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Q&A: Question and Answer System Based on Gemini LLM and Langchain for E-learning company

This is an end to end LLM project based on Google Palm and Langchain. We are building a Q&A system for an e-learning company that sells data related courses and bootcamps. They have thousands of learners who uses discord server or email to ask questions. This system will provide a streamlit based user interface for students where they can ask questions and get answers.

Project Highlights

• Use a real CSV file of FAQs that promtsandresponse company is using right now.
• Their human staff will use this file to assist their course learners.
• We will build an LLM based question and answer system that can reduce the workload of their human staff.
• Students should be able to use this system to ask questions directly and get answers within seconds

You will learn following,

• Langchain + Gemini Ai: LLM based Q&A
• Streamlit: UI
• Huggingface instructor embeddings: Text embeddings
• FAISS: Vector databse

Installation

1.Clone this repository to your local machine using:

  https://github.com/Subee567/GenAI.git

2. Install the required dependencies using pip:

  pip install -r requirements.txt

3.Acquire an api key through makersuite.google.com and put it in .env file

  GOOGLE_API_KEY="your_api_key_here"

Usage

1. Run the Streamlit app by executing:

streamlit run main.py

2.The web app will open in your browser.

• To create a knowledebase of FAQs, click on Create Knolwedge Base button. It will take some time before knowledgebase is created so please wait.

• Once knowledge base is created you will see a directory called faiss_index in your current folder

• Now you are ready to ask questions. Type your question in Question box and hit Enter

Sample Questions

• Do you guys provide internship and also do you offer EMI payments?
• Do you have javascript course?
• Should I learn power bi or tableau?
• I've a MAC computer. Can I use powerbi on it?
• I don't see power pivot. how can I enable it?

Project Structure

• main.py: The main Streamlit application script.
• langchain_helper.py: This has all the langchain code
• requirements.txt: A list of required Python packages for the project.
• .env: Configuration file for storing your Google API key.

Contributing

Contributions are welcome! If you want to add more features or improve the code, feel free to fork this repository and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.