Q&A: Question and Answer System Based on Google Palm LLM and Langchain

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. Company sells data related courses and bootcamps. They have thousands of learners who uses discord server or email to ask questions. This system will provide an API and 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 Codebasics 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 + Google Palm: LLM based Q&A
Streamlit: UI
Huggingface instructor embeddings: Text embeddings
FAISS: Vector databse

Installation

1.Clone this repository to your local machine using:

   git clone https://github.com/AquibPy/Q-A-LLM.git

2.Navigate to the project directory:

  cd dir_name

3.Install the required dependencies using pip:

  pip install -r requirements.txt

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

  GOOGLE_API_KEY="your_api_key_here"

Usage

Run the Streamlit app by executing:
```
streamlit run ui.py
```
Run the API executing:
```
uvicorn api:app --reload
```

FastAPI framework, high performance, easy to learn, fast to code, ready for production

Documentation: https://fastapi.tiangolo.com

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

The key features are:

Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available.
Fast to code: Increase the speed to develop features by about 200% to 300%.
Fewer bugs: Reduce about 40% of human (developer) induced errors.
Intuitive: Great editor support. Completion everywhere. Less time debugging.
Easy: Designed to be easy to use and learn. Less time reading docs.
Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.
Robust: Get production-ready code. With automatic interactive documentation.
Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

Interactive API docs

Now go to http://127.0.0.1:8000/docs.

You will see the automatic interactive API documentation (provided by Swagger UI):

3.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

ui.py: The main Streamlit application script.
helper_function.py: This contains helper functions
requirements.txt: A list of required Python packages for the project.
.env: Configuration file for storing your Google API key and Hugging Face API key.

AquibPy / Q-A-LLM