https://github.com/madihanazir/pdfai
Develop a full-stack application that allows users to upload PDF documents and ask questions regarding the content of these documents. The backend will process these documents and utilize natural language processing to provide answers to the questions posed by the users.
https://github.com/madihanazir/pdfai
Last synced: 3 months ago
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Develop a full-stack application that allows users to upload PDF documents and ask questions regarding the content of these documents. The backend will process these documents and utilize natural language processing to provide answers to the questions posed by the users.
- Host: GitHub
- URL: https://github.com/madihanazir/pdfai
- Owner: madihanazir
- Created: 2025-01-03T18:18:47.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-02-25T12:12:04.000Z (4 months ago)
- Last Synced: 2025-02-25T12:33:29.350Z (4 months ago)
- Language: Python
- Homepage:
- Size: 2.76 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
Awesome Lists containing this project
README
Frontend README.md
# PDF QnA App - Frontend
## Overview
This is the frontend of the PDF QnA application, built with React and Tailwind CSS. It allows users to upload PDFs and ask questions about their content.
## Features
- Upload a PDF file.
- Ask questions about the uploaded PDF.
- Responsive design with a clean layout.
## Setup Instructions
1. Clone the repository:
```bash
git clone https://github.com/your-repo-name/frontend.git
Navigate to the project directory:
cd frontend
Install dependencies:
npm install
Start the development server:
npm run dev
Access the app at http://localhost:5173.
Folder Structure
src/components/: Contains React components (AskQuestion.jsx, UploadPDF.jsx).
App.jsx: Main application file.
index.css: Tailwind CSS styles.
Technologies Used
React
Tailwind CSS
Axios
#### **Backend README.md**
```markdown
# PDF QnA App - Backend
## Overview
This is the backend of the PDF QnA application, built with FastAPI. It handles PDF uploads, processes their content, and provides answers to user questions using a vector database and LLM.
## Features
- Upload PDF files.
- Store extracted content in a database.
- Use vector embeddings for retrieval-based Q&A.
## Setup Instructions
1. Clone the repository:
```bash
git clone https://github.com/your-repo-name/backend.git
Navigate to the project directory:
cd backend
Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Start the FastAPI server:
uvicorn app.main:app --reload
Access the API docs at http://127.0.0.1:8000/docs.
Folder Structure
app/routers/: Contains route handlers (documents.py, questions.py).
app/services/: NLP and PDF processing logic (nlp_service.py, pdf_processor.py).
app/database/: Database connection and models.
API Endpoints
POST /documents/upload: Upload a PDF file.
Request Body: Form-data with the file.
Response: { "message": "Uploaded successfully", "document_id": 1 }
POST /questions/ask: Ask a question about a document.
Request Body: { "document_id": 1, "question": "What is this about?" }
Response: { "answer": "This document is about..." }
Technologies Used
FastAPI
Chroma (for vector database)
LangChain (for LLM integration)
SQLite (for database)
---
### 3. **Brief Architecture Overview**
#### **Architecture Overview**
The application consists of two main components:
1. **Frontend**:
- Built with React for a responsive and interactive user interface.
- Users can upload PDFs and ask questions about their content.
2. **Backend**:
- Built with FastAPI to handle file uploads, content processing, and question answering.
- Uses a vector database (Chroma) to store document embeddings and retrieve relevant content for questions.
- Integrates with an LLM for answer generation.
#### **Data Flow**
1. **Upload PDF**:
- Frontend sends the PDF to the backend (`/documents/upload`).
- Backend processes the PDF, extracts text, and stores it in the database and vector database.
- Backend returns the `document_id`.
2. **Ask Question**:
- Frontend sends the `document_id` and question to the backend (`/questions/ask`).
- Backend retrieves relevant document content using vector similarity search.
- LLM generates an answer based on the retrieved content.
- Backend returns the answer to the frontend.