mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 16:36:21 +02:00
Structured data, minor features (#500)
- Sorted out confusing --auto mode with tg-load-structured-data - Fixed tests & added CLI tests
This commit is contained in:
cybermaggedon
GitHub
parent
0b7620bc04
commit
5537fac731
7 changed files with 3318 additions and 360 deletions
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@ -31,6 +31,7 @@ def load_structured_data(
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suggest_schema: bool = False,
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generate_descriptor: bool = False,
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parse_only: bool = False,
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auto: bool = False,
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output_file: str = None,
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sample_size: int = 100,
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sample_chars: int = 500,
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@ -49,6 +50,7 @@ def load_structured_data(
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suggest_schema: Analyze data and suggest matching schemas
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generate_descriptor: Generate descriptor from data sample
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parse_only: Parse data but don't import to TrustGraph
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auto: Run full automatic pipeline (suggest schema + generate descriptor + import)
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output_file: Path to write output (descriptor/parsed data)
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sample_size: Number of records to sample for analysis
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sample_chars: Maximum characters to read for sampling
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@ -62,7 +64,90 @@ def load_structured_data(
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logging.basicConfig(level=logging.INFO)
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# Determine operation mode
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if suggest_schema:
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if auto:
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logger.info(f"🚀 Starting automatic pipeline for {input_file}...")
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logger.info("Step 1: Analyzing data to discover best matching schema...")
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# Step 1: Auto-discover schema (reuse suggest_schema logic)
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discovered_schema = _auto_discover_schema(api_url, input_file, sample_chars, logger)
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if not discovered_schema:
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logger.error("Failed to discover suitable schema automatically")
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print("❌ Could not automatically determine the best schema for your data.")
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print("💡 Try running with --suggest-schema first to see available options.")
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return None
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logger.info(f"✅ Discovered schema: {discovered_schema}")
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print(f"🎯 Auto-selected schema: {discovered_schema}")
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# Step 2: Auto-generate descriptor
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logger.info("Step 2: Generating descriptor configuration...")
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auto_descriptor = _auto_generate_descriptor(api_url, input_file, discovered_schema, sample_chars, logger)
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if not auto_descriptor:
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logger.error("Failed to generate descriptor automatically")
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print("❌ Could not automatically generate descriptor configuration.")
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return None
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logger.info("✅ Generated descriptor configuration")
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print("📝 Generated descriptor configuration")
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# Step 3: Parse and preview data
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logger.info("Step 3: Parsing and validating data...")
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preview_records = _auto_parse_preview(input_file, auto_descriptor, min(sample_size, 5), logger)
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if preview_records is None:
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logger.error("Failed to parse data with generated descriptor")
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print("❌ Could not parse data with generated descriptor.")
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return None
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# Show preview
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print("📊 Data Preview (first few records):")
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print("=" * 50)
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for i, record in enumerate(preview_records[:3], 1):
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print(f"Record {i}: {record}")
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print("=" * 50)
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# Step 4: Import (unless dry_run)
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if dry_run:
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logger.info("✅ Dry run complete - data is ready for import")
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print("✅ Dry run successful! Data is ready for import.")
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print(f"💡 Run without --dry-run to import {len(preview_records)} records to TrustGraph.")
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return None
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else:
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logger.info("Step 4: Importing data to TrustGraph...")
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print("🚀 Importing data to TrustGraph...")
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# Recursively call ourselves with the auto-generated descriptor
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# This reuses all the existing import logic
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import tempfile
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import os
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# Save auto-generated descriptor to temp file
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temp_descriptor = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False)
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json.dump(auto_descriptor, temp_descriptor, indent=2)
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temp_descriptor.close()
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try:
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# Call the full pipeline mode with our auto-generated descriptor
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result = load_structured_data(
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api_url=api_url,
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input_file=input_file,
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descriptor_file=temp_descriptor.name,
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flow=flow,
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dry_run=False, # We already handled dry_run above
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verbose=verbose
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)
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print("✅ Auto-import completed successfully!")
