API which were defined in AI Foundry (based on Azure AI Service). When defining agent in project in AI Foundry I can use it in playground via web browser. The issue appears when I'm trying to access them via API (call from Power Automate). When executing Run on agent I get info that agent cannot be found. The issue doesn't exist when using Azure OpenAI and defining assistants. I can use them both via API and web browser. I guess that another layer of management which is project might be an issue here. I saw usage of SDK in Python and first call is to connect to a project and then get an agent. Does anyone of you experienced the same? Is a way to select and run agent via API?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 Repository
Principle Does not have Access to API/Operation
Hi all, I am trying to connect Azure OpenAI service to Azure AI Search service to Azure Gen 2 Data lake. In the Azure AI Foundry studio Chat Playground, I am able to add my data source, which is a .csv file in the data lake that has been indexed successfully. I use "System Assigned Managed Identity". The following RBAC has been applied: AI Search service has Cognitive Services OpenAI Contributor in Azure Open AI service Azure OpenAI service has Search Index Data Reader in AI Search Service Azure OpenAI service has Search Service Contributor in AI Search Service AI Search Service has Storage Blob Data Reader in Storage account (Data Lake) As mentioned when adding the data source it passes validation but when I try to ask a question, I get the error "We couldn't connect your data Principal does not have access to API/Operation"Building a Scalable Web Crawling and Indexing Pipeline with Azure storage and AI Search
In the ever-evolving world of data management, keeping search indexes up-to-date with dynamic data can be challenging. Traditional approaches, such as manual or scheduled indexing, are resource-intensive, delay-prone, and difficult to scale. Azure Blob Trigger combined with an AI Search Indexer offers a cutting-edge solution to overcome these challenges, enabling real-time, scalable, and enriched data indexing. This blog explores how Blob Trigger, integrated with Azure Cognitive Search, transforms the indexing process by automating workflows and enriching data with AI capabilities. It highlights the step-by-step process of configuring Blob Storage, creating Azure Functions for triggers, and seamlessly connecting with an AI-powered search index. The approach leverages Azure's event-driven architecture, ensuring efficient and cost-effective data management.Building AI-Powered Clinical Knowledge Stores with Azure AI Search
👀 Missed Session 01? Don’t worry—you can still catch up. But first, here’s what AI HLS Ignited is all about: What is AI HLS Ignited? AI HLS Ignited is a Microsoft-led technical series for healthcare innovators, solution architects, and AI engineers. Each session brings to life real-world AI solutions that are reshaping the Healthcare and Life Sciences (HLS) industry. Through live demos, architectural deep dives, and GitHub-hosted code, we equip you with the tools and knowledge to build with confidence. Session 01 Recap: In our first session, we introduced the accelerator MedIndexer - which is an indexing framework designed for the automated creation of structured knowledge bases from unstructured clinical sources. Whether you're dealing with X-rays, clinical notes, or scanned documents, MedIndexer converts these inputs into a schema-driven format optimized for Azure AI Search. This will allow your applications to leverage state-of-the-art retrieval methodologies, including vector search and re-ranking. Moreover, by applying a well-defined schema and vectorizing the data into high-dimensional representations, MedIndexer empowers AI applications to retrieve more precise and context-aware information... The result? AI systems that surface more relevant, accurate, and context-aware insights—faster. 🔍 Turning Your Unstructured Data into Value "About 80% of medical data remains unstructured and untapped after it is created (e.g., text, image, signal, etc.)" — Healthcare Informatics Research, Chungnam National University In the era of AI, the rise of AI copilots and assistants has led to a shift in how we access knowledge. But retrieving clinical data that lives in disparate formats is no trivial task. Building retrieval systems takes effort—and how you structure your knowledge store matters. It’s a cyclic, iterative, and constantly evolving process. That’s why we believe in leveraging enterprise-ready retrieval platforms like Azure AI Search—designed to power intelligent search experiences across structured and unstructured data. It serves as the foundation for building advanced retrieval systems in healthcare. However, implementing Azure AI Search alone is not enough. Mastering its capabilities and applying well-defined patterns can significantly enhance your ability to address repetitive tasks and complex retrieval scenarios. This project aims to accelerate your ability to transform raw clinical data into high-fidelity, high-value knowledge structures that can power your next-generation AI healthcare applications. 