commercetxt 1.0.3

Python reference parser + RAG tools for the CommerceTXT protocol.

CommerceTXT Python Reference Parser

Production-ready Python parser for the CommerceTXT Protocol v1.0.1

Robust, secure implementation with enterprise-grade security, async support, RAG tools, and AI integration.

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✨ Key Features

Core Parser

• ✅ Full Spec Compliance - CommerceTXT Protocol v1.0.1 (Tier 1, 2, 3 directives)
• ✅ UTF-16/32 Support - Auto-detect Excel exports and international encodings
• ✅ Fractal Inheritance - Multi-file resolution with circular dependency detection
• ✅ Indent Auto-Detection - Handles 2-space, 4-space, mixed indentation
• ✅ BOM Handling - UTF-8/UTF-16/UTF-32 Byte Order Mark detection
• ✅ Source Mapping - Track line numbers for debugging

Security & Performance

• 🔒 SSRF Protection - Blocks private IPs, localhost, exotic IP notations
• 🔒 DoS Mitigation - File size (10MB), nesting depth (100), rate limits
• ⚡ LRU Caching - High-performance caching for repeated parses
• ⚡ Async Support - Concurrent bulk parsing via AsyncCommerceTXTParser
• 📊 Performance Metrics - Real-time timing and memory tracking

AI & RAG Tools

• 🤖 AI Bridge - Low-token prompts (~120 tokens vs 8,500+ HTML)
• 🏥 Health Checker - AI readiness scoring (0-100)
• 🌐 Schema.org Bridge - JSON-LD conversion with full mappings
• 🔧 Semantic Normalizer - Standardize attributes across catalogs
• 📦 RAG Pipeline - Vector database integration
• ⚡ Async RAG - Stream-based processing

Vector Database Support

• Pinecone, Qdrant, Redis, FAISS, In-Memory

Validation & Testing

• ✅ Tiered Validation - Tier 1/2/3 compliance checks
• ✅ 80%+ Coverage - Comprehensive test suite
• ✅ Property-Based Tests - Hypothesis for edge cases
• ✅ Fuzz Testing - Random input stress testing
• ✅ Security Audits - SSRF/DoS prevention

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🚀 Installation

Basic Install

pip install commercetxt

With Optional Features

pip install commercetxt[cli]      # Colored CLI output
pip install commercetxt[async]    # Async file support
pip install commercetxt[rag]      # RAG tools (local bundle)
pip install commercetxt[rag-all]  # All RAG drivers
pip install commercetxt[dev]      # Development tools

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📖 Usage Examples

Basic Parsing

from commercetxt import parse_file

# Parse commerce.txt file
result = parse_file('commerce.txt')

# Access directives
identity = result.directives.get('IDENTITY', {})
product = result.directives.get('PRODUCT', {})
offer = result.directives.get('OFFER', {})

print(f"Store: {identity.get('Name')}")
print(f"Product: {product.get('Name')}")
print(f"Price: ${offer.get('Price')}")

# Check for issues
if result.errors:
    print(f"Errors: {result.errors}")

With Validation

from commercetxt import parse_file, CommerceTXTValidator

result = parse_file('commerce.txt')

# Validate
validator = CommerceTXTValidator(strict=False)
validated = validator.validate(result)

print(f"Errors: {len(validated.errors)}")
print(f"Warnings: {len(validated.warnings)}")

AI Bridge (Low-Token Prompts)

from commercetxt import parse_file
from commercetxt.bridge import CommerceAIBridge

result = parse_file('product.txt')
bridge = CommerceAIBridge(result)

# Generate ~120 token prompt
prompt = bridge.generate_low_token_prompt()
print(prompt)

# Get AI readiness score
score = bridge.calculate_readiness_score()
print(f"Score: {score}/100")

Async Bulk Processing

import asyncio
from commercetxt.async_parser import AsyncCommerceTXTParser
from pathlib import Path

async def process_catalog():
    parser = AsyncCommerceTXTParser()
    
    # Read file contents
    files = ['p1.txt', 'p2.txt', 'p3.txt']
    contents = [Path(f).read_text() for f in files]
    
    # Parse concurrently
    results = await parser.parse_many(contents)
    
    for result in results:
        product = result.directives.get('PRODUCT', {})
        print(f"Processed: {product.get('Name')}")

asyncio.run(process_catalog())