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logger.info("Auto-import pipeline completed successfully")
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return result
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finally:
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# Clean up temp descriptor file
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try:
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os.unlink(temp_descriptor.name)
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except:
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pass
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elif suggest_schema:
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logger.info(f"Analyzing {input_file} to suggest schemas...")
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logger.info(f"Sample size: {sample_size} records")
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logger.info(f"Sample chars: {sample_chars} characters")
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@ -497,123 +582,144 @@ def load_structured_data(
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print(f"- Records processed: {len(output_records)}")
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print(f"- Target schema: {schema_name}")
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print(f"- Field mappings: {len(mappings)}")
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# Helper functions for auto mode
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def _auto_discover_schema(api_url, input_file, sample_chars, logger):
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"""Auto-discover the best matching schema for the input data"""
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try:
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# Read sample data
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with open(input_file, 'r', encoding='utf-8') as f:
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sample_data = f.read(sample_chars)
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# Import API modules
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from trustgraph.api import Api
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api = Api(api_url)
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config_api = api.config()
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# Get available schemas
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schema_keys = config_api.list("schema")
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if not schema_keys:
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logger.error("No schemas available in TrustGraph configuration")
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return None
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else:
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# Full pipeline: parse and import
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if not descriptor_file:
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# Auto-generate descriptor if not provided
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logger.info("No descriptor provided, auto-generating...")
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logger.info(f"Schema name: {schema_name}")
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# Read sample data for descriptor generation
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# Get schema definitions
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schemas = {}
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for key in schema_keys:
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try:
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with open(input_file, 'r', encoding='utf-8') as f:
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sample_data = f.read(sample_chars)
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logger.info(f"Read {len(sample_data)} characters for descriptor generation")
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schema_def = config_api.get("schema", key)
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schemas[key] = schema_def
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except Exception as e:
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logger.error(f"Failed to read input file for descriptor generation: {e}")
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raise
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logger.warning(f"Could not load schema {key}: {e}")
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if not schemas:
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logger.error("No valid schemas could be loaded")
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return None
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# Generate descriptor using TrustGraph prompt service
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# Use prompt service for schema selection
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flow_api = api.flow().id("default")
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prompt_client = flow_api.prompt()
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prompt = f"""Analyze this data sample and determine the best matching schema:
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DATA SAMPLE:
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{sample_data[:1000]}
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AVAILABLE SCHEMAS:
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{json.dumps(schemas, indent=2)}
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Return ONLY the schema name (key) that best matches this data. Consider:
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1. Field names and types in the data
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2. Data structure and format
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3. Domain and use case alignment
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Schema name:"""
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response = prompt_client.schema_selection(
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schemas=schemas,
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sample=sample_data[:1000]
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)
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# Extract schema name from response
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if isinstance(response, dict) and 'schema' in response:
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return response['schema']
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elif isinstance(response, str):
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# Try to extract schema name from text response
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response_lower = response.lower().strip()
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for schema_key in schema_keys:
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if schema_key.lower() in response_lower:
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return schema_key
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# If no exact match, try first mentioned schema
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words = response.split()
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for word in words:
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clean_word = word.strip('.,!?":').lower()
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if clean_word in [s.lower() for s in schema_keys]:
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matching_schema = next(s for s in schema_keys if s.lower() == clean_word)
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return matching_schema
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logger.warning(f"Could not parse schema selection from response: {response}")
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# Fallback: return first available schema
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logger.info(f"Using fallback: first available schema '{schema_keys[0]}'")
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return schema_keys[0]
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except Exception as e:
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logger.error(f"Schema discovery failed: {e}")
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return None
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def _auto_generate_descriptor(api_url, input_file, schema_name, sample_chars, logger):
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"""Auto-generate descriptor configuration for the discovered schema"""
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try:
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# Read sample data
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with open(input_file, 'r', encoding='utf-8') as f:
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sample_data = f.read(sample_chars)
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# Import API modules
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from trustgraph.api import Api
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api = Api(api_url)
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config_api = api.config()
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# Get schema definition
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schema_def = config_api.get("schema", schema_name)
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# Use prompt service for descriptor generation
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flow_api = api.flow().id("default")
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prompt_client = flow_api.prompt()
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response = prompt_client.diagnose_structured_data(
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sample=sample_data,
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schema_name=schema_name,
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schema=schema_def
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)
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if isinstance(response, str):
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try:
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from trustgraph.api import Api
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from trustgraph.api.types import ConfigKey
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api = Api(api_url)
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config_api = api.config()
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# Get available schemas
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logger.info("Fetching available schemas for descriptor generation...")
