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Back to Chat with your data README

Local setup

Note for macOS Developers: If you are using macOS on Apple Silicon (ARM64) the DevContainer will not work. This is due to a limitation with the Azure Functions Core Tools (see here). We recommend using the Non DevContainer Setup instructions to run the accelerator locally.

The easiest way to run this accelerator is in a VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:

  1. Start Docker Desktop (install it if not already installed)

  2. Open the project: Open in Dev Containers

  3. In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window

  4. Run azd auth login

  5. Run azd env set AZURE_APP_SERVICE_HOSTING_MODEL code - This sets your environment to deploy code rather than rely on public containers, like the "Deploy to Azure" button.

  6. Run azd up - This will provision Azure resources and deploy the accelerator to those resources.

    • Important: Beware that the resources created by this command will incur immediate costs, primarily from the AI Search resource. These resources may accrue costs even if you interrupt the command before it is fully executed. You can run azd down or delete the resources manually to avoid unnecessary spending.
    • You will be prompted to select a subscription, and a location. That location list is based on the OpenAI model availability table and may become outdated as availability changes.
    • If you do, accidentally, chose the wrong location; you will have to ensure that you use azd down or delete the Resource Group as the deployment bases the location from this Resource Group.
  7. After the application has been successfully deployed you will see a URL printed to the console. Click that URL to interact with the application in your browser.

NOTE: It may take up to an hour for the application to be fully deployed. If you see a "Python Developer" welcome screen or an error page, then wait a bit and refresh the page.

NOTE: The default auth type uses keys that are stored in the Azure Keyvault. If you want to use RBAC-based auth (more secure), please run before deploying:

azd env set AZURE_AUTH_TYPE rbac
azd env set USE_KEY_VAULT false

Also please refer to the section on setting up RBAC auth.

Detailed Development Container setup instructions

The solution contains a development container with all the required tooling to develop and deploy the accelerator. To deploy the Chat With Your Data accelerator using the provided development container you will also need:

If you are running this on Windows, we recommend you clone this repository in WSL

git clone https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator

Open the cloned repository in Visual Studio Code and connect to the development container.

code .

!!! tip Visual Studio Code should recognize the available development container and ask you to open the folder using it. For additional details on connecting to remote containers, please see the Open an existing folder in a container quickstart.

When you start the development container for the first time, the container will be built. This usually takes a few minutes. Please use the development container for all further steps.

The files for the dev container are located in /.devcontainer/ folder.

Local debugging

To customize the accelerator or run it locally, you must provision the Azure resources by running azd provision in a Terminal. This will generate a .env for you and you can use the "Run and Debug" (Ctrl + Shift + D) command to chose which part of the accelerator to run. There is an environment variable values table below.

To run the accelerator in local when the solution is secured by RBAC you need to assign some roles to your principal id. You can do it either manually or programatically.

Manually assign roles

You need to assign the following roles to your PRINCIPALID (you can get your 'principal id' from Microsoft Entra ID):

Role GUID
Cognitive Services OpenAI Contributor a001fd3d-188f-4b5d-821b-7da978bf7442
Search Service Contributor 7ca78c08-252a-4471-8644-bb5ff32d4ba0
Search Index Data Contributor 8ebe5a00-799e-43f5-93ac-243d3dce84a7
Storage Blob Data Reader 2a2b9908-6ea1-4ae2-8e65-a410df84e7d1
Reader acdd72a7-3385-48ef-bd42-f606fba81ae7

Programatically assign roles

You can also update the principalId value with your own principalId in the main.bicep file.

Authenticate using RBAC

To authenticate using API Keys, update the value of AZURE_AUTH_TYPE to keys. For accessing using 'rbac', manually make changes by following the below steps:

  1. Ensure role assignments listed on this page have been created.
  2. Navigate to your Search service in the Azure Portal
  3. Under Settings, select Keys
  4. Select either Role-based access control or Both
  5. Navigate to your App service in the Azure Portal
  6. Under Settings, select Configuration
  7. Set the value of the AZURE_AUTH_TYPE setting to rbac
  8. Restart the application

Deploy services manually

You can deploy the full solution from local with the following command azd deploy. You can also deploy services individually

Service Description
azd deploy web A python app, enabling you to chat on top of your data.
azd deploy adminweb A Streamlit app for the "admin" site where you can upload and explore your data.
azd deploy function A python function app processing requests.

