PandaAI Platform

Picture an intelligent assistant that dives into your local knowledge base, harnessing cutting-edge Retrieval Augmented Generation (RAG) technology to deliver precise answers to your every question! The PandaAI Platform is a versatile powerhouse, supporting text, images, videos, and all sorts of files, while seamlessly adapting to any role—be it a college advisor, customer support guru, technical expert, or even a trusty notetaker. Fully customizable and endlessly adaptable, it empowers you to conquer any scenario with ease!

Features

• Local knowledge base management
• Text chunking and vector embedding
• Support for text and file uploads
• Question answering based on similarity search
• Integration with LM Studio for local generation
• Role selection when handling different user scenario

Installation

1. Clone the repository
2. Install dependencies

pip install -r requirements.txt

For Advanced Feature (video and image file input), you can skip this if you only have txt based file!

1. We need Rust compiler(https://www.rust-lang.org/tools/install)

Install ffmpeg (do not forget this step if you want to upload video : )

1. Install ffmpeg from https://ffmpeg.org/download.html
2. Add ffmpeg to the system path

Install Tesseract (do not forget this step if you want to upload image : )

1. Install Tesseract from https://github.com/tesseract-ocr/tesseract?tab=readme-ov-file#installing-tesseract
2. Add Tesseract to the system path

pip install -r videorequirements.txt

Running the Application

Run the following command to start the service:

python -m simple_pandaaiqa.app

Then access in your browser: http://localhost:8000

Technology Stack

• Backend: FastAPI, Python
• Frontend: HTML, CSS, JavaScript
• Embedding: LM Studio API integration
• Text Generation: LM Studio API integration

LM Studio Integration

This project supports integration with LM Studio to provide higher quality answer generation.

Steps to use:

1. Install LM Studio locally and start the server
2. Make sure LM Studio is listening on http://localhost:1234
3. Configure the API endpoints in config.py
4. Start the LM Studio Server

If LM Studio cannot be connected, the system will automatically display an error message.