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schema_keys = config_api.list("schema")
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logger.info(f"Found {len(schema_keys)} schemas: {schema_keys}")
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if not schema_keys:
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logger.warning("No schemas found in configuration")
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print("No schemas available in TrustGraph configuration")
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return
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# Fetch each schema definition
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schemas = []
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config_keys = [ConfigKey(type="schema", key=key) for key in schema_keys]
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schema_values = config_api.get(config_keys)
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for value in schema_values:
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try:
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schema_def = json.loads(value.value) if isinstance(value.value, str) else value.value
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schemas.append(schema_def)
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logger.debug(f"Loaded schema: {value.key}")
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except json.JSONDecodeError as e:
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logger.warning(f"Failed to parse schema {value.key}: {e}")
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continue
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logger.info(f"Successfully loaded {len(schemas)} schema definitions")
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# Generate descriptor using diagnose-structured-data prompt
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flow_api = api.flow().id(flow)
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logger.info("Calling TrustGraph diagnose-structured-data prompt for descriptor generation...")
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response = flow_api.prompt(
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id="diagnose-structured-data",
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variables={
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"schemas": schemas,
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"sample": sample_data
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}
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)
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# Parse the generated descriptor
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if isinstance(response, str):
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try:
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descriptor = json.loads(response)
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except json.JSONDecodeError:
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logger.error("Generated descriptor is not valid JSON")
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raise ValueError("Failed to generate valid descriptor")
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else:
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descriptor = response
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# Override schema_name if provided
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if schema_name:
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descriptor.setdefault('output', {})['schema_name'] = schema_name
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logger.info("Successfully generated descriptor from data sample")
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except ImportError as e:
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logger.error(f"Failed to import TrustGraph API: {e}")
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raise
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except Exception as e:
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logger.error(f"Failed to generate descriptor: {e}")
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raise
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return json.loads(response)
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except json.JSONDecodeError:
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logger.error("Generated descriptor is not valid JSON")
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return None
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else:
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# Load existing descriptor
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try:
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with open(descriptor_file, 'r', encoding='utf-8') as f:
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descriptor = json.load(f)
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logger.info(f"Loaded descriptor configuration from {descriptor_file}")
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except Exception as e:
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logger.error(f"Failed to load descriptor file: {e}")
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raise
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return response
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except Exception as e:
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logger.error(f"Descriptor generation failed: {e}")
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return None
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def _auto_parse_preview(input_file, descriptor, max_records, logger):
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"""Parse and preview data using the auto-generated descriptor"""
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try:
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# Simplified parsing logic for preview (reuse existing logic)
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format_info = descriptor.get('format', {})
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format_type = format_info.get('type', 'csv').lower()
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encoding = format_info.get('encoding', 'utf-8')
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logger.info(f"Processing {input_file} for import...")
