Question-Answer-Large-Language-Model

Health Lifestyle Nutrition Q&A: Question and Answer System Based on Google Palm LLM and Langchain for learning purpose

This is an end-to-end LLM project based on Google Palm and Langchain. I have build a Q&A system for Health, Lifestyle, and Nutrition related question answers.

I have used the following libraries for project implementation

• Langchain + Google Palm: LLM based Q&A
• Streamlit: UI
• Huggingface instructor embeddings: Text embeddings
• Annoy: Vector database

Installation

1. Clone this repository to your local machine using: git clone (https://github.com/data-pioneer/Question-Answer-Chatbot-LLM.git)

2. Install the required dependencies using pip: pip install -r requirements.txt pip install google-generativeai pip install InstructorEmbedding pip3 install sentence-transformers pip install annoy

3. Acquire an API key through makersuite.google.com and put it in the .env file GOOGLE_API_KEY="your_api_key_here"

Usage

1. Run the long chain implementation file by executing: Python langchain_imp.py

2. Run the Streamlit app by executing: streamlit run web_main.py

3. The web app will open in your browser.

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

Sample Questions

• What are the benefits of regular exercise?
• How many glasses of water should you drink per day?
• What is a balanced diet??

Project Structure

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