docprocessor 1.1.0

Intelligent document processing with OCR, chunking, and AI summarization

DocProcessor

A Python library for processing documents with OCR, semantic chunking, and LLM-based summarization. Designed for building semantic search systems and document analysis workflows.

Table of Contents

• Features
• Installation
• Quick Start
• Advanced Usage
• API Reference
• Development
• Contributing
• License

Features

• Multi-format Support: PDF, DOCX, PPTX, TXT, MD, and images (PNG, JPG, GIF, BMP)
• Intelligent OCR: Layout-aware PDF text extraction with OCR fallback for images
• Semantic Chunking: Smart text segmentation using LangChain's RecursiveCharacterTextSplitter
• LLM Summarization: Generate concise document summaries (with fallback)
• Meilisearch Integration: Built-in support for indexing to Meilisearch
• Flexible API: Use components individually or as a unified pipeline

Installation

From PyPI (Coming Soon)

pip install docprocessor

From GitHub

pip install git+https://github.com/Knowledge-Innovation-Centre/doc-processor.git

For Development

git clone https://github.com/Knowledge-Innovation-Centre/doc-processor.git
cd doc-processor
pip install -e ".[dev]"

System Dependencies

For OCR functionality, install system packages:

Ubuntu/Debian:

sudo apt-get install tesseract-ocr poppler-utils

macOS:

brew install tesseract poppler

Quick Start

Basic Usage

from docprocessor import DocumentProcessor

# Initialize processor
processor = DocumentProcessor()

# Process a document
result = processor.process(
    file_path="document.pdf",
    extract_text=True,
    chunk=True,
    summarize=False  # Requires LLM client
)

print(f"Extracted {len(result.text)} characters")
print(f"Created {len(result.chunks)} chunks")

With LLM Summarization

from docprocessor import DocumentProcessor

# Your LLM client (must have a complete_chat method)
class MyLLMClient:
    def complete_chat(self, messages, temperature):
        # Call your LLM API (OpenAI, Anthropic, Mistral, etc.)
        return {"content": "Generated summary here"}

llm_client = MyLLMClient()

processor = DocumentProcessor(
    llm_client=llm_client,
    summary_target_words=500
)

result = processor.process(
    file_path="document.pdf",
    summarize=True
)

print(f"Summary: {result.summary}")

With Meilisearch Indexing

from docprocessor import DocumentProcessor, MeiliSearchIndexer

# Process document
processor = DocumentProcessor()
result = processor.process("document.pdf", chunk=True)

# Index to Meilisearch
indexer = MeiliSearchIndexer(
    url="http://localhost:7700",
    api_key="your_master_key",
    index_prefix="dev_"  # Optional environment prefix
)

# Convert chunks to search documents
search_docs = processor.chunks_to_search_documents(result.chunks)

# Index chunks
indexer.index_chunks(
    chunks=search_docs,
    index_name="document_chunks"
)

# Search
results = indexer.search(
    query="artificial intelligence",
    index_name="document_chunks",
    limit=10
)

Advanced Usage

Custom Chunking Parameters

processor = DocumentProcessor(
    chunk_size=1024,      # Larger chunks
    chunk_overlap=100,    # More overlap
    min_chunk_size=200    # Higher minimum
)

chunks = processor.chunk_text(
    text="Your long document text here...",
    filename="document.txt"
)

Extract Text Only

processor = DocumentProcessor()

extraction = processor.extract_text("document.pdf")

print(f"Text: {extraction['text']}")
print(f"Pages: {extraction['page_count']}")
print(f"Format: {extraction['metadata']['format']}")

Multi-Environment Indexing

# Index to multiple environments
environments = {
    "dev": {
        "url": "http://localhost:7700",
        "api_key": "dev_key",
        "prefix": "dev_"
    },
    "prod": {
        "url": "https://search.production.com",
        "api_key": "prod_key",
        "prefix": "prod_"
    }
}

for env_name, config in environments.items():
    indexer = MeiliSearchIndexer(
        url=config["url"],
        api_key=config["api_key"],
        index_prefix=config["prefix"]
    )

    indexer.index_chunks(search_docs, "document_chunks")
    print(f"Indexed to {env_name}")

API Reference

DocumentProcessor

Main class for document processing.

