Automated Document Parser Documentation

Welcome to the Automated Document Parser documentation!

Automated Document Parser is a powerful and intelligent document processing library built on top of LangChain. It provides automatic file type detection and loading for various document formats, making it easy to build RAG (Retrieval-Augmented Generation) applications.

Features

  • Automatic File Type Detection - Intelligently detects and processes 10+ file formats

  • Multiple PDF Loaders - 9 different PDF loading methods for various use cases

  • Modular Architecture - Clean separation with file_load/ and pdf_load/ modules

  • Easy Integration - Simple API that works seamlessly with LangChain

  • Structured Data Support - Handle CSV, JSON, and other structured formats

  • Extensible Design - Easy to add custom loaders

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 methods):

  • pypdf - Basic PDF text extraction (default)

  • 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

Quick Start

Installation

pip install automated-document-parser

Basic Usage

Step 1: Automatic File Type Detection

The parser automatically detects file types and loads them with sensible defaults:

from automated_document_parser import DocumentParser

# Initialize the parser
parser = DocumentParser()

# Parse a single document (auto-detects file type)
documents = parser.parse("path/to/document.pdf")

# Parse multiple documents (auto-detects each file type)
files = ["doc1.txt", "data.csv", "report.pdf"]
results = parser.parse_multiple(files)

Step 2: Specify Loading Methods

You can specify loading methods and parameters that apply to all appropriate files:

# Specify PDF loading method for all PDFs
files = ["doc1.txt", "data.csv", "report.pdf", "paper.pdf"]
results = parser.parse_multiple(
    files,
    pdf_loader_method="pdfplumber"  # Applies to all PDF files
)

# Specify encoding for text files
results = parser.parse_multiple(
    files,
    encoding="utf-8"  # Applies to all text files
)

# Combine multiple parameters
results = parser.parse_multiple(
    files,
    pdf_loader_method="pymupdf",
    encoding="utf-8"
)

Single File with Parameters

# Parse single PDF with specific method
documents = parser.parse(
    "document.pdf",
    pdf_loader_method="pdfplumber"
)

# Parse text file with encoding
documents = parser.parse(
    "file.txt",
    encoding="utf-8"
)

Advanced PDF Loading

For more control, use PDFLoader directly:

from automated_document_parser.loaders import PDFLoader

# Use specific PDF loading method
loader = PDFLoader("document.pdf", method="pdfplumber")
documents = loader.load()

# Use Mathpix for mathematical content
loader = PDFLoader(
    "math_paper.pdf",
    method="mathpix",
    mathpix_app_id="your_id",
    mathpix_app_key="your_key"
)
documents = loader.load()

Direct Loader Usage

from automated_document_parser.loaders.file_load import (
    TextFileLoader,
    CSVFileLoader,
    JSONFileLoader
)

# Load text file with specific encoding
loader = TextFileLoader("file.txt", encoding="utf-8")
docs = loader.load()

# Load CSV file
csv_loader = CSVFileLoader("data.csv")
csv_docs = csv_loader.load()

Contents

API Reference

Additional Resources

Indices and tables