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
Prerequisites
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:
- API which serves the FastAPI endpoints
- Workflows which serve the Hatchet workers and workflows
Here's what our project directory structure should look like as we build:
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:
[tool.poetry.dependencies]
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"
[tool.poetry.scripts]
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