Ed Tech Company QA Assistant

Ed-Tech Company QA Assistant

This project is a QA (Question-Answering) assistant designed for an Ed-Tech company. It leverages vector databases, state-of-the-art language models, and custom document embeddings to provide accurate responses to user queries based on a pre-defined knowledge base.

Table of Contents

• Features
• Installation
• Usage
• Project Structure
• Future Enhancements
• Contributing
• License

Features

• Vector Database: Utilizes FAISS for efficient similarity search across the knowledge base.
• Custom Embeddings: Powered by HuggingFaceEmbeddings using the sentence-transformers/all-MiniLM-L6-v2 model.
• Large Language Model (LLM): Integrates Groq's LLaMA3 model (llama3-8b-8192) for generating context-aware responses.
• CSV Loader: Loads FAQs or other structured data from CSV files to populate the knowledge base.
• Streamlit Interface: A simple and interactive UI for asking questions and receiving answers.

Installation

1. Clone the Repository:

git clone https://github.com/shaadclt/Ed-Tech-Company-QA-Assistant.git
cd Ed-Tech-Company-QA-Assistant

2. Set Up a Virtual Environment:

python -m venv venv
source venv/bin/activate  # On Windows, use `venvScriptsactivate`

3. Install Dependencies:

pip install -r requirements.txt

4. Environment Variables: Create a .env file in the project root and add your Groq API key:

GROQ_API_KEY=your_groq_api_key_here

Usage

1. Run the Streamlit App

streamlit run main.py

2. Create the Knowledge Base: To create the knowledge base from a CSV file, click the button Create Knowledgebase

3. Ask a Question: Enter a question in the Streamlit interface, and the assistant will provide a contextually accurate response based on the loaded knowledge base.

Project Structure

• langchain_helper.py: Contains the logic for loading data, creating the vector database, and generating responses.
• main.py: The Streamlit application file.
• faqs.csv: A sample CSV file used to populate the knowledge base.
• requirements.txt: Lists all required Python packages.
• .env: Stores environment variables like API keys (not included in the repository for security reasons).

Future Enhancements

• Expand Knowledge Base: Add more documents or data sources.
• Fine-Tune Models: Adjust the embeddings or LLM for more accurate responses.
• Advanced UI: Improve the Streamlit interface with additional features like response filtering or feedback mechanisms.

Contributing

Feel free to fork this repository, create a feature branch, and submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License. See the License file for details.