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Patient Information App

Overview

This project features a reasoning agent designed to gather patient information before a doctor's visit. The agent interacts with patients through a conversational interface, collecting essential details such as name, date of birth, allergies, current medications, and reason for visit. This agent is built on top of the xRx framework, providing a seamless experience for patient intake.

Getting Started

Prerequisites

  • We assume you have already cloned the repository, as explained in the general README. For the sake of clarity, here's the command again:

    git clone --recursive https://github.com/8090-inc/xrx-sample-apps.git

  • If the submodule was not installed, or you want to update it, use the following command:

    git submodule update --init --recursive

  • Install Docker, Python3, and Pip3 with homebrew on macOS or apt-get update on Debian/Ubuntu based Linux systems:

    brew cask install docker
brew install [email protected]

How To Run

Using Docker

  1. Build the Docker image:

    docker build -t patient-information-agent:latest .

  2. Run the container:

    docker run -p 8003:8003 --env-file .env patient-information-agent:latest

The agent will be accessible at http://localhost:8003.

Locally without Docker

  1. Set up the Python virtual environment:

    python3 -m venv venv
source venv/bin/activate

  2. Install requirements:

    pip install -r requirements.txt

  3. Start a local redis cluster

    redis-server --port 6379

  4. Run the application:

    cd app
uvicorn main:app --host 127.0.0.1 --port 8003 --reload

The agent will now be running at http://localhost:8003

Project Structure

  • reasoning/: Contains the main application code
    • app/: backend application folder
      • agent/: agent logic
        • context_manager.py: Manages session context
        • executor.py: Main agent execution logic
      • __init__.py: Initializes the app module
      • main.py: FastAPI application setup and endpoint definition
    • Dockerfile: Docker configuration for containerization
    • requirements.txt: Python dependencies
  • nextjs-client/: Next.js frontend for the patient intake application
  • test/: Contains test files
  • xrx-core/: Core xRx framework (submodule)
  • docker-compose.yaml: Docker Compose configuration file
  • env-example.txt: Example environment variables file
  • README.md: Project documentation

API Usage

The agent exposes a single endpoint using FastAPI:

  • POST /run-reasoning-agent: Submit a query to the reasoning agent and receive streaming responses.

Example request using curl

curl -X POST http://localhost:8003/run-reasoning-agent \
     -H "Content-Type: application/json" \
     -H "Accept: text/event-stream" \
     -d '{
         "session": {
             "id": "1234567890"
         },
         "messages": [
             {"role": "user", "content": "Hi, I'm here for my appointment."}
         ]
     }

Example request using Python's requests library with streaming

import requests
import json

url = "http://localhost:8003/run-reasoning-agent"
headers = {
    "Content-Type": "application/json",
    "Accept": "text/event-stream",
}
payload = {
    "session": {"id": "1234567890"},
    "messages": [{"role": "user", "content": "Hi, I'm here for my appointment."}]
}

response = requests.post(url, headers=headers, data=json.dumps(payload), stream=True)

for line in response.iter_lines(decode_unicode=True):
    if line.startswith('data: '):
        data = json.loads(line[6:])
        print(data)

Example response (streaming)

{
    "messages": [
        {"role": "assistant", "content": "Hello! Welcome to our clinic. I'd be happy to help you check in for your appointment. Can you please tell me your full name?"}
    ],
    "session": {"id": "1234567890"},
    "node": "CustomerResponse",
    "output": "Hello! Welcome to our clinic. I'd be happy to help you check in for your appointment. Can you please tell me your full name?"
}

To view the interactive API documentation, visit http://localhost:8093/docs in your web browser after starting the server.

Environment Variables

Ensure you have the following environment variables set in your .env file:

  • LLM_API_KEY: API key for the language model service
  • LLM_BASE_URL: Base URL for the language model API
  • LLM_MODEL_ID: ID of the language model to use
  • REDIS_HOST: Hostname for the Redis server (default: "localhost")

Testing

To run tests, use the following command from the project root:

python -m unittest discover test

Frontend

The project includes a Next.js frontend in the nextjs-client/ directory. To run the frontend:

  1. Navigate to the nextjs-client/ directory
  2. Install dependencies: npm install
  3. Run the development server: npm run dev

The frontend will be available at http://localhost:3000.

Contributing

Contributions to improve the Patient Information Agent are welcome. Please follow these steps:

  1. Open a new issue on GitHub describing the proposed change or improvement
  2. Fork the repository
  3. Create a new branch for your feature
  4. Commit your changes
  5. Push to your branch
  6. Create a pull request, referencing the issue you created

Note: pull requests not backed by published issues will not be considered. This process ensures that all contributions are discussed and aligned with the project's goals before implementation.