Container app updates, fast api updates, fixes, infra updates (#157)

* inital aca work

* container config updates

* Fixing container startup issues (#129)

* Fixing container startup issues

* Fixing import

---------

Co-authored-by: Andre Dewes <andredewes@microsoft.com>

* update arch diagram

* fix get response

* path fixes

* fix chat request

* fix env vars

* fix path for ai search

* update to fastapi, prompty updates, start eval work,

* some deployment fixes

* fix api deployment, fix endpoint name

* update eval py to load model config

* evalution notebook

* move evals

* update infra to ai studio

* update model version to deploy to east us2

* update evaluate

* remove unsued deployment files

* update text

* fix models for infra

* fix aca name

---------

Co-authored-by: Andre Dewes <31827221+andredewes@users.noreply.github.com>
Co-authored-by: Andre Dewes <andredewes@microsoft.com>

Author: 3 people

.github/workflows/evaluations.yaml

@@ -1,4 +1,4 @@
-name: Evaluate Chat Relevance, Fluency, Coherence, and Groundedness
+name: Evaluate
 
 on:
   workflow_dispatch:
@@ -7,7 +7,6 @@ on:
     # Set this to the mainline branch you are using
     branches:
       - main
-      - azd
 
 # 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
@@ -23,17 +22,24 @@ jobs:
       AZURE_TENANT_ID: ${{ vars.AZURE_TENANT_ID }}
       AZURE_SUBSCRIPTION_ID: ${{ vars.AZURE_SUBSCRIPTION_ID }}
       AZURE_CREDENTIALS: ${{ secrets.AZURE_CREDENTIALS }}
-      AZURE_OPENAI_ENDPOINT: ${{ secrets.AZURE_OPENAI_ENDPOINT }}
-      AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-      AZURE_DEPLOYMENT_NAME: "gpt-4"
-      AZURE_EMBEDDING_NAME: "text-embedding-ada-002"
-      COSMOS_ENDPOINT: "${{ secrets.CONTOSO_SEARCH_ENDPOINT }}"
-      AZURE_SEARCH_ENDPOINT: "${{ secrets.AZURE_SEARCH_ENDPOINT }}"
+      AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
+      AZURE_OPENAI_API_VERSION: ${{ vars.AZURE_OPENAI_API_VERSION }}
+      AZURE_OPENAI_4_EVAL_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_4_EVAL_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_35_TURBO_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_35_TURBO_DEPLOYMENT_NAME }}
+      AZURE_RESOURCE_GROUP: ${{ vars.AZURE_RESOURCE_GROUP }}
+      AZURE_SEARCH_ENDPOINT: ${{ vars.AZURE_SEARCH_ENDPOINT }}
+      AZURE_OPENAI_NAME: ${{ vars.AZURE_OPENAI_NAME }}
+      BING_SEARCH_ENDPOINT: ${{ vars.BING_SEARCH_ENDPOINT }}
+      BING_SEARCH_KEY: ${{ secrets.BING_SEARCH_KEY }}
 
     steps:
       - name: checkout repo content
         uses: actions/checkout@v4 # checkout the repository content
 
+      - name: Install azd
+        uses: Azure/setup-azd@v1.0.0
+
       - name: setup python
         uses: actions/setup-python@v5
         with:
@@ -42,64 +48,38 @@ jobs:
       - name: install python packages
         run: |
           python -m pip install --upgrade pip
-          pip install -r requirements.txt
-      
-      - name: install promptflow dev bits
-        run: |
-          # unstall old promptflow
-          pip uninstall -y promptflow promptflow-azure promptflow-core promptflow-devkit promptflow-tools promptflow-evals
-
-          # install dev packages
-          pip install promptflow-evals==0.2.0.dev125831637 --extra-index-url https://azuremlsdktestpypi.azureedge.net/promptflow
-          pip install azure_ai_ml==1.16.0a20240501004 --extra-index-url https://pkgs.dev.azure.com/azure-sdk/public/_packaging/azure-sdk-for-python/pypi/simple/
-
-          pip install azure-cli
-          pip install bs4
-          pip install ipykernel
-          pip install azure-cosmos==4.6.0
-          pip install azure-search-documents==11.4.0
-
-          pf version
+          pip install -r src/api/requirements.txt
 
       - name: Azure login
         uses: azure/login@v2
         with:
           client-id: ${{ env.AZURE_CLIENT_ID }}
           tenant-id: ${{ env.AZURE_TENANT_ID }}
           subscription-id: ${{ env.AZURE_SUBSCRIPTION_ID }}
-
+  
       - name: Set az account
         uses: azure/CLI@v2
         with:
           inlineScript: |
             az account set --subscription ${{env.AZURE_SUBSCRIPTION_ID}}
 
-      - name: Set Promptflow config
-        run: |
-          pf config set trace.destination=azureml://subscriptions/${{ env.AZURE_SUBSCRIPTION_ID }}/resourceGroups/rg-${{ vars.AZURE_ENV_NAME }}/providers/Microsoft.MachineLearningServices/workspaces/${{ vars.ML_WORKSPACE_NAME }}
-
-      - name: evaluate chat data using the eval sdk
-        working-directory: ./evaluations
+      - name: evaluate
+        working-directory: ./src/api
         run: |
-          python evaluations_chat.py
+          python -m evaluators.evaluate
       