Caching

from commercetxt.cache import parse_cached

# First call - parses
result1 = parse_cached(content)

# Second call - from cache
result2 = parse_cached(content)

Fractal Inheritance

from commercetxt.resolver import CommerceTXTResolver

resolver = CommerceTXTResolver()
# Resolves @INHERIT and merges
merged = resolver.resolve('https://example.com/product.txt')

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🤖 RAG Tools

AI Health Check

from commercetxt.rag.tools import AIHealthChecker

checker = AIHealthChecker()
health = checker.check(result)

print(f"Score: {health.score}/100")
print(f"Grade: {health.grade}")

Schema.org Export

from commercetxt.rag.tools import SchemaBridge

bridge = SchemaBridge()
json_ld = bridge.to_json_ld(result)
print(json_ld)

Semantic Normalization

from commercetxt.rag.tools import SemanticNormalizer

normalizer = SemanticNormalizer()
normalized = normalizer.normalize({
    'color': 'midnight black',
    'capacity': '128GB'
})
# Output: {'color': 'black', 'storage': '128'}

RAG Pipeline

from commercetxt.rag import RAGGenerator
from commercetxt import parse_file

result = parse_file('commerce.txt')

generator = RAGGenerator()
shards = generator.generate(result)

for shard in shards:
    print(f"Text: {shard.text[:50]}...")
    print(f"Tags: {shard.semantic_tags}")

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🖥️ CLI Commands

Basic

commercetxt commerce.txt               # Parse and validate
commercetxt commerce.txt --json        # JSON output
commercetxt commerce.txt --strict      # Warnings as errors

Validation

commercetxt commerce.txt --validate    # Full validation report
commercetxt product.txt --health       # AI health check
commercetxt commerce.txt --metrics     # Performance metrics

AI Tools

commercetxt product.txt --prompt       # Low-token LLM prompt
commercetxt commerce.txt --schema      # Schema.org JSON-LD

Product Tools

commercetxt p1.txt p2.txt --compare    # Compare products
commercetxt product.txt --normalize    # Normalize attributes

Advanced

commercetxt file.txt --log-level DEBUG
commercetxt file.txt --validate --metrics --json

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🏗️ Architecture

commercetxt/
├── parser.py         # Core parsing engine
├── async_parser.py   # Async concurrent parser
├── validator.py      # Validation facade
├── validators/       # Tier validators
├── bridge.py         # AI prompt generator
├── resolver.py       # Fractal inheritance
├── cache.py          # LRU caching
├── security.py       # SSRF/DoS protection
├── cli.py            # CLI interface
└── rag/              # RAG tools
    ├── pipeline.py   # RAG pipeline
    ├── core/         # Core logic
    ├── drivers/      # Vector DB drivers
    ├── tools/        # Utilities
    └── monitoring/   # Health checks

Security Limits

• MAX_FILE_SIZE: 10 MB
• MAX_SECTIONS: 1,000
• MAX_LINE_LENGTH: 100 KB
• MAX_NESTING_DEPTH: 100

Blocked Networks:

• Localhost (127.0.0.0/8)
• Private IPs (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16)
• Link-local (169.254.0.0/16)

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🧪 Testing

Run Tests

pytest tests/                          # All tests
pytest --cov=commercetxt               # With coverage
pytest tests/test_parser.py -v        # Specific suite

Test Strategy

• Unit Tests (150+ vectors)
• Integration Tests
• Property-Based (Hypothesis)
• Fuzz Tests
• Security Tests
• Performance Tests

Coverage: 80%+ (verified 82%)

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🔧 Configuration

Environment Variables

export COMMERCETXT_CACHE_SIZE=1000
export COMMERCETXT_LOG_LEVEL=INFO

Programmatic

from commercetxt import CommerceTXTParser

parser = CommerceTXTParser(
    strict=True,
    auto_detect_indent=True,
    indent_width=4
)

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📚 Examples

See examples directory:

• Basic product catalog
• Multi-language stores
• Category hierarchies
• Google Store example

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🤝 Contributing

• Repository: https://github.com/commercetxt/commercetxt
• Issues: https://github.com/commercetxt/commercetxt/issues
• Discussions: https://github.com/commercetxt/commercetxt/discussions

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📄 License

MIT License - see LICENSE

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🔗 Links

• Protocol Spec v1.0.1: spec/README.md
• PyPI: https://pypi.org/project/commercetxt/
• Website: https://commercetxt.org
• Changelog: https://github.com/commercetxt/commercetxt/releases

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Parser v1.0.3 | Protocol v1.0.1 | Built for the Agentic Web