Running All Services Locally Using Docker Compose

To run all applications using Docker Compose, you first need a .env file containing the configuration for your provisioned resources. This file can be created manually at the root of the project. Alternatively, if resources were provisioned using azd provision or azd up, a .env file is automatically generated in the .azure/<env-name>/.env file. To get your <env-name> run azd env list to see which env is default.

The AzureWebJobsStorage needs to be added to your .env file manually. This can be retrieved from the function settings via the Azure Portal.

To start the services, you can use either of the following commands:

  • make docker-compose-up
  • cd docker && AZD_ENV_FILE=<path-to-env-file> docker-compose up

Note: By default, these commands will run the latest Docker images built from the main branch. If you wish to use a different image, you will need to modify the docker/docker-compose.yml file accordingly.

Develop & run the frontend locally

For faster development, you can run the frontend Typescript React UI app and the Python Flask api app in development mode. This allows the app to "hot reload" meaning your changes will automatically be reflected in the app without having to refresh or restart the local servers.

They can be launched locally from vscode (Ctrl+Shift+D) and selecting "Launch Frontend (api)" and "Launch Frontend (UI). You will also be able to place breakpoints in the code should you wish. This will automatically install any dependencies for Node and Python.

Starting the Flask app in dev mode from the command line (optional)

This step is included if you cannot use the Launch configuration in VSCode. Open a terminal and enter the following commands

cd code
poetry run flask run

Starting the Typescript React app in dev mode (optional)

This step is included if you cannot use the Launch configuration in VSCode. Open a new separate terminal and enter the following commands:

cd code\frontend
npm install
npm run dev

The local vite server will return a url that you can use to access the chat interface locally, such as http://localhost:5174/.

Develop & run the admin app

The admin app can be launched locally from vscode (Ctrl+Shift+D) and selecting "Launch Admin site". You will also be able to place breakpoints in the Python Code should you wish.

This should automatically open http://localhost:8501/ and render the admin interface.

Develop & run the batch processing functions

If you want to develop and run the batch processing functions container locally, use the following commands.

Running the batch processing locally

First, install Azure Functions Core Tools.

cd code\backend\batch
poetry run func start

Or use the Azure Functions VS Code extension.

Debugging the batch processing functions locally

Rename the file local.settings.json.sample in the batch folder to local.settings.json and update the AzureWebJobsStorage value with the storage account connection string.

Copy the .env file from previous section to the batch folder.

Execute the above shell command to run the function locally. You may need to stop the deployed function on the portal so that all requests are debugged locally. To trigger the function, you can click on the corresponding URL that will be printed to the terminal.

