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 } }
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 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 RepositoryNLP London Meetup - Microsoft Reactor
Hey everyone! Thanks for joining out session today at the NLP London meetup in the Microsoft Reactor. Here you can find the resources that we have shared during the session and our contact links. Resources Azure OpenAI Docs The Azure Developer CLI azd AI App Templates Azure AI Search Docs Azure OpenAI Assistants Responsible AI Contoso Creative Writer Example Application Our next event London Reactor meetup - 10th December Liam Hampton LinkedIn Chris Noring LinkedInDell APEX File Storage for Microsoft Azure brings a powerful new option to our customers
Dell PowerScale OneFS has been trusted by customers across all industries to provide performant, resilient, and scalable multiprotocol file storage for nearly two decades. At Ignite 2024 we announced a new Dell managed variant that compliments the existing offering to give you powerful choice from a proven industry leader.AMA on Camera - AI Revolution: Azure OpenAI's Game-Changing Enhancements
Discover the latest enhancements to Azure OpenAI Service offering and deployments. Join our engineering experts to learn about Azure OpenAI Data Zones for flexible, multi-regional data processing and compliance. Explore simplified deployment flows, industry-leading performance guarantees, and cost-efficiency at scale. Perfect for anyone looking to leverage adaptable, reliable, and scalable AI solutions for enterprise applications. Please check out this blog post for more information: Accelerate scale with Azure OpenAI Service Provisioned offering | Microsoft Azure Blog Azure OpenAI Global Batch offering is designed to handle large-scale and high-volume processing tasks efficiently. Process asynchronous groups of requests with separate quota, a 24-hour turnaround time, at 50% less cost than global standard. Learn more There will be presentations on Batch, Data Zone Standard and Data Zone Provisioned and we will be taking questions throughout. Please share any questions that you would like our experts to address in the comments below. Questions can be posted anytime in the comments below beforehand, if it fits your schedule or time zone better, though questions will not be answered until the live hour. Questions will be answered both in the live broadcast on video and in text below. This will be a live stream video directly on this event page. NOTE: Please be aware the link sent via the private message to folks will not work due to some platform changes. We are working on redirecting it, but just in case, the new event link (the page you are on) is here: AMA on Camera - AI Revolution: Azure OpenAI's Game-Changing Enhancements | Microsoft Community Hub )Automate 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.