🚀 How to Get Started with MedIndexer New to Azure AI Search? Begin with our guided labs to build a strong foundation and get hands-on with the core capabilities. Already familiar with the tech? Jump ahead to the real-world use cases—learn how to build Coded Policy Knowledge Stores and X-ray Knowledge Stores. 🧪 Labs 🧪 Building Your Azure AI Search Index: 🧾 Notebook - Building your first Index Learn how to create and configure an Azure AI Search index to enable intelligent search capabilities for your applications. 🧪 Indexing Data into Azure AI Search: 🧾 Notebook - Ingest and Index Clinical Data Understand how to ingest, preprocess, and index clinical data into Azure AI Search using schema-first principles. 🧪 Retrieval Methods for Azure AI Search: 🧾 Notebook - Exploring Vector Search and Hybrid Retrieval Dive into retrieval techniques such as vector search, hybrid retrieval, and reranking to enhance the accuracy and relevance of search results. 🧪 Evaluation Methods for Azure AI Search: 🧾 Notebook - Evaluating Search Quality and Relevance Learn how to evaluate the performance of your search index using relevance metrics and ground truth datasets to ensure high-quality search results. 🏥 Use Cases 📝 Creating Coded Policy Knowledge Stores In many healthcare systems, policy documents such as pre-authorization guidelines are still trapped in static, scanned PDFs. These documents are critical—they contain ICD codes, drug name coverage, and payer-specific logic—but are rarely structured or accessible in real-time. To solve this, we built a pipeline that transforms these documents into intelligent, searchable knowledge stores. This diagram shows how pre-auth policy PDFs are ingested via blob storage, passed through an OCR and embedding skillset, and then indexed into Azure AI Search. The result: fast access to coded policy data for AI apps. 🧾 Notebook - Creating Coded Policies Knowledge Stores Transform payer policies into machine-readable formats. This use case includes: Preprocessing and cleaning PDF documents Building custom OCR skills Leveraging out-of-the-box Indexer capabilities and embedding skills Enabling real-time AI-assisted querying for ICDs, payer names, drug names, and policy logic Why it matters: This streamlines prior authorization and coding workflows for providers and payors, reducing manual effort and increasing transparency. 🩻 Creating X-ray Knowledge Stores In radiology workflows, X-ray reports and image metadata contain valuable clinical insights—but these are often underutilized. Traditionally, they’re stored as static entries in PACS systems or loosely connected databases. The goal of this use case is to turn those X-ray reports into a searchable, intelligent asset that clinicians can explore and interact with in meaningful ways. This diagram illustrates a full retrieval pipeline where radiology reports are uploaded, enriched through foundational models, embedded, and indexed. The output powers an AI-driven web app for similarity search and decision support. 🧾 Notebook - Creating X-rays Knowledge Stores Turn imaging reports and metadata into a searchable knowledge base. This includes: Leveraging push APIs with custom event-driven indexing pipeline triggered on new X-ray uploads Generating embeddings using Microsoft Healthcare foundation models Providing an AI-powered front-end for X-ray similarity search Why it matters: Supports clinical decision-making by retrieving similar past cases, aiding diagnosis and treatment planning with contextual relevance. 📣 Join Us for the Next Session Help shape the future of healthcare by sharing AI HLS Ignited with your network—and don’t miss what’s coming next! 📅 Register for the upcoming session → AI HLS Ignited Event Page 💻 Explore the code, demos, and architecture → AI HLS Ignited GitHub RepositoryBuilding an AI-Powered ESG Consultant Using Azure AI Services: A Case Study
