RAG Book Reviews Chatbot

This project implements a Retrieval-Augmented Generation (RAG) system to create a chatbot that can answer questions about book reviews and opinions. The chatbot uses reports from Goodreads and Reddit to provide comprehensive information about popular psychology books.

Project Structure

README.md
poetry.lock
pyproject.toml
reports/
    ├── goodreads_report.txt
    └── reddit_report.txt
src/
    └── rag_book_reviews/
        ├── __init__.py
        ├── vector_db.py
        ├── chat_interface.py
        ├── chainlit_app.py
        ├── populate_db.py
        └── read_reports.py

Components

vector_db.py: Handles the creation and management of the vector database using DeepLake.
chat_interface.py: Implements the chatbot interface using LangChain and OpenAI's GPT model.
read_reports.py: Reads and processes the Goodreads and Reddit reports.
populate_db.py: Populates the vector database with the processed reports.
chainlit_app.py: The entry point of the application, integrating Chainlit for the chat interface.

Setup and Installation

Clone the repository:

git clone https://github.com/yourusername/rag-book-reviews-chatbot.git
cd rag-book-reviews-chatbot

Install dependencies using Poetry:
```
poetry install
```

Set up environment variables: Create a .env file in the root directory with the following content:

OPENAI_API_KEY=your_openai_api_key
ACTIVELOOP_TOKEN=your_activeloop_token
ACTIVELOOP_ID=your_activeloop_id

Usage

Populate the vector database:

python src/rag_book_reviews/populate_db.py

Run the chatbot using Chainlit:

chainlit run src/rag_book_reviews/chainlit_app.py

This will start the Chainlit server and open the chat interface in your default web browser.

How it works

The populate_db.py script reads the Goodreads and Reddit reports, processes them, and stores them in a DeepLake vector database.
The chat_interface.py uses LangChain and OpenAI's GPT model to create a retrieval-based question-answering system.
When a user asks a question, the system uses a RetrievalQAWithSourcesChain to retrieve relevant information from the vector database.
The retrieved information is used to generate an informed response, and the sources of the information are also returned.
The Chainlit interface in chainlit_app.py provides a user-friendly chat experience, allowing users to interact with the chatbot seamlessly and view the sources of the information provided.

Features

Retrieval-Augmented Generation for accurate and context-aware responses
Use of RetrievalQAWithSourcesChain for advanced retrieval and answer generation
Return of source information for each response, enhancing transparency and credibility
Integration with DeepLake for efficient vector storage and retrieval
User-friendly chat interface powered by Chainlit
Comprehensive book reviews and opinions from Goodreads and Reddit

sosanzma/rag_book_reviews