Business Recommendation Chatbot based on Yelp Data

How to run

Prerequisite

• Poetry 1.8.3
• Miniconda or alternatives that can create new Python environment with a specified Python version

Set up

• Download the Yelp Data at https://www.yelp.com/dataset, unzip the reviews file yelp_dataset.tar (4.34 GB) and put the data at data/yelp_dataset. For example: data/yelp_dataset/yelp_academic_dataset_business.json
• Create a new .env file based on .env.example and populate the variables there
• Create a new Python 3.11.9 environment: conda create --prefix ./.venv python=3.11.9
• Make sure Poetry use the new Python 3.11.9 environment: poetry env use ./.venv
• Install Python dependencies with Poetry: poetry install
• Start the Jupyterlab notebook: poetry run jupyter lab

Build the RAG pipeline

• Sequentially run the notebooks 001 and 003 to prepare the data: notebooks/001-sample-yelp-dataset, notebooks/003-collate-metadata-into-review.ipynb
• Run the notebook 005 to fine-tune the embedding model notebooks/005-finetune-embeddings.ipynb. On a machine with 4 GB of vRAM GPU, the fine-tuning process would take about 2 hours.
• Then explore main RAG-building notebook it notebooks/009-pipeline-v1.ipynb

Start Chatbot UI

• Navigate to ui folder: cd ui
• Run: poetry run chainlit run chat_v2.py -hw
• Access the Chatbot UI at http://localhost:8000