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https://github.com/trustgraph-ai/trustgraph.git
synced 2026-07-24 12:41:02 +02:00
Refactor to remove dupes
This commit is contained in:
Cyber MacGeddon
parent
985464b090
commit
630d3502d5
1 changed files with 56 additions and 147 deletions
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@ -152,154 +152,45 @@ def load_structured_data(
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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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# Read sample data from input file
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try:
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with open(input_file, 'r', encoding='utf-8') as f:
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# Read up to sample_chars characters
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sample_data = f.read(sample_chars)
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if len(sample_data) < sample_chars:
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logger.info(f"Read entire file ({len(sample_data)} characters)")
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else:
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logger.info(f"Read sample ({sample_chars} characters)")
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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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# Use the helper function to discover schema (get raw response for display)
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response = _auto_discover_schema(api_url, input_file, sample_chars, logger, return_raw_response=True)
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# Fetch available schemas from Config API
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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 list of available schema keys
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logger.info("Fetching available schemas from Config API...")
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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 values are JSON strings, parse them
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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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# Use TrustGraph prompt service for schema suggestion
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flow_api = api.flow().id("default")
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# Call schema-selection prompt with actual schemas and data sample
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logger.info("Calling TrustGraph schema-selection prompt...")
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response = flow_api.prompt(
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id="schema-selection",
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variables={
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"schemas": schemas, # Array of actual schema definitions (note: plural 'schemas')
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"data": sample_data
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}
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)
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if response:
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print("Schema Suggestion Results:")
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print("=" * 50)
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print(response)
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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 call TrustGraph prompt service: {e}")
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raise
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print("=" * 50)
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print("\nUse --generate-descriptor with --schema-name to create a descriptor configuration.")
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else:
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print("Could not determine the best matching schema for your data.")
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print("Available schemas can be viewed using: tg-config-list schema")
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elif generate_descriptor:
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logger.info(f"Generating descriptor from {input_file}...")
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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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if schema_name:
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# If no schema specified, discover it first
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if not schema_name:
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logger.info("No schema specified, auto-discovering...")
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schema_name = _auto_discover_schema(api_url, input_file, sample_chars, logger)
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if not schema_name:
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print("Error: Could not determine schema automatically.")
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print("Please specify a schema using --schema-name or run --suggest-schema first.")
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return
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logger.info(f"Auto-selected schema: {schema_name}")
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else:
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logger.info(f"Target schema: {schema_name}")
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# Read sample data from input file
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try:
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with open(input_file, 'r', encoding='utf-8') as f:
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# Read up to sample_chars characters
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sample_data = f.read(sample_chars)
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if len(sample_data) < sample_chars:
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logger.info(f"Read entire file ({len(sample_data)} characters)")
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else:
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logger.info(f"Read sample ({sample_chars} characters)")
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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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# Generate descriptor using helper function
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descriptor = _auto_generate_descriptor(api_url, input_file, schema_name, sample_chars, logger)
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# Fetch available schemas from Config API (same as suggest-schema mode)
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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 list of available schema keys
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logger.info("Fetching available schemas from Config API...")
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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 values are JSON strings, parse them
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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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# Use TrustGraph prompt service for descriptor generation
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flow_api = api.flow().id("default")
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# Call diagnose-structured-data prompt with schemas and data sample
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logger.info("Calling TrustGraph diagnose-structured-data prompt...")
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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, # Array of actual schema definitions
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"sample": sample_data # Note: using 'sample' instead of 'data'
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}
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)
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if descriptor:
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# Output the generated descriptor
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if output_file:
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try:
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with open(output_file, 'w', encoding='utf-8') as f:
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if isinstance(response, str):
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f.write(response)
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else:
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f.write(json.dumps(response, indent=2))
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f.write(json.dumps(descriptor, indent=2))
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print(f"Generated descriptor saved to: {output_file}")
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logger.info(f"Descriptor saved to {output_file}")
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except Exception as e:
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@ -308,17 +199,12 @@ def load_structured_data(
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else:
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print("Generated Descriptor:")
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print("=" * 50)
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if isinstance(response, str):
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print(response)
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else:
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print(json.dumps(response, indent=2))
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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 call TrustGraph prompt service: {e}")
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raise
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print(json.dumps(descriptor, indent=2))
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print("=" * 50)
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print("Use this descriptor with --parse-only to validate or without modes to import.")
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else:
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print("Error: Failed to generate descriptor.")
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print("Check the logs for details or try --suggest-schema to verify schema availability.")
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elif parse_only:
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if not descriptor_file:
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@ -585,12 +471,24 @@ def load_structured_data(
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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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def _auto_discover_schema(api_url, input_file, sample_chars, logger, return_raw_response=False):
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"""Auto-discover the best matching schema for the input data
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Args:
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api_url: TrustGraph API URL
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input_file: Path to input data file
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sample_chars: Number of characters to sample from file
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logger: Logger instance
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return_raw_response: If True, return raw prompt response; if False, parse to extract schema name
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Returns:
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Schema name (str) if return_raw_response=False, or full response if True
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"""
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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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logger.info(f"Read {len(sample_data)} characters for analysis")
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# Import API modules
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from trustgraph.api import Api
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@ -599,7 +497,10 @@ def _auto_discover_schema(api_url, input_file, sample_chars, logger):
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config_api = api.config()
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# Get available schemas
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logger.info("Fetching available schemas from Config API...")
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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.error("No schemas available in TrustGraph configuration")
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return None
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@ -613,6 +514,7 @@ def _auto_discover_schema(api_url, input_file, sample_chars, logger):
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if schema_values:
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schema_def = json.loads(schema_values[0].value) if isinstance(schema_values[0].value, str) else schema_values[0].value
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schemas[key] = schema_def
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logger.debug(f"Loaded schema: {key}")
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except Exception as e:
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logger.warning(f"Could not load schema {key}: {e}")
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@ -620,18 +522,25 @@ def _auto_discover_schema(api_url, input_file, sample_chars, logger):
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logger.error("No valid schemas could be loaded")
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return None
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logger.info(f"Successfully loaded {len(schemas)} schema definitions")
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# Use prompt service for schema selection
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flow_api = api.flow().id("default")
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# Call schema-selection prompt with actual schemas and data sample
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logger.info("Calling TrustGraph schema-selection prompt...")
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response = flow_api.prompt(
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id="schema-selection",
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variables={
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"schemas": list(schemas.values()), # Array of actual schema definitions
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"data": sample_data[:1000] # Truncate sample data
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"question": sample_data # Truncate sample data
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}
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)
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# Return raw response if requested (for suggest_schema mode)
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if return_raw_response:
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return response
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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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@ -988,4 +897,4 @@ For more information on the descriptor format, see:
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if __name__ == "__main__":
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main()
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main()
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