Lerøy HR assistant

Welcome to the Lerøy HR Assistant repository! The project is based on Microsoft's sample-app-aoai-chatGPT and has been customized to our needs. See the workflow overview section for details on the various components used in this project.

Note: Some components may use preview APIs from Azure OpenAI and Azure AI sSearch .

Workflow overview

1. Data acquisition and processing

• Data extraction: Utilize Power Automate to extract .aspx files from SharePoint pages.
• Data processing: Employ Azure Functions with Python to process data, converting documents to HTML format.
• Data storage: Store processed data in Azure Blob Storage.

2. Azure AI Search index creation

• Searchable content: Programmatically create a search index using Azure AI Search. Our implementation is based on Azure Search Vector Samples.
• Configuration: Create Azure resources and add them as environment secrets and variables.
• Deployment: Create and update indexes using GitHub Actions.

3. Chat Application

• User interface and backend: Develop a chat application based on the sample-app-aoai-chatGPT repository.
• Configuration: Create Azure resources and add them as environment secrets and variables. For more information, see Configuration Guide (TODO: Add link to configuration guide).
• Deployment: Create and update the application using GitHub Actions.

HR assistant system message template

This is a generalized version of the system message used in our HR chatbot. Customize it to fit your specific needs and company policies.

You are an AI assistant specializing in HR-related tasks for employees. Adhere to these guidelines:

## Core Functions
- Provide information on company policies
- Clearly state limitations when information is unavailable

## Language and Communication
- Use professional yet friendly language in [specify language(s)]

## User Interaction
- Ask clarifying questions when needed

## Information Presentation
- Structure responses with clear headings, subheadings, and bullet points

## Continuous Improvement
- Ask if the response was helpful

## Limitations
- Clearly state that you are an AI Assistant and cannot approve requests

Important note on formatting for environment variables

When using this system message as a value for the environment variable AZURE_OPENAI_SYSTEM_MESSAGE, it must be formatted as a single line of text with escaped characters. Follow these steps:

1. Replace all newline characters with \\n.
2. Escape all double quotes with a backslash (\").

⚠️ Note: Do not enclose the final value in double quotes when setting the environment variable value on GitHub.

def print_formatted_system_message(input_text: str):
    """Format multi-line text for use as a system message in an environment variable"""
    single_line = input_text.replace('\n', '\\n')
    escaped_quotes = single_line.replace('"', '\\"')
    print(escaped_quotes)

Running locally

1. Create a .env file based on the .env.sample provided.
2. Execute start.cmd to run the application.

For a detailed description, se sample-app-aoai-chatGPT.

Deployment

Our application is deployed using GitHub Actions. When code is pushed or merged to the main branch, it is automatically deployed to the test environment. Upon creating a release, the code is deployed to the prod environment. To view the detailed configuration, see deployment configuration

Configuration

Create test and prod environments for storing secrets and variables.

Adding an identity provider

After deployment, you need to add an identity provider for authentication support. For more information, see this tutorial.

Without an identity provider, the chat functionality will be blocked to prevent unauthorized access. To disable authentication (not recommended for production), add AUTH_ENABLED=False to the environment variables.

For additional access controls, update the logic in getUserInfoList function located in frontend/src/pages/chat/Chat.tsx.

Scalability

Configure the number of threads and workers in gunicorn.conf.py. Redeploy the app after making changes. See the Oryx documentation for more details.

Debugging deployed app

1. Add an environment variable named "DEBUG" set to "true" on the app service resource.
2. Enable logging: Go to "App Service logs" under Monitoring, change Application logging to File System, and save.
3. View logs using "Log stream" under Monitoring.

Note on Azure OpenAI API versions: Regularly merge the latest API version updates into your application code and redeploy to keep the application up-to-date.