End-to-End QA Chatbot

This repository provides an end-to-end QA (Question-Answering) Chatbot that integrates multiple state-of-the-art technologies. The chatbot can scrape websites using BeautifulSoup (bs4), retrieve relevant documents via vector retrieval, and maintain conversation history to provide context-aware responses. It leverages advanced tools and models including Groq, Huggingface, OpenAI, and Ollama models, and uses prompt templates to structure queries.

Overview

The project demonstrates how to:

• Build a conversational chatbot that interacts with websites and answers questions contextually.
• Scrape websites using BeautifulSoup (bs4).
• Retrieve context-relevant documents using vector retrieval techniques.
• Maintain conversation history to provide context-aware responses.
• Use prompt templates for effective query structuring.
• Integrate multiple models including Groq, Huggingface, OpenAI, and Ollama models.

Features

• Groq Integration:
  Efficient querying with the Groq API for language model inference.

• Huggingface Models:
  Leverage state-of-the-art NLP models and utilities from Huggingface.

• Web Scraping with BeautifulSoup (bs4):
  Extract and process content from websites to support conversational queries.

• Conversational Chatbot:
  Maintains conversation history for context-aware answers.

• Vector Retrieval:
  Uses vector-based methods to index and retrieve relevant documents.

• Prompt Templating:
  Structures queries for effective interaction with language models.

• Multiple Model Integrations:
  Combines capabilities from OpenAI and Ollama models.

Installation

1. Clone the Repository:

   git clone https://github.com/EniolaAdemola/End-to-End-QA-Chatbot.git
   cd End-to-End-QA-Chatbot
   

2. Create and activate a virtual environment (optional but recommended) for me i used conda:

   conda create -p venv python==3.10 -y

   conda activate venv
   

3. Install the required libraries:

   pip install -r requirements.txt
   

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

   GROQ_API_KEY=your_groq_api_key
   HF_TOKEN=your_hf_token

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Run the bot

To start the streamlit application, you can run either ollama-bot.py or openai-bot.py:

streamlit run openai-qa-chatbot\openai-bot.py

🤝 Contributing

Contributions are welcome! Feel free to submit a PR or open an issue.

📞 Contact

For inquiries, reach out to Eniola Ademola.

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