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with open(input_file, 'r', encoding=encoding) as f:
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raw_data = f.read()
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# Parse data using the same logic as parse-only mode, but with full dataset
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try:
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format_info = descriptor.get('format', {})
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format_type = format_info.get('type', 'csv').lower()
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encoding = format_info.get('encoding', 'utf-8')
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logger.info(f"Input format: {format_type}, encoding: {encoding}")
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with open(input_file, 'r', encoding=encoding) as f:
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raw_data = f.read()
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logger.info(f"Read {len(raw_data)} characters from input file")
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except Exception as e:
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logger.error(f"Failed to read input file: {e}")
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raise
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# Parse data (reuse parse-only logic but process all records)
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parsed_records = []
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batch_size = descriptor.get('output', {}).get('options', {}).get('batch_size', 1000)
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if format_type == 'csv':
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import csv
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@ -623,261 +729,50 @@ def load_structured_data(
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delimiter = options.get('delimiter', ',')
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has_header = options.get('has_header', True) or options.get('header', True)
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logger.info(f"CSV options - delimiter: '{delimiter}', has_header: {has_header}")
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reader = csv.DictReader(StringIO(raw_data), delimiter=delimiter)
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if not has_header:
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first_row = next(reader)
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fieldnames = [f"field_{i+1}" for i in range(len(first_row))]
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reader = csv.DictReader(StringIO(raw_data), fieldnames=fieldnames, delimiter=delimiter)
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try:
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reader = csv.DictReader(StringIO(raw_data), delimiter=delimiter)
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if not has_header:
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first_row = next(reader)
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fieldnames = [f"field_{i+1}" for i in range(len(first_row))]
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reader = csv.DictReader(StringIO(raw_data), fieldnames=fieldnames, delimiter=delimiter)
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record_count = 0
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for row in reader:
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parsed_records.append(row)
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record_count += 1
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# Process in batches to avoid memory issues
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if record_count % batch_size == 0:
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logger.info(f"Parsed {record_count} records...")
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except Exception as e:
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logger.error(f"Failed to parse CSV data: {e}")
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raise
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count = 0
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for row in reader:
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if count >= max_records:
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break
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parsed_records.append(dict(row))
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count += 1
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elif format_type == 'json':
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try:
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data = json.loads(raw_data)
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if isinstance(data, list):
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parsed_records = data
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elif isinstance(data, dict):
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root_path = format_info.get('options', {}).get('root_path')
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if root_path:
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if root_path.startswith('$.'):
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key = root_path[2:]
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data = data.get(key, data)
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if isinstance(data, list):
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parsed_records = data
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else:
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parsed_records = [data]
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except Exception as e:
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logger.error(f"Failed to parse JSON data: {e}")
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raise
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elif format_type == 'xml':
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import xml.etree.ElementTree as ET
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import json
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data = json.loads(raw_data)
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options = format_info.get('options', {})
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record_path = options.get('record_path', '//record')
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field_attribute = options.get('field_attribute')
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# Legacy support for old options format
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if 'root_element' in options or 'record_element' in options:
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root_element = options.get('root_element')
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record_element = options.get('record_element', 'record')
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if root_element:
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record_path = f"//{root_element}/{record_element}"