Parameters:

• ocr_enabled (bool): Enable OCR for PDFs/images. Default: True
• chunk_size (int): Target chunk size in tokens. Default: 512
• chunk_overlap (int): Overlap between chunks. Default: 50
• min_chunk_size (int): Minimum chunk size. Default: 100
• summary_target_words (int): Target summary length. Default: 500
• llm_client (Optional[Any]): LLM client for summarization
• llm_temperature (float): LLM temperature. Default: 0.3

Methods:

• process(): Full pipeline (extract, chunk, summarize)
• extract_text(): Extract text from document
• chunk_text(): Chunk text into segments
• summarize_text(): Generate summary
• chunks_to_search_documents(): Convert chunks for indexing

MeiliSearchIndexer

Interface for Meilisearch operations.

Parameters:

• url (str): Meilisearch server URL
• api_key (str): Meilisearch API key
• index_prefix (Optional[str]): Prefix for index names

Methods:

• index_chunks(): Index multiple documents
• index_document(): Index single document
• search(): Search an index
• delete_document(): Delete by ID
• delete_documents_by_filter(): Delete by filter
• create_index(): Create new index

DocumentChunk

Data class representing a text chunk.

Attributes:

• chunk_id (str): Unique identifier
• file_id (str): Source file identifier
• output_id (str): Output identifier
• project_id (int): Project identifier
• filename (str): Source filename
• chunk_number (int): Chunk sequence number
• total_chunks (int): Total chunks in document
• chunk_text (str): The chunk text content
• token_count (int): Number of tokens
• pages (List[int]): Page numbers (for PDFs)
• metadata (Dict): Additional metadata

Architecture

DocProcessor consists of several independent components:

1. ContentExtractor: Extracts text from various file formats
2. DocumentChunker: Splits text into semantic segments
3. DocumentSummarizer: Generates LLM-based summaries
4. MeiliSearchIndexer: Indexes documents to Meilisearch

Each component can be used independently or through the unified DocumentProcessor API.

Requirements

Python: 3.10+ (tested on 3.10, 3.11, 3.12)

Core Dependencies:

• pdfminer.six - PDF text extraction
• pdf2image - PDF to image conversion
• pytesseract - OCR engine
• opencv-python - Image preprocessing
• Pillow - Image handling
• python-docx - DOCX extraction
• python-pptx - PPTX extraction
• langchain-text-splitters - Semantic chunking
• tiktoken - Token counting

Optional:

• meilisearch - Search engine integration

Examples

See the examples/ directory for more usage examples:

• basic_usage.py - Simple document processing
• multi_environment.py - Indexing to multiple environments
• custom_chunking.py - Advanced chunking options

Development

Using GitHub Codespaces (Recommended)

The easiest way to start developing:

1. Click the Code button on GitHub
2. Select Codespaces → Create codespace on main
3. Wait for the environment to build (includes all dependencies)
4. Start coding!

The devcontainer automatically installs:

• Python 3.11
• All system dependencies (Tesseract, Poppler)
• Python dependencies in editable mode
• Pre-commit hooks
• VS Code extensions (Black, isort, flake8, etc.)

Local Development Setup

# Clone the repository
git clone https://github.com/Knowledge-Innovation-Centre/doc-processor.git
cd doc-processor

# Install system dependencies
# Ubuntu/Debian
sudo apt-get install tesseract-ocr poppler-utils

# macOS
brew install tesseract poppler

# Install Python dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

# Run tests with coverage
pytest --cov=docprocessor

Code Quality

We use automated tools to maintain code quality:

# Format code
black docprocessor tests

# Sort imports
isort docprocessor tests

# Lint
flake8 docprocessor tests

# Type check
mypy docprocessor

# Or run all checks with pre-commit
pre-commit run --all-files

Running Tests

# Run all tests
pytest

# With coverage report
pytest --cov=docprocessor --cov-report=html

# Run specific test file
pytest tests/test_processor.py -v

# Run tests matching pattern
pytest -k "test_extract" -v

Contributing

We love contributions! Please see CONTRIBUTING.md for details on:

• Development setup
• Code style guidelines
• Testing requirements
• Pull request process
• Issue reporting

Quick tips:

• Use the devcontainer for consistent environment
• Write tests for new features
• Follow PEP 8 and use pre-commit hooks
• Update documentation for API changes
• Add entries to CHANGELOG.md

Changelog

See CHANGELOG.md for version history and release notes.

License

MIT License - see LICENSE file for details.

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

Citation

If you use docprocessor in your research or project, please cite:

@software{docprocessor2025,
  title = {docprocessor: Intelligent Document Processing Library},
  author = {Knowledge Innovation Centre},
  year = {2025},
  url = {https://github.com/Knowledge-Innovation-Centre/doc-processor}
}

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Made with ❤️ by Knowledge Innovation Centre