https://github.com/auth0-samples/auth0-ai-smart-hr-assistant
https://github.com/auth0-samples/auth0-ai-smart-hr-assistant
Last synced: 10 months ago
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- Host: GitHub
- URL: https://github.com/auth0-samples/auth0-ai-smart-hr-assistant
- Owner: auth0-samples
- License: apache-2.0
- Created: 2025-01-29T17:00:42.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-25T08:48:39.000Z (over 1 year ago)
- Last Synced: 2025-07-19T09:20:48.028Z (11 months ago)
- Language: TypeScript
- Size: 448 KB
- Stars: 1
- Watchers: 5
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SmartHR Assistant - AI Agent with Fine-Grained Authorization
A sample application demonstrating how to implement an AI agent with fine-grained authorization controls when accessing sensitive user data.
> For a CLI version of this application, please refer to the [cli-only-app](https://github.com/oktadev/auth0-ai-smart-hr-assistant/tree/cli-only-app) branch.
## Overview
SmartHR Assistant is an intelligent document management system that:
1. Provides secure access to HR documents based on complex authorization rules:
- Salary information (viewable only by HR and the specific employee)
- Performance reviews (viewable by employee, their manager, and HR)
- Employee information (viewable by HR and the specific employee)
- Team documents (viewable by team members only)
- Company policies (public to all employees)
2. Offers AI-powered capabilities:
- Answers questions about policies and procedures
- Generates document summaries
- Assists with document search
## Data Categories with Different Authorization Levels
### 1. Employee Information
- Salary information
- Performance reviews
- Employee personal and public information
### 2. Team Information
- Team documents
- Team members
### 3. Company Information
- Company policies
- Company documents
## Authorization Levels
### 1. Employee Level
- Full access to their own records
- Limited access to other employees' public information
- Full access to public company documents
- Full access to their teams documents
### 2. Manager Level
Employee Level +
- Full access to their team members information
- Full access to performance reviews of their team
### 3. HR Level
Manager Level +
- Full access to all employee information
- Full access to all team information
- Full access to all performance reviews
### 4. Admin Level
- Full access to all data
## Future Enhancements
- Integrate Auth0 and get the user information from Auth0
- Add SQL DB for realtime data
## How to run
### Prerequisites
- An Okta FGA account, you can create one [here](https://dashboard.fga.dev).
- An OpenAI account and API key create one [here](https://platform.openai.com).
### Setup
1. Install the dependencies
```sh
bun install # or npm install
```
2. Create a `.env.local` file using the format below:
```sh
# OpenAI
OPENAI_API_KEY=xx-xxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxx
# Okta FGA
FGA_STORE_ID=xxxxxxxxxxxxxxxxxxxxxxxxxxx
FGA_CLIENT_ID=xxxxxxxxxxxxxxxxxxxxxxxxxxx
FGA_CLIENT_SECRET=xxxxxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxx
# Required only for non-US regions
FGA_API_URL=https://api.xxx.fga.dev
FGA_API_AUDIENCE=https://api.xxx.fga.dev/
```
### Obtain OpenAI API Key
[Use this page for instructions on how to find your OpenAI API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key). Once you have your key, update the `.env` file accordingly.
### Configure Okta FGA
1. **Create a client**
Navigate to _Settings_ and in the _Authorized Clients_ section click **+ Create Client** button. On the new page give your client a name and mark all three client permissions then click **Create**.
2. Copy the information on the modal and update your `.env.local` file with the values you now have for `FGA_STORE_ID`, `FGA_CLIENT_ID`, and `FGA_CLIENT_SECRET`.
3. Run the `bun run fga:init` script to initialize the FGA store with the model and tuples.
### Run the application
```sh
bun start # or npm start
```
## Technical Stack
This application is built with TypeScript, Next.js, TailwindCSS, LlamaIndex, and OpenFGA.
## Data Sources
All data will be in PDF and markdown files and will be loaded into an in-memory vector store on application startup.