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else:
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record_path = f"//{record_element}"
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logger.info(f"XML options - record_path: '{record_path}', field_attribute: '{field_attribute}'")
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try:
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root = ET.fromstring(raw_data)
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if isinstance(data, list):
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parsed_records = data[:max_records]
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else:
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parsed_records = [data]
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# Find record elements using XPath
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xpath_expr = record_path
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if xpath_expr.startswith('/ROOT/'):
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xpath_expr = xpath_expr[6:]
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elif xpath_expr.startswith('/'):
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xpath_expr = '.' + xpath_expr
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records = root.findall(xpath_expr)
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logger.info(f"Found {len(records)} records using XPath: {record_path} (converted to: {xpath_expr})")
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# Convert XML elements to dictionaries
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for element in records:
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record = {}
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if field_attribute:
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# Handle field elements with name attributes (UN data format)
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for child in element:
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if child.tag == 'field' and field_attribute in child.attrib:
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field_name = child.attrib[field_attribute]
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field_value = child.text.strip() if child.text else ""
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record[field_name] = field_value
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else:
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# Handle standard XML structure
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record.update(element.attrib)
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for child in element:
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if child.text:
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record[child.tag] = child.text.strip()
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else:
|
||||
record[child.tag] = ""
|
||||
|
||||
if not record and element.text:
|
||||
record['value'] = element.text.strip()
|
||||
|
||||
parsed_records.append(record)
|
||||
|
||||
except ET.ParseError as e:
|
||||
logger.error(f"Failed to parse XML data: {e}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to process XML data: {e}")
|
||||
raise
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unsupported format type: {format_type}")
|
||||
|
||||
logger.info(f"Successfully parsed {len(parsed_records)} records")
|
||||
|
||||
# Apply transformations and create TrustGraph objects
|
||||
# Apply basic field mappings for preview
|
||||
mappings = descriptor.get('mappings', [])
|
||||
processed_records = []
|
||||
schema_name = descriptor.get('output', {}).get('schema_name', 'default')
|
||||
confidence = descriptor.get('output', {}).get('options', {}).get('confidence', 0.9)
|
||||
preview_records = []
|
||||
|
||||
logger.info(f"Applying {len(mappings)} field mappings...")
|
||||
|
||||
for record_num, record in enumerate(parsed_records, start=1):
|
||||
for record in parsed_records:
|
||||
processed_record = {}
|
||||
|
||||
for mapping in mappings:
|
||||
source_field = mapping.get('source_field') or mapping.get('source')
|
||||
target_field = mapping.get('target_field') or mapping.get('target')
|
||||
source_field = mapping.get('source_field')
|
||||
target_field = mapping.get('target_field', source_field)
|
||||
|
||||
if source_field in record:
|
||||
value = record[source_field]
|
||||
|
||||
# Apply transforms
|
||||
transforms = mapping.get('transforms', [])
|
||||
for transform in transforms:
|
||||
transform_type = transform.get('type')
|
||||
|
||||
if transform_type == 'trim' and isinstance(value, str):
|
||||
value = value.strip()
|
||||
elif transform_type == 'upper' and isinstance(value, str):
|
||||
value = value.upper()
|
||||
elif transform_type == 'lower' and isinstance(value, str):
|
||||
value = value.lower()
|
||||
elif transform_type == 'title_case' and isinstance(value, str):
|
||||
value = value.title()
|
||||
elif transform_type == 'to_int':
|
||||
try:
|
||||
value = int(value) if value != '' else None
|
||||
except (ValueError, TypeError):
|
||||
logger.warning(f"Failed to convert '{value}' to int in record {record_num}")
|
||||
elif transform_type == 'to_float':
|
||||
try:
|
||||
value = float(value) if value != '' else None
|
||||
except (ValueError, TypeError):
|
||||
logger.warning(f"Failed to convert '{value}' to float in record {record_num}")
|
||||
|
||||
# Convert all values to strings as required by ExtractedObject schema
|
||||
processed_record[target_field] = str(value) if value is not None else ""
|
||||
else:
|
||||
logger.warning(f"Source field '{source_field}' not found in record {record_num}")
|
||||
|
||||
# Create TrustGraph ExtractedObject
|
||||
output_record = {
|
||||
"metadata": {
|
||||
"id": f"import-{record_num}",
|
||||
"metadata": [],
|
||||
"user": "trustgraph",
|
||||
"collection": "default"
|
||||
},
|
||||
"schema_name": schema_name,
|
||||
"values": processed_record,
|
||||
"confidence": confidence,
|
||||
"source_span": ""
|
||||
}
|
||||
processed_records.append(output_record)
|
||||
|
||||
logger.info(f"Processed {len(processed_records)} records with transformations")
|
||||
|
||||
if dry_run:
|
||||
print(f"Dry run mode - would import {len(processed_records)} records to TrustGraph")
|
||||
print(f"Target schema: {schema_name}")
|
||||
print(f"Sample record:")
|
||||
if processed_records:
|
||||
# Show what the batched format will look like
|
||||
sample_batch = processed_records[:min(3, len(processed_records))]
|
||||
batch_values = [record["values"] for record in sample_batch]
|
||||
first_record = processed_records[0]
|
||||
batched_sample = {
|
||||
"metadata": first_record["metadata"],
|
||||
"schema_name": first_record["schema_name"],
|
||||
"values": batch_values,
|
||||
"confidence": first_record["confidence"],
|
||||
"source_span": first_record["source_span"]
|
||||
}
|
||||
print(json.dumps(batched_sample, indent=2))
|
||||
return
|
||||
|
||||
# Import to TrustGraph using objects import endpoint via WebSocket
|
||||
logger.info(f"Importing {len(processed_records)} records to TrustGraph...")
