User Guide
Fullstack - FastAPI/React
Project Setup

Build a Fullstack RAG Chatbot with Hatchet and FastAPI

In this tutorial, we'll walk through the steps to implement a Retrevial Augmented Generation (RAG) Chatbot to answer questions based on the contents of a website.

This tutorial covers the following skills:

  • serving user requests with FastAPI and Hatchet
  • real-time progress streaming using Hatchet
  • advanced LLM prompt-chaining and RAG design
  • basic webscraping with requests and Beautiful Soup


This tutorial assumes you have a working understanding of Python, FastAPI, and React/Typescript.

  • Python 3.8 or higher
  • Poetry (pip install poetry)

We'll be splitting the backend into two services:

  1. API which serves the FastAPI endpoints
  2. Workflows which serve the Hatchet workers and workflows

Here's what our project directory structure should look like as we build: API Dir

Init Poetry

Start by initializing a new Poetry project with poetry init.

Next, add the following dependences and scripts to your pyproject.toml in your project root:

python = "^3.8"
python-dotenv = "^1.0.0"
uvicorn = {extras = ["standard"], version = "^0.27.0"}
fastapi = "^0.109.0"
openai = "^1.11.0"
beautifulsoup4 = "^4.12.3"
requests = "^2.31.0"
urllib3 = "1.26.15"
hatchet-sdk = "0.10.5"

api = "src.api.main:start"
hatchet = "src.workflows.main:start"

View Complete File on GitHub (opens in a new tab)

Finally, install the dependences with: poetry install

Next Steps: Define a Hatchet Workflow and Worker →