RestoRecommender: AI-Powered Restaurant Recommendation System

RestoRecommender leverages the power of artificial intelligence (AI) to suggest restaurants and bars based on user preferences and review analysis. It utilizes Retrieval-Augmented Generation (RAG) to extract meaningful insights from customer reviews.

Functionality Breakdown:

• Data Collection (places.ipynb):

  • This Jupyter Notebook scrapes data from Google Maps's Places API and stores the extracted information in CSV files (places.csv and reviews.csv) within the data directory.

• RAG LLM Model Creation (rag_chatbit.ipynb):

  • This script outlines the process of building a RAG-based large language model (LLM). The steps involve:
    • Data loading from CSV files
    • Vector database construction
    • Embedding model training
    • LLM model creation
  • The notebook also showcases demonstration use cases and evaluations based on synthetically generated questions.

• Answer Generation (experiments.ipynb):

  • This Jupyter Notebook utilizes the questions.txt file containing user queries to generate corresponding answers and saves them in the Question-Answer.txt file. It allows for the inclusion of additional user questions for enhanced personalization.

• Web User Interface (web-UI folder):

  • This folder houses the necessary files to create a web-based user interface (UI) that facilitates interaction with the RestoRecommender chatbot. It employs the Flask framework.
  • To launch the UI:
    1. Navigate to the web-UI directory in your terminal.
    2. Run the command: python app.py
    3. Open your web browser and visit the following URL: http://127.0.0.1:5000/

References:

• https://pypi.org/project/googlemaps/
• https://github.com/googlemaps/google-maps-services-python/blob/master/googlemaps/places.py
• https://python.langchain.com/v0.1/docs/get_started/introduction
• https://ai.google.dev/gemini-api/docs
• https://github.com/facebookresearch/faiss