|
||||
|
||||
try:
|
||||
import asyncio
|
||||
from websockets.asyncio.client import connect
|
||||
|
||||
# Construct objects import URL similar to load_knowledge pattern
|
||||
if not api_url.endswith("/"):
|
||||
api_url += "/"
|
||||
|
||||
# Convert HTTP URL to WebSocket URL if needed
|
||||
ws_url = api_url.replace("http://", "ws://").replace("https://", "wss://")
|
||||
objects_url = ws_url + f"api/v1/flow/{flow}/import/objects"
|
||||
|
||||
logger.info(f"Connecting to objects import endpoint: {objects_url}")
|
||||
|
||||
async def import_objects():
|
||||
async with connect(objects_url) as ws:
|
||||
imported_count = 0
|
||||
|
||||
# Process records in batches
|
||||
for i in range(0, len(processed_records), batch_size):
|
||||
batch_records = processed_records[i:i + batch_size]
|
||||
|
||||
# Extract values from each record in the batch
|
||||
batch_values = [record["values"] for record in batch_records]
|
||||
|
||||
# Create batched ExtractedObject message using first record as template
|
||||
first_record = batch_records[0]
|
||||
batched_record = {
|
||||
"metadata": first_record["metadata"],
|
||||
"schema_name": first_record["schema_name"],
|
||||
"values": batch_values, # Array of value dictionaries
|
||||
"confidence": first_record["confidence"],
|
||||
"source_span": first_record["source_span"]
|
||||
}
|
||||
|
||||
# Send batched ExtractedObject
|
||||
await ws.send(json.dumps(batched_record))
|
||||
imported_count += len(batch_records)
|
||||
|
||||
if imported_count % 100 == 0:
|
||||
logger.info(f"Imported {imported_count}/{len(processed_records)} records...")
|
||||
|
||||
logger.info(f"Successfully imported {imported_count} records to TrustGraph")
|
||||
return imported_count
|
||||
|
||||
# Run the async import
|
||||
imported_count = asyncio.run(import_objects())
|
||||
print(f"Import completed: {imported_count} records imported to schema '{schema_name}'")
|
||||
|
||||
except ImportError as e:
|
||||
logger.error(f"Failed to import required modules: {e}")
|
||||
print(f"Error: Required modules not available - {e}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to import data to TrustGraph: {e}")
|
||||
print(f"Import failed: {e}")
|
||||
raise
|
||||
if processed_record: # Only add if we got some data
|
||||
preview_records.append(processed_record)
|
||||
|
||||
return preview_records if preview_records else parsed_records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Preview parsing failed: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
|
|
@ -908,26 +803,29 @@ Examples:
|
|||
%(prog)s --input customers.csv --descriptor descriptor.json
|
||||
%(prog)s --input products.xml --descriptor xml_descriptor.json
|
||||
|
||||
# All-in-one: Auto-generate descriptor and import (for simple cases)
|
||||
%(prog)s --input customers.csv --schema-name customer
|
||||
# FULLY AUTOMATIC: Discover schema + generate descriptor + import (zero manual steps!)
|
||||
%(prog)s --input customers.csv --auto
|
||||
%(prog)s --input products.xml --auto --dry-run # Preview before importing
|
||||
|
||||
# Dry run to validate without importing
|
||||
%(prog)s --input customers.csv --descriptor descriptor.json --dry-run
|
||||
|
||||
Use Cases:
|
||||
--auto : 🚀 FULLY AUTOMATIC: Discover schema + generate descriptor + import data
|
||||
(zero manual configuration required!)
