kodexa-document 8.0.0.dev20609788083

High-performance Python bindings for the Go-based Kodexa Document SDK with in-memory processing

Kodexa Document Python

High-performance Python bindings for the Go-based Kodexa Document SDK using CFFI. Provides comprehensive document processing capabilities with ~100x performance improvement through in-memory operations.

Overview

This package provides mature Python bindings for the Go-based Kodexa Document SDK. It uses CFFI (C Foreign Function Interface) to communicate with the Go library, offering full access to hierarchical document processing, advanced querying, and rich metadata management.

Key Highlights:

• Production Ready: 413+ comprehensive tests covering all functionality
• High Performance: ~100x faster with in-memory mode (1.19ms vs 121ms)
• Full Feature Set: Complete document manipulation, querying, and persistence
• Cross Platform: Linux, macOS (Intel/ARM), Windows, AWS Lambda

Features

Core Document Operations

• Document Creation: From text, JSON, KDDB files, or scratch
• In-Memory Processing: ~100x performance boost for temporary operations
• Context Managers: Automatic resource cleanup with with statements
• Multiple Formats: JSON export/import, KDDB persistence, dict conversion

Content Structure & Navigation

• Hierarchical Nodes: Document tree structure like DOM for web pages
• Content Operations: Rich text handling with content parts
• Tree Navigation: Parent/child relationships, sibling traversal, path queries
• Node Management: Create, modify, remove nodes with full hierarchy support

Advanced Querying

• Selector Language: XPath-like queries (//paragraph[contains(@content, 'text')])
• Variable Support: Parameterized queries with variable substitution
• Performance Options: First-only results, relative queries from nodes
• Rich Filtering: Content-based, tag-based, and feature-based selection

Metadata & Annotations

• Features System: Key-value metadata with type organization
• Tagging: Content annotation with confidence scores and values
• Document Labels: Classification and categorization
• Mixins: Capability flags and behavior markers
• External Data: Arbitrary data storage with custom keys
• Processing Steps: Workflow tracking and validation rules

Spatial & Geometric Operations

• Bounding Boxes: Position and dimension tracking
• Spatial Queries: Location-based content selection
• Coordinate Systems: Flexible positioning support

Enterprise Features

• Extraction Engine: Advanced content extraction with taxonomies
• Validation Framework: Rule-based document validation
• Statistics: Comprehensive document metrics and analysis
• Error Handling: Comprehensive exception system with specific error types
• Memory Management: Automatic cleanup with finalizers

Installation

pip install kodexa-document

Quick Start

from kodexa_document import Document

# Create high-performance in-memory document
with Document(inmemory=True) as doc:
    # Create document structure
    root = doc.create_node("document", "My Document")
    doc.content_node = root

    section = doc.create_node("section", "Introduction", parent=root)
    para = doc.create_node("paragraph", "Important content", parent=section)

    # Add rich metadata
    para.tag("important", confidence=0.95, value="key-point")
    para.add_feature("style", "emphasis", "bold")
    doc.add_label("technical-document")

    # Query with selectors
    important_nodes = doc.select("//paragraph[@tag='important']")
    all_content = doc.select("//*[contains(@content, 'content')]")

    # Export to different formats
    json_str = doc.to_json(indent=2)
    doc.save("output.kddb")

print(f"Found {len(important_nodes)} important paragraphs")

Advanced Usage Examples

Document Processing Pipeline

from kodexa_document import Document
from kodexa_document.errors import DocumentError

def process_document(input_path, output_path):
    """Complete document processing pipeline."""
    with Document.from_kddb(input_path, inmemory=True) as doc:
        # Analyze structure
        all_nodes = doc.select("//*")
        paragraphs = doc.select("//paragraph")

        # Process content
        for i, para in enumerate(paragraphs):
            if len(para.content) > 100:  # Long paragraphs
                para.tag("detailed", confidence=0.8)
                para.add_feature("analysis", "length", len(para.content))

            if i == 0:  # First paragraph
                para.tag("introduction")

        # Add document metadata
        doc.set_metadata("processed", True)
        doc.set_metadata("node_count", len(all_nodes))
        doc.add_label("processed-document")

        # Save results
        doc.save(output_path)

        return {
            "uuid": doc.uuid,
            "nodes": len(all_nodes),
            "tagged": len(doc.get_all_tagged_nodes())
        }

# Process with error handling
try:
    result = process_document("input.kddb", "processed.kddb")
    print(f"Processed document {result['uuid']}: {result['nodes']} nodes")
except DocumentError as e:
    print(f"Processing failed: {e}")

Content Analysis and Extraction

# Load and analyze document structure
with Document.from_text("Chapter 1\nIntroduction\nContent here",
                       separator="\n", inmemory=True) as doc:

    # Navigate document hierarchy
    root = doc.content_node
    children = root.get_children()

    # Rich querying
    headers = doc.select("//paragraph[1]")  # First paragraphs (likely headers)
    long_content = doc.select("//paragraph[string-length(@content) > 50]")

    # Feature analysis
    for node in children:
        node.add_feature("position", "index", node.index)
        if "Chapter" in node.content:
            node.tag("chapter-header")
            node.add_feature("structure", "type", "header")

    # Get comprehensive statistics
    stats = doc.get_statistics()
    tagged_nodes = doc.get_all_tagged_nodes()

    print(f"Document structure: {len(children)} top-level nodes")
    print(f"Tagged content: {len(tagged_nodes)} nodes")
    print(f"Statistics: {stats}")

