path2dream-processors 0.1.17

A template for a private Python package

Path2Dream Processors

A powerful and extensible file processing library that converts various file types into clean, structured text using cutting-edge AI APIs. Perfect for building AI applications that need to process diverse content sources.

🚀 Features

Supported File Types

• 📄 Documents: PDF files using LlamaParse API (balanced mode)
• 🎵 Audio: MP3, WAV, M4A, FLAC using OpenAI Whisper API
• 🌐 URLs: Web pages using Jina Reader API (clean content extraction)
• 📹 Video: Coming soon
• 🖼️ Images: Coming soon

Key Benefits

• Async Processing: Full async/await support for concurrent file processing
• Clean Content Extraction: Automatically removes ads, cookie banners, and navigation elements from web pages
• High Accuracy: Uses industry-leading APIs (OpenAI Whisper, LlamaParse, Jina Reader)
• Robust Error Handling: Comprehensive error handling with descriptive messages
• Easy Integration: Simple interface with full type hints
• Comprehensive Testing: 95%+ test coverage with both unit and integration tests

📦 Installation

pip install path2dream-processors

🔧 Quick Start

Basic Usage

import asyncio
from path2dream_processors.file_parser import APIBasedFileParser

async def main():
    # Initialize the parser
    parser = APIBasedFileParser()

    # Process multiple files of different types concurrently
    files = [
        "presentation.pdf",           # Document
        "meeting_recording.mp3",      # Audio
        "https://example.com/article" # Web page
    ]

    # Get clean text content (all files processed in parallel)
    result = await parser.parse_files(files)
    print(result)

# Run the async function
asyncio.run(main())

Environment Setup

Create a .env file with your API keys:

OPENAI_API_KEY=your_openai_api_key_here
LLAMA_CLOUD_API_KEY=your_llama_cloud_api_key_here  
JINA_API_KEY=your_jina_api_key_here

🎯 API Reference

FileParser Interface

The main interface for file processing:

from abc import ABC, abstractmethod
from typing import List

class FileParser(ABC):
    """Interface for parsing files to text representation."""
    
    @abstractmethod
    async def parse_files(self, file_paths: List[str]) -> str:
        """
        Convert files to text representation asynchronously.
        
        Args:
            file_paths: List of local file paths or URLs
            
        Returns:
            Text representation of file contents
        """
        pass

APIBasedFileParser Implementation

Production-ready async file parser with full API integration:

class APIBasedFileParser:
    """Real file parser using APIs with async support."""
    
    async def parse_files(self, file_paths: List[str]) -> str:
        """Parse multiple files concurrently and return combined text representation."""
        
    async def _parse_audio(self, file_path: str) -> str:
        """Parse audio file using OpenAI Whisper API asynchronously."""
        
    async def _parse_document(self, file_path: str) -> str:
        """Parse PDF document using LlamaParse API."""
        
    async def _parse_url(self, file_path: str) -> str:
        """Parse URL content using Jina Reader API asynchronously."""
        
    def _get_file_type(self, file_path: str) -> FileType:
        """Determine file type by extension or URL pattern."""

File Type Detection

The parser automatically detects file types:

from path2dream_processors.file_parser import FileType

# Supported extensions
AUDIO_EXTENSIONS = {'.mp3', '.wav', '.m4a', '.flac', '.ogg', '.aac', '.wma'}
DOCUMENT_EXTENSIONS = {'.pdf', '.docx', '.txt', '.xlsx', ...}
# URLs starting with http:// or https://

📝 Detailed Examples

Processing Audio Files

import asyncio

async def process_audio():
    parser = APIBasedFileParser()

    # Transcribe audio to text
    result = await parser.parse_files(["interview.mp3"])
    print(result)
    # Output:
    # === PARSED FILE CONTENT ===
    # 
    # File: interview.mp3
    # Audio transcription: This is the transcribed content from the audio file...
    # ----------------------------------------

asyncio.run(process_audio())

Processing Documents

import asyncio

async def process_document():
    parser = APIBasedFileParser()
    
    # Extract text from PDF with structure preservation
    result = await parser.parse_files(["report.pdf"])
    print(result)
    # Output:
    # === PARSED FILE CONTENT ===
    # 
    # File: report.pdf  
    # Document content: # Executive Summary
    # 
    # This report analyzes...
    # ----------------------------------------

asyncio.run(process_document())

Processing Web Pages

import asyncio

async def process_webpage():
    parser = APIBasedFileParser()
    
    # Extract clean content from web pages
    result = await parser.parse_files(["https://example.com/article"])
    print(result)
    # Output:
    # === PARSED FILE CONTENT ===
    # 
    # File: example.com
    # Web content from: Article Title
    # 
    # Clean article content without ads or navigation...
    # ----------------------------------------

asyncio.run(process_webpage())

Mixed File Processing (Concurrent)

import asyncio

async def process_mixed_files():
    parser = APIBasedFileParser()
    
    # Process multiple file types concurrently
    files = [
        "audio.mp3",
        "document.pdf", 
        "https://news.site/article"
    ]

    # All files are processed in parallel for faster execution
    result = await parser.parse_files(files)
    print(result)
    # Returns combined clean text from all sources

asyncio.run(process_mixed_files())

🔑 API Keys Setup

OpenAI API Key

1. Visit OpenAI API Keys
2. Create a new API key
3. Add to your .env file as OPENAI_API_KEY

LlamaParse API Key

1. Visit LlamaIndex Cloud
2. Generate an API key
3. Add to your .env file as LLAMA_CLOUD_API_KEY

Jina API Key

1. Visit Jina AI
2. Sign up and get your API key
3. Add to your .env file as JINA_API_KEY

🧪 Testing

The package includes comprehensive testing with async support:

# Run all tests
pytest

# Run with coverage
pytest --cov=path2dream_processors

# Run only unit tests (fast)
pytest tests/ -k "not Integration"

# Run integration tests (requires API keys)
pytest tests/ -k "Integration"

🏗️ Architecture

Design Principles

• Async-First: Full async/await support for concurrent processing
• Interface Segregation: Clean abstract interface for easy testing and mocking
• Single Responsibility: Each parser method handles one file type
• Dependency Inversion: Relies on external APIs through well-defined interfaces
• Error Handling: Graceful degradation with informative error messages

Extension Points

To add support for new file types:

1. Add file extensions to the appropriate constant
2. Implement async _parse_<type> method in APIBasedFileParser
3. Add corresponding FileType enum value
4. Update _get_file_type method
5. Add comprehensive tests with async support

Example: Adding Image Support

async def _parse_image(self, file_path: str) -> str:
    """Parse image file using vision API."""
    try:
        # Your async image processing logic here
        async with aiohttp.ClientSession() as session:
            # API call logic
            pass
        return f"Image description: {description}"
    except Exception as e:
        return f"Error processing image: {str(e)}"

🤝 Contributing

We welcome contributions! Please see our contributing guidelines:

1. Fork the repository
2. Create a feature branch
3. Add tests for your changes (including async tests)
4. Ensure all tests pass
5. Submit a pull request

Development Setup

# Clone the repository
git clone https://github.com/your-username/path2dream_processors.git
cd path2dream_processors

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• 📚 Documentation: Check this README and inline code documentation
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

🎯 Roadmap

• Image processing with vision APIs
• Video processing with audio extraction
• Batch processing optimization
• Custom output format support
• Streaming API support
• Plugin architecture for custom processors