Automated Document Parser

A powerful and automated document parser built with LangChain for intelligent document processing. This library automatically detects file types and uses the appropriate loader to parse documents into LangChain-compatible formats.

Features

• Automatic file type detection based on file extensions
• Multiple PDF loading methods - 9 different PDF loaders for various use cases
• Modular architecture - Clean separation with file_load/ and pdf_load/ modules
• Support for multiple document formats: PDF, TXT, CSV, JSON, DOCX, HTML, Markdown
• Built on LangChain for seamless integration with RAG applications
• Type-safe implementation with comprehensive error handling
• Batch processing support for multiple documents

Supported File Types

Text Files

• .txt - Plain text files
• .md - Markdown files

Structured Data

• .csv - CSV files with encoding support
• .json - JSON files with jq schema filtering

Documents

• .docx - Microsoft Word documents
• .html - HTML files

PDF Files (9 loading methods)

• pypdf - Basic PDF text extraction (default, no extra dependencies)
• unstructured - Advanced OCR and layout detection
• amazon_textract - AWS Textract for high-accuracy OCR
• mathpix - Specialized for mathematical formulas
• pdfplumber - High accuracy text and table extraction
• pypdfium2 - Google PDFium library
• pymupdf - PyMuPDF (fitz) backend
• pymupdf4llm - LLM-optimized extraction
• opendataloader - Advanced multi-format parsing

Installation

Install from PyPI:

pip install automated-document-parser

Or using uv:

uv add automated-document-parser

Quick Start

Basic Usage - Automatic File Type Detection

The primary feature is automatic file type detection. Just point to any supported file and the parser handles the rest:

from automated_document_parser import DocumentParser

# Initialize the parser
parser = DocumentParser()

# Parse any single file - automatically detects type and uses the right loader
documents = parser.parse("document.pdf")        # Auto-detects PDF
documents = parser.parse("data.csv")            # Auto-detects CSV
documents = parser.parse("notes.txt")           # Auto-detects text file

# Parse multiple files of different types - all formats handled automatically
file_paths = ["report.pdf", "data.csv", "notes.txt", "info.docx"]
all_docs = parser.parse_multiple(file_paths)  # Each file auto-detected and loaded

# Access parsed content
for file_path, docs in all_docs.items():
    print(f"File: {file_path}")
    for doc in docs:
        print(f"  Content: {doc.page_content[:100]}...")
        print(f"  Metadata: {doc.metadata}")

Specify Loading Methods

Specify the PDF loading method and other parameters to apply to all files:

from automated_document_parser import DocumentParser

parser = DocumentParser()

# Step 1: Specify the method for PDFs
# Step 2: Parser automatically detects file types and loads them with specified method
file_paths = ["report1.pdf", "report2.pdf", "data.csv", "notes.txt"]
all_docs = parser.parse_multiple(
    file_paths,
    pdf_loader_method="pdfplumber",  # All PDFs will use pdfplumber
    encoding="utf-8"                  # All text files will use UTF-8 encoding
)

# Each file is automatically detected and loaded with the specified settings
for file_path, docs in all_docs.items():
    print(f"Loaded {file_path}: {len(docs)} documents")

Documentation

Full documentation is available at: https://pulkit12dhingra.github.io/automated-document-parser/

Architecture

The library uses a modular architecture:

automated_document_parser/
├── loaders/
│   ├── file_load/          # File loaders module
│   │   ├── base.py         # Base file loader class
│   │   ├── text_loader.py  # Text file loader
│   │   ├── csv_loader.py   # CSV loader
│   │   ├── json_loader.py  # JSON loader
│   │   ├── docx_loader.py  # DOCX loader
│   │   └── html_loader.py  # HTML loader
│   ├── pdf_load/           # PDF loaders module
│   │   ├── base.py         # Base PDF loader class
│   │   ├── pypdf_loader.py
│   │   ├── mathpix_loader.py
│   │   ├── pdfplumber_loader.py
│   │   └── ... (9 PDF loaders total)
│   └── file_loaders.py     # Main orchestrator
└── core.py                 # DocumentParser class

License

MIT License - see LICENSE file for details.

Contributing

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