File Talk AI - Backend

A Django-based backend API for interacting with PDF documents using AI. This application leverages GROQ's LLM to provide intelligent document analysis, question answering, summarization, and question generation capabilities.

Features

Question Answering: Ask questions about your PDF documents and get AI-powered answers
Document Summarization: Generate concise summaries of PDF content (configurable page ranges)
Question Generation: Automatically generate relevant questions from document content
PDF Processing: Upload and process PDF files with text extraction
RESTful API: Clean, versioned API endpoints (v1)
Enterprise Architecture: Domain-driven design with clear separation of concerns
Comprehensive Error Handling: Custom exceptions with detailed error responses
Configurable AI Models: Support for different GROQ model configurations
Logging & Monitoring: Structured logging with JSON/text format support

Architecture

This project follows Clean Architecture principles with the following layers:

chat_bot_api/
├── api/                    # API Layer (Controllers)
│   ├── middlewares/        # Custom middleware
│   └── v1/                 # API version 1
│       ├── views.py        # Request handlers
│       ├── serializers.py  # Input validation
│       └── validators.py   # Custom validators
│
├── application/            # Application Layer (Use Cases)
│   ├── dto/                # Data Transfer Objects
│   │   ├── request_dto.py  # Request DTOs
│   │   └── response_dto.py # Response DTOs
│   ├── services/           # Business logic services
│   │   ├── agent_service.py              # AI agent orchestration
│   │   ├── question_answer_service.py    # Q&A service
│   │   ├── summary_service.py            # Summarization service
│   │   └── question_generation_service.py # Question generation
│   └── use_cases/          # Application use cases
│
├── domain/                 # Domain Layer (Business Logic)
│   ├── enums/              # Enumerations
│   │   ├── action_types.py # Action type enums
│   │   └── user_types.py   # User type enums
│   ├── exceptions/         # Custom exceptions
│   │   ├── base.py         # Base exception classes
│   │   ├── agent_exceptions.py
│   │   └── pdf_exceptions.py
│   └── models/             # Domain models
│
├── infrastructure/         # Infrastructure Layer
│   ├── cache/              # Caching implementations
│   ├── external/           # External service integrations
│   ├── repositories/       # Data access layer
│   └── storage/            # File storage (local/S3)
│
├── core/                   # Core Utilities
│   ├── decorators/         # Custom decorators (e.g., retry)
│   ├── mixins/             # Reusable mixins
│   └── utils/              # Helper utilities
│       ├── logger.py       # Logging utilities
│       ├── helpers.py      # General helpers
│       └── validators.py   # Validation utilities
│
└── tests/                  # Test Suite
    ├── unit/               # Unit tests
    └── integration/        # Integration tests

Tech Stack

Framework & Core

Django 4.2+
Django REST Framework
Django CORS Headers

AI & ML

Phidata - AI agent framework
GROQ API - LLM inference

PDF Processing

PyMuPDF (fitz) - PDF text extraction

Database

SQLite (development)
PostgreSQL support (production-ready)

Production Tools

Gunicorn - WSGI server
WhiteNoise - Static file serving
python-dotenv - Environment management

Project Structure

file-talk-ai/
├── chat_bot_api/           # Main application
├── config/                 # Configuration module
│   ├── env_config.py       # Environment configuration
│   ├── constants.py        # Application constants
│   └── settings/           # Django settings
├── file_talk_ai_project/   # Django project settings
├── media/                  # Uploaded files (gitignored)
├── manage.py               # Django management script
├── requirements.txt        # Python dependencies
└── .env.example            # Environment variables template

Getting Started

Prerequisites

Python 3.8+
pip (Python package manager)
Virtual environment (recommended)
GROQ API key (Get one here)

Installation

Clone the repository

git clone https://github.com/AkshayPanchivala/File-Talk-AI-Backend.git
cd File-Talk-AI-Backend-main/file-talk-ai

Create and activate virtual environment

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

Install dependencies
```
pip install -r requirements.txt
```

Configure environment variables

# Create .env file (see Environment Configuration section)
cp .env.example .env
# Edit .env and add your GROQ API key

Run database migrations
```
python manage.py migrate
```
Start the development server
```
python manage.py runserver
```

The API will be available at http://localhost:8000

Environment Configuration

Create a .env file in the project root (file-talk-ai/) directory:

# ===================================
# Required Configuration
# ===================================
groqApiKey=your_groq_api_key_here

# ===================================
# AI Model Configuration (Optional)
# ===================================
GROQ_MODEL_ID=llama-3.3-70b-versatile
AI_TEMPERATURE=0.7
AI_MAX_TOKENS=8000

# ===================================
# PDF Processing (Optional)
# ===================================
PDF_DOWNLOAD_TIMEOUT=30
PDF_MAX_PAGES=100
PDF_DEFAULT_MIN_PAGE=1
PDF_DEFAULT_MAX_PAGE=5
PDF_STORAGE_PATH=media/pdfs

# ===================================
# Summary Configuration (Optional)
# ===================================
SUMMARY_MIN_WORDS=8000

# ===================================
# Question Generation (Optional)
# ===================================
QUESTIONS_COUNT=20