Environment variables

App Setting Value Note
AZURE_SEARCH_SERVICE The URL of your Azure AI Search resource. e.g. https://.search.windows.net
AZURE_SEARCH_INDEX The name of your Azure AI Search Index
AZURE_SEARCH_KEY An admin key for your Azure AI Search resource
AZURE_SEARCH_USE_SEMANTIC_SEARCH False Whether or not to use semantic search
AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG default The name of the semantic search configuration to use if using semantic search.
AZURE_SEARCH_TOP_K 5 The number of documents to retrieve from Azure AI Search.
AZURE_SEARCH_ENABLE_IN_DOMAIN True Limits responses to only queries relating to your data.
AZURE_SEARCH_CONTENT_COLUMN List of fields in your Azure AI Search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "
AZURE_SEARCH_CONTENT_VECTOR_COLUMN Field from your Azure AI Search index for storing the content's Vector embeddings
AZURE_SEARCH_DIMENSIONS 1536 Azure OpenAI Embeddings dimensions. 1536 for text-embedding-ada-002. A full list of dimensions can be found here.
AZURE_SEARCH_FIELDS_ID id AZURE_SEARCH_FIELDS_ID: Field from your Azure AI Search index that gives a unique idenitfier of the document chunk. id if you don't have a specific requirement.
AZURE_SEARCH_FILENAME_COLUMN AZURE_SEARCH_FILENAME_COLUMN: Field from your Azure AI Search index that gives a unique idenitfier of the source of your data to display in the UI.
AZURE_SEARCH_TITLE_COLUMN Field from your Azure AI Search index that gives a relevant title or header for your data content to display in the UI.
AZURE_SEARCH_URL_COLUMN Field from your Azure AI Search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.
AZURE_SEARCH_FIELDS_TAG tag Field from your Azure AI Search index that contains tags for the document. tag if you don't have a specific requirement.
AZURE_SEARCH_FIELDS_METADATA metadata Field from your Azure AI Search index that contains metadata for the document. metadata if you don't have a specific requirement.
AZURE_SEARCH_FILTER Filter to apply to search queries.
AZURE_SEARCH_USE_INTEGRATED_VECTORIZATION Whether to use Integrated Vectorization
AZURE_OPENAI_RESOURCE the name of your Azure OpenAI resource
AZURE_OPENAI_MODEL The name of your model deployment
AZURE_OPENAI_MODEL_NAME gpt-35-turbo The name of the model
AZURE_OPENAI_MODEL_VERSION 0613 The version of the model to use
AZURE_OPENAI_API_KEY One of the API keys of your Azure OpenAI resource
AZURE_OPENAI_EMBEDDING_MODEL text-embedding-ada-002 The name of your Azure OpenAI embeddings model deployment
AZURE_OPENAI_EMBEDDING_MODEL_NAME text-embedding-ada-002 The name of the embeddings model (can be found in Azure AI Studio)
AZURE_OPENAI_EMBEDDING_MODEL_VERSION 2 The version of the embeddings model to use (can be found in Azure AI Studio)
AZURE_OPENAI_TEMPERATURE 0 What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. A value of 0 is recommended when using your data.
AZURE_OPENAI_TOP_P 1.0 An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. We recommend setting this to 1.0 when using your data.
AZURE_OPENAI_MAX_TOKENS 1000 The maximum number of tokens allowed for the generated answer.
AZURE_OPENAI_STOP_SEQUENCE Up to 4 sequences where the API will stop generating further tokens. Represent these as a string joined with "
AZURE_OPENAI_SYSTEM_MESSAGE You are an AI assistant that helps people find information. A brief description of the role and tone the model should use
AZURE_OPENAI_API_VERSION 2024-02-01 API version when using Azure OpenAI on your data
AzureWebJobsStorage The connection string to the Azure Blob Storage for the Azure Functions Batch processing
BACKEND_URL The URL for the Backend Batch Azure Function. Use http://localhost:7071 for local execution
DOCUMENT_PROCESSING_QUEUE_NAME doc-processing The name of the Azure Queue to handle the Batch processing
AZURE_BLOB_ACCOUNT_NAME The name of the Azure Blob Storage for storing the original documents to be processed
AZURE_BLOB_ACCOUNT_KEY The key of the Azure Blob Storage for storing the original documents to be processed
AZURE_BLOB_CONTAINER_NAME The name of the Container in the Azure Blob Storage for storing the original documents to be processed
AZURE_FORM_RECOGNIZER_ENDPOINT The name of the Azure Form Recognizer for extracting the text from the documents
AZURE_FORM_RECOGNIZER_KEY The key of the Azure Form Recognizer for extracting the text from the documents
APPLICATIONINSIGHTS_CONNECTION_STRING The Application Insights connection string to store the application logs
ORCHESTRATION_STRATEGY openai_function Orchestration strategy. Use Azure OpenAI Functions (openai_function), Semantic Kernel (semantic_kernel), LangChain (langchain) or Prompt Flow (prompt_flow) for messages orchestration. If you are using a new model version 0613 select any strategy, if you are using a 0314 model version select "langchain". Note that both openai_function and semantic_kernel use OpenAI function calling. Prompt Flow option is still in development and does not support RBAC or integrated vectorization as of yet.
AZURE_CONTENT_SAFETY_ENDPOINT The endpoint of the Azure AI Content Safety service
AZURE_CONTENT_SAFETY_KEY The key of the Azure AI Content Safety service
AZURE_SPEECH_SERVICE_KEY The key of the Azure Speech service
AZURE_SPEECH_SERVICE_REGION The region (location) of the Azure Speech service
AZURE_AUTH_TYPE keys The default is to use API keys. Change the value to 'rbac' to authenticate using Role Based Access Control. For more information refer to section Authenticate using RBAC

Bicep

A Bicep file is used to generate the ARM template. You can deploy this accelerator by the following command if you do not want to use azd.

az deployment sub create --template-file ./infra/main.bicep --subscription {your_azure_subscription_id} --location {search_location}