This repo contains all of the code to complete the freeCodeCamp course, "Build a Semantic Book Recommender with LLMs – Full Course". There are five components to this tutorial:
- Text data cleaning (code in the notebook
data-exploration.ipynb) - Semantic (vector) search and how to build a vector database (code in the notebook
vector-search.ipynb). This allows users to find the most similar books to a natural language query (e.g., "a book about a person seeking revenge"). - Doing text classification using zero-shot classification in LLMs (code in the notebook
text-classification.ipynb). This allows us to classify the books as "fiction" or "non-fiction", creating a facet that users can filter the books on. - Doing sentiment analysis using LLMs and extracting the emotions from text (code in the notebook
sentiment-analysis.ipynb). This will allow users to sort books by their tone, such as how suspenseful, joyful or sad the books are. - Creating a web application using Gradio for users to get book recommendations (code in the file
gradio-dashboard.py).
This project was initially created in Python 3.11. In order to run the project, the following dependencies are required:
- kagglehub
- pandas
- matplotlib
- seaborn
- python-dotenv
- langchain-community
- langchain-opencv
- langchain-chroma
- transformers
- gradio
- notebook
- ipywidgets
A requirements.txt file containing all the project dependencies is provided as part of this repo.
In order to create your vector database, you'll need to create a .env file in your root directory containing your OpenAI API key. Instructions on how to do this are part of the tutorial.
The data for this project can be downloaded from Kaggle. Instructions on how to do this are also in the repo.