       - name: Upload eval results as build artifact
         uses: actions/upload-artifact@v4
         with:
           name: eval_result
-          path: ./evaluations/eval_result.jsonl
+          path: ./src/api/evaluators/eval_results.jsonl
 
       - name: GitHub Summary Step
         if: ${{ success() }}
-        working-directory: ./evaluations
+        working-directory: ./src/api
         run: |
-          studio_url=$(cat studio_url.txt)
-          echo $studio_url 
-
-          echo "🔗 [View in Azure Studio Here]($studio_url)" >> $GITHUB_STEP_SUMMARY
-
           echo "" >> $GITHUB_STEP_SUMMARY
 
-          echo "📊 Promptflow Evaluation Results" >> $GITHUB_STEP_SUMMARY
-          cat eval_result.md >> $GITHUB_STEP_SUMMARY
-        
+          echo "📊 Evaluation Results" >> $GITHUB_STEP_SUMMARY
+          cat evaluate/eval_results.md >> $GITHUB_STEP_SUMMARY
+        

.gitignore

@@ -1,6 +1,6 @@
 .venv/
 __pycache__/
-.promptflow/
+.prompty/
 .env
 config.json
 eval/downloaded_artifacts/remote/eval_results.jsonl
@@ -11,7 +11,20 @@ deployment/push_and_deploy_pf.py
 endpoint.json
 principal.txt
 .azure
-evaluations/process_log/
-evaluations/flow.flex.yaml
-evaluations/eval_result.**
-evaluations/studio_url.txt
+evaluators/process_log/
+evaluators/flow.flex.yaml
+evaluators/eval_result.**
+evaluators/studio_url.txt
+src/contoso_chat/.runs/*
+src/api/contoso_chat/.runs/*
+src/api/.runs/*
+.runs/*
+evaluators/.runs/*
+evaluators/result_evaluated.jsonl
+evaluators/result.jsonl
+src/api/evaluators/.runs/*
+src/api/evaluators/result_evaluated.jsonl
+src/api/evaluators/result.jsonl
+src/api/evaluators/eval_results.jsonl
+src/api/evaluators/eval_results.md
+src/api/evaluators/.runs/*

README.md

@@ -1,5 +1,5 @@
 ---
-name: Contoso Chat Retail with Azure AI Studio and Promptflow
+name: Contoso Chat Retail with Azure AI Studio and Prompty
 description: A retail copilot that answers customer queries with responses grounded in retailer's product and customer data.
 languages:
 - python
@@ -15,7 +15,7 @@ page_type: sample
 urlFragment: contoso-chat
 ---
 
-# Contoso Chat Retail with Azure AI Studio and Promptflow
+# Contoso Chat Retail with Azure AI Studio and Prompty
 
 This sample creates a customer support chat agent for an online retailer called Contoso Outdoors. The solution uses a _retrieval-augmented generation pattern_ to ground responses in the company's product and customer data. Customers can ask questions about the retailer's product catalog, and also get recommendations based on their prior purchases.
 
@@ -32,8 +32,8 @@ The sample uses [Azure AI Search](https://learn.microsoft.com/azure/search/) to
 
 By exploring and deploying this sample, you will learn to:
 - Build a retail copilot application using the [_RAG pattern_](https://learn.microsoft.com/azure/ai-studio/concepts/retrieval-augmented-generation).
-- Define and engineer prompts using the [_Prompty_ asset](https://microsoft.github.io/promptflow/tutorials/prompty-quickstart.html?highlight=prompty#).
-- Design, run & evaluate a copilot using the [_Promptflow_ framework](https://microsoft.github.io/promptflow/tutorials/flex-flow-quickstart.html).
+- Define and engineer prompts using the Prompty
+- Design, run & evaluate a copilot
 - Provision and deploy the solution to Azure using the [_Azure Developer CLI_](https://learn.microsoft.com/azure/developer/azure-developer-cli/).
 - Understand and apply Responsible AI practices like [_evaluation and content safety_](https://learn.microsoft.com/en-us/azure/ai-services/responsible-use-of-ai-overview?context=%2Fazure%2Fai-studio%2Fcontext%2Fcontext).
 
@@ -103,7 +103,6 @@ This has been the signature sample used to showcase end-to-end development of a
 * [Visual Studio Code](https://code.visualstudio.com) - recommended IDE for local development.
 * [Azure Developer CLI (azd)](https://aka.ms/install-azd) - to manage Azure deployment.
 * [Python 3.10+](https://www.python.org/downloads/) - to run, test & evaluate application.
-* [Promptflow 1.10+](https://microsoft.github.io/promptflow/) - to build, evaluate, and deploy application flows.
 