# ===================================
# Cache Configuration (Optional)
# ===================================
CACHE_ENABLED=False
CACHE_TTL=3600
REDIS_URL=redis://localhost:6379/0

# ===================================
# Storage Configuration (Optional)
# ===================================
STORAGE_BACKEND=local  # Options: local, s3
# AWS_ACCESS_KEY_ID=your_aws_access_key
# AWS_SECRET_ACCESS_KEY=your_aws_secret_key
# AWS_STORAGE_BUCKET_NAME=your_bucket_name
# AWS_S3_REGION_NAME=us-east-1

# ===================================
# Logging Configuration (Optional)
# ===================================
LOG_LEVEL=INFO          # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
LOG_FORMAT=json         # Options: json, text

# ===================================
# Feature Flags (Optional)
# ===================================
ENABLE_RATE_LIMITING=False
ENABLE_MONITORING=False

# ===================================
# Environment (Optional)
# ===================================
ENVIRONMENT=development # Options: development, testing, production
DEBUG=True

Getting Your GROQ API Key

Visit GROQ Console
Sign up or log in to your account
Navigate to API Keys section
Click Create API Key
Copy the generated key
Add it to your .env file

API Documentation

Base URL

http://localhost:8000/api/v1/chat-bot/

Endpoints

1. Get API Information

GET /api/v1/chat-bot/conversation/

Response:

{
  "message": "File Talk AI - Conversation API",
  "version": "v1",
  "endpoints": {
    "POST /conversation/": "Process conversation",
    "POST /options/": "Get available options"
  }
}

2. Process Conversation (Question Answering)

POST /api/v1/chat-bot/conversation/
Content-Type: application/json

Request Body:

{
  "action": "question_answer",
  "documentUrl": "https://example.com/document.pdf",
  "question": "What is the main topic of this document?"
}

Response:

{
  "data": "The main topic of this document is...",
  "message": "Question answered successfully"
}

3. Document Summarization

POST /api/v1/chat-bot/conversation/
Content-Type: application/json

Request Body:

{
  "action": "summarizer",
  "documentUrl": "https://example.com/document.pdf",
  "minPage": 1,
  "maxPage": 5
}

Response:

{
  "data": "Summary of the document...",
  "message": "Document summarized successfully"
}

4. Question Generation

POST /api/v1/chat-bot/conversation/
Content-Type: application/json

Request Body:

{
  "action": "generate_questions",
  "documentUrl": "https://example.com/document.pdf",
  "minPage": 1,
  "maxPage": 5
}

Response:

{
  "data": [
    "Question 1?",
    "Question 2?",
    ...
  ],
  "message": "Questions generated successfully"
}

5. Get Options

POST /api/v1/chat-bot/options/
Content-Type: application/json

Request Body:

{
  "startedChatbot": true
}

Response:

{
  "options": [
    "question_answer",
    "summarizer",
    "generate_questions"
  ]
}

Action Types

question_answer - Answer questions from PDF content
summarizer - Summarize PDF document
generate_questions - Generate questions from PDF content

Error Responses

All errors follow this structure:

{
  "error": {
    "code": "ERROR_CODE",
    "message": "Human-readable error message",
    "details": {
      "field": "Additional error context"
    }
  }
}

Common Error Codes:

VALIDATION_ERROR - Invalid request data
PDF_PROCESSING_ERROR - PDF processing failed
AGENT_ERROR - AI agent processing error
INTERNAL_ERROR - Server error

Frontend Repository

This backend is designed to work with the File Talk AI frontend:

Frontend Repository: File Talk AI - Frontend (React + Vite)

Make sure to configure the frontend's VITE_API_BASE_URL environment variable to point to this backend:

VITE_API_BASE_URL=http://localhost:8000

Development

Running Tests

python manage.py test

Creating Superuser

python manage.py createsuperuser

Accessing Admin Panel

Navigate to http://localhost:8000/admin/ after creating a superuser.

Production Deployment

For production deployment:

Set ENVIRONMENT=production in .env
Set DEBUG=False
Configure PostgreSQL database
Use Gunicorn as WSGI server
Configure static file serving with WhiteNoise
Set up proper CORS settings
Enable rate limiting and monitoring
Consider using Redis for caching

License

This project is licensed under the MIT License.

Acknowledgements

Django - Web framework
Django REST Framework - API toolkit
GROQ - LLM inference platform
Phidata - AI agent framework
PyMuPDF - PDF processing library

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on the GitHub repository.

Built with ❤️ by Akshay Panchivala

Name		Name	Last commit message	Last commit date
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file-talk-ai		file-talk-ai
.gitignore		.gitignore
README.md		README.md

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File Talk AI - Backend

Table of Contents

Features

Architecture

Tech Stack

Project Structure

Getting Started

Prerequisites

Installation

Environment Configuration

Getting Your GROQ API Key

API Documentation

Base URL

Endpoints

1. Get API Information

2. Process Conversation (Question Answering)

3. Document Summarization

4. Question Generation

5. Get Options

Action Types

Error Responses

Frontend Repository

Development

Running Tests

Creating Superuser

Accessing Admin Panel

Production Deployment

License

Acknowledgements

Contributing

Support

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