|
||||
--suggest-schema : Diagnose which TrustGraph schemas might match your data
|
||||
(uses --sample-chars to limit data sent for analysis)
|
||||
--generate-descriptor: Create/review the structured data language configuration
|
||||
(uses --sample-chars to limit data sent for analysis)
|
||||
--parse-only : Validate that parsed data looks correct before import
|
||||
(uses --sample-size to limit records processed, ignores --sample-chars)
|
||||
(no mode flags) : Full pipeline - parse and import to TrustGraph
|
||||
|
||||
For more information on the descriptor format, see:
|
||||
docs/tech-specs/structured-data-descriptor.md
|
||||
""".strip()
|
||||
""",
|
||||
)
|
||||
|
||||
# Required arguments
|
||||
parser.add_argument(
|
||||
'-u', '--api-url',
|
||||
default=default_url,
|
||||
|
|
@ -968,6 +866,11 @@ For more information on the descriptor format, see:
|
|||
action='store_true',
|
||||
help='Parse data using descriptor but don\'t import to TrustGraph'
|
||||
)
|
||||
mode_group.add_argument(
|
||||
'--auto',
|
||||
action='store_true',
|
||||
help='Run full automatic pipeline: discover schema + generate descriptor + import data'
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'-o', '--output',
|
||||
|
|
@ -1026,7 +929,12 @@ For more information on the descriptor format, see:
|
|||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Validate argument combinations
|
||||
# Input validation
|
||||
if not os.path.exists(args.input):
|
||||
print(f"Error: Input file not found: {args.input}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
# Mode-specific validation
|
||||
if args.parse_only and not args.descriptor:
|
||||
print("Error: --descriptor is required when using --parse-only", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
|
@ -1038,11 +946,15 @@ For more information on the descriptor format, see:
|
|||
if (args.suggest_schema or args.generate_descriptor) and args.sample_size != 100: # 100 is default
|
||||
print("Warning: --sample-size is ignored in analysis modes, use --sample-chars instead", file=sys.stderr)
|
||||
|
||||
if not any([args.suggest_schema, args.generate_descriptor, args.parse_only]) and not args.descriptor:
|
||||
# Full pipeline mode without descriptor - schema_name should be provided
|
||||
if not args.schema_name:
|
||||
print("Error: --descriptor or --schema-name is required for full import", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
# Require explicit mode selection - no implicit behavior
|
||||
if not any([args.suggest_schema, args.generate_descriptor, args.parse_only, args.auto]):
|
||||
print("Error: Must specify an operation mode", file=sys.stderr)
|
||||
print("Available modes:", file=sys.stderr)
|
||||
print(" --auto : Discover schema + generate descriptor + import", file=sys.stderr)
|
||||
print(" --suggest-schema : Analyze data and suggest schemas", file=sys.stderr)
|
||||
print(" --generate-descriptor : Generate descriptor from data", file=sys.stderr)
|
||||
print(" --parse-only : Parse data without importing", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
try:
|
||||
load_structured_data(
|
||||
|
|
@ -1052,6 +964,7 @@ For more information on the descriptor format, see:
|
|||
suggest_schema=args.suggest_schema,
|
||||
generate_descriptor=args.generate_descriptor,
|
||||
parse_only=args.parse_only,
|
||||
auto=args.auto,
|
||||
output_file=args.output,
|
||||
sample_size=args.sample_size,
|
||||
sample_chars=args.sample_chars,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue