Skip to content

README.md

Latest commit

 

History

History
72 lines (48 loc) · 2.3 KB

README.md

File metadata and controls

72 lines (48 loc) · 2.3 KB
Raw

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.