diff --git a/.github/workflows/azure-dev.yml b/.github/workflows/azure-dev.yml index 4957d802..0d903df1 100644 --- a/.github/workflows/azure-dev.yml +++ b/.github/workflows/azure-dev.yml @@ -10,7 +10,7 @@ on: - main # Set up permissions for deploying with secretless Azure federated credentials -# https://learn.microsoft.com/en-us/azure/developer/github/connect-from-azure?tabs=azure-portal%2Clinux#set-up-azure-login-with-openid-connect-authentication +# https://learn.microsoft.com/azure/developer/github/connect-from-azure?tabs=azure-portal%2Clinux#set-up-azure-login-with-openid-connect-authentication permissions: id-token: write contents: read diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..9b388533 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "python.testing.pytestArgs": [ + "tests" + ], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true +} \ No newline at end of file diff --git a/README.md b/README.md index 041a4848..13f34c35 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Getting Started with Agents Using Azure AI Foundry -The agent leverages the Azure AI Agent service and utilizes file search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance. +The agent leverages Foundry Agent Service and utilizes file search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance.
@@ -8,21 +8,23 @@ The agent leverages the Azure AI Agent service and utilizes file search for know
+**Note**: With any AI solutions you create using these templates, you are responsible for assessing all associated risks, and for complying with all applicable laws and safety standards. Learn more in the transparency documents for [Agent Service](https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Flearn.microsoft.com%2Fen-us%2Fazure%2Fai-foundry%2Fresponsible-ai%2Fagents%2Ftransparency-note&data=05%7C02%7Chowieleung%40microsoft.com%7C42645ec29da244bd920508de2095bcad%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638984024651276233%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Un4HtoksTeodWPQMQp7zh8BNW6j%2BeIw4mcs6gbS4e6E%3D&reserved=0) and [Agent Framework](https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fmicrosoft%2Fagent-framework%2Fblob%2Fmain%2FTRANSPARENCY_FAQ.md&data=05%7C02%7Chowieleung%40microsoft.com%7C42645ec29da244bd920508de2095bcad%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638984024651325701%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=19nfzxn8ZN1qr7Hy77fn%2BgFXD1sc%2BXiuPuUi3H2NNz4%3D&reserved=0). + ## Solution Overview This solution deploys a web-based chat application with an AI agent running in Azure Container App. -The agent leverages the Azure AI Agent service and utilizes Azure AI Search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance. +The agent leverages the Foundry Agent Service and utilizes Azure AI Search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance. -This solution creates an Azure AI Foundry project and Azure AI services. More details about the resources can be found in the [resources](#resources) documentation. There are options to enable logging, tracing, and monitoring. +This solution creates a Microsoft Foundry project and Foundry Tools. More details about the resources can be found in the [resources](#resources) documentation. There are options to enable logging, tracing, and monitoring. Instructions are provided for deployment through GitHub Codespaces, VS Code Dev Containers, and your local development environment. ### Solution Architecture -![Architecture diagram showing that user input is provided to the Azure Container App, which contains the app code. With user identity and resource access through managed identity, the input is used to form a response. The input and the Azure monitor are able to use the Azure resources deployed in the solution: Application Insights, Azure AI Foundry Project, Azure AI Services, Storage account, Azure Container App, and Log Analytics Workspace.](docs/images/architecture.png) +![Architecture diagram showing that user input is provided to the Azure Container App, which contains the app code. With user identity and resource access through managed identity, the input is used to form a response. The input and the Azure monitor are able to use the Azure resources deployed in the solution: Application Insights, Microsoft Foundry Project, Foundry Tools, Storage account, Azure Container App, and Log Analytics Workspace.](docs/images/architecture.png) -The app code runs in Azure Container App to process the user input and generate a response to the user. It leverages Azure AI projects and Azure AI services, including the model and agent. +The app code runs in an Azure Container App to process user input and generate a response to the user. It leverages Microsoft Foundry projects and Foundry Tools, including the model and agent. ### Key Features @@ -32,17 +34,20 @@ The AI agent uses file search or Azure AI Search to retrieve knowledge from uplo - **[Customizable AI Model Deployment](./docs/deploy_customization.md#customizing-model-deployments)**
The solution allows users to configure and deploy AI models, such as gpt-4o-mini, with options to adjust model capacity, and knowledge retrieval methods. -- **[Built-in Monitoring and Tracing](./docs/other_features.md#tracing-and-monitoring)**
+- **[Built-in Monitoring and Tracing](./docs/observability.md#tracing-and-monitoring)**
Integrated monitoring capabilities, including Azure Monitor and Application Insights, enable tracing and logging for easier troubleshooting and performance optimization. - **[Flexible Deployment Options](./docs/deployment.md)**
The solution supports deployment through GitHub Codespaces, VS Code Dev Containers, or local environments, providing flexibility for different development workflows. -- **[Agent Evaluation](./docs/other_features.md#agent-evaluation)**
-This solution demonstrates how you can evaluate your agent's performance and quality during local development and incorporate it into monitoring and CI/CD workflow. +- **[Continuous Evaluation](./docs/observability.md#continuous-evaluation)**
+Proactively monitor and assess your agent's performance over time with continuous evaluation that automatically checks real-world interactions to identify potential issues before they impact users. + +- **[Agent Evaluation](./docs/observability.md#agent-evaluation)**
+This solution demonstrates how you can evaluate your agent's performance and quality through Pytest. -- **[AI Red Teaming Agent](./docs/other_features.md#ai-red-teaming-agent)**
-Facilitates the creation of an AI Red Teaming Agent that can run batch automated scans for safety and security scans on your Agent solution to check your risk posture before deploying it into production. +- **[AI Red Teaming Agent](./docs/observability.md#ai-red-teaming-agent)**
+Facilitates the creation of an AI Red Teaming Agent through Pytest that can run batch automated scans for safety and security on your Agent solution to check your risk posture before deploying it into production.
@@ -111,9 +116,9 @@ The majority of the Azure resources used in this infrastructure are on usage-bas You can try the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator) for the resources: -- **Azure AI Foundry**: Free tier. [Pricing](https://azure.microsoft.com/pricing/details/ai-studio/) +- **Microsoft Foundry**: Free tier. [Pricing](https://azure.microsoft.com/pricing/details/ai-studio/) - **Azure Storage Account**: Standard tier, LRS. Pricing is based on storage and operations. [Pricing](https://azure.microsoft.com/pricing/details/storage/blobs/) -- **Azure AI Services**: S0 tier, defaults to gpt-4o-mini. Pricing is based on token count. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/) +- **Foundry Tools**: S0 tier, defaults to gpt-4o-mini. Pricing is based on token count. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/) - **Azure Container App**: Consumption tier with 0.5 CPU, 1GiB memory/storage. Pricing is based on resource allocation, and each month allows for a certain amount of free usage. [Pricing](https://azure.microsoft.com/pricing/details/container-apps/) - **Log analytics**: Pay-as-you-go tier. Costs based on data ingested. [Pricing](https://azure.microsoft.com/pricing/details/monitor/) - **Agent Evaluations**: Incurs the cost of your provided model deployment used for local evaluations. @@ -135,7 +140,7 @@ You may want to consider additional security measures, such as: > **Important Security Notice**
This template, the application code and configuration it contains, has been built to showcase Microsoft Azure specific services and tools. We strongly advise our customers not to make this code part of their production environments without implementing or enabling additional security features.

-For a more comprehensive list of best practices and security recommendations for Intelligent Applications, [visit our official documentation](https://learn.microsoft.com/en-us/azure/ai-foundry/). +For a more comprehensive list of best practices and security recommendations for Intelligent Applications, [visit our official documentation](https://learn.microsoft.com/azure/ai-foundry/). ### Resources diff --git a/SECURITY.md b/SECURITY.md index f7b89984..9842a84a 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -4,7 +4,7 @@ Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). -If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below. +If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below. ## Reporting Security Issues @@ -12,7 +12,7 @@ If you believe you have found a security vulnerability in any Microsoft-owned re Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report). -If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc). +If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/msrc/pgp-key-msrc). You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). @@ -36,6 +36,6 @@ We prefer all communications to be in English. ## Policy -Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd). +Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/msrc/cvd). \ No newline at end of file diff --git a/airedteaming/ai_redteaming.py b/airedteaming/ai_redteaming.py deleted file mode 100644 index 39d9ba62..00000000 --- a/airedteaming/ai_redteaming.py +++ /dev/null @@ -1,107 +0,0 @@ -# ------------------------------------ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. -# ------------------------------------ - -from typing import Optional, Dict, Any -import os -import time -from pathlib import Path -from dotenv import load_dotenv - -# Azure imports -from azure.identity import DefaultAzureCredential, get_bearer_token_provider -from azure.ai.evaluation.red_team import RedTeam, RiskCategory, AttackStrategy -from azure.ai.projects import AIProjectClient -import azure.ai.agents -from azure.ai.agents.models import ListSortOrder - -async def run_red_team(): - # Load environment variables from .env file - current_dir = Path(__file__).parent - env_path = current_dir / "../src/.env" - load_dotenv(dotenv_path=env_path) - - # Initialize Azure credentials - credential = DefaultAzureCredential() - - # Get AI project parameters from environment variables (matching evaluate.py) - project_endpoint = os.environ.get("AZURE_EXISTING_AIPROJECT_ENDPOINT") - deployment_name = os.getenv("AZURE_AI_AGENT_DEPLOYMENT_NAME") # Using getenv for consistency with evaluate.py - agent_id = os.environ.get("AZURE_EXISTING_AGENT_ID") - agent_name = os.environ.get("AZURE_AI_AGENT_NAME") - - # Validate required environment variables - if not project_endpoint: - raise ValueError("Please set the AZURE_EXISTING_AIPROJECT_ENDPOINT environment variable.") - - if not agent_id and not agent_name: - raise ValueError("Please set either AZURE_EXISTING_AGENT_ID or AZURE_AI_AGENT_NAME environment variable.") - - with DefaultAzureCredential(exclude_interactive_browser_credential=False) as credential: - with AIProjectClient(endpoint=project_endpoint, credential=credential) as project_client: - # Look up the agent by name if agent ID is not provided (matching evaluate.py) - if not agent_id and agent_name: - for agent in project_client.agents.list_agents(): - if agent.name == agent_name: - agent_id = agent.id - break - - if not agent_id: - raise ValueError("Agent ID not found. Please provide a valid agent ID or name.") - - agent = project_client.agents.get_agent(agent_id) - - # Use model from agent if not provided - matching evaluate.py - if not deployment_name: - deployment_name = agent.model - - thread = project_client.agents.threads.create() - - def agent_callback(query: str) -> str: - message = project_client.agents.messages.create(thread_id=thread.id, role="user", content=query) - run = project_client.agents.runs.create(thread_id=thread.id, agent_id=agent.id) - - # Poll the run as long as run status is queued or in progress - while run.status in ["queued", "in_progress", "requires_action"]: - # Wait for a second - time.sleep(1) - run = project_client.agents.runs.get(thread_id=thread.id, run_id=run.id) - # [END create_run] - print(f"Run status: {run.status}") - - if run.status == "failed": - print(f"Run error: {run.last_error}") - return "Error: Agent run failed." - messages = project_client.agents.messages.list(thread_id=thread.id, order=ListSortOrder.DESCENDING) - for msg in messages: - if msg.text_messages: - return msg.text_messages[0].text.value - return "Could not get a response from the agent." - - - # Print agent details to verify correct targeting - print(f"Running Red Team evaluation against agent:") - print(f" - Agent ID: {agent.id}") - print(f" - Agent Name: {agent.name}") - print(f" - Using Model: {deployment_name}") - - red_team = RedTeam( - azure_ai_project=project_endpoint, - credential=credential, - risk_categories=[RiskCategory.Violence], - num_objectives=1, - output_dir="redteam_outputs/" - ) - - print("Starting Red Team scan...") - result = await red_team.scan( - target=agent_callback, - scan_name="Agent-Scan", - attack_strategies=[AttackStrategy.Flip], - ) - print("Red Team scan complete.") - -if __name__ == "__main__": - import asyncio - asyncio.run(run_red_team()) \ No newline at end of file diff --git a/azure.yaml b/azure.yaml index 00ad34cf..af4cc48e 100644 --- a/azure.yaml +++ b/azure.yaml @@ -4,7 +4,7 @@ name: azd-get-started-with-ai-agents metadata: - template: azd-get-started-with-ai-agents@1.0.4 + template: azd-get-started-with-ai-agents@2.0.0b1 requiredVersions: azd: ">=1.14.0" @@ -85,4 +85,4 @@ pipeline: - AZURE_EXISTING_AIPROJECT_ENDPOINT - AZURE_EXISTING_AGENT_ID - ENABLE_AZURE_MONITOR_TRACING - - AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED + - OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT diff --git a/docs/deploy_customization.md b/docs/deploy_customization.md index dc6123d4..78321a37 100644 --- a/docs/deploy_customization.md +++ b/docs/deploy_customization.md @@ -50,7 +50,7 @@ To override any of those resource names, run `azd env set ` before ## Customizing model deployments -For more information on the Azure OpenAI models and non-Microsoft models that can be used in your deployment, view the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support). +For more information on the Azure OpenAI models and non-Microsoft models that can be used in your deployment, view the [list of models supported by Foundry Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support). To customize the model deployments, you can set the following environment variables: diff --git a/docs/deployment.md b/docs/deployment.md index d068a193..37c6c35f 100644 --- a/docs/deployment.md +++ b/docs/deployment.md @@ -12,13 +12,13 @@ To deploy this Azure environment successfully, your Azure account (the account y You can view the permissions for your account and subscription by going to Azure portal, clicking 'Subscriptions' under 'Navigation' and then choosing your subscription from the list. If cannot find the subscription, make sure no filters are selected. After selecting your subscription, select 'Access control (IAM)' and you can see the roles that are assigned to your account for this subscription. To get more information about the roles, go to the 'Role assignments' tab, search by your account name and click the role you want to view more information about. -Check the [Azure Products by Region](https://azure.microsoft.com/en-us/explore/global-infrastructure/products-by-region/?products=all®ions=all) page and select a **region** where the following services are available: +Check the [Azure Products by Region](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/?products=all®ions=all) page and select a **region** where the following services are available: -- [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/) -- [Azure Container Apps](https://learn.microsoft.com/en-us/azure/container-apps/) -- [Azure Container Registry](https://learn.microsoft.com/en-us/azure/container-registry/) -- [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/) -- [GPT Model Capacity](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) +- [Azure AI Foundry](https://learn.microsoft.com/azure/ai-foundry/) +- [Azure Container Apps](https://learn.microsoft.com/azure/container-apps/) +- [Azure Container Registry](https://learn.microsoft.com/azure/container-registry/) +- [Azure AI Search](https://learn.microsoft.com/azure/search/) +- [GPT Model Capacity](https://learn.microsoft.com/azure/ai-services/openai/concepts/models) Here are some examples of the regions where the services are available: East US, East US2, Japan East, UK South, Sweden Central. @@ -184,12 +184,12 @@ When you start a deployment, most parameters will have default values. You can c | **Setting** | **Description** | **Default value** | |------------|----------------| ------------| -| **Existing Project Resource ID** | Specify an existing project resource ID to be used instead of provisioning new Azure AI Foundry project and Azure AI services. | | +| **Existing Project Resource ID** | Specify an existing project resource ID to be used instead of provisioning new Azure AI Foundry project and Foundry Tools. | | | **Azure Region** | Select a region with quota which supports your selected model. | | -| **Model** | Choose from the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support) for your selected region. | gpt-4o-mini | +| **Model** | Choose from the [list of models supported by Foundry Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support) for your selected region. | gpt-4o | | **Model Format** | Choose from OpenAI or Microsoft, depending on your model. | OpenAI | | **Model Deployment Capacity** | Configure capacity for your model. | 80k | -| **Embedding Model** | Choose from text-embedding-3-large, text-embedding-3-small, and text-embedding-ada-002. This may only be deployed if Azure AI Search is enabled. | text-embedding-3-small | +| **Embedding Model** | Choose from text-embedding-3-large, azdtext-embedding-3-small, and text-embedding-ada-002. This may only be deployed if Azure AI Search is enabled. | text-embedding-3-small | | **Embedding Model Capacity** | Configure capacity for your embedding model. | 50k | | **Knowledge Retrieval** | Choose OpenAI's file search or Azure AI Search Index. | OpenAI's file search | @@ -225,7 +225,7 @@ azd env set ENABLE_AZURE_MONITOR_TRACING true To enable message contents to be included in the traces, set the following environment variable. Note that the messages may contain personally identifiable information. ```shell -azd env set AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED true +azd env set OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT true ``` You can view the App Insights tracing in Azure AI Foundry. Select your project on the Azure AI Foundry page and then click 'Tracing'. @@ -239,9 +239,8 @@ You can view the App Insights tracing in Azure AI Foundry. Select your project o The default for the model capacity in deployment is 80k tokens for chat model and 50k for embedded model for AI Search. For optimal performance, it is recommended to increase to 100k tokens. You can change the capacity by following the steps in [setting capacity and deployment SKU](deploy_customization.md#customizing-model-deployments). -- Navigate to the home screen of the [Azure AI Foundry Portal](https://ai.azure.com/) -- Select Quota Management buttom at the bottom of the home screen -* In the Quota tab, click the GlobalStandard dropdown and select the model and region you are using for this accelerator to see your available quota. Please note gpt-4o-mini and text-embedding-3-small are used as default. +- Navigate to [Monitor and track your quota usage](https://ai.azure.com/managementCenter/quota) +* In the Quota tab, click the GlobalStandard dropdown and select the model and region you are using for this accelerator to see your available quota. Please note gpt-4o and text-embedding-3-small are used as default. - Request more quota or delete any unused model deployments as needed. @@ -269,7 +268,7 @@ Once you've opened the project in [Codespaces](#github-codespaces) or in [Dev Co 3. You will be prompted to provide an `azd` environment name (like "azureaiapp"), select a subscription from your Azure account, and select a location which has quota for all the resources. Then, it will provision the resources in your account and deploy the latest code. - - For guidance on selecting a region with quota and model availability, follow the instructions in the [quota recommendations](#quota-recommendations) section and ensure that your model is available in your selected region by checking the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support) + - For guidance on selecting a region with quota and model availability, follow the instructions in the [quota recommendations](#quota-recommendations) section and ensure that your model is available in your selected region by checking the [list of models supported by Foundry Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support) - This deployment will take 7-10 minutes to provision the resources in your account and set up the solution with sample data. - If you get an error or timeout with deployment, changing the location can help, as there may be availability constraints for the resources. You can do this by running `azd down` and deleting the `.azure` folder from your code, and then running `azd up` again and selecting a new region. diff --git a/docs/images/agent_id_in_foundry_ui.png b/docs/images/agent_id_in_foundry_ui.png index 484ec1c6..49357a58 100644 Binary files a/docs/images/agent_id_in_foundry_ui.png and b/docs/images/agent_id_in_foundry_ui.png differ diff --git a/docs/images/agent_monitor.png b/docs/images/agent_monitor.png new file mode 100644 index 00000000..1ae92705 Binary files /dev/null and b/docs/images/agent_monitor.png differ diff --git a/docs/images/architecture.png b/docs/images/architecture.png index 76e797e8..7fde9d5d 100644 Binary files a/docs/images/architecture.png and b/docs/images/architecture.png differ diff --git a/docs/images/enable_cont_eval.png b/docs/images/enable_cont_eval.png new file mode 100644 index 00000000..b766b21e Binary files /dev/null and b/docs/images/enable_cont_eval.png differ diff --git a/docs/images/eval_link.png b/docs/images/eval_link.png new file mode 100644 index 00000000..d615a7a9 Binary files /dev/null and b/docs/images/eval_link.png differ diff --git a/docs/images/eval_report.png b/docs/images/eval_report.png new file mode 100644 index 00000000..117280f7 Binary files /dev/null and b/docs/images/eval_report.png differ diff --git a/docs/images/red_teaming_report.png b/docs/images/red_teaming_report.png new file mode 100644 index 00000000..6d20e67d Binary files /dev/null and b/docs/images/red_teaming_report.png differ diff --git a/docs/images/red_teaming_report_link.png b/docs/images/red_teaming_report_link.png new file mode 100644 index 00000000..9bc7dd71 Binary files /dev/null and b/docs/images/red_teaming_report_link.png differ diff --git a/docs/images/tracing_tab.png b/docs/images/tracing_tab.png index 2163497b..46f1d1b7 100644 Binary files a/docs/images/tracing_tab.png and b/docs/images/tracing_tab.png differ diff --git a/docs/observability.md b/docs/observability.md new file mode 100644 index 00000000..62518a98 --- /dev/null +++ b/docs/observability.md @@ -0,0 +1,146 @@ +# Observability features + +Observability is a key aspect of building and maintaining high-quality AI applications. It encompasses monitoring, tracing, and evaluating the performance and behavior of AI systems to ensure they meet desired standards and provide a safe and reliable user experience. + +In **pre-deployment** stage, you can leverage [Agent Evaluation](#agent-evaluation) and [AI Red Teaming Agent](#ai-red-teaming-agent) features to assess and improve the quality, safety, and reliability of your AI agents before they are released to end users. You will establish a test baseline for your agent and continuously monitor its performance during development iterations. For example, you find 85% passing rate for [task completion rate](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/agent-evaluators#system-evaluation) to be the acceptance threshold for your agents before deployment. + +In **post-deployment** stage, you can utilize [Tracing and monitoring](#tracing-and-monitoring) and [Continuous Evaluation](#continuous-evaluation) capabilities to maintain ongoing visibility into your agent's performance and behavior in production. With the baselines established in pre-deployment, you can set up alerts for a desirable passing rate, so that you can review the failing traces that helps you quickly identify and address any issues that may arise, ensuring a consistent and high-quality user experience. + +## Prequisites + +Execute `azd up` to generate most of these environment variables in `.azure/.env`. To specify the Agent ID, navigate to the Azure AI Foundry Portal: + + 1. Go to [Azure AI Foundry Portal](https://ai.azure.com/) and sign in + 2. Click on your project from the homepage + 3. In the top navigation, select **Build** + 4. In the left-hand menu, select **Agents** + 5. Locate your agent in the list - the agent name and version will be displayed + 6. The Agent ID follows the format: `{agent_name}:{agent_version}` (e.g., `agent-template-assistant:1`) + + ![Agent ID in Foundry UI](./images/agent_id_in_foundry_ui.png) + +## Agent Evaluation + +Azure AI Foundry offers a number of [built-in evaluators](https://learn.microsoft.com/azure/ai-foundry/concepts/observability#what-are-evaluators) to measure the quality, efficiency, risk and safety of your agents. For example, intent resolution, tool call accuracy, and task adherence evaluators are targeted to assess the end-to-end and tool call process quality of agent workflow, while content safety evaluator checks for inappropriate content in the responses such as violence or hate. +You can also create custom evaluators tailored to your specific requirements, including custom prompt-based evaluators or code-based evaluators that implement your unique assessment criteria. + +In this template, we show how the evaluation of your agent can be intergrated into the test suite of your AI application. + +You can use the [evaluation test script](../tests/test_evaluation.py) to validate your agent's performance using built-in Azure AI evaluators. The test demonstrates how to: + - Define testing criteria using Azure AI evaluators: + - [Agent evaluators](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/agent-evaluators): process and system level evaluators specifically designed for agent workflows. + - [Retrieval-augmented Generation (RAG) evaluators](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/rag-evaluators): evaluate the quality of end-to-end and retrieval process of RAG in agents or standalone systems. + - [Risk and safety evaluators](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/risk-safety-evaluators): assess potential risks and safety concerns in agent responses. + - [General purpose evaluators](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-evaluators/general-purpose-evaluators): evaluate coherence and fluency in business writing scenarios. + - [Textual similarity evaluators](https://learn.microsoft.com/azure/ai-foundry/concepts//evaluation-evaluators/textual-similarity-evaluators): measure semantic similarity of AI-generated texts with respect to expected ground truth texts. + - Run evaluation against specific test queries + - Retrieve and analyze evaluation results + + The test reads the following environment variables: + - `AZURE_EXISTING_AIPROJECT_ENDPOINT`: AI Project endpoint + - `AZURE_EXISTING_AGENT_ID`: AI Agent Id in the format `agent_name:agent_version` (with fallback logic to look up the latest version by name using `AZURE_AI_AGENT_NAME`) + - `AZURE_AI_AGENT_DEPLOYMENT_NAME`: The judge model deployment name used by evaluators + + Follow the [prerequisites](#prerequisites) to set up these environment variables. To install required packages and run the evaluation test in your python environment: + + ```shell + python -m pip install -r src/requirements.txt + + pytest tests/test_evaluation.py + ``` + + **Tip:** Add the `-s` flag to see detailed print output during test execution: + ```shell + pytest tests/test_evaluation.py -s + ``` + + Upon completion, the test will display a **Report URL** in the output where you can review the detailed evaluation results in the Azure AI Foundry UI, including individual evaluator passing scores and explanations. + + ![Evaluation Report Link](./images/eval_link.png) + + ![Evaluation Report](./images/eval_report.png) + +## AI Red Teaming Agent + +The [AI Red Teaming Agent](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent) is a powerful tool designed to help organizations proactively find security and safety risks associated with generative AI systems during design and development of generative AI models and applications. + +In the [red teaming test script](../tests/test_red_teaming.py), you will be able to set up an AI Red Teaming Agent to run an automated scan of your agent in this sample. The test demonstrates how to: +- Create a red-teaming evaluation +- Generate taxonomies for risk categories (e.g., prohibited actions) +- Configure attack strategies (Flip, Base64) with multi-turn conversations +- Retrieve and analyze red teaming results + +No test dataset or adversarial LLM is needed as the AI Red Teaming Agent will generate all the attack prompts for you. + + Follow the [prerequisites](#prerequisites) to set up these environment variables. To install required packages and run the red teaming test in your local development environment: + +```shell +python -m pip install -r src/requirements.txt + +pytest tests/test_red_teaming.py +``` + +**Tip:** Add the `-s` flag to see detailed print output during test execution: +```shell +pytest tests/test_red_teaming.py -s +``` + +Upon completion, the test will display a **Report URL** in the output where you can review the detailed red teaming evaluation results in the Azure AI Foundry UI, including attack inputs, outcomes, and reasons. + +![Red Teaming Report Link](./images/red_teaming_report_link.png) + +![Red Teaming Evaluation Report](./images/red_teaming_report.png) + +Read more on supported attack techniques and risk categories in our [documentation](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/run-scans-ai-red-teaming-agent). + +## Tracing and monitoring + +**Enable tracing by setting the environment variable (if not already enabled):** + +```shell +azd env set ENABLE_AZURE_MONITOR_TRACING true +azd deploy +``` + +### Console traces + +You can view console traces in the Azure portal. You can get the link to the resource group with the azd tool: + +```shell +azd show +``` + +Or if you want to navigate from the Azure portal main page, select your resource group from the 'Recent' list, or by clicking the 'Resource groups' and searching your resource group there. + +After accessing your resource group in Azure portal, choose your container app from the list of resources. Then open 'Monitoring' and 'Log Stream'. Choose the 'Application' radio button to view application logs. You can choose between real-time and historical using the corresponding radio buttons. Note that it may take some time for the historical view to be updated with the latest logs. + +### Agent traces + +You can view both the server-side and client-side traces, cost and evaluation data in Azure AI Foundry. Go to the agent under your project on the Azure AI Foundry page and then click 'Tracing'. + +![Tracing Tab](./images/tracing_tab.png) + +### Monitor + +Once App Insights is connected to your foundry project, you can also visit the monitoring dashboard to view trends such as agent runs and tokens count, error rates, evaluation results, and other key metrics that help you monitor agent performance and usage. + +![Monitor Dashboard](./images/agent_monitor.png) + +## Continuous Evaluation + +Continuous evaluation is an automated monitoring capability that continuously assesses your agent's quality, performance, and safety as it handles real user interactions in production. + +During container startup, continuous evaluation is `enabled` by default and pre-configured with a sample evaluator set to evaluate up to `5` agent responses per hour. Continuous evaluation does not generate test inputs—instead, it evaluates real user conversations as they occur. This means evaluation runs are triggered only when actual users interact with your agent, and if there are no user interactions, there will be no evaluation entries. + +To customize continuous evaluation from the Azure AI Foundry: + +1. Go to [Azure AI Foundry Portal](https://ai.azure.com/) and sign in +2. Click on your project from the homepage +3. In the top navigation, select **Build** +4. In the left-hand menu, select **Agents** +5. Select **Monitor** +6. Choose the agent you want to enable continuous evaluation for from the agent list +7. Click on **Settings** +8. Select evaluators and adjust maximal number of runs per hour + +![Configure Continuous Evaluation](./images/enable_cont_eval.png) diff --git a/docs/other_features.md b/docs/other_features.md deleted file mode 100644 index 26e2fd33..00000000 --- a/docs/other_features.md +++ /dev/null @@ -1,100 +0,0 @@ -# Other Features - -## Tracing and Monitoring - -**First, if tracing isn't enabled yet, enable tracing by setting the environment variable:** - -```shell -azd env set ENABLE_AZURE_MONITOR_TRACING true -azd deploy -``` - -You can view console logs in the Azure portal. You can get the link to the resource group with the azd tool: - -```shell -azd show -``` - -Or if you want to navigate from the Azure portal main page, select your resource group from the 'Recent' list, or by clicking the 'Resource groups' and searching your resource group there. - -After accessing your resource group in Azure portal, choose your container app from the list of resources. Then open 'Monitoring' and 'Log Stream'. Choose the 'Application' radio button to view application logs. You can choose between real-time and historical using the corresponding radio buttons. Note that it may take some time for the historical view to be updated with the latest logs. - -You can view the App Insights tracing in Azure AI Foundry. Select your project on the Azure AI Foundry page and then click 'Tracing'. - -![Tracing Tab](../docs/images/tracing_tab.png) - -## Agent Evaluation - -**First, make sure tracing is working by following the steps in the [Tracing and Monitoring](#tracing-and-monitoring) section above.** - -AI Foundry offers a number of [built-in evaluators](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/agent-evaluate-sdk) to measure the quality, efficiency, risk and safety of your agents. For example, intent resolution, tool call accuracy, and task adherence evaluators are targeted to assess the performance of agent workflow, while content safety evaluator checks for inappropriate content in the responses such as violence or hate. (screenshot) - -In this template, we show how these evaluations can be performed during different phases of your development cycle. - -- **Local development**: You can use this [local evaluation script](../evals/evaluate.py) to get performance and evaluation metrics based on a set of [test queries](../evals/eval-queries.json) for a sample set of built-in evaluators. - - The script reads the following environment variables: - - `AZURE_EXISTING_AIPROJECT_ENDPOINT`: AI Project endpoint - - `AZURE_EXISTING_AGENT_ID`: AI Agent Id, with fallback logic to look up agent Id by name `AZURE_AI_AGENT_NAME` - - `AZURE_AI_AGENT_DEPLOYMENT_NAME`: Deployment model used by the AI-assisted evaluators, with fallback logic to your agent model - -** (Optional) All of these are generated locally in [`.env`](../src/.env) after executing `azd up` except `AZURE_EXISTING_AGENT_ID` which is generated remotely. To find this variables remotely in Container App, follow this: - -1. Go to [Azure AI Foundry Portal](https://ai.azure.com/) and sign in -2. Click on your project from the homepage -3. In the left-hand menu, select Agents -4. Choose the agent you want to inspect -5. The Agent ID will be shown in the agent’s detail panel—usually near the top or under the “Properties” or “Overview” tab [Entra Agent ID Spec] -![Agent ID in Foundry UI](./images/agent_id_in_foundry_ui.png) - - -To install required packages and run the script: - - ```shell - python -m pip install -r src/requirements.txt - python -m pip install azure-ai-evaluation - - python evals/evaluate.py - ``` - -- **Monitoring**: When tracing is enabled, the [application code](../src/api/routes.py) sends an asynchronous evaluation request after processing a thread run, allowing continuous monitoring of your agent. You can view results from the AI Foundry Tracing tab. - ![Tracing](./images/tracing_eval_screenshot.png) - Alternatively, you can go to your Application Insights logs for an interactive experience. To access Application Insights logs in the Azure portal: - - 1. Navigate to your resource group (use `azd show` to get the link) - 2. Find and click on the Application Insights resource (usually named starts with `appi-`) - 3. In the left menu, click on **Logs** under the **Monitoring** section - 4. You can now run KQL queries in the query editor - - Here is an example query to see logs on thread runs and related events: - - ```kql - let thread_run_events = traces - | extend thread_run_id = tostring(customDimensions.["gen_ai.thread.run.id"]); - dependencies - | extend thread_run_id = tostring(customDimensions.["gen_ai.thread.run.id"]) - | join kind=leftouter thread_run_events on thread_run_id - | where isnotempty(thread_run_id) - | project timestamp, thread_run_id, name, success, duration, event_message = message, event_dimensions=customDimensions1 - ``` - -![Application Insight Logs Query](../docs/images/app_insight_logs_query.png) - -- **Continuous Integration**: You can try the [AI Agent Evaluation GitHub action](https://github.com/microsoft/ai-agent-evals) using the [sample GitHub workflow](../.github/workflows/ai-evaluation.yaml) in your CI/CD pipeline. This GitHub action runs a set of queries against your agent, performs evaluations with evaluators of your choice, and produce a summary report. It also supports a comparison mode with statistical test, allowing you to iterate agent changes on your production environment with confidence. See [documentation](https://github.com/microsoft/ai-agent-evals) for more details. - -## AI Red Teaming Agent - -The [AI Red Teaming Agent](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent) is a powerful tool designed to help organizations proactively find security and safety risks associated with generative AI systems during design and development of generative AI models and applications. - -In this [script](../airedteaming/ai_redteaming.py), you will be able to set up an AI Red Teaming Agent to run an automated scan of your agent in this sample. No test dataset or adversarial LLM is needed as the AI Red Teaming Agent will generate all the attack prompts for you. - -To install required extra packages from Azure AI Evaluation SDK and run the script in your local development environment: - -```shell -python -m pip install -r src/requirements.txt -python -m pip install azure-ai-evaluation[redteam] - -python airedteaming/ai_redteaming.py -``` - -Read more on supported attack techniques and risk categories in our [documentation](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/run-scans-ai-red-teaming-agent). diff --git a/evals/eval-action-data-path.json b/evals/eval-action-data-path.json deleted file mode 100644 index 87d2f9a8..00000000 --- a/evals/eval-action-data-path.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "name": "test-dataset", - "evaluators": [ - "IntentResolutionEvaluator", - "TaskAdherenceEvaluator", - "CoherenceEvaluator", - "RelevanceEvaluator", - "FluencyEvaluator", - "ViolenceEvaluator", - "SexualEvaluator", - "SelfHarmEvaluator", - "HateUnfairnessEvaluator", - "IndirectAttackEvaluator", - "ProtectedMaterialEvaluator", - "CodeVulnerabilityEvaluator", - "ContentSafetyEvaluator" - ], - "data": [ - { - "query": "What features do the SmartView Glasses have?" - }, - { - "query": "How long is the warranty on the SmartView Glasses?" - } - ] - } \ No newline at end of file diff --git a/evals/eval-queries.json b/evals/eval-queries.json deleted file mode 100644 index f4508ab7..00000000 --- a/evals/eval-queries.json +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "query": "What features do the SmartView Glasses have?" - }, - { - "query": "How long is the warranty on the SmartView Glasses?" - } -] \ No newline at end of file diff --git a/evals/evaluate.py b/evals/evaluate.py deleted file mode 100644 index aff3c0e7..00000000 --- a/evals/evaluate.py +++ /dev/null @@ -1,191 +0,0 @@ -import os -import time -import json - -from pathlib import Path -from dotenv import load_dotenv -from urllib.parse import urlparse - -from azure.ai.agents.models import RunStatus, MessageRole -from azure.ai.projects import AIProjectClient -from azure.ai.evaluation import ( - AIAgentConverter, evaluate, ToolCallAccuracyEvaluator, IntentResolutionEvaluator, - TaskAdherenceEvaluator, CodeVulnerabilityEvaluator, ContentSafetyEvaluator, - IndirectAttackEvaluator) - -from azure.identity import DefaultAzureCredential - -def run_evaluation(): - """Demonstrate how to evaluate an AI agent using the Azure AI Project SDK""" - current_dir = Path(__file__).parent - eval_queries_path = current_dir / "eval-queries.json" - eval_input_path = current_dir / f"eval-input.jsonl" - eval_output_path = current_dir / f"eval-output.json" - - env_path = current_dir / "../src/.env" - load_dotenv(dotenv_path=env_path) - - # Get AI project parameters from environment variables - project_endpoint = os.environ.get("AZURE_EXISTING_AIPROJECT_ENDPOINT") - parsed_endpoint = urlparse(project_endpoint) - model_endpoint = f"{parsed_endpoint.scheme}://{parsed_endpoint.netloc}" - deployment_name = os.getenv("AZURE_AI_AGENT_DEPLOYMENT_NAME") - agent_name = os.environ.get("AZURE_AI_AGENT_NAME") - agent_id = os.environ.get("AZURE_EXISTING_AGENT_ID") - - # Validate required environment variables - if not project_endpoint: - raise ValueError("Please set the AZURE_EXISTING_AIPROJECT_ENDPOINT environment variable.") - - if not agent_id and not agent_name: - raise ValueError("Please set either AZURE_EXISTING_AGENT_ID or AZURE_AI_AGENT_NAME environment variable.") - - # Initialize the AIProjectClient - credential = DefaultAzureCredential() - ai_project = AIProjectClient( - credential=credential, - endpoint=project_endpoint, - api_version = "2025-05-15-preview" # Evaluations yet not supported on stable (api_version="2025-05-01") - ) - - # Look up the agent by name if agent Id is not provided - if not agent_id and agent_name: - for agent in ai_project.agents.list_agents(): - if agent.name == agent_name: - agent_id = agent.id - break - - if not agent_id: - raise ValueError("Agent ID not found. Please provide a valid agent ID or name.") - - agent = ai_project.agents.get_agent(agent_id) - - # Use model from agent if not provided - if not deployment_name: - deployment_name = agent.model - - # Setup required evaluation config - model_config = { - "azure_deployment": deployment_name, - "azure_endpoint": model_endpoint, - "api_version": "", - } - thread_data_converter = AIAgentConverter(ai_project) - - # Read test queries from input file - with open(eval_queries_path, "r", encoding="utf-8") as f: - test_data = json.load(f) - - # Execute the test queries against the agent and prepare the evaluation input - with open(eval_input_path, "w", encoding="utf-8") as f: - - for row in test_data: - # Create a new thread for each query to isolate conversations - thread = ai_project.agents.threads.create() - - # Create the user query - ai_project.agents.messages.create( - thread.id, role=MessageRole.USER, content=row.get("query") - ) - - # Run agent on thread and measure performance - start_time = time.time() - run = ai_project.agents.runs.create_and_process( - thread_id=thread.id, agent_id=agent.id - ) - end_time = time.time() - - if run.status != RunStatus.COMPLETED: - raise ValueError(run.last_error or "Run failed to complete") - - operational_metrics = { - "server-run-duration-in-seconds": ( - run.completed_at - run.created_at - ).total_seconds(), - "client-run-duration-in-seconds": end_time - start_time, - "completion-tokens": run.usage.completion_tokens, - "prompt-tokens": run.usage.prompt_tokens, - "ground-truth": row.get("ground-truth", '') - } - - # Add thread data + operational metrics to the evaluation input - evaluation_data = thread_data_converter.prepare_evaluation_data(thread_ids=thread.id) - eval_item = evaluation_data[0] - eval_item["metrics"] = operational_metrics - f.write(json.dumps(eval_item) + "\n") - - - # Now, run a sample set of evaluators using the evaluation input - # See https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/agent-evaluate-sdk - # for the full list of evaluators available. - results = evaluate( - evaluation_name="evaluation-test", - data=eval_input_path, - evaluators={ - "operational_metrics": OperationalMetricsEvaluator(), - "tool_call_accuracy": ToolCallAccuracyEvaluator(model_config=model_config), - "intent_resolution": IntentResolutionEvaluator(model_config=model_config), - "task_adherence": TaskAdherenceEvaluator(model_config=model_config), - "code_vulnerability": CodeVulnerabilityEvaluator(credential=credential, azure_ai_project=project_endpoint), - "content_safety": ContentSafetyEvaluator(credential=credential, azure_ai_project=project_endpoint), - "indirect_attack": IndirectAttackEvaluator(credential=credential, azure_ai_project=project_endpoint) - }, - output_path=eval_output_path, # raw evaluation results - azure_ai_project=project_endpoint, # if you want results uploaded to AI Foundry - ) - - # Format and print the evaluation results - print_eval_results(results, eval_input_path, eval_output_path) - - -class OperationalMetricsEvaluator: - """Propagate operational metrics to the final evaluation results""" - def __init__(self): - pass - def __call__(self, *, metrics: dict, **kwargs): - return metrics - - -def print_eval_results(results, input_path, output_path): - """Print the evaluation results in a formatted table""" - metrics = results.get("metrics", {}) - - # Get the maximum length for formatting - key_len = max(len(key) for key in metrics.keys()) + 5 - value_len = 20 - full_len = key_len + value_len + 5 - - # Format the header - print("\n" + "=" * full_len) - print("Evaluation Results".center(full_len)) - print("=" * full_len) - - # Print all metrics, see evaluation output file for full details - print(f"{'Metric':<{key_len}} | {'Value'}") - print("-" * (key_len) + "-+-" + "-" * value_len) - - for key, value in sorted(metrics.items()): - if isinstance(value, float): - formatted_value = f"{value:.2f}" - else: - formatted_value = str(value) - - print(f"{key:<{key_len}} | {formatted_value}") - - print("=" * full_len + "\n") - - # Print additional information - print(f"Evaluation input: {input_path}") - print(f"Evaluation output: {output_path}") - if results.get("studio_url") is not None: - print(f"AI Foundry URL: {results['studio_url']}") - - print("\n" + "=" * full_len + "\n") - - -if __name__ == "__main__": - try: - run_evaluation() - except Exception as e: - print(f"Error during evaluation: {e}") - diff --git a/infra/api.bicep b/infra/api.bicep index e9747273..f1c082d7 100644 --- a/infra/api.bicep +++ b/infra/api.bicep @@ -15,8 +15,9 @@ param searchServiceEndpoint string param agentName string param agentID string param enableAzureMonitorTracing bool -param azureTracingGenAIContentRecordingEnabled bool +param otelInstrumentationGenAICaptureMessageContent bool param projectEndpoint string +param searchConnectionId string resource apiIdentity 'Microsoft.ManagedIdentity/userAssignedIdentities@2023-01-31' = { name: identityName @@ -73,13 +74,17 @@ var env = [ value: enableAzureMonitorTracing } { - name: 'AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED' - value: azureTracingGenAIContentRecordingEnabled + name: 'OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT' + value: otelInstrumentationGenAICaptureMessageContent } { name: 'AZURE_EXISTING_AIPROJECT_ENDPOINT' value: projectEndpoint } + { + name: 'SEARCH_CONNECTION_ID' + value: searchConnectionId + } ] diff --git a/infra/core/ai/cognitiveservices.bicep b/infra/core/ai/cognitiveservices.bicep index c0bd0cdc..62a3b655 100644 --- a/infra/core/ai/cognitiveservices.bicep +++ b/infra/core/ai/cognitiveservices.bicep @@ -5,7 +5,7 @@ param location string = resourceGroup().location param tags object = {} @description('The custom subdomain name used to access the API. Defaults to the value of the name parameter.') param customSubDomainName string = aiServiceName -param disableLocalAuth bool = true +param disableLocalAuth bool = false param deployments array = [] param appInsightsId string param appInsightConnectionString string @@ -52,9 +52,12 @@ resource aiServiceConnection 'Microsoft.CognitiveServices/accounts/connections@2 parent: account properties: { category: 'AzureOpenAI' - authType: 'AAD' + authType: 'ApiKey' isSharedToAll: true target: account.properties.endpoints['OpenAI Language Model Instance API'] + credentials: { + key: account.listKeys().key1 + } metadata: { ApiType: 'azure' ResourceId: account.id diff --git a/infra/core/host/ai-environment.bicep b/infra/core/host/ai-environment.bicep index 424532d2..b18372d8 100644 --- a/infra/core/host/ai-environment.bicep +++ b/infra/core/host/ai-environment.bicep @@ -115,6 +115,16 @@ module projectStorageRoleAssignment '../../core/security/role.bicep' = { } } +module projectAIUserRoleAssignment '../../core/security/role.bicep' = { + name: 'ai-project-role-ai-user' + params: { + principalType: 'ServicePrincipal' + principalId: cognitiveServices.outputs.projectPrincipalId + roleDefinitionId: '53ca6127-db72-4b80-b1b0-d745d6d5456d' // Azure AI User + } +} + + module searchService '../search/search-services.bicep' = if (!empty(searchServiceName)) { dependsOn: [cognitiveServices] diff --git a/infra/core/search/search-services.bicep b/infra/core/search/search-services.bicep index 0813da10..146b1bef 100644 --- a/infra/core/search/search-services.bicep +++ b/infra/core/search/search-services.bicep @@ -107,5 +107,5 @@ output id string = search.id output endpoint string = 'https://${name}.search.windows.net/' output name string = search.name output principalId string = !empty(searchIdentityProvider) ? search.identity.principalId : '' -output searchConnectionId string = '' +output searchConnectionId string = !empty(searchIdentityProvider) ? aiServices::project::searchConnection.id : '' diff --git a/infra/main.bicep b/infra/main.bicep index 24484900..5c38c134 100644 --- a/infra/main.bicep +++ b/infra/main.bicep @@ -7,7 +7,7 @@ param environmentName string @description('Location for all resources') // Based on the model, creating an agent is not supported in all regions. -// The combination of allowed and usageName below is for AZD to check AI model gpt-4o-mini quota only for the allowed regions for creating an agent. +// The combination of allowed and usageName below is for AZD to check AI model gpt-4o quota only for the allowed regions for creating an agent. // If using different models, update the SKU,capacity depending on the model you use. // https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support @allowed([ @@ -20,9 +20,9 @@ param environmentName string @metadata({ azd: { type: 'location' - // quota-validation for ai models: gpt-4o-mini + // quota-validation for ai models: gpt-4o usageName: [ - 'OpenAI.GlobalStandard.gpt-4o-mini,80' + 'OpenAI.GlobalStandard.gpt-4o,80' ] } }) @@ -62,9 +62,9 @@ param aiAgentID string = '' @description('ID of the existing agent') param azureExistingAgentId string = '' @description('Name of the chat model to deploy') -param agentModelName string = 'gpt-4o-mini' +param agentModelName string = 'gpt-4o' @description('Name of the model deployment') -param agentDeploymentName string = 'gpt-4o-mini' +param agentDeploymentName string = 'gpt-4o' @description('Version of the chat model to deploy') // See version availability in this table: @@ -112,13 +112,16 @@ param useSearchService bool = false param enableAzureMonitorTracing bool = false @description('Do we want to use the Azure Monitor tracing for GenAI content recording') -param azureTracingGenAIContentRecordingEnabled bool = false +param otelInstrumentationGenAICaptureMessageContent bool = false param templateValidationMode bool = false @description('Random seed to be used during generation of new resources suffixes.') param seed string = newGuid() +param searchServiceEndpoint string = '' +param searchConnectionId string = '' + var runnerPrincipalType = templateValidationMode? 'ServicePrincipal' : 'User' var abbrs = loadJsonContent('./abbreviations.json') @@ -204,10 +207,18 @@ module ai 'core/host/ai-environment.bicep' = if (empty(azureExistingAIProjectRes } } -var searchServiceEndpoint = !useSearchService +var searchServiceEndpointFromAIOutput = !useSearchService ? '' : empty(azureExistingAIProjectResourceId) ? ai!.outputs.searchServiceEndpoint : '' +var searchConnectionIdFromAIOutput = !useSearchService + ? '' + : empty(azureExistingAIProjectResourceId) ? ai!.outputs.searchConnectionId : '' + +var searchServiceEndpoint_final = empty(searchServiceEndpoint) ? searchServiceEndpointFromAIOutput : searchServiceEndpoint + +var searchConnectionId_final = empty(searchConnectionId) ? searchConnectionIdFromAIOutput : searchConnectionId + // If bringing an existing AI project, set up the log analytics workspace here module logAnalytics 'core/monitor/loganalytics.bicep' = if (!empty(azureExistingAIProjectResourceId)) { name: 'logAnalytics' @@ -288,14 +299,15 @@ module api 'api.bicep' = { agentDeploymentName: agentDeploymentName searchConnectionName: searchConnectionName aiSearchIndexName: aiSearchIndexName - searchServiceEndpoint: searchServiceEndpoint + searchServiceEndpoint: searchServiceEndpointFromAIOutput embeddingDeploymentName: embeddingDeploymentName embeddingDeploymentDimensions: embeddingDeploymentDimensions agentName: agentName agentID: agentID enableAzureMonitorTracing: enableAzureMonitorTracing - azureTracingGenAIContentRecordingEnabled: azureTracingGenAIContentRecordingEnabled + otelInstrumentationGenAICaptureMessageContent: otelInstrumentationGenAICaptureMessageContent projectEndpoint: projectEndpoint + searchConnectionId: searchConnectionId_final } } @@ -331,6 +343,16 @@ module userAzureAIUser 'core/security/role.bicep' = if (empty(azureExistingAIPr } } +module backendAzureAIUser 'core/security/role.bicep' = if (empty(azureExistingAIProjectResourceId)) { + name: 'backend-role-azure-ai-user' + scope: rg + params: { + principalType: 'ServicePrincipal' + principalId: api.outputs.SERVICE_API_IDENTITY_PRINCIPAL_ID + roleDefinitionId: '53ca6127-db72-4b80-b1b0-d745d6d5456d' + } +} + module backendCognitiveServicesUser 'core/security/role.bicep' = if (empty(azureExistingAIProjectResourceId)) { name: 'backend-role-cognitive-services-user' scope: rg @@ -431,13 +453,13 @@ output AZURE_AI_AGENT_DEPLOYMENT_NAME string = agentDeploymentName output AZURE_AI_SEARCH_CONNECTION_NAME string = searchConnectionName output AZURE_AI_EMBED_DEPLOYMENT_NAME string = embeddingDeploymentName output AZURE_AI_SEARCH_INDEX_NAME string = aiSearchIndexName -output AZURE_AI_SEARCH_ENDPOINT string = searchServiceEndpoint +output AZURE_AI_SEARCH_ENDPOINT string = searchServiceEndpoint_final output AZURE_AI_EMBED_DIMENSIONS string = embeddingDeploymentDimensions output AZURE_AI_AGENT_NAME string = agentName output AZURE_EXISTING_AGENT_ID string = agentID output AZURE_EXISTING_AIPROJECT_ENDPOINT string = projectEndpoint output ENABLE_AZURE_MONITOR_TRACING bool = enableAzureMonitorTracing -output AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED bool = azureTracingGenAIContentRecordingEnabled +output OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT bool = otelInstrumentationGenAICaptureMessageContent // Outputs required by azd for ACA output AZURE_CONTAINER_ENVIRONMENT_NAME string = containerApps.outputs.environmentName @@ -445,5 +467,5 @@ output SERVICE_API_IDENTITY_PRINCIPAL_ID string = api.outputs.SERVICE_API_IDENTI output SERVICE_API_NAME string = api.outputs.SERVICE_API_NAME output SERVICE_API_URI string = api.outputs.SERVICE_API_URI output SERVICE_API_ENDPOINTS array = ['${api.outputs.SERVICE_API_URI}'] -output SEARCH_CONNECTION_ID string = '' +output SEARCH_CONNECTION_ID string = searchConnectionId_final output AZURE_CONTAINER_REGISTRY_ENDPOINT string = containerApps.outputs.registryLoginServer diff --git a/infra/main.parameters.json b/infra/main.parameters.json index 75f41629..59b46387 100644 --- a/infra/main.parameters.json +++ b/infra/main.parameters.json @@ -60,16 +60,16 @@ "value": "${AZURE_EXISTING_AGENT_ID}" }, "agentDeploymentName": { - "value": "${AZURE_AI_AGENT_MODEL_NAME=gpt-4o-mini}" + "value": "${AZURE_AI_AGENT_MODEL_NAME=gpt-4o}" }, "agentModelFormat": { "value": "${AZURE_AI_AGENT_MODEL_FORMAT=OpenAI}" }, "agentModelName": { - "value": "${AZURE_AI_AGENT_MODEL_NAME=gpt-4o-mini}" + "value": "${AZURE_AI_AGENT_MODEL_NAME=gpt-4o}" }, "agentModelVersion": { - "value": "${AZURE_AI_AGENT_MODEL_VERSION=2024-07-18}" + "value": "${AZURE_AI_AGENT_MODEL_VERSION=2024-11-20}" }, "agentDeploymentSku": { "value": "${AZURE_AI_AGENT_DEPLOYMENT_SKU=GlobalStandard}" @@ -95,7 +95,7 @@ "embedDeploymentCapacity": { "value": "${AZURE_AI_EMBED_DEPLOYMENT_CAPACITY=50}" }, - "embeddingDeploymentDimensions": { + "embeddingDeploymentDimensions": { "value": "${AZURE_AI_EMBED_DIMENSIONS=100}" }, "apiAppExists": { @@ -110,11 +110,17 @@ "enableAzureMonitorTracing": { "value": "${ENABLE_AZURE_MONITOR_TRACING=false}" }, - "azureTracingGenAIContentRecordingEnabled": { - "value": "${AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED=false}" + "otelInstrumentationGenAICaptureMessageContent": { + "value": "${OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=false}" }, "templateValidationMode": { "value": "${TEMPLATE_VALIDATION_MODE=false}" + }, + "searchServiceEndpoint": { + "value": "${AZURE_AI_SEARCH_ENDPOINT}" + }, + "searchConnectionId": { + "value": "${SEARCH_CONNECTION_ID}" } } } diff --git a/next-steps.md b/next-steps.md index de03a199..283e109e 100644 --- a/next-steps.md +++ b/next-steps.md @@ -19,13 +19,6 @@ To troubleshoot any issues, see [troubleshooting](#troubleshooting). Configure environment variables for running services by updating `settings` in [main.parameters.json](./infra/main.parameters.json). -### Configure CI/CD pipeline - -1. Create a workflow pipeline file locally. The following starters are available: - - [Deploy with GitHub Actions](https://github.com/Azure-Samples/azd-starter-bicep/blob/main/.github/workflows/azure-dev.yml) - - [Deploy with Azure Pipelines](https://github.com/Azure-Samples/azd-starter-bicep/blob/main/.azdo/pipelines/azure-dev.yml) -2. Run `azd pipeline config` to configure the deployment pipeline to connect securely to Azure. - ## What was added ### Infrastructure configuration diff --git a/requirements-dev.txt b/requirements-dev.txt index 32b97c12..0b69d05c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,3 +1,4 @@ -r src/requirements.txt +pytest ruff pre-commit \ No newline at end of file diff --git a/scripts/set_default_models.ps1 b/scripts/set_default_models.ps1 deleted file mode 100644 index a3db2f67..00000000 --- a/scripts/set_default_models.ps1 +++ /dev/null @@ -1,129 +0,0 @@ -$SubscriptionId = ([System.Environment]::GetEnvironmentVariable('AZURE_SUBSCRIPTION_ID', "Process")) -$Location = ([System.Environment]::GetEnvironmentVariable('AZURE_LOCATION', "Process")) - -$Errors = 0 - -if (-not $SubscriptionId) { - Write-Error "❌ ERROR: Missing AZURE_SUBSCRIPTION_ID" - $Errors++ -} - -if (-not $Location) { - Write-Error "❌ ERROR: Missing AZURE_LOCATION" - $Errors++ -} - -if ($Errors -gt 0) { - exit 1 -} - - -$defaultEnvVars = @{ - AZURE_AI_EMBED_DEPLOYMENT_NAME = 'text-embedding-3-small' - AZURE_AI_EMBED_MODEL_NAME = 'text-embedding-3-small' - AZURE_AI_EMBED_MODEL_FORMAT = 'OpenAI' - AZURE_AI_EMBED_MODEL_VERSION = '1' - AZURE_AI_EMBED_DEPLOYMENT_SKU = 'Standard' - AZURE_AI_EMBED_DEPLOYMENT_CAPACITY = '50' - AZURE_AI_AGENT_DEPLOYMENT_NAME = 'gpt-4o-mini' - AZURE_AI_AGENT_MODEL_NAME = 'gpt-4o-mini' - AZURE_AI_AGENT_MODEL_VERSION = '2024-07-18' - AZURE_AI_AGENT_MODEL_FORMAT = 'OpenAI' - AZURE_AI_AGENT_DEPLOYMENT_SKU = 'GlobalStandard' - AZURE_AI_AGENT_DEPLOYMENT_CAPACITY = '80' -} - -$envVars = @{} - -foreach ($key in $defaultEnvVars.Keys) { - $val = [System.Environment]::GetEnvironmentVariable($key, "Process") - $envVars[$key] = $val - if (-not $val) { - $envVars[$key] = $defaultEnvVars[$key] - } - azd env set $key $envVars[$key] -} - -# --- If we do not use existing AI Project, we don't deploy models, so skip validation --- -$resourceId = [System.Environment]::GetEnvironmentVariable('AZURE_EXISTING_AIPROJECT_RESOURCE_ID', "Process") -if (-not [string]::IsNullOrEmpty($resourceId)) { - Write-Host "✅ AZURE_EXISTING_AIPROJECT_RESOURCE_ID is set, skipping model deployment validation." - exit 0 -} - -$chatDeployment = @{ - name = $envVars.AZURE_AI_AGENT_DEPLOYMENT_NAME - model = @{ - name = $envVars.AZURE_AI_AGENT_MODEL_NAME - version = $envVars.AZURE_AI_AGENT_MODEL_VERSION - format = $envVars.AZURE_AI_AGENT_MODEL_FORMAT - } - sku = @{ - name = $envVars.AZURE_AI_AGENT_DEPLOYMENT_SKU - capacity = $envVars.AZURE_AI_AGENT_DEPLOYMENT_CAPACITY - } - capacity_env_var_name = 'AZURE_AI_AGENT_DEPLOYMENT_CAPACITY' -} - - - -$aiModelDeployments = @($chatDeployment) - -$useSearchService = ([System.Environment]::GetEnvironmentVariable('USE_AZURE_AI_SEARCH_SERVICE', "Process")) - -if ($useSearchService -eq 'true') { - $embedDeployment = @{ - name = $envVars.AZURE_AI_EMBED_DEPLOYMENT_NAME - model = @{ - name = $envVars.AZURE_AI_EMBED_MODEL_NAME - version = $envVars.AZURE_AI_EMBED_MODEL_VERSION - format = $envVars.AZURE_AI_EMBED_MODEL_FORMAT - } - sku = @{ - name = $envVars.AZURE_AI_EMBED_DEPLOYMENT_SKU - capacity = $envVars.AZURE_AI_EMBED_DEPLOYMENT_CAPACITY - min_capacity = 30 - } - capacity_env_var_name = 'AZURE_AI_EMBED_DEPLOYMENT_CAPACITY' - } - - $aiModelDeployments += $embedDeployment -} - - -az account set --subscription $SubscriptionId -Write-Host "🎯 Active Subscription: $(az account show --query '[name, id]' --output tsv)" - -$QuotaAvailable = $true - -try { - Write-Host "🔍 Validating model deployments against quotas..." -} catch { - Write-Error "❌ ERROR: Failed to validate model deployments. Ensure you have the necessary permissions." - exit 1 -} - -foreach ($deployment in $aiModelDeployments) { - $name = $deployment.name - $model = $deployment.model.name - $type = $deployment.sku.name - $format = $deployment.model.format - $capacity = $deployment.sku.capacity - $capacity_env_var_name = $deployment.capacity_env_var_name - Write-Host "🔍 Validating model deployment: $name ..." - & .\scripts\resolve_model_quota.ps1 -Location $Location -Model $model -Format $format -Capacity $capacity -CapacityEnvVarName $capacity_env_var_name -DeploymentType $type - - # Check if the script failed - if ($LASTEXITCODE -ne 0) { - Write-Error "❌ ERROR: Quota validation failed for model deployment: $name" - $QuotaAvailable = $false - } -} - - -if (-not $QuotaAvailable) { - exit 1 -} else { - Write-Host "✅ All model deployments passed quota validation successfully." - exit 0 -} \ No newline at end of file diff --git a/scripts/set_default_models.sh b/scripts/set_default_models.sh deleted file mode 100755 index 4a1f77f0..00000000 --- a/scripts/set_default_models.sh +++ /dev/null @@ -1,117 +0,0 @@ -#!/bin/bash - -set -e - -# --- Check Required Environment Variables --- -SubscriptionId="${AZURE_SUBSCRIPTION_ID}" -Location="${AZURE_LOCATION}" - -Errors=0 - -if [ -z "$SubscriptionId" ]; then - echo "❌ ERROR: Missing AZURE_SUBSCRIPTION_ID" >&2 - Errors=$((Errors + 1)) -fi - -if [ -z "$Location" ]; then - echo "❌ ERROR: Missing AZURE_LOCATION" >&2 - Errors=$((Errors + 1)) -fi - -if [ "$Errors" -gt 0 ]; then - exit 1 -fi - -# --- Default Values --- -declare -A defaultEnvVars=( - [AZURE_AI_EMBED_DEPLOYMENT_NAME]="text-embedding-3-small" - [AZURE_AI_EMBED_MODEL_NAME]="text-embedding-3-small" - [AZURE_AI_EMBED_MODEL_FORMAT]="OpenAI" - [AZURE_AI_EMBED_MODEL_VERSION]="1" - [AZURE_AI_EMBED_DEPLOYMENT_SKU]="Standard" - [AZURE_AI_EMBED_DEPLOYMENT_CAPACITY]="50" - [AZURE_AI_AGENT_DEPLOYMENT_NAME]="gpt-4o-mini" - [AZURE_AI_AGENT_MODEL_NAME]="gpt-4o-mini" - [AZURE_AI_AGENT_MODEL_VERSION]="2024-07-18" - [AZURE_AI_AGENT_MODEL_FORMAT]="OpenAI" - [AZURE_AI_AGENT_DEPLOYMENT_SKU]="GlobalStandard" - [AZURE_AI_AGENT_DEPLOYMENT_CAPACITY]="80" -) - -# --- Set Env Vars and azd env --- -declare -A envVars -for key in "${!defaultEnvVars[@]}"; do - val="${!key}" - if [ -z "$val" ]; then - val="${defaultEnvVars[$key]}" - fi - envVars[$key]="$val" - azd env set "$key" "$val" -done - -# --- If we do not use existing AI Project, we don't deploy models, so skip validation --- -resourceId="${AZURE_EXISTING_AIPROJECT_RESOURCE_ID}" -if [ -n "$resourceId" ]; then - echo "✅ AZURE_EXISTING_AIPROJECT_RESOURCE_ID is set, skipping model deployment validation." - exit 0 -fi - -# --- Build Chat Deployment --- -chatDeployment_name="${envVars[AZURE_AI_AGENT_DEPLOYMENT_NAME]}" -chatDeployment_model_name="${envVars[AZURE_AI_AGENT_MODEL_NAME]}" -chatDeployment_model_version="${envVars[AZURE_AI_AGENT_MODEL_VERSION]}" -chatDeployment_model_format="${envVars[AZURE_AI_AGENT_MODEL_FORMAT]}" -chatDeployment_sku_name="${envVars[AZURE_AI_AGENT_DEPLOYMENT_SKU]}" -chatDeployment_capacity="${envVars[AZURE_AI_AGENT_DEPLOYMENT_CAPACITY]}" -chatDeployment_capacity_env="AZURE_AI_AGENT_DEPLOYMENT_CAPACITY" - -aiModelDeployments=( - "$chatDeployment_name|$chatDeployment_model_name|$chatDeployment_model_version|$chatDeployment_model_format|$chatDeployment_sku_name|$chatDeployment_capacity|$chatDeployment_capacity_env" -) - -# --- Optional Embed Deployment --- -if [ "$USE_AZURE_AI_SEARCH_SERVICE" == "true" ]; then - embedDeployment_name="${envVars[AZURE_AI_EMBED_DEPLOYMENT_NAME]}" - embedDeployment_model_name="${envVars[AZURE_AI_EMBED_MODEL_NAME]}" - embedDeployment_model_version="${envVars[AZURE_AI_EMBED_MODEL_VERSION]}" - embedDeployment_model_format="${envVars[AZURE_AI_EMBED_MODEL_FORMAT]}" - embedDeployment_sku_name="${envVars[AZURE_AI_EMBED_DEPLOYMENT_SKU]}" - embedDeployment_capacity="${envVars[AZURE_AI_EMBED_DEPLOYMENT_CAPACITY]}" - embedDeployment_capacity_env="AZURE_AI_EMBED_DEPLOYMENT_CAPACITY" - - aiModelDeployments+=( - "$embedDeployment_name|$embedDeployment_model_name|$embedDeployment_model_version|$embedDeployment_model_format|$embedDeployment_sku_name|$embedDeployment_capacity|$embedDeployment_capacity_env" - ) -fi - -# --- Set Subscription --- -az account set --subscription "$SubscriptionId" -echo "🎯 Active Subscription: $(az account show --query '[name, id]' --output tsv)" - -QuotaAvailable=true - -# --- Validate Quota --- -for entry in "${aiModelDeployments[@]}"; do - IFS="|" read -r name model model_version format type capacity capacity_env_var_name <<< "$entry" - echo "🔍 Validating model deployment: $name ..." - ./scripts/resolve_model_quota.sh \ - -Location "$Location" \ - -Model "$model" \ - -Format "$format" \ - -Capacity "$capacity" \ - -CapacityEnvVarName "$capacity_env_var_name" \ - -DeploymentType "$type" - - if [ $? -ne 0 ]; then - echo "❌ ERROR: Quota validation failed for model deployment: $name" >&2 - QuotaAvailable=false - fi -done - -# --- Final Check --- -if [ "$QuotaAvailable" != "true" ]; then - exit 1 -else - echo "✅ All model deployments passed quota validation successfully." - exit 0 -fi \ No newline at end of file diff --git a/scripts/write_env.ps1 b/scripts/write_env.ps1 index 8f164b0b..92ee0ba1 100755 --- a/scripts/write_env.ps1 +++ b/scripts/write_env.ps1 @@ -18,7 +18,7 @@ $azureAISearchIndexName = azd env get-value AZURE_AI_SEARCH_INDEX_NAME 2>$null $azureAISearchEndpoint = azd env get-value AZURE_AI_SEARCH_ENDPOINT 2>$null $serviceAPIUri = azd env get-value SERVICE_API_URI 2>$null $enableAzureMonitorTracing = azd env get-value ENABLE_AZURE_MONITOR_TRACING 2>$null -$azureTracingGenAIContentRecordingEnabled = azd env get-value AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED 2>$null +$otelInstrumentationGenAICaptureMessageContent = azd env get-value OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT 2>$null Add-Content -Path $envFilePath -Value "AZURE_EXISTING_AIPROJECT_RESOURCE_ID=$aiProjectResourceId" Add-Content -Path $envFilePath -Value "AZURE_EXISTING_AIPROJECT_ENDPOINT=$aiProjectEndpoint" @@ -31,6 +31,5 @@ Add-Content -Path $envFilePath -Value "AZURE_AI_EMBED_DIMENSIONS=$azureAIEmbedDi Add-Content -Path $envFilePath -Value "AZURE_AI_SEARCH_INDEX_NAME=$azureAISearchIndexName" Add-Content -Path $envFilePath -Value "AZURE_AI_SEARCH_ENDPOINT=$azureAISearchEndpoint" Add-Content -Path $envFilePath -Value "AZURE_AI_AGENT_NAME=$azureAiAgentName" -Add-Content -Path $envFilePath -Value "AZURE_TENANT_ID=$azureTenantId" Add-Content -Path $envFilePath -Value "ENABLE_AZURE_MONITOR_TRACING=$enableAzureMonitorTracing" -Add-Content -Path $envFilePath -Value "AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED=$azureTracingGenAIContentRecordingEnabled" +Add-Content -Path $envFilePath -Value "OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=$otelInstrumentationGenAICaptureMessageContent" diff --git a/scripts/write_env.sh b/scripts/write_env.sh index 862f4ab9..65eda214 100755 --- a/scripts/write_env.sh +++ b/scripts/write_env.sh @@ -16,9 +16,8 @@ echo "AZURE_AI_EMBED_DIMENSIONS=$(azd env get-value AZURE_AI_EMBED_DIMENSIONS 2> echo "AZURE_AI_SEARCH_INDEX_NAME=$(azd env get-value AZURE_AI_SEARCH_INDEX_NAME 2>/dev/null)" >> $ENV_FILE_PATH echo "AZURE_AI_SEARCH_ENDPOINT=$(azd env get-value AZURE_AI_SEARCH_ENDPOINT 2>/dev/null)" >> $ENV_FILE_PATH echo "AZURE_AI_AGENT_NAME=$(azd env get-value AZURE_AI_AGENT_NAME 2>/dev/null)" >> $ENV_FILE_PATH -echo "AZURE_TENANT_ID=$(azd env get-value AZURE_TENANT_ID 2>/dev/null)" >> $ENV_FILE_PATH echo "AZURE_EXISTING_AIPROJECT_ENDPOINT=$(azd env get-value AZURE_EXISTING_AIPROJECT_ENDPOINT 2>/dev/null)" >> $ENV_FILE_PATH echo "ENABLE_AZURE_MONITOR_TRACING=$(azd env get-value ENABLE_AZURE_MONITOR_TRACING 2>/dev/null)" >> $ENV_FILE_PATH -echo "AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED=$(azd env get-value AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED 2>/dev/null)" >> $ENV_FILE_PATH +echo "OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=$(azd env get-value OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT 2>/dev/null)" >> $ENV_FILE_PATH exit 0 \ No newline at end of file diff --git a/src/Dockerfile b/src/Dockerfile index 2f0a31c7..13badda6 100644 --- a/src/Dockerfile +++ b/src/Dockerfile @@ -6,7 +6,6 @@ WORKDIR /code COPY . . - RUN pip install --no-cache-dir --upgrade -r requirements.txt # Install Node.js and pnpm with specific versions @@ -14,14 +13,17 @@ RUN apt-get update \ && apt-get install -y curl \ && curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \ && apt-get install -y nodejs \ - && npm install -g pnpm@10.4.1 \ + && npm install -g pnpm@10.6.0 \ && node --version \ && pnpm --version # Build React frontend WORKDIR /code/frontend -RUN pnpm install \ - && pnpm build +RUN pnpm install --frozen-lockfile=false \ + && pnpm build \ + && rm -rf node_modules \ + && rm -rf /root/.local/share/pnpm \ + && rm -rf /root/.pnpm-store RUN apt-get purge -y krb5-user libkrb5-3 libkrb5support0 libgssapi-krb5-2 diff --git a/src/api/main.py b/src/api/main.py index 7d1a55ba..6a286438 100644 --- a/src/api/main.py +++ b/src/api/main.py @@ -5,7 +5,8 @@ import os from azure.ai.projects.aio import AIProjectClient -from azure.identity import DefaultAzureCredential +from azure.identity.aio import DefaultAzureCredential +from azure.ai.projects.telemetry import AIProjectInstrumentor import fastapi from fastapi.staticfiles import StaticFiles @@ -20,73 +21,60 @@ @contextlib.asynccontextmanager async def lifespan(app: fastapi.FastAPI): - agent = None - + agent_version_obj = None proj_endpoint = os.environ.get("AZURE_EXISTING_AIPROJECT_ENDPOINT") - agent_id = os.environ.get("AZURE_EXISTING_AGENT_ID") + agent_id = os.environ.get("AZURE_EXISTING_AGENT_ID") try: - ai_project = AIProjectClient( - credential=DefaultAzureCredential(exclude_shared_token_cache_credential=True), - endpoint=proj_endpoint, - api_version = "2025-05-15-preview" # Evaluations yet not supported on stable (api_version="2025-05-01") - ) - logger.info("Created AIProjectClient") - - if enable_trace: - application_insights_connection_string = "" - try: - application_insights_connection_string = await ai_project.telemetry.get_connection_string() - except Exception as e: - e_string = str(e) - logger.error("Failed to get Application Insights connection string, error: %s", e_string) - if not application_insights_connection_string: - logger.error("Application Insights was not enabled for this project.") - logger.error("Enable it via the 'Tracing' tab in your AI Foundry project page.") - exit() - else: - from azure.monitor.opentelemetry import configure_azure_monitor - configure_azure_monitor(connection_string=application_insights_connection_string) - app.state.application_insights_connection_string = application_insights_connection_string - logger.info("Configured Application Insights for tracing.") - - if agent_id: - try: - agent = await ai_project.agents.get_agent(agent_id) - logger.info("Agent already exists, skipping creation") - logger.info(f"Fetched agent, agent ID: {agent.id}") - logger.info(f"Fetched agent, model name: {agent.model}") - except Exception as e: - logger.error(f"Error fetching agent: {e}", exc_info=True) - - if not agent: - # Fallback to searching by name - agent_name = os.environ["AZURE_AI_AGENT_NAME"] - agent_list = ai_project.agents.list_agents() - if agent_list: - async for agent_object in agent_list: - if agent_object.name == agent_name: - agent = agent_object - logger.info(f"Found agent by name '{agent_name}', ID={agent_object.id}") - break - - if not agent: - raise RuntimeError("No agent found. Ensure qunicorn.py created one or set AZURE_EXISTING_AGENT_ID.") - - app.state.ai_project = ai_project - app.state.agent = agent - - yield + + async with ( + DefaultAzureCredential() as credential, + AIProjectClient(endpoint=proj_endpoint, credential=credential) as project_client, + project_client.get_openai_client() as openai_client, + ): + logger.info("Created AIProjectClient") + + if enable_trace: + application_insights_connection_string = "" + try: + application_insights_connection_string = await project_client.telemetry.get_application_insights_connection_string() + except Exception as e: + e_string = str(e) + logger.error("Failed to get Application Insights connection string, error: %s", e_string) + if not application_insights_connection_string: + logger.error("Application Insights was not enabled for this project.") + logger.error("Enable it via the 'Tracing' tab in your AI Foundry project page.") + exit() + else: + from azure.monitor.opentelemetry import configure_azure_monitor + configure_azure_monitor(connection_string=application_insights_connection_string) + AIProjectInstrumentor().instrument(True) + app.state.application_insights_connection_string = application_insights_connection_string + logger.info("Configured Application Insights for tracing.") + + if agent_id: + try: + agent_name = agent_id.split(":")[0] + agent_version = agent_id.split(":")[1] + agent_version_obj = await project_client.agents.get_version(agent_name, agent_version) + logger.info("Agent already exists, skipping creation") + logger.info(f"Fetched agent, agent ID: {agent_version_obj.id}") + except Exception as e: + logger.error(f"Error fetching agent: {e}", exc_info=True) + + if not agent_version_obj: + raise RuntimeError("No agent found. Ensure qunicorn.py created one or set AZURE_EXISTING_AGENT_ID.") + + app.state.ai_project = project_client + app.state.agent_version_obj = agent_version_obj + app.state.openai_client = openai_client + yield except Exception as e: logger.error(f"Error during startup: {e}", exc_info=True) raise RuntimeError(f"Error during startup: {e}") finally: - try: - await ai_project.close() - logger.info("Closed AIProjectClient") - except Exception as e: - logger.error("Error closing AIProjectClient", exc_info=True) + logger.info("Closed AIProjectClient") def create_app(): diff --git a/src/api/routes.py b/src/api/routes.py index d5beeb0c..d2909be9 100644 --- a/src/api/routes.py +++ b/src/api/routes.py @@ -4,7 +4,9 @@ import asyncio import json import os -from typing import AsyncGenerator, Optional, Dict +from datetime import datetime, timezone +from typing import AsyncGenerator, Mapping, Optional, Dict + import fastapi from fastapi import Request, Depends, HTTPException @@ -14,24 +16,17 @@ import logging from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator +from azure.ai.projects.models import AgentVersionObject, AgentReference +from openai.types.conversations.message import Message +from openai.types.responses import Response, ResponseOutputText, ResponseOutputMessage, ResponseInputText, ResponseInputMessageItem +from openai.types.conversations import Conversation +from openai.types.responses.response_output_text import AnnotationFileCitation + +from azure.ai.projects.aio import AIProjectClient -from azure.ai.agents.aio import AgentsClient -from azure.ai.agents.models import ( - Agent, - MessageDeltaChunk, - ThreadMessage, - ThreadRun, - AsyncAgentEventHandler, - RunStep -) -from azure.ai.projects import AIProjectClient -from azure.ai.projects.models import ( - AgentEvaluationRequest, - AgentEvaluationSamplingConfiguration, - AgentEvaluationRedactionConfiguration, - EvaluatorIds -) +from openai.types.responses import ResponseTextDeltaEvent, ResponseCompletedEvent, ResponseTextDoneEvent, ResponseCreatedEvent, ResponseOutputItemDoneEvent +from openai import AsyncOpenAI # Create a logger for this module logger = logging.getLogger("azureaiapp") @@ -40,6 +35,7 @@ logging.getLogger("azure.core.pipeline.policies.http_logging_policy").setLevel(logging.WARNING) from opentelemetry import trace + tracer = trace.get_tracer(__name__) # Define the directory for your templates. @@ -78,108 +74,101 @@ def authenticate(credentials: Optional[HTTPBasicCredentials] = Depends(security) auth_dependency = Depends(authenticate) if basic_auth else None +def cleanup_created_at_metadata(metadata: Mapping[str, str]) -> None: + """Remove oldest created_at timestamp entries to keep metadata under 16 items limit.""" + if not metadata: + return + + # metadata go to be up to 16 items. If there is more than that, remove the one ended with _created_at key with smallest value + while len(metadata) > 16: + created_at_keys = [k for k in metadata if k.endswith("_created_at")] + if not created_at_keys: + break # No more _created_at keys to remove + min_key = min(created_at_keys, key=metadata.get) + del metadata[min_key] def get_ai_project(request: Request) -> AIProjectClient: return request.app.state.ai_project -def get_agent_client(request: Request) -> AgentsClient: - return request.app.state.agent_client +def get_agent_version_obj(request: Request) -> AgentVersionObject: + return request.app.state.agent_version_obj -def get_agent(request: Request) -> Agent: - return request.app.state.agent +def get_openai_client(request: Request) -> AsyncOpenAI: + return request.app.state.openai_client -def get_app_insights_conn_str(request: Request) -> str: - if hasattr(request.app.state, "application_insights_connection_string"): - return request.app.state.application_insights_connection_string - else: - return None +def get_created_at_label(message_id: str) -> str: + return f"{message_id}_created_at" def serialize_sse_event(data: Dict) -> str: return f"data: {json.dumps(data)}\n\n" -async def get_message_and_annotations(agent_client : AgentsClient, message: ThreadMessage) -> Dict: +async def get_or_create_conversation( + openai_client: AsyncOpenAI, + conversation_id: Optional[str], + agent_id: Optional[str], + current_agent_id: str +) -> Conversation: + """ + Get an existing conversation or create a new one. + Returns the conversation_id. + """ + conversation: Optional[Conversation] = None + + # Attempt to get an existing conversation if we have matching agent and conversation IDs + if conversation_id and agent_id == current_agent_id: + try: + logger.info(f"Using existing conversation with ID {conversation_id}") + conversation = await openai_client.conversations.retrieve(conversation_id=conversation_id) + logger.info(f"Retrieved conversation: {conversation.id}") + except Exception as e: + logger.error(f"Error retrieving conversation: {e}") + + # Create a new conversation if we don't have one + if not conversation: + try: + logger.info("Creating a new conversation") + conversation = await openai_client.conversations.create() + logger.info(f"Generated new conversation ID: {conversation.id}") + except Exception as e: + logger.error(f"Error creating conversation: {e}") + raise HTTPException(status_code=400, detail=f"Error handling conversation: {e}") + + return conversation + +async def get_message_and_annotations(event: Message | ResponseOutputMessage) -> Dict: annotations = [] # Get file annotations for the file search. - for annotation in (a.as_dict() for a in message.file_citation_annotations): - file_id = annotation["file_citation"]["file_id"] - logger.info(f"Fetching file with ID for annotation {file_id}") - openai_file = await agent_client.files.get(file_id) - annotation["file_name"] = openai_file.filename - logger.info(f"File name for annotation: {annotation['file_name']}") - annotations.append(annotation) + text = "" + content = event.content[0] + if content.type == "output_text" or content.type == "input_text": + text = content.text + if content.type == "output_text": + for annotation in content.annotations: + if annotation.type == "file_citation": + ann = { + 'label': annotation.filename, + "index": annotation.index + } + annotations.append(ann) + elif annotation.type == "url_citation": + ann = { + 'label': annotation.title, + "index": annotation.start_index + } + annotations.append(ann) # Get url annotation for the index search. - for url_annotation in message.url_citation_annotations: - annotation = url_annotation.as_dict() - annotation["file_name"] = annotation['url_citation']['title'] - logger.info(f"File name for annotation: {annotation['file_name']}") - annotations.append(annotation) + # for url_annotation in event.url_citation_annotations: + # annotation = url_annotation.as_dict() + # annotation["file_name"] = annotation['url_citation']['title'] + # logger.info(f"File name for annotation: {annotation['file_name']}") + # annotations.append(annotation) return { - 'content': message.text_messages[0].text.value, + 'content': text, 'annotations': annotations } -class MyEventHandler(AsyncAgentEventHandler[str]): - def __init__(self, ai_project: AIProjectClient, app_insights_conn_str: str): - super().__init__() - self.agent_client = ai_project.agents - self.ai_project = ai_project - self.app_insights_conn_str = app_insights_conn_str - - async def on_message_delta(self, delta: MessageDeltaChunk) -> Optional[str]: - stream_data = {'content': delta.text, 'type': "message"} - return serialize_sse_event(stream_data) - - async def on_thread_message(self, message: ThreadMessage) -> Optional[str]: - try: - logger.info(f"MyEventHandler: Received thread message, message ID: {message.id}, status: {message.status}") - if message.status != "completed": - return None - - logger.info("MyEventHandler: Received completed message") - - stream_data = await get_message_and_annotations(self.agent_client, message) - stream_data['type'] = "completed_message" - return serialize_sse_event(stream_data) - except Exception as e: - logger.error(f"Error in event handler for thread message: {e}", exc_info=True) - return None - - async def on_thread_run(self, run: ThreadRun) -> Optional[str]: - logger.info("MyEventHandler: on_thread_run event received") - run_information = f"ThreadRun status: {run.status}, thread ID: {run.thread_id}" - stream_data = {'content': run_information, 'type': 'thread_run'} - if run.status == "failed": - stream_data['error'] = run.last_error.as_dict() - # automatically run agent evaluation when the run is completed - if run.status == "completed": - run_agent_evaluation(run.thread_id, run.id, self.ai_project, self.app_insights_conn_str) - return serialize_sse_event(stream_data) - - async def on_error(self, data: str) -> Optional[str]: - logger.error(f"MyEventHandler: on_error event received: {data}") - stream_data = {'type': "stream_end"} - return serialize_sse_event(stream_data) - - async def on_done(self) -> Optional[str]: - logger.info("MyEventHandler: on_done event received") - stream_data = {'type': "stream_end"} - return serialize_sse_event(stream_data) - - async def on_run_step(self, step: RunStep) -> Optional[str]: - logger.info(f"Step {step['id']} status: {step['status']}") - step_details = step.get("step_details", {}) - tool_calls = step_details.get("tool_calls", []) - - if tool_calls: - logger.info("Tool calls:") - for call in tool_calls: - azure_ai_search_details = call.get("azure_ai_search", {}) - if azure_ai_search_details: - logger.info(f"azure_ai_search input: {azure_ai_search_details.get('input')}") - logger.info(f"azure_ai_search output: {azure_ai_search_details.get('output')}") - return None @router.get("/", response_class=HTMLResponse) async def index(request: Request, _ = auth_dependency): @@ -190,149 +179,154 @@ async def index(request: Request, _ = auth_dependency): } ) +async def save_user_message_created_at(openai_client: AsyncOpenAI, conversation: Conversation, input_created_at: float): + conversation.metadata = conversation.metadata or {} + try: + logger.info(f"Saving created_at.") + messages = await openai_client.conversations.items.list(conversation_id=conversation.id, order="desc") + last_input_message = None + async for message in messages: + if isinstance(message, Message) and message.role == "user": + last_input_message = message + break + if last_input_message: + conversation.metadata[get_created_at_label(last_input_message.id)] = str(input_created_at) + cleanup_created_at_metadata(conversation.metadata) + + await openai_client.conversations.update(conversation.id, metadata=conversation.metadata) + + logger.info(f"Successfully saved created_at for user message") + return # Success, exit the retry loop + + except Exception as e: + logger.error(f"Error updating message created_at.") + + async def get_result( - request: Request, - thread_id: str, - agent_id: str, - ai_project: AIProjectClient, - app_insight_conn_str: Optional[str], + agent: AgentVersionObject, + conversation: Conversation, + user_message: str, + openAI: AsyncOpenAI, carrier: Dict[str, str] ) -> AsyncGenerator[str, None]: ctx = TraceContextTextMapPropagator().extract(carrier=carrier) with tracer.start_as_current_span('get_result', context=ctx): - logger.info(f"get_result invoked for thread_id={thread_id} and agent_id={agent_id}") + logger.info(f"get_result invoked for conversation={conversation.id}") + input_created_at = datetime.now(timezone.utc).timestamp() try: - agent_client = ai_project.agents - async with await agent_client.runs.stream( - thread_id=thread_id, - agent_id=agent_id, - event_handler=MyEventHandler(ai_project, app_insight_conn_str), - ) as stream: - logger.info("Successfully created stream; starting to process events") - async for event in stream: - _, _, event_func_return_val = event - logger.debug(f"Received event: {event}") - if event_func_return_val: - logger.info(f"Yielding event: {event_func_return_val}") - yield event_func_return_val - else: - logger.debug("Event received but no data to yield") + response = await openAI.responses.create( + conversation=conversation.id, + input=user_message, + extra_body={"agent": AgentReference(name=agent.name, version=agent.version).as_dict()}, + stream=True + ) + logger.info("Successfully created stream; starting to process events") + async for event in response: + print(event) + if event.type == "response.created": + logger.info(f"Stream response created with ID: {event.response.id}") + elif event.type == "response.output_text.delta": + logger.info(f"Delta: {event.delta}") + stream_data = {'content': event.delta, 'type': "message"} + yield serialize_sse_event(stream_data) + elif event.type == "response.output_item.done" and event.item.type == "message": + stream_data = await get_message_and_annotations(event.item) + stream_data['type'] = "completed_message" + yield serialize_sse_event(stream_data) + elif event.type == "response.completed": + logger.info(f"Response completed with full message: {event.response.output_text}") + except Exception as e: logger.exception(f"Exception in get_result: {e}") - yield serialize_sse_event({'type': "error", 'message': str(e)}) + error_data = { + 'content': str(e), + 'annotations': [], + 'type': "completed_message" + } + yield serialize_sse_event(error_data) + finally: + stream_data = {'type': "stream_end"} + await save_user_message_created_at(openAI, conversation, input_created_at) + yield serialize_sse_event(stream_data) + @router.get("/chat/history") async def history( request: Request, - ai_project : AIProjectClient = Depends(get_ai_project), - agent : Agent = Depends(get_agent), + agent: AgentVersionObject = Depends(get_agent_version_obj), + openai_client : AsyncOpenAI = Depends(get_openai_client), _ = auth_dependency ): with tracer.start_as_current_span("chat_history"): - # Retrieve the thread ID from the cookies (if available). - thread_id = request.cookies.get('thread_id') + conversation_id = request.cookies.get('conversation_id') agent_id = request.cookies.get('agent_id') - # Attempt to get an existing thread. If not found, create a new one. + # Get or create conversation using the reusable function + conversation = await get_or_create_conversation( + openai_client, conversation_id, agent_id, agent.id + ) + agent_id = agent.id + # Create a new message from the user's input. try: - agent_client = ai_project.agents - if thread_id and agent_id == agent.id: - logger.info(f"Retrieving thread with ID {thread_id}") - thread = await agent_client.threads.get(thread_id) - else: - logger.info("Creating a new thread") - thread = await agent_client.threads.create() + content = [] + items = await openai_client.conversations.items.list(conversation_id=conversation.id, order="desc", limit=16) + async for item in items: + if item.type == "message": + formatteded_message = await get_message_and_annotations(item) + formatteded_message['role'] = item.role + formatteded_message['created_at'] = conversation.metadata.get(get_created_at_label(item.id), "") + content.append(formatteded_message) + + + logger.info(f"List message, conversation ID: {conversation_id}") + response = JSONResponse(content=content) + + # Update cookies to persist the conversation IDs. + response.set_cookie("conversation_id", conversation_id) + response.set_cookie("agent_id", agent_id) + return response except Exception as e: - logger.error(f"Error handling thread: {e}") - raise HTTPException(status_code=400, detail=f"Error handling thread: {e}") - - thread_id = thread.id - agent_id = agent.id - - # Create a new message from the user's input. - try: - content = [] - response = agent_client.messages.list( - thread_id=thread_id, - ) - async for message in response: - formatteded_message = await get_message_and_annotations(agent_client, message) - formatteded_message['role'] = message.role - formatteded_message['created_at'] = message.created_at.astimezone().strftime("%m/%d/%y, %I:%M %p") - content.append(formatteded_message) - - - logger.info(f"List message, thread ID: {thread_id}") - response = JSONResponse(content=content) - - # Update cookies to persist the thread and agent IDs. - response.set_cookie("thread_id", thread_id) - response.set_cookie("agent_id", agent_id) - return response - except Exception as e: - logger.error(f"Error listing message: {e}") - raise HTTPException(status_code=500, detail=f"Error list message: {e}") + logger.error(f"Error listing message: {e}") + raise HTTPException(status_code=500, detail=f"Error list message: {e}") @router.get("/agent") async def get_chat_agent( - request: Request + agent: AgentVersionObject = Depends(get_agent_version_obj), ): - return JSONResponse(content=get_agent(request).as_dict()) + return JSONResponse(content={"name": agent.name, "metadata": agent.metadata}) @router.post("/chat") async def chat( request: Request, - agent : Agent = Depends(get_agent), - ai_project: AIProjectClient = Depends(get_ai_project), - app_insights_conn_str : str = Depends(get_app_insights_conn_str), + openai_client : AsyncOpenAI = Depends(get_openai_client), + agent: AgentVersionObject = Depends(get_agent_version_obj), + _ = auth_dependency ): - # Retrieve the thread ID from the cookies (if available). - thread_id = request.cookies.get('thread_id') - agent_id = request.cookies.get('agent_id') + # Retrieve the conversation ID from the cookies (if available). + conversation_id = request.cookies.get('conversation_id') + agent_id = request.cookies.get('agent_id') with tracer.start_as_current_span("chat_request"): carrier = {} TraceContextTextMapPropagator().inject(carrier) - - # Attempt to get an existing thread. If not found, create a new one. - try: - agent_client = ai_project.agents - if thread_id and agent_id == agent.id: - logger.info(f"Retrieving thread with ID {thread_id}") - thread = await agent_client.threads.get(thread_id) - else: - logger.info("Creating a new thread") - thread = await agent_client.threads.create() - except Exception as e: - logger.error(f"Error handling thread: {e}") - raise HTTPException(status_code=400, detail=f"Error handling thread: {e}") - thread_id = thread.id + # if the connection no longer exist or agent is changed, create a new one + conversation = await get_or_create_conversation( + openai_client, conversation_id, agent_id, agent.id + ) + conversation_id = conversation.id agent_id = agent.id - + # Parse the JSON from the request. try: user_message = await request.json() except Exception as e: logger.error(f"Invalid JSON in request: {e}") raise HTTPException(status_code=400, detail=f"Invalid JSON in request: {e}") - - logger.info(f"user_message: {user_message}") - # Create a new message from the user's input. - try: - message = await agent_client.messages.create( - thread_id=thread_id, - role="user", - content=user_message.get('message', '') - ) - logger.info(f"Created message, message ID: {message.id}") - except Exception as e: - logger.error(f"Error creating message: {e}") - raise HTTPException(status_code=500, detail=f"Error creating message: {e}") # Set the Server-Sent Events (SSE) response headers. headers = { @@ -340,13 +334,13 @@ async def chat( "Connection": "keep-alive", "Content-Type": "text/event-stream" } - logger.info(f"Starting streaming response for thread ID {thread_id}") + logger.info(f"Starting streaming response for conversation ID {conversation_id}") # Create the streaming response using the generator. - response = StreamingResponse(get_result(request, thread_id, agent_id, ai_project, app_insights_conn_str, carrier), headers=headers) + response = StreamingResponse(get_result(agent, conversation, user_message.get('message', ''), openai_client, carrier), headers=headers) - # Update cookies to persist the thread and agent IDs. - response.set_cookie("thread_id", thread_id) + # Update cookies to persist the conversation and agent IDs. + response.set_cookie("conversation_id", conversation_id) response.set_cookie("agent_id", agent_id) return response @@ -354,46 +348,6 @@ def read_file(path: str) -> str: with open(path, 'r') as file: return file.read() - -def run_agent_evaluation( - thread_id: str, - run_id: str, - ai_project: AIProjectClient, - app_insights_conn_str: str): - - if app_insights_conn_str: - agent_evaluation_request = AgentEvaluationRequest( - run_id=run_id, - thread_id=thread_id, - evaluators={ - "Relevance": {"Id": EvaluatorIds.RELEVANCE.value}, - "TaskAdherence": {"Id": EvaluatorIds.TASK_ADHERENCE.value}, - "ToolCallAccuracy": {"Id": EvaluatorIds.TOOL_CALL_ACCURACY.value}, - }, - sampling_configuration=AgentEvaluationSamplingConfiguration( - name="default", - sampling_percent=100, - ), - redaction_configuration=AgentEvaluationRedactionConfiguration( - redact_score_properties=False, - ), - app_insights_connection_string=app_insights_conn_str, - ) - - async def run_evaluation(): - try: - logger.info(f"Running agent evaluation on thread ID {thread_id} and run ID {run_id}") - agent_evaluation_response = await ai_project.evaluations.create_agent_evaluation( - evaluation=agent_evaluation_request - ) - logger.info(f"Evaluation response: {agent_evaluation_response}") - except Exception as e: - logger.error(f"Error creating agent evaluation: {e}") - - # Create a new task to run the evaluation asynchronously - asyncio.create_task(run_evaluation()) - - @router.get("/config/azure") async def get_azure_config(_ = auth_dependency): """Get Azure configuration for frontend use""" diff --git a/src/frontend/package.json b/src/frontend/package.json index 6bed8968..0974d2c8 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -6,14 +6,14 @@ "scripts": { "dev": "vite", "build": "tsc && vite build", - "setup": "npm install -g pnpm@10.4.1 && pnpm install && pnpm build", + "setup": "npm install -g pnpm@10.6.0 && pnpm install && pnpm build", "preview": "vite preview", "typecheck": "tsc --noEmit" }, "keywords": [], "author": "", "license": "ISC", - 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'@esbuild/android-arm64@0.25.3': + '@esbuild/android-arm@0.27.0': optional: true - '@esbuild/android-arm@0.25.3': + '@esbuild/android-x64@0.27.0': optional: true - '@esbuild/android-x64@0.25.3': + '@esbuild/darwin-arm64@0.27.0': optional: true - '@esbuild/darwin-arm64@0.25.3': + '@esbuild/darwin-x64@0.27.0': optional: true - '@esbuild/darwin-x64@0.25.3': + '@esbuild/freebsd-arm64@0.27.0': optional: true - '@esbuild/freebsd-arm64@0.25.3': + '@esbuild/freebsd-x64@0.27.0': optional: true - '@esbuild/freebsd-x64@0.25.3': + '@esbuild/linux-arm64@0.27.0': optional: true - '@esbuild/linux-arm64@0.25.3': + '@esbuild/linux-arm@0.27.0': optional: true - '@esbuild/linux-arm@0.25.3': + '@esbuild/linux-ia32@0.27.0': optional: true - '@esbuild/linux-ia32@0.25.3': + '@esbuild/linux-loong64@0.27.0': optional: true - '@esbuild/linux-loong64@0.25.3': + '@esbuild/linux-mips64el@0.27.0': optional: true - '@esbuild/linux-mips64el@0.25.3': + '@esbuild/linux-ppc64@0.27.0': optional: true - '@esbuild/linux-ppc64@0.25.3': + '@esbuild/linux-riscv64@0.27.0': optional: true - '@esbuild/linux-riscv64@0.25.3': + '@esbuild/linux-s390x@0.27.0': optional: true - '@esbuild/linux-s390x@0.25.3': + '@esbuild/linux-x64@0.27.0': optional: true - '@esbuild/linux-x64@0.25.3': + '@esbuild/netbsd-arm64@0.27.0': optional: true - '@esbuild/netbsd-arm64@0.25.3': + '@esbuild/netbsd-x64@0.27.0': optional: true - '@esbuild/netbsd-x64@0.25.3': + '@esbuild/openbsd-arm64@0.27.0': optional: true - '@esbuild/openbsd-arm64@0.25.3': + '@esbuild/openbsd-x64@0.27.0': optional: true - '@esbuild/openbsd-x64@0.25.3': + '@esbuild/openharmony-arm64@0.27.0': optional: true - '@esbuild/sunos-x64@0.25.3': + '@esbuild/sunos-x64@0.27.0': optional: true - '@esbuild/win32-arm64@0.25.3': + '@esbuild/win32-arm64@0.27.0': optional: true - '@esbuild/win32-ia32@0.25.3': + '@esbuild/win32-ia32@0.27.0': optional: true - '@esbuild/win32-x64@0.25.3': + '@esbuild/win32-x64@0.27.0': optional: true '@floating-ui/core@1.6.9': @@ -4434,14 +4444,14 @@ snapshots: '@ungap/structured-clone@1.3.0': {} - '@vitejs/plugin-react@4.4.1(vite@6.3.3(@types/node@22.14.1))': + '@vitejs/plugin-react@4.4.1(vite@6.4.1(@types/node@22.14.1))': dependencies: '@babel/core': 7.26.10 '@babel/plugin-transform-react-jsx-self': 7.25.9(@babel/core@7.26.10) '@babel/plugin-transform-react-jsx-source': 7.25.9(@babel/core@7.26.10) '@types/babel__core': 7.20.5 react-refresh: 0.17.0 - vite: 6.3.3(@types/node@22.14.1) + vite: 6.4.1(@types/node@22.14.1) transitivePeerDependencies: - supports-color @@ -4521,33 +4531,34 @@ snapshots: entities@6.0.0: {} - esbuild@0.25.3: + esbuild@0.27.0: optionalDependencies: - '@esbuild/aix-ppc64': 0.25.3 - '@esbuild/android-arm': 0.25.3 - '@esbuild/android-arm64': 0.25.3 - '@esbuild/android-x64': 0.25.3 - '@esbuild/darwin-arm64': 0.25.3 - '@esbuild/darwin-x64': 0.25.3 - '@esbuild/freebsd-arm64': 0.25.3 - '@esbuild/freebsd-x64': 0.25.3 - '@esbuild/linux-arm': 0.25.3 - '@esbuild/linux-arm64': 0.25.3 - '@esbuild/linux-ia32': 0.25.3 - '@esbuild/linux-loong64': 0.25.3 - '@esbuild/linux-mips64el': 0.25.3 - '@esbuild/linux-ppc64': 0.25.3 - '@esbuild/linux-riscv64': 0.25.3 - '@esbuild/linux-s390x': 0.25.3 - '@esbuild/linux-x64': 0.25.3 - '@esbuild/netbsd-arm64': 0.25.3 - '@esbuild/netbsd-x64': 0.25.3 - '@esbuild/openbsd-arm64': 0.25.3 - '@esbuild/openbsd-x64': 0.25.3 - '@esbuild/sunos-x64': 0.25.3 - '@esbuild/win32-arm64': 0.25.3 - '@esbuild/win32-ia32': 0.25.3 - '@esbuild/win32-x64': 0.25.3 + '@esbuild/aix-ppc64': 0.27.0 + '@esbuild/android-arm': 0.27.0 + '@esbuild/android-arm64': 0.27.0 + '@esbuild/android-x64': 0.27.0 + '@esbuild/darwin-arm64': 0.27.0 + '@esbuild/darwin-x64': 0.27.0 + '@esbuild/freebsd-arm64': 0.27.0 + '@esbuild/freebsd-x64': 0.27.0 + '@esbuild/linux-arm': 0.27.0 + '@esbuild/linux-arm64': 0.27.0 + '@esbuild/linux-ia32': 0.27.0 + '@esbuild/linux-loong64': 0.27.0 + '@esbuild/linux-mips64el': 0.27.0 + '@esbuild/linux-ppc64': 0.27.0 + '@esbuild/linux-riscv64': 0.27.0 + '@esbuild/linux-s390x': 0.27.0 + '@esbuild/linux-x64': 0.27.0 + '@esbuild/netbsd-arm64': 0.27.0 + '@esbuild/netbsd-x64': 0.27.0 + '@esbuild/openbsd-arm64': 0.27.0 + '@esbuild/openbsd-x64': 0.27.0 + '@esbuild/openharmony-arm64': 0.27.0 + '@esbuild/sunos-x64': 0.27.0 + '@esbuild/win32-arm64': 0.27.0 + '@esbuild/win32-ia32': 0.27.0 + '@esbuild/win32-x64': 0.27.0 escalade@3.2.0: {} @@ -4760,7 +4771,7 @@ snapshots: lexical@0.12.6: {} - lib0@0.2.105: + lib0@0.2.114: dependencies: isomorphic.js: 0.2.5 @@ -5517,9 +5528,9 @@ snapshots: '@types/unist': 3.0.3 vfile-message: 4.0.2 - vite@6.3.3(@types/node@22.14.1): + vite@6.4.1(@types/node@22.14.1): dependencies: - esbuild: 0.25.3 + esbuild: 0.27.0 fdir: 6.4.4(picomatch@4.0.2) picomatch: 4.0.2 postcss: 8.5.3 @@ -5537,6 +5548,6 @@ snapshots: yjs@13.6.26: dependencies: - lib0: 0.2.105 + lib0: 0.2.114 zwitch@2.0.4: {} diff --git a/src/frontend/src/components/agents/AgentPreview.tsx b/src/frontend/src/components/agents/AgentPreview.tsx index 0e8b5202..07ea759d 100644 --- a/src/frontend/src/components/agents/AgentPreview.tsx +++ b/src/frontend/src/components/agents/AgentPreview.tsx @@ -15,7 +15,8 @@ import { AgentPreviewChatBot } from "./AgentPreviewChatBot"; import { MenuButton } from "../core/MenuButton/MenuButton"; import { IChatItem } from "./chatbot/types"; import { Waves } from "./Waves"; -import { BuiltWithBadge } from "./BuiltWithBadge"; +/* temporarily disable BuiltWithBadge */ +// import { BuiltWithBadge } from "./BuiltWithBadge"; import styles from "./AgentPreview.module.css"; @@ -46,34 +47,69 @@ interface IAgentPreviewProps { } interface IAnnotation { - file_name?: string; - text: string; - start_index: number; - end_index: number; + label: string; + index: number; } const preprocessContent = ( content: string, annotations?: IAnnotation[] ): string => { - if (annotations) { - // Process annotations in reverse order so that the indexes remain valid - annotations - .slice() - .reverse() - .forEach((annotation) => { - // If there's a file_name, show it (wrapped in brackets), otherwise fall back to annotation.text. - const linkText = annotation.file_name - ? `[${annotation.file_name}]` - : annotation.text; - - content = - content.slice(0, annotation.start_index) + - linkText + - content.slice(annotation.end_index); - }); + if (!annotations || annotations.length === 0) { + return content; + } + + // Process annotations in descending order index, ascending label, remove duplicates + let processedContent = content; + annotations + .slice() + .sort((a, b) => { + // Primary sort: descending index + if (b.index !== a.index) { + return b.index - a.index; + } + // Secondary sort: descending label (as tiebreaker) + return b.label.localeCompare(a.label); + }) + .filter((annotation, index, self) => + index === self.findIndex(a => a.label === annotation.label && a.index === annotation.index)) + .forEach((annotation) => { + // Only process if the index is valid and within bounds + if (annotation.index >= 0 && annotation.index <= processedContent.length) { + // If there's a label, show it (wrapped in brackets), inserting after the index + processedContent = + processedContent.slice(0, annotation.index + 1) + + ` [${annotation.label}]` + + processedContent.slice(annotation.index + 1); + } + }); + return processedContent; +}; + +const formatTimestampToLocalTime = (timestampStr: string): string => { + // Convert timestamp string to local timezone with specific format + let localTime = new Date().toLocaleString(); + if (timestampStr) { + try { + // Parse timestamp (assuming it's a Unix timestamp in seconds as string, could be float) + const timestamp = parseFloat(timestampStr); + if (!isNaN(timestamp)) { + const date = new Date(timestamp * 1000); // Convert to milliseconds + localTime = date.toLocaleDateString('en-US', { + month: '2-digit', + day: '2-digit', + year: '2-digit' + }) + ', ' + date.toLocaleTimeString('en-US', { + hour: 'numeric', + minute: '2-digit', + hour12: true + }); + } + } catch (e) { + console.error('Error parsing timestamp:', e); + } } - return content; + return localTime; }; export function AgentPreview({ agentDetails }: IAgentPreviewProps): ReactNode { @@ -105,12 +141,14 @@ export function AgentPreview({ agentDetails }: IAgentPreviewProps): ReactNode { const reversedResponse = [...json_response].reverse(); for (const entry of reversedResponse) { + const localTime = formatTimestampToLocalTime(entry.created_at); + if (entry.role === "user") { historyMessages.push({ id: crypto.randomUUID(), content: entry.content, role: "user", - more: { time: entry.created_at }, // Or use timestamp from history if available + more: { time: localTime }, }); } else { historyMessages.push({ @@ -118,29 +156,33 @@ export function AgentPreview({ agentDetails }: IAgentPreviewProps): ReactNode { content: preprocessContent(entry.content, entry.annotations), role: "assistant", // Assuming 'assistant' role for non-user isAnswer: true, // Assuming this property for assistant messages - more: { time: entry.created_at }, // Or use timestamp from history if available + more: { time: localTime }, // annotations: entry.annotations, // If you plan to use annotations }); } } setMessageList((prev) => [...historyMessages, ...prev]); // Prepend history } else { - const errorChatItem = createAssistantMessageDiv(); // This will add an empty message first - appendAssistantMessage( - errorChatItem, - "Error occurs while loading chat history!", - false - ); + // For error messages, add directly to messageList without preprocessing + const errorMessage: IChatItem = { + id: crypto.randomUUID(), + content: "Error occurs while loading chat history!", + isAnswer: true, + more: { time: new Date().toISOString() }, + }; + setMessageList(prev => [...prev, errorMessage]); } setIsLoadingChatHistory(false); } catch (error) { console.error("Failed to load chat history:", error); - const errorChatItem = createAssistantMessageDiv(); - appendAssistantMessage( - errorChatItem, - "Error occurs while loading chat history!", - false - ); + // For error messages, add directly to messageList without preprocessing + const errorMessage: IChatItem = { + id: crypto.randomUUID(), + content: "Error occurs while loading chat history!", + isAnswer: true, + more: { time: new Date().toISOString() }, + }; + setMessageList(prev => [...prev, errorMessage]); setIsLoadingChatHistory(false); } }; @@ -236,6 +278,7 @@ export function AgentPreview({ agentDetails }: IAgentPreviewProps): ReactNode { let isStreaming = true; let buffer = ""; let annotations: IAnnotation[] = []; + let hasReceivedCompletedMessage = false; // Create a reader for the SSE stream const reader = stream.getReader(); @@ -277,23 +320,6 @@ export function AgentPreview({ agentDetails }: IAgentPreviewProps): ReactNode { console.log("[ChatClient] Parsed SSE event:", data); - if (data.error) { - if (!chatItem) { - chatItem = createAssistantMessageDiv(); - console.log( - "[ChatClient] Created new messageDiv for assistant." - ); - } - - setIsResponding(false); - appendAssistantMessage( - chatItem, - data.error.message || "An error occurred.", - false - ); - return; - } - // Check the data type to decide how to update the UI if (data.type === "stream_end") { // End of the stream @@ -313,18 +339,50 @@ export function AgentPreview({ agentDetails }: IAgentPreviewProps): ReactNode { } if (data.type === "completed_message") { - clearAssistantMessage(chatItem); - accumulatedContent = data.content; - annotations = data.annotations; - isStreaming = false; + // Each completed_message should get its own balloon + if (hasReceivedCompletedMessage) { + // We've already processed a completed message, so create a new balloon for this one + chatItem = createAssistantMessageDiv(); + console.log( + "[ChatClient] Created new messageDiv for additional completed message." + ); + + // Reset for the new message + accumulatedContent = data.content; + annotations = data.annotations || []; + } else { + // First completed message in this stream + clearAssistantMessage(chatItem); + accumulatedContent = data.content; + annotations = data.annotations || []; + hasReceivedCompletedMessage = true; + } + console.log( "[ChatClient] Received completed message:", accumulatedContent ); - + + isStreaming = false; setIsResponding(false); } else { + // Handle streaming content + if (hasReceivedCompletedMessage) { + // We've had a completed message before, so this is new streaming content + // Create a new balloon for the new streaming content + chatItem = createAssistantMessageDiv(); + console.log( + "[ChatClient] Created new messageDiv for streaming after completed message." + ); + + // Reset for new streaming content + annotations = []; + accumulatedContent = ""; + hasReceivedCompletedMessage = false; // Reset for this new cycle + } accumulatedContent += data.content; + isStreaming = true; + console.log( "[ChatClient] Received streaming chunk:", data.content @@ -543,7 +601,8 @@ export function AgentPreview({ agentDetails }: IAgentPreviewProps): ReactNode { )} - + {/* temporarily disable BuiltWithBadge */} + {/* */} {/* Settings Panel */} diff --git a/src/gunicorn.conf.py b/src/gunicorn.conf.py index c32dce20..87d6a92c 100644 --- a/src/gunicorn.conf.py +++ b/src/gunicorn.conf.py @@ -1,7 +1,7 @@ # Copyright (c) Microsoft. All rights reserved. # Licensed under the MIT license. # See LICENSE file in the project root for full license information. -from typing import Dict, List +from typing import Dict, List, Optional import asyncio import csv @@ -12,18 +12,25 @@ import sys from azure.ai.projects.aio import AIProjectClient -from azure.ai.agents.models import ( - Agent, - AsyncToolSet, - AzureAISearchTool, - FilePurpose, - FileSearchTool, - Tool, -) -from azure.ai.projects.models import ConnectionType, ApiKeyCredentials +from azure.ai.projects.models import ConnectionType, ApiKeyCredentials, AgentVersionObject from azure.identity.aio import DefaultAzureCredential from azure.core.credentials_async import AsyncTokenCredential +from azure.ai.projects.models import PromptAgentDefinition +from azure.ai.projects.models import FileSearchTool, AzureAISearchAgentTool, Tool, AgentVersionObject, AzureAISearchToolResource, AISearchIndexResource + +from azure.ai.projects.models import ( + PromptAgentDefinition, + EvaluationRule, + ContinuousEvaluationRuleAction, + EvaluationRuleFilter, + EvaluationRuleEventType, + EvaluatorCategory, + EvaluatorDefinitionType, + EvaluationRuleActionType +) + +from openai import AsyncOpenAI from dotenv import load_dotenv from logging_config import configure_logging @@ -33,12 +40,6 @@ logger = configure_logging(os.getenv("APP_LOG_FILE", "")) -agentID = os.environ.get("AZURE_EXISTING_AGENT_ID") if os.environ.get( - "AZURE_EXISTING_AGENT_ID") else os.environ.get( - "AZURE_AI_AGENT_ID") - -proj_endpoint = os.environ.get("AZURE_EXISTING_AIPROJECT_ENDPOINT") - def list_files_in_files_directory() -> List[str]: # Get the absolute path of the 'files' directory files_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), 'files')) @@ -116,6 +117,7 @@ def _get_file_path(file_name: str) -> str: async def get_available_tool( project_client: AIProjectClient, + openai_client: AsyncOpenAI, creds: AsyncTokenCredential) -> Tool: """ Get the toolset and tool definition for the agent. @@ -124,106 +126,162 @@ async def get_available_tool( :param creds: The credentials, used for the index. :return: The tool set, available based on the environment. """ - # File name -> {"id": file_id, "path": file_path} - file_ids: List[str] = [] # First try to get an index search. - conn_id = "" - if os.environ.get('AZURE_AI_SEARCH_INDEX_NAME'): - conn_list = project_client.connections.list() - async for conn in conn_list: - if conn.type == ConnectionType.AZURE_AI_SEARCH: - conn_id = conn.id - break - - toolset = AsyncToolSet() - if conn_id: + conn_id = os.environ.get('SEARCH_CONNECTION_ID') + search_index_name = os.environ.get('AZURE_AI_SEARCH_INDEX_NAME') + if search_index_name and conn_id: await create_index_maybe(project_client, creds) - return AzureAISearchTool( - index_connection_id=conn_id, - index_name=os.environ.get('AZURE_AI_SEARCH_INDEX_NAME')) + return AzureAISearchAgentTool( + azure_ai_search=AzureAISearchToolResource(indexes=[AISearchIndexResource( + project_connection_id=conn_id, + index_name=search_index_name, + query_type="simple" + )]) + ) else: logger.info( "agent: index was not initialized, falling back to file search.") # Upload files for file search - for file_name in FILES_NAMES: - file_path = _get_file_path(file_name) - file = await project_client.agents.files.upload_and_poll( - file_path=file_path, purpose=FilePurpose.AGENTS) - # Store both file id and the file path using the file name as key. - file_ids.append(file.id) - - # Create the vector store using the file IDs. - vector_store = await project_client.agents.vector_stores.create_and_poll( - file_ids=file_ids, - name="sample_store" - ) + file_streams = [open(_get_file_path(file_name), "rb") for file_name in FILES_NAMES] + + try: + vector_store = await openai_client.vector_stores.create() + await openai_client.vector_stores.file_batches.upload_and_poll( + vector_store_id=vector_store.id, files=file_streams + ) + print(f"File uploaded to vector store (id: {vector_store.id})") + except FileNotFoundError: + print(f"Warning: Asset file not found.") + print("Creating vector store without file for demonstration...") + + logger.info("agent: file store and vector store success") return FileSearchTool(vector_store_ids=[vector_store.id]) -async def create_agent(ai_client: AIProjectClient, - creds: AsyncTokenCredential) -> Agent: +async def create_agent(ai_project: AIProjectClient, + openai_client: AsyncOpenAI, + creds: AsyncTokenCredential) -> AgentVersionObject: logger.info("Creating new agent with resources") - tool = await get_available_tool(ai_client, creds) - toolset = AsyncToolSet() - toolset.add(tool) - - instructions = "Use AI Search always. Avoid to use base knowledge." if isinstance(tool, AzureAISearchTool) else "Use File Search always. Avoid to use base knowledge." + tool = await get_available_tool(ai_project, openai_client, creds) + + instructions = "Use File Search always with citations. Avoid to use base knowledge." - agent = await ai_client.agents.create_agent( - model=os.environ["AZURE_AI_AGENT_DEPLOYMENT_NAME"], - name=os.environ["AZURE_AI_AGENT_NAME"], - instructions=instructions, - toolset=toolset + if isinstance(tool, AzureAISearchAgentTool): + instructions = """Use AI Search always. + You must always provide citations for answers using the tool and render them as: `\u3010message_idx:search_idx\u2020source\u3011`. + Avoid to use base knowledge.""" + + agent = await ai_project.agents.create_version( + agent_name=os.environ["AZURE_AI_AGENT_NAME"], + definition=PromptAgentDefinition( + model=os.environ["AZURE_AI_AGENT_DEPLOYMENT_NAME"], + instructions=instructions, + tools=[tool], + ), ) return agent -async def initialize_resources(): +async def initialize_eval(project_client: AIProjectClient, openai_client: AsyncOpenAI, agent_obj: AgentVersionObject, credential: AsyncTokenCredential): + eval_rule_id = f"eval-rule-for-{agent_obj.name}" try: - async with DefaultAzureCredential( - exclude_shared_token_cache_credential=True) as creds: - async with AIProjectClient( - credential=creds, - endpoint=proj_endpoint - ) as ai_client: - # If the environment already has AZURE_AI_AGENT_ID or AZURE_EXISTING_AGENT_ID, try - # fetching that agent - if agentID is not None: - try: - agent = await ai_client.agents.get_agent( - agentID) - logger.info(f"Found agent by ID: {agent.id}") - return - except Exception as e: - logger.warning( - "Could not retrieve agent by AZURE_EXISTING_AGENT_ID = " - f"{agentID}, error: {e}") - - # Check if an agent with the same name already exists - agent_list = ai_client.agents.list_agents() - if agent_list: - async for agent_object in agent_list: - if agent_object.name == os.environ[ - "AZURE_AI_AGENT_NAME"]: - logger.info( - "Found existing agent named " - f"'{agent_object.name}'" - f", ID: {agent_object.id}") - os.environ["AZURE_EXISTING_AGENT_ID"] = agent_object.id - return - - # Create a new agent - agent = await create_agent(ai_client, creds) - os.environ["AZURE_EXISTING_AGENT_ID"] = agent.id - logger.info(f"Created agent, agent ID: {agent.id}") + eval_rules = project_client.evaluation_rules.list( + action_type=EvaluationRuleActionType.CONTINUOUS_EVALUATION, + agent_name=agent_obj.name) + rules_list = [rule async for rule in eval_rules] + + if len(rules_list) >= 1: + print(f"Continuous Evaluation Rule for agent {agent_obj.name} already exists") + else: + # Create an evaluation with testing criteria + data_source_config = {"type": "azure_ai_source", "scenario": "responses"} + testing_criteria = [ + { "type": "azure_ai_evaluator", + "name": "violence", + "evaluator_name": "builtin.violence", + "initialization_parameters": {"deployment_name": os.environ["AZURE_AI_AGENT_DEPLOYMENT_NAME"]}, + } + ] + eval_object = await openai_client.evals.create( + name=f"{agent_obj.name} Continuous Evaluation", + data_source_config=data_source_config, # type: ignore + testing_criteria=testing_criteria, # type: ignore + ) + print(f"Evaluation created (id: {eval_object.id}, name: {eval_object.name})") + + # Configure a rule that triggers the evaluation on agent responses + continuous_eval_rule = await project_client.evaluation_rules.create_or_update( + id=eval_rule_id, + evaluation_rule=EvaluationRule( + display_name=f"{agent_obj.name} Continuous Eval Rule", + description="An eval rule that runs on agent response completions", + action=ContinuousEvaluationRuleAction( + eval_id=eval_object.id, # link to evaluation created above + max_hourly_runs=5), # set max eval run limit per hour + event_type=EvaluationRuleEventType.RESPONSE_COMPLETED, + filter=EvaluationRuleFilter(agent_name=agent_obj.name), + enabled=True, + ), + ) + print( + f"Continuous Evaluation Rule created (id: {continuous_eval_rule.id}, name: {continuous_eval_rule.display_name})" + ) + except Exception as e: + logger.error(f"Error creating Continuous Evaluation Rule: {e}", exc_info=True) +async def initialize_resources(): + proj_endpoint = os.environ.get("AZURE_EXISTING_AIPROJECT_ENDPOINT") + try: + async with ( + DefaultAzureCredential() as credential, + AIProjectClient(endpoint=proj_endpoint, credential=credential) as project_client, + project_client.get_openai_client() as openai_client, + ): + # If the environment already has AZURE_AI_AGENT_ID or AZURE_EXISTING_AGENT_ID, try + # fetching that agent + agent_obj: Optional[AgentVersionObject] = None + + agentID = os.environ.get("AZURE_EXISTING_AGENT_ID") + + if agentID: + try: + agent_name = agentID.split(":")[0] + agent_version = agentID.split(":")[1] + agent_obj = await project_client.agents.get_version(agent_name, agent_version) + logger.info(f"Found agent by ID: {agent_obj.id}") + except Exception as e: + logger.warning( + "Could not retrieve agent by AZURE_EXISTING_AGENT_ID = " + f"{agentID}, error: {e}") + else: + logger.info("No existing agent ID found.") + + # Check if an agent with the same name already exists + if not agent_obj: + try: + agent_name = os.environ["AZURE_AI_AGENT_NAME"] + logger.info(f"Retrieving agent by name: {agent_name}") + agents = await project_client.agents.get(agent_name) + agent_obj = agents.versions.latest + logger.info(f"Agent with agent id, {agent_obj.id} retrieved.") + except Exception as e: + logger.info(f"Agent name, {agent_name} not found.") + + # Create a new agent + if not agent_obj: + agent_obj = await create_agent(project_client, openai_client, credential) + logger.info(f"Created agent, agent ID: {agent_obj.id}") + + os.environ["AZURE_EXISTING_AGENT_ID"] = agent_obj.id + + await initialize_eval(project_client, openai_client, agent_obj, credential) except Exception as e: logger.info("Error creating agent: {e}", exc_info=True) - raise RuntimeError(f"Failed to create the agent: {e}") + raise RuntimeError(f"Failed to create the agent: {e}") def on_starting(server): diff --git a/src/requirements.txt b/src/requirements.txt index 777d4447..ba3f93c0 100644 --- a/src/requirements.txt +++ b/src/requirements.txt @@ -1,17 +1,15 @@ -fastapi==0.115.13 +fastapi>=0.121.2 uvicorn[standard]==0.29.0 gunicorn==23.0.0 azure-identity==1.19.0 -aiohttp==3.13.0 - -azure_ai_agents==1.0.0 -azure_ai_projects==1.0.0b11 - -azure-core==1.34.0 # other versions might not compatible -azure-core-tracing-opentelemetry -azure-monitor-opentelemetry==1.6.9 # version such as 1.6.11 isn't compatible +aiohttp==3.13.1 +openai +azure_ai_projects==2.0.0b2 +azure-core==1.36.0 # other versions might not compatible +azure-core-tracing-opentelemetry==1.0.0b12 +azure-monitor-opentelemetry-exporter==1.0.0b44 +azure-monitor-opentelemetry==1.8.1 # version such as 1.6.11 isn't compatible azure-search-documents -opentelemetry-sdk setuptools==80.9.0 -starlette>=0.40.0 # fix vulnerability +starlette>=0.47.2 # fix GHSA-2c2j-9gv5-cj73 (CVE-2025-54121) - DoS when parsing large multipart forms jinja2 # new dependent of fastapi \ No newline at end of file diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py new file mode 100644 index 00000000..f105a143 --- /dev/null +++ b/tests/test_evaluation.py @@ -0,0 +1,137 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ + +import time +from azure.identity import DefaultAzureCredential +from azure.ai.projects import AIProjectClient +from openai.types.eval_create_params import DataSourceConfigCustom + +from test_utils import retrieve_agent, retrieve_endpoint, retrieve_model_deployment + + +def test_evaluation(): + with ( + DefaultAzureCredential(exclude_interactive_browser_credential=False) as credential, + AIProjectClient(endpoint=retrieve_endpoint(), credential=credential) as project_client, + project_client.get_openai_client() as openai_client, + ): + + agent = retrieve_agent(project_client) + model = retrieve_model_deployment() + + data_source_config = DataSourceConfigCustom( + type="custom", + item_schema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}, + include_sample_schema=True, + ) + + # Define testing criteria. Explore the evaluator catalog for more built-in evaluators. + testing_criteria = [ + # quality evaluation of agent messages (sample.output_items) + { + "type": "azure_ai_evaluator", + "name": "task_completion", + "evaluator_name": "builtin.task_completion", + "data_mapping": { + "query": "{{item.query}}", + "response": "{{sample.output_items}}" + }, + "initialization_parameters": {"deployment_name": f"{model}"}, + }, + { + "type": "azure_ai_evaluator", + "name": "task_adherence", + "evaluator_name": "builtin.task_adherence", + "data_mapping": { + "query": "{{item.query}}", + "response": "{{sample.output_items}}" + }, + "initialization_parameters": {"deployment_name": f"{model}"}, + }, + { + "type": "azure_ai_evaluator", + "name": "tool_call_success", + "evaluator_name": "builtin.tool_call_success", + "data_mapping": { + "response": "{{sample.output_items}}" + }, + "initialization_parameters": {"deployment_name": f"{model}"}, + }, + # safety evalution of agent responses (sample.output_text) + { + "type": "azure_ai_evaluator", + "name": "violence", + "evaluator_name": "builtin.violence", + "data_mapping": { + "query": "{{item.query}}", + "response": "{{sample.output_text}}" + }, + }, + { + "type": "azure_ai_evaluator", + "name": "indirect_attack", + "evaluator_name": "builtin.indirect_attack", + "data_mapping": { + "query": "{{item.query}}", + "response": "{{sample.output_text}}" + }, + }, + ] + + eval_object = openai_client.evals.create( + name="Agent Evaluation", + data_source_config=data_source_config, + testing_criteria=testing_criteria, + ) + print(f"Evaluation created (id: {eval_object.id}, name: {eval_object.name})") + + # Define data source for evaluation run + data_source = { + "type": "azure_ai_target_completions", + "source": { + "type": "file_content", + "content": [ + {"item": {"query": "Tell me a joke about a robot"}}, + {"item": {"query": "What are the best places to visit in Tokyo?"}}, + ], + }, + "input_messages": { + "type": "template", + "template": [ + {"type": "message", "role": "user", "content": {"type": "input_text", "text": "{{item.query}}"}} + ], + }, + "target": { + "type": "azure_ai_agent", + "name": agent.name, + "version": agent.version, # Version is optional. Defaults to latest version if not specified + }, + } + + # Submit evaluation run + agent_eval_run = openai_client.evals.runs.create( + eval_id=eval_object.id, name=f"Evaluation Run for Agent {agent.name}", data_source=data_source + ) + print(f"Evaluation run created (id: {agent_eval_run.id})") + + # Poll for completion + while agent_eval_run.status not in ["completed", "failed"]: + agent_eval_run = openai_client.evals.runs.retrieve(run_id=agent_eval_run.id, eval_id=eval_object.id) + print(f"Waiting for eval run to complete... current status: {agent_eval_run.status}") + time.sleep(5) + + if agent_eval_run.status == "completed": + print("\n Evaluation run completed successfully!") + print(f"Result Counts: {agent_eval_run.result_counts}") + print(f"Report URL: {agent_eval_run.report_url}") + + # Assertions + assert agent_eval_run.status == "completed", "Evaluation run did not complete successfully. Review logs from the evaluation report." + assert agent_eval_run.result_counts.errored == 0, f"There were errored evaluation items. Review evaluation results in the evaluation report in {agent_eval_run.report_url}." + assert agent_eval_run.result_counts.failed == 0, f"There were failed evaluation items. Review evaluation results in {agent_eval_run.report_url}." + + +if __name__ == "__main__": + test_evaluation() \ No newline at end of file diff --git a/tests/test_red_teaming.py b/tests/test_red_teaming.py new file mode 100644 index 00000000..0f1c03cd --- /dev/null +++ b/tests/test_red_teaming.py @@ -0,0 +1,124 @@ +# pylint: disable=line-too-long,useless-suppression +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ + +from pprint import pprint +from azure.identity import DefaultAzureCredential +from azure.ai.projects import AIProjectClient +from azure.ai.projects.models import ( + AgentVersionObject, + EvaluationTaxonomy, + AzureAIAgentTarget, + AgentTaxonomyInput, + RiskCategory, +) +import time +from azure.ai.projects.models import EvaluationTaxonomy, AgentVersionObject +from test_utils import retrieve_agent, retrieve_endpoint + +def test_red_teaming() -> None: + + with ( + DefaultAzureCredential(exclude_interactive_browser_credential=False) as credential, + AIProjectClient(endpoint=retrieve_endpoint(), credential=credential) as project_client, + project_client.get_openai_client() as client, + ): + + agent = retrieve_agent(project_client) + + eval_group_name = "Red Team Agent Safety evaluation -" + str(int(time.time())) + eval_run_name = f"Red Team Agent Safety evaluation run for {agent.name} -" + str(int(time.time())) + data_source_config = {"type": "azure_ai_source", "scenario": "red_team"} + + # Define testing criteria for red teaming. + # Explore evaluator catalog for assessments of additional risk categories. + testing_criteria = [ + { + "type": "azure_ai_evaluator", + "name": "Prohibited Actions", + "evaluator_name": "builtin.prohibited_actions" + } + ] + pprint(testing_criteria) + + eval_object = client.evals.create( + name=eval_group_name, + data_source_config=data_source_config, + testing_criteria=testing_criteria, + ) + print(f"Red team evaluation created for red teaming: {eval_group_name}") + + risk_categories_for_taxonomy = [RiskCategory.PROHIBITED_ACTIONS] + target = AzureAIAgentTarget( + name=agent.name, version=agent.version, tool_descriptions=_get_tool_descriptions(agent) + ) + agent_taxonomy_input = AgentTaxonomyInput(risk_categories=risk_categories_for_taxonomy, target=target) + eval_taxonomy_input = EvaluationTaxonomy( + description="Taxonomy for red teaming evaluation", taxonomy_input=agent_taxonomy_input + ) + taxonomy = project_client.evaluation_taxonomies.create(name=agent.name, body=eval_taxonomy_input) + + # Submit evaluation run for red teaming + eval_run_object = client.evals.runs.create( + eval_id=eval_object.id, + name=eval_run_name, + data_source={ + "type": "azure_ai_red_team", + "item_generation_params": { + "type": "red_team_taxonomy", + "attack_strategies": ["Flip", "Base64"], + "num_turns": 1, # number of interaction turns per item + "source": {"type": "file_id", "id": taxonomy.id}, + }, + "target": target.as_dict(), + }, + ) + + print(f"Eval Run created for red teaming: {eval_run_name}") + + # Poll for completion + while True: + run = client.evals.runs.retrieve(run_id=eval_run_object.id, eval_id=eval_object.id) + if run.status == "completed" or run.status == "failed": + print(f"Result Counts: {run.result_counts}") + print(f"Report URL: {run.report_url}") + break + time.sleep(5) + print(f"Waiting for eval run to complete... {run.status}") + + # assertions + assert run.status == "completed", "Evaluation run did not complete successfully. Review logs from the evaluation report." + assert run.result_counts.errored == 0, f"There were errored evaluation items. Review error details in the evaluation report in {run.report_url}." + assert run.result_counts.failed == 0, f"Some vulnerability has been exposed by red-teaming attacks in your application. Review evaluation results in the evaluation report in {run.report_url}." + + +def _get_tool_descriptions(agent: AgentVersionObject): + tools = agent.definition.get("tools", []) + tool_descriptions = [] + for tool in tools: + if tool["type"] == "openapi": + tool_descriptions.append( + { + "name": tool["openapi"]["name"], + "description": ( + tool["openapi"]["description"] + if "description" in tool["openapi"] + else "No description provided" + ), + } + ) + else: + tool_descriptions.append( + { + "name": tool["name"] if "name" in tool else "Unnamed Tool", + "description": tool["description"] if "description" in tool else "No description provided", + } + ) + + return tool_descriptions + + +if __name__ == "__main__": + test_red_teaming() \ No newline at end of file diff --git a/tests/test_search_index_manager.py b/tests/test_search_index_manager.py deleted file mode 100644 index e17a7b31..00000000 --- a/tests/test_search_index_manager.py +++ /dev/null @@ -1,307 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. -# Licensed under the MIT license. -# See LICENSE file in the project root for full license information. -import csv -import json -import os -import tempfile -import unittest -from unittest.mock import AsyncMock, patch -from azure.identity.aio import DefaultAzureCredential - -from search_index_manager import SearchIndexManager -from azure.ai.projects.aio import AIProjectClient -from azure.ai.projects.models._enums import ConnectionType -from azure.core.exceptions import HttpResponseError - -from ddt import ddt, data - -connection_string = os.environ.get("AZURE_EXISTING_AIPROJECT_CONNECTION_STRING") if os.environ.get("AZURE_EXISTING_AIPROJECT_CONNECTION_STRING") else os.environ.get("AZURE_AIPROJECT_CONNECTION_STRING") - -class MockAsyncIterator: - - def __init__(self, list_data): - assert list_data and isinstance(list_data, list) - self._data = list_data - - async def __aiter__(self): - for dt in self._data: - yield dt - - -@ddt -class TestSearchIndexManager(unittest.IsolatedAsyncioTestCase): - """Tests for the RAG helper.""" - - INPUT_DIR = os.path.join( - os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'files') - # INPUT_DIR = os.path.join( - # os.path.dirname( - # os.path.dirname( - # os.path.dirname(os.path.dirname(__file__)))), 'data_') - EMBEDDINGS_FILE = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), - 'data', 'embeddings.csv') - - @classmethod - def setUpClass(cls) -> None: - super(TestSearchIndexManager, cls).setUpClass() - - def setUp(self) -> None: - self.search_endpoint = os.environ["SEARCH_ENDPOINT"] - self.index_name = "test_index" - self.embed_key = os.environ['EMBED_API_KEY'] - self.model = "text-embedding-3-small" - unittest.TestCase.setUp(self) - - async def test_create_delete_mock(self): - """Test that if index is deleteed the appropriate error is raised.""" - mock_ix_client = AsyncMock() - mock_aenter = AsyncMock() - with patch( - 'search_index_manager.SearchIndexClient', - return_value=mock_ix_client): - mock_ix_client.__aenter__.return_value = mock_aenter - rag = self._get_mock_rag(AsyncMock()) - self.assertTrue(await rag.create_index()) - mock_aenter.create_index.assert_called_once() - mock_aenter.get_index.assert_not_called() - mock_aenter.create_index.reset_mock() - mock_aenter.create_index.side_effect = HttpResponseError( - 'Mock http error') - self.assertFalse(await rag.create_index()) - mock_aenter.create_index.assert_called_once() - mock_aenter.get_index.assert_called_once() - with self.assertRaisesRegex(HttpResponseError, 'Mock http error'): - await rag.create_index(raise_on_error=True) - await rag.delete_index() - mock_aenter.create_index.side_effect = ValueError( - 'Mock value error') - with self.assertRaisesRegex(ValueError, 'Mock value error'): - await rag.create_index() - - mock_aenter.delete_index.assert_called_once() - with self.assertRaisesRegex( - ValueError, - "Unable to perform the operation " - "as the index is absent.+"): - await rag.delete_index() - - async def test_exception_no_dinmensions(self): - """Test the exception shown if no dimensions were provided.""" - rag = SearchIndexManager( - endpoint=self.search_endpoint, - credential=AsyncMock(), - index_name=self.index_name, - dimensions=None, - model=self.model, - deployment_name="mock_embedding_model", - embedding_endpoint="", - embedding_client=AsyncMock(), - embed_api_key=self.embed_key, - ) - with self.assertRaisesRegex( - ValueError, "No embedding dimensions were provided.+"): - await rag.create_index(vector_index_dimensions=None) - - async def test_exception_different_dimmensions(self): - """Test the exception shown if dimensions - and dinensions_override are different.""" - rag = SearchIndexManager( - endpoint=self.search_endpoint, - credential=AsyncMock(), - index_name=self.index_name, - dimensions=41, - model=self.model, - embedding_client=AsyncMock(), - deployment_name=self.model, - embedding_endpoint=self.search_endpoint, - embed_api_key=self.embed_key, - ) - with self.assertRaisesRegex( - ValueError, - "vector_index_dimensions is different " - "from dimensions provided to constructor."): - await rag.create_index(vector_index_dimensions=42) - - @unittest.skip("Only for live tests.") - async def test_e2e(self): - """Run search end to end.""" - async with DefaultAzureCredential() as creds: - async with AIProjectClient.from_connection_string( - credential=creds, - conn_str=connection_string, - ) as project: - aoai_connection = await project.connections.get_default( - connection_type=ConnectionType.AZURE_OPEN_AI, - include_credentials=True) - self.assertIsNotNone(aoai_connection) - rag = SearchIndexManager( - endpoint=self.search_endpoint, - credential=creds, - index_name=self.index_name, - dimensions=100, - model=self.model, - deployment_name=self.model, - embedding_endpoint=aoai_connection.endpoint_url, - embed_api_key=aoai_connection.key, - ) - self.assertTrue(await rag.create_index(raise_on_error=True)) - await rag.upload_documents( - os.path.join( - os.path.dirname( - os.path.dirname( - __file__)), 'data', 'embeddings.csv')) - - result = await rag.search( - "What is the temperature rating " - "of the cozynights sleeping bag?") - result_semantic = await rag.semantic_search( - "What is the temperature rating " - "of the cozynights sleeping bag?") - await rag.delete_index() - await rag.close() - self.assertTrue(bool(result), "The regular search is empty.") - self.assertTrue(bool(result_semantic), "The semantic search is empty.") - - async def test_life_cycle_mock(self): - """Test create, upload, search and delete""" - mock_ix_client = AsyncMock() - mock_aenter = AsyncMock() - mock_serch_client = AsyncMock() - mock_serch_client.search.return_value = MockAsyncIterator([ - {'token': 'a', 'title': 'a.txt'}, - {'token': 'b', 'title': 'b.txt'} - ]) - with patch( - 'search_index_manager.SearchIndexClient', - return_value=mock_ix_client): - with patch( - 'search_index_manager.SearchClient', - return_value=mock_serch_client): - mock_ix_client.__aenter__.return_value = mock_aenter - rag = self._get_mock_rag(AsyncMock()) - self.assertTrue(await rag.create_index()) - - # Upload documents. - await rag.upload_documents( - TestSearchIndexManager.EMBEDDINGS_FILE) - mock_serch_client.upload_documents.assert_called_once() - - search_result = await rag.search('test') - mock_serch_client.search.assert_called_once() - self.assertEqual(search_result, - "a, source: a.txt\n------\nb, source: b.txt") - - @data(2, 4) - async def test_build_embeddings_file_mock(self, sentences_per_embedding): - """Use this test to build - the new embeddings file in the data directory.""" - embedding_client = AsyncMock() - embedding_client.embed.retun_value = {'data': [[0, 0], [1, 1], [ - 2, 2]] if sentences_per_embedding == 4 else [[0, 0], [1, 1]]} - rag = SearchIndexManager( - endpoint=self.search_endpoint, - credential=AsyncMock(), - index_name=self.index_name, - dimensions=2, - model=self.model, - deployment_name=self.model, - embedding_endpoint=self.search_endpoint, - embed_api_key=self.embed_key, - embedding_client=embedding_client - ) - sentences = [ - f"This is {v} sentence" for v in [ - 'first', 'second', 'third', 'forth', 'fifth']] - with tempfile.TemporaryDirectory() as d: - data = ' '.join(sentences) - input_file = os.path.join(d, 'input.csv') - with open(input_file, 'w') as f: - f.write(data) - out_file = os.path.join(d, 'embeddings.csv') - await rag.build_embeddings_file( - input_directory=input_file, - output_file=out_file, - sentences_per_embedding=sentences_per_embedding - ) - index = 1 - with open(out_file, newline='') as fp: - reader = csv.DictReader(fp) - for row in reader: - self.assertEqual( - ' '.join( - sentences[ - index * sentences_per_embedding: ( - index + 1) * sentences_per_embedding])) - self.assertListEqual( - json.loads( - row['embedding']), [ - index, index]) - self.assertEqual(row['document_reference'], 'input.csv') - index += 1 - - @unittest.skip("Only for live tests.") - async def test_build_embeddings_file(self): - """Use this test to build the new - embeddings file in the data directory.""" - async with DefaultAzureCredential() as creds: - async with AIProjectClient.from_connection_string( - credential=creds, - conn_str=connection_string, - ) as project: - aoai_connection = await project.connections.get_default( - connection_type=ConnectionType.AZURE_OPEN_AI) - self.assertIsNotNone(aoai_connection) - async with ( - await project.inference.get_embeddings_client()) as embed: - rag = SearchIndexManager( - endpoint=self.search_endpoint, - credential=creds, - index_name=self.index_name, - dimensions=100, - model=self.model, - deployment_name=self.model, - embedding_endpoint=aoai_connection.endpoint_url, - embed_api_key=self.embed_key, - embedding_client=embed - ) - await rag.build_embeddings_file( - input_directory=TestSearchIndexManager.INPUT_DIR, - output_file=TestSearchIndexManager.EMBEDDINGS_FILE, - sentences_per_embedding=10 - ) - - @unittest.skip("Only for live tests.") - async def test_get_or_create(self): - """Test index_name creation.""" - async with DefaultAzureCredential() as cred: - self.AssertTrue(await SearchIndexManager.create_index( - endpoint=self.search_endpoint, - credential=cred, - index_name=self.index_name, - dimensions=100)) - self.AssertFalse(await SearchIndexManager.create_index( - endpoint=self.search_endpoint, - credential=cred, - index_name=self.index_name, - dimensions=100500)) - - def _get_mock_rag(self, embedding_client): - """Return the mock RAG """ - return SearchIndexManager( - endpoint=self.search_endpoint, - credential=AsyncMock(), - index_name=self.index_name, - dimensions=100, - model="mock_embedding_model", - deployment_name="mock_embedding_model", - embedding_client=embedding_client, - embedding_endpoint="", - embed_api_key=self.embed_key - - ) - - -if __name__ == "__main__": - # import sys;sys.argv = ['', 'Test.testName'] - unittest.main() diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 00000000..366fa86f --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,49 @@ +# ------------------------------------ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. +# ------------------------------------ + +import os +from pprint import pprint +from dotenv import load_dotenv +from azure.ai.projects import AIProjectClient + +env_file = os.path.abspath(os.path.join(os.path.dirname(__file__), "../src/.env")) +load_dotenv(env_file) + +def retrieve_agent(project_client: AIProjectClient): + agent_id = os.environ.get("AZURE_EXISTING_AGENT_ID", "") + agent_name = os.environ.get("AZURE_AI_AGENT_NAME", "") + agent_version = "" + + if not agent_name and (not agent_id or ":" not in agent_id): + raise ValueError("Please set AZURE_EXISTING_AGENT_ID environment variable in the format 'agent_name:agent_version'.") + + if agent_id: + agent_name = agent_id.split(":")[0] + agent_version = agent_id.split(":")[1] + + + if agent_version: + agent = project_client.agents.get_version( + agent_name=agent_name, agent_version=agent_version + ) + print(f"Agent retrieved (id: {agent.id}, name: {agent.name}, version: {agent.version})") + else: + agent_obj = project_client.agents.get(agent_name=agent_name) + agent = agent_obj.versions.latest + print(f"Latest agent version retrieved (id: {agent.id}, name: {agent.name}, version: {agent.version})") + + return agent + +def retrieve_endpoint(): + endpoint = os.environ.get("AZURE_EXISTING_AIPROJECT_ENDPOINT", "") + if not endpoint: + raise ValueError("Please set AZURE_EXISTING_AIPROJECT_ENDPOINT environment variable.") + return endpoint + +def retrieve_model_deployment(): + deployment = os.environ.get("AZURE_AI_AGENT_DEPLOYMENT_NAME", "") + if not deployment: + raise ValueError("Please set AZURE_AI_AGENT_DEPLOYMENT_NAME environment variable.") + return deployment \ No newline at end of file