In today's corporate landscape, Environmental, Social, and Governance (ESG) compliance has become increasingly important for stakeholders. To address the challenges of analyzing vast amounts of ESG data efficiently, a comprehensive AI-powered solution called ESGai has been developed. This blog explores how Azure AI services were leveraged to create a sophisticated ESG consultant for publicly listed companies. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAh The Challenge: Making Sense of Complex ESG Data Organizations face significant challenges when analyzing ESG compliance data. Manual analysis is time-consuming, prone to errors, and difficult to scale. ESGai was designed to address these pain points by creating an AI-powered virtual consultant that provides detailed insights based on publicly available ESG data. Solution Architecture: The Three-Agent System ESGai implements a sophisticated three-agent architecture, all powered by Azure's AI capabilities: Manager Agent: Breaks down complex user queries into manageable sub-questions containing specific keywords that facilitate vector search retrieval. The system prompt includes generalized document headers from the vector database for context. Worker Agent: Processes the sub-questions generated by the Manager, connects to the vector database to retrieve relevant text chunks, and provides answers to the sub-questions. Results are stored in Cosmos DB for later use. Director Agent: Consolidates the answers from the Worker agent into a comprehensive final response tailored specifically to the user's original query. It's important to note that while conceptually there are three agents, the Worker is actually a single agent that gets called multiple times - once for each sub-question generated by the Manager. Current Implementation State The current MVP implementation has several limitations that are planned for expansion: Limited Company Coverage: The vector database currently stores data for only 2 companies, with 3 documents per company (Sustainability Report, XBRL, and BRSR). Single Model Deployment: Only one GPT-4o model is currently deployed to handle all agent functions. Basic Storage Structure: The Blob container has a simple structure with a single directory. While Azure Blob storage doesn't natively support hierarchical folders, the team plans to implement virtual folders in the future. Free Tier Limitations: Due to funding constraints, the AI Search service is using the free tier, which limits vector data storage to 50MB. Simplified Vector Database: The current index stores all 6 files (3 documents × 2 companies) in a single vector database without filtering capabilities or schema definition. Azure Services Powering ESGai The implementation of ESGai leverages multiple Azure services for a robust and scalable architecture: Azure AI Services: Provides pre-built APIs, SDKs, and services that incorporate AI capabilities without requiring extensive machine learning expertise. This includes access to 62 pre-trained models for chat completions through the AI Foundry portal. Azure OpenAI: Hosts the GPT-4o model for generating responses and the Ada embedding model for vectorization. The service combines OpenAI's advanced language models with Azure's security and enterprise features. Azure AI Foundry: Serves as an integrated platform for developing, deploying, and governing generative AI applications. It offers a centralized management centre that consolidates subscription information, connected resources, access privileges, and usage quotas. Azure AI Search (formerly Cognitive Search): Provides both full-text and vector search capabilities using the OpenAI ada-002 embedding model for vectorization. It's configured with hybrid search algorithms (BM25 RRF) for optimal chunk ranking. Azure Storage Services: Utilizes Blob Storage for storing PDFs, Business Responsibility Sustainability Reports (BRSRs), and other essential documents. It integrates seamlessly with AI Search using indexers to track database changes. Cosmos DB: Employs MongoDB APIs within Cosmos DB as a NoSQL database for storing chat history between agents and users. Azure App Services: Hosts the web application using a B3-tier plan optimized for cost efficiency, with GitHub Actions integrated for continuous deployment. Project Evolution: From Concept to Deployment The development of ESGai followed a structured approach through several phases: Phase 1: Data Cleaning Extracted specific KPIs from XML/XBRL datasets and BRSR reports containing ESG data for 1,000 listed companies Cleaned and standardized data to ensure consistency and accuracy Phase 2: RAG Framework Development Implemented Retrieval-Augmented Generation (RAG) to enhance responses by dynamically fetching relevant information Created a workflow that includes query processing, data retrieval, and response generation Phase 3: Initial Deployment Deployed models locally using Docker and n8n automation tools for testing Identified the need for more scalable web services Phase 4: Transition to Azure Services Migrated automation workflows from n8n to Azure AI Foundry services Leveraged Azure's comprehensive suite of AI services, storage solutions, and app hosting capabilities Technical Implementation Details Model Configurations: The GPT model is configured with: Model version: 2024-11-20 Temperature: 0.7 Max Response Token: 800 Past Messages: 10 Top-p: 0.95 Frequency/Presence Penalties: 0 The embedding model uses OpenAI-text-embedding-Ada-002 with 1536 dimensions and hybrid semantic search (BM25 RRF) algorithms. Cost Analysis and Efficiency A detailed cost breakdown per user query reveals: App Server: $390-400 AI Search: $5 per query RAG Query Processing: $4.76 per query Agent-specific costs: Manager: $0.05 (30 input tokens, 210 output tokens) Worker: $3.71 (1500 input tokens, 1500 output tokens) Director: $1.00 (600 input tokens, 600 output tokens) Challenges and Solutions The team faced several challenges during implementation: Quota Limitations: Initial deployments encountered token quota restrictions, which were resolved through Azure support requests (typically granted within 24 hours). Cost Optimization: High costs associated with vectorization required careful monitoring. The team addressed this by shutting down unused services and deploying on services with free tiers. Integration Issues: GitHub Actions raised errors during deployment, which were resolved using GitHub's App Service Build Service. Azure UI Complexity: The team noted that Azure AI service naming conventions were sometimes confusing, as the same name is used for both parent and child resources. Free Tier Constraints: The AI Search service's free tier limitation of 50MB for vector data storage restricts the amount of company information that can be included in the current implementation. Future Roadmap The current implementation is an MVP with several areas for expansion: Expand the database to include more publicly available sustainability reports beyond the current two companies Optimize token usage by refining query handling processes Research alternative embedding models to reduce costs while maintaining accuracy Implement a more structured storage system with virtual folders in Blob storage Upgrade from the free tier of AI Search to support larger data volumes Develop a proper schema for the vector database to enable filtering and more targeted searches Scale to multiple GPT model deployments for improved performance and redundancy Conclusion ESGai demonstrates how advanced AI techniques like Retrieval-Augmented Generation can transform data-intensive domains such as ESG consulting. By leveraging Azure's comprehensive suite of AI services alongside a robust agent-based architecture, this solution provides users with actionable insights while maintaining scalability and cost efficiency. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAh
Azure OpenAI Content Filter Result is always content_filter_error
I'm exploring blocklists as a solution for OpenAI not detecting sensitive words (specifically "wrist-cutting" in my local language (Cantonese) (to be fair not even Chinese AIs know the word) I have created a Blocklist with 1 entry: Term: [鎅𰾛𠝹]手 Type: Regex It can block inputs with ease: { "error": { "message": "The response was filtered due to the prompt triggering Azure OpenAI's content management policy. Please modify your prompt and retry. To learn more about our content filtering policies please read our documentation: https://go.microsoft.com/fwlink/?linkid=2198766", "type": null, "param": "prompt", "code": "content_filter", "status": 400, "innererror": { "code": "ResponsibleAIPolicyViolation", "content_filter_result": { "custom_blocklists": { "details": [ { "filtered": true, "id": "ChineseBlockList" } ], "filtered": true }, "hate": { "filtered": false, "severity": "safe" }, "profanity": { "filtered": false, "detected": false }, "self_harm": { "filtered": false, "severity": "safe" }, "sexual": { "filtered": false, "severity": "safe" }, "violence": { "filtered": false, "severity": "safe" } } } } } However, it cannot block outputs. { "choices": [ { "content_filter_result": { "error": { "code": "content_filter_error", "message": "The contents are not filtered" } }, "content_filter_results": {}, "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "content": "𠝹手（也寫作“拍手”）是一種手部動作，通常是將雙手合攏並用力拍打在一起，發出聲音。這個動作常用於表達讚賞、鼓勵或慶祝，像是在演出結束後觀眾的掌聲，或是在某些活動中用來引起注意。𠝹手也可以用於節奏感的表達，像是在音樂中隨著節拍拍手。這個動作在許多文化中都有其獨特的意義和用途。", "refusal": null, "role": "assistant" } } ], "created": 1737702254, "id": "chatcmpl-At81eUTIzDkZPCKznSKr19YMJU1ud", "model": "gpt-4o-mini-2024-07-18", "object": "chat.completion", "prompt_filter_results": [ { "prompt_index": 0, "content_filter_results": { "custom_blocklists": { "filtered": false }, "hate": { "filtered": false, "severity": "safe" }, "profanity": { "filtered": false, "detected": false }, "self_harm": { "filtered": false, "severity": "safe" }, "sexual": { "filtered": false, "severity": "safe" }, "violence": { "filtered": false, "severity": "safe" } } } ], "system_fingerprint": "fp_5154047bf2", "usage": { "completion_tokens": 138, "completion_tokens_details": { "accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0 }, "prompt_tokens": 34, "prompt_tokens_details": { "audio_tokens": 0, "cached_tokens": 0 }, "total_tokens": 172 } }