Performance Comparison

import time

# In-memory processing (recommended for temporary operations)
start = time.time()
with Document(inmemory=True) as doc:
    root = doc.create_node("document", "Fast processing")
    doc.content_node = root
    for i in range(1000):
        doc.create_node("item", f"Item {i}", parent=root)
    nodes = doc.select("//*")
inmemory_time = time.time() - start

# File-based processing (for persistence)
start = time.time()
with Document(inmemory=False) as doc:
    root = doc.create_node("document", "Persistent processing")
    doc.content_node = root
    for i in range(1000):
        doc.create_node("item", f"Item {i}", parent=root)
    nodes = doc.select("//*")
file_time = time.time() - start

print(f"In-memory: {inmemory_time:.3f}s")
print(f"File-based: {file_time:.3f}s")
print(f"Performance improvement: {file_time/inmemory_time:.1f}x faster")

Loading Documents

The from_kddb method supports flexible loading modes:

# Standard loading modes
doc = Document.from_kddb("input.kddb")  # Detached copy (safe, default)
doc = Document.from_kddb("input.kddb", detached=False)  # In-place editing
doc = Document.from_kddb("input.kddb", inmemory=True)  # 100x performance boost

# Load from bytes (API responses, downloads, etc.)
with open("document.kddb", "rb") as f:
    kddb_bytes = f.read()
doc = Document.from_kddb(kddb_bytes, inmemory=True)

# Temporary files with auto-cleanup
doc = Document.from_kddb("temp.kddb", delete_on_close=True)

Parameter	Default	Description
detached	True	Creates working copy vs editing original
inmemory	False	Loads into memory for ~100x performance
delete_on_close	False	Auto-deletes file when document closes

Error Handling

from kodexa_document.errors import DocumentError, DocumentNotFoundError

# Robust error handling
try:
    with Document.from_kddb("document.kddb", inmemory=True) as doc:
        # Process document
        nodes = doc.select("//paragraph")
        for node in nodes:
            node.tag("processed")

        # Validate results
        if not doc.uuid:
            raise DocumentError("Invalid document state")

except DocumentNotFoundError:
    print("Document file not found")
except DocumentError as e:
    print(f"Document processing error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Architecture

Python Application
       ↓
CFFI Python Wrapper (413+ Tests)
       ↓
Go Shared Library (CGO)
       ↓
GORM Domain Layer
       ↓
SQLite Database (File/Memory)

Performance Modes:

• In-Memory SQLite: :memory: database for maximum speed
• File-Based SQLite: Persistent .kddb files for storage
• Hybrid Mode: Load from file, process in-memory, save back

Requirements

• Python 3.12+
• cffi >= 1.14.0
• Go shared library (automatically bundled in wheel)

Platform Support

• Linux x86_64 - Primary development platform
• macOS x86_64 & ARM64 - Intel and Apple Silicon support
• Windows x86_64 - Full Windows compatibility
• AWS Lambda - Amazon Linux 2 optimization

Testing & Quality

• 413+ Comprehensive Tests covering all functionality
• 100% Feature Coverage - All advertised features are tested and working
• Error Path Testing - Comprehensive error handling validation
• Performance Testing - Memory usage and speed benchmarks
• Cross-Platform Testing - Validated on all supported platforms

# Run comprehensive test suite
cd lib/python
source ../../venv/bin/activate
python -m pytest tests/ -v

# Test categories
python -m pytest tests/test_document.py -v                    # Core document operations
python -m pytest tests/test_contentnode_features_tags.py -v  # Features and tags
python -m pytest tests/test_contentnode_selectors.py -v      # Query system
python -m pytest tests/test_extraction.py -v                 # Advanced extraction

Development Setup

# Quick setup from repository root
python3 -m venv venv
source venv/bin/activate
pip install cffi pytest

# Build Go library and Python bindings
cd lib/go && make linux  # or: make darwin, make windows
cd ../python

# Test installation
python -c "from kodexa_document import Document; print('Success!')"

# Run tests
python -m pytest tests/ -v

Documentation

User Documentation

• USAGE.md - Comprehensive usage examples and best practices
• docs/API_REFERENCE.md - Complete API reference

Build Documentation

• docs/BUILD_SCRIPTS_GUIDE.md - Build automation guide
• build/docs/BUILD.md - Detailed build instructions
• build/docs/WINDOWS_SETUP.md - Windows development setup

Best Practices

1. Use inmemory=True for temporary processing (~100x faster)
2. Use context managers (with statements) for automatic cleanup
3. Handle specific exceptions (DocumentError, DocumentNotFoundError)
4. Structure documents hierarchically with proper parent-child relationships
5. Leverage selectors for efficient document querying
6. Use features and tags for rich content annotation
7. Set meaningful metadata for document tracking and organization

Use Cases

• Document Processing Pipelines - ETL workflows for structured documents
• Content Analysis - Text mining, information extraction, document understanding
• Document Transformation - Format conversion, structure normalization
• Search and Indexing - Content indexing with rich metadata
• Validation and Quality - Document structure validation and quality assessment
• Machine Learning - Feature extraction for ML pipelines
• Enterprise Integration - High-performance document processing systems

Performance Characteristics

Operation	In-Memory	File-Based	Improvement
Document Creation	~1.2ms	~121ms	100x
Node Creation (1000 nodes)	~15ms	~1.5s	100x
Selector Queries	~2ms	~45ms	22x
Feature/Tag Operations	~0.5ms	~25ms	50x

License

Same as the main Kodexa Document SDK.

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Ready to get started? Check out USAGE.md for comprehensive examples and run the test suite to see all features in action!