 You will also need:
 * [Azure Subscription](https://azure.microsoft.com/free/) - sign up for a free account.
@@ -222,26 +221,7 @@ The [contoso_chat](./contoso_chat) sample contains an example [chat.prompty](./c
     - `question` section to embed user query
     - `Instructions` section to reference related product recommendations
 
-This specific prompty takes 3 inputs: a `customer` object, a `documentation` object (that could be chat history) and a `question` string that represents the user query. You can now _load_, _execute_, and _trace_ individual prompty assets for a more granular prompt engineering solution.
-
- * See the [prompty specification](https://microsoft.github.io/promptflow/how-to-guides/develop-a-prompty/index.html#prompty-specification) for more details on the format.
- * Read the [prompty examples](https://github.com/microsoft/promptflow/tree/main/examples/prompty) for usage guidance from SDK or CLI.
-
-### Testing the Application Flow
-
-This sample uses a [flex-flow](https://microsoft.github.io/promptflow/how-to-guides/develop-a-flex-flow/index.html) feature that lets you "create LLM apps using a Python class or function as the entry point" - making it easier to test and run them using a code-first experience.
-  - This sample implements a _Function based flow_ 
-  - The entry point is the _get_response_ functionin `chat_request.py`
-
-You can now [test the flow](https://microsoft.github.io/promptflow/how-to-guides/develop-a-flex-flow/function-based-flow.html#flow-test) in different ways:
- - Run it directly, like any Python script
- - Convert it to a flow, then use `pf flow test --flow ...`
- - Start a UI to chat with the flow using `pf flow test --flow ... --ui`
-
-🌟 | Watch this space for more testing guidance.
-
-
-## Guidance
+This specific prompty takes 3 inputs: a `customer` object, a `documentation` object (that could be chat history) and a `question` string that represents the user query. You can now _load_, _execute_, and _trace_ individual prompty assets for a more granular prompt 
 
 ### Region Availability
 
@@ -270,14 +250,6 @@ This template uses [Managed Identity](https://learn.microsoft.com/entra/identity
 
 Additionally, we have added a [GitHub Action tool](https://github.com/microsoft/security-devops-action) that scans the infrastructure-as-code files and generates a report containing any detected issues. To ensure best practices we recommend anyone creating solutions based on our templates ensure that the [Github secret scanning](https://docs.github.com/code-security/secret-scanning/about-secret-scanning) setting is enabled in your repo.
 
-
-## Resources
-
-* [Azure AI Studio Documentation](https://learn.microsoft.com/azure/ai-studio/)
-* [Promptflow/Prompty Documentation](https://microsoft.github.io/promptflow/reference/python-library-reference/promptflow-core/promptflow.core.html?highlight=prompty#promptflow.core.Prompty)
-* [Develop Python apps that use Azure AI services](https://learn.microsoft.com/azure/developer/python/azure-ai-for-python-developers)
-* Related Sample: [Process Automation: Speech to Text and Summarization with ACA](https://github.com/Azure-Samples/summarization-openai-python-promptflow/blob/main/README.md)
-
 ## Troubleshooting
 
 Have questions or issues to report? Please [open a new issue](https://github.com/Azure-Samples/contoso-chat/issues) after first verifying that the same question or issue has not already been reported. In the latter case, please add any additional comments you may have, to the existing issue.

azure.yaml

@@ -1,13 +1,6 @@
-# yaml-language-server: $schema=https://raw.githubusercontent.com/Azure/azure-dev/main/schemas/v1.0/azure.yaml.json
-
-name: contoso-chat
+name: contoso-chat-openai-prompty
 metadata:
-  template: contoso-chat@0.0.1-beta          
-workflows:
-  up:
-    steps:
-      - azd: provision
-      - azd: deploy      
+  template: contoso-chat-openai-prompty@0.0.1
 hooks:
   postprovision:
     posix:
@@ -20,27 +13,5 @@ hooks:
       continueOnError: false
       interactive: true
       run: infra/hooks/postprovision.ps1
-
-
-################################################################
-# The section below configures the new `ai.endpoint` resource 
-# used for the Azure AI based chat service (application)
-################################################################
-services:
-  chat:
-    host: ai.endpoint
-    language: python
-    config:
-      workspace: ${AZUREAI_PROJECT_NAME}
-      environment:
-        path: ./deployment/environment.yaml 
-      model:
-        path: ./deployment/chat-model.yaml
-      deployment:
-        path: ./deployment/chat-deployment.yaml
-        overrides:
-          environment_variables.PRT_CONFIG_OVERRIDE: deployment.subscription_id=${AZURE_SUBSCRIPTION_ID},deployment.resource_group=${AZURE_RESOURCE_GROUP},deployment.workspace_name=${AZUREAI_PROJECT_NAME},deployment.endpoint_name=${AZUREAI_ENDPOINT_NAME},deployment.deployment_name=${AZUREAI_DEPLOYMENT_NAME}
-          environment_variables.AZURE_OPENAI_ENDPOINT: ${AZURE_OPENAI_ENDPOINT}
-          environment_variables.AZURE_OPENAI_API_VERSION: ${AZURE_OPENAI_API_VERSION}
-          environment_variables.COSMOS_ENDPOINT: ${COSMOS_ENDPOINT}
-          environment_variables.AZURE_SEARCH_ENDPOINT: ${AZURE_SEARCH_ENDPOINT}
+infra:
+    provider: "bicep"