metadata

title: AutoQuizzer
emoji: 🧑‍🏫
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 4.31.1
app_file: app.py
header: mini
pinned: true
models:
  - meta-llama/Meta-Llama-3-8B-Instruct

🧑‍🏫 AutoQuizzer

Generates a quiz from a URL. You can play the quiz, or let the LLM play it.

Built using: 🏗️ Haystack • 🦙 Llama 3 8B Instruct • ⚡ Groq

How does it work?

Quiz generation Pipeline: downloads HTML content from the URL, extracts the text and passes it to Llama 3 to generate a quiz in JSON format.
You can play the quiz and get a score.
You can let Llama 3 play the quiz:
- closed book answer Pipeline: the LLM is provided given the general quiz topic and questions. It can answer the questions based on its parametric knowledge and reasoning abilities.
- Web RAG answer Pipeline: for each question, a Google search is performed and the top 3 snippets are included in the prompt for the LLM.

How to run it locally?

Clone the repo: git clone https://github.com/anakin87/autoquizzer
Install: cd autoquizzer && pip install -r requirements.txt
Export two environment variables:
- GROQ_API_KEY: API key for the Groq API, used to serve Llama 3.
- SERPERDEV_API_KEY: API key for the SerperDev API, used to fetch search results.
Run the webapp: gradio app.py

Customization and potential improvements

To not exceed the rate limits of Groq's free API, we truncate the text extracted from the URL to 4000 characters. This means that the generated quiz focuses on the beginning of the text. You can increase/remove this limit, taking into account the maximum context length of the model.
We are using the OpenAIGenerator to query Llama 3 on Groq. You can use an OpenAI model with the same generator. You can also use a different generator (Haystack generators).
In the quiz generation pipeline, we ask the model to generate a JSON. Llama-3-8B-Instruct usually works. You can also create a more robust JSON generation pipeline, as shown in this tutorial: Generating Structured Output with Loop-Based Auto-Correction.