참고소스 수정본
This commit is contained in:
LASTA_DEV01\lasta
@@ -0,0 +1,790 @@
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"""
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Shared validation functions for Firecrawl v2 API.
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"""
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from typing import Optional, Dict, Any, List
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from ..types import ScrapeOptions, ScrapeFormats
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def _convert_format_string(format_str: str) -> str:
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"""
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Convert format string from snake_case to camelCase.
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Args:
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format_str: Format string in snake_case
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Returns:
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Format string in camelCase
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"""
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format_mapping = {
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"raw_html": "rawHtml",
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"change_tracking": "changeTracking",
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"screenshot_full_page": "screenshot@fullPage"
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}
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return format_mapping.get(format_str, format_str)
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def normalize_schema_for_openai(schema: Any) -> Any:
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"""
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Normalize a schema for OpenAI compatibility by handling recursive references.
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Args:
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schema: Schema to normalize
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Returns:
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Normalized schema
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"""
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if not schema or not isinstance(schema, dict):
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return schema
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visited = set()
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def normalize_object(obj: Any) -> Any:
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if not isinstance(obj, dict):
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if isinstance(obj, list):
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return [normalize_object(item) for item in obj]
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return obj
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obj_id = id(obj)
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if obj_id in visited:
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return obj
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visited.add(obj_id)
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normalized = dict(obj)
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# Handle $ref recursion
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if "$ref" in normalized:
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visited.discard(obj_id)
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return normalized
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if "$defs" in normalized:
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defs = normalized.pop("$defs")
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processed_rest = {}
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for key, value in normalized.items():
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if isinstance(value, dict) and "$ref" not in value:
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processed_rest[key] = normalize_object(value)
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else:
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processed_rest[key] = value
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normalized_defs = {}
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for key, value in defs.items():
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normalized_defs[key] = normalize_object(value)
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result = {**processed_rest, "$defs": normalized_defs}
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visited.discard(obj_id)
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return result
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if (normalized.get("type") == "object" and
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"properties" in normalized and
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normalized.get("additionalProperties") is True):
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del normalized["additionalProperties"]
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if (normalized.get("type") == "object" and
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"required" in normalized and
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"properties" in normalized):
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if (isinstance(normalized["required"], list) and
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isinstance(normalized["properties"], dict)):
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valid_required = [field for field in normalized["required"]
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if field in normalized["properties"]]
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if valid_required:
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normalized["required"] = valid_required
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else:
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del normalized["required"]
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else:
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del normalized["required"]
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for key, value in list(normalized.items()):
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if isinstance(value, dict) and "$ref" not in value:
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normalized[key] = normalize_object(value)
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elif isinstance(value, list):
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normalized[key] = [normalize_object(item) if isinstance(item, dict) else item for item in value]
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visited.discard(obj_id)
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return normalized
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return normalize_object(schema)
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def validate_schema_for_openai(schema: Any) -> bool:
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"""
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Validate schema for OpenAI compatibility.
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Args:
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schema: Schema to validate
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Returns:
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True if schema is valid, False otherwise
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"""
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if not schema or not isinstance(schema, dict):
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return True
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visited = set()
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def has_invalid_structure(obj: Any) -> bool:
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if not isinstance(obj, dict):
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return False
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obj_id = id(obj)
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if obj_id in visited:
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return False
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visited.add(obj_id)
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if "$ref" in obj:
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visited.discard(obj_id)
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return False
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if (obj.get("type") == "object" and
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"properties" not in obj and
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"patternProperties" not in obj and
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obj.get("additionalProperties") is True):
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visited.discard(obj_id)
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return True
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for value in obj.values():
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if isinstance(value, dict) and "$ref" not in value:
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if has_invalid_structure(value):
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visited.discard(obj_id)
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return True
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elif isinstance(value, list):
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for item in value:
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if isinstance(item, dict) and "$ref" not in item:
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if has_invalid_structure(item):
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visited.discard(obj_id)
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return True
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visited.discard(obj_id)
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return False
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return not has_invalid_structure(schema)
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OPENAI_SCHEMA_ERROR_MESSAGE = (
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"Schema contains invalid structure for OpenAI: object type with no 'properties' defined "
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"but 'additionalProperties: true' (schema-less dictionary not supported by OpenAI). "
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"Please define specific properties for your object. Note: Recursive schemas using '$ref' are supported."
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)
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def _contains_recursive_ref(obj: Any, target_def_name: str, defs: Dict[str, Any], visited: Optional[set] = None) -> bool:
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"""
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Check if an object contains a recursive reference to a specific definition.
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Args:
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obj: Object to check
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target_def_name: Name of the definition to check for recursion
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defs: Dictionary of definitions
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visited: Set of visited object keys to detect cycles
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Returns:
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True if recursive reference is found, False otherwise
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"""
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if not obj or not isinstance(obj, (dict, list)):
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return False
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if visited is None:
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visited = set()
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import json
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obj_key = json.dumps(obj, sort_keys=True, default=str)
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if obj_key in visited:
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return False
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visited.add(obj_key)
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try:
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if isinstance(obj, dict):
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if "$ref" in obj and isinstance(obj["$ref"], str):
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ref_path = obj["$ref"].split("/")
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if len(ref_path) >= 3 and ref_path[0] == "#" and ref_path[1] == "$defs":
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def_name = ref_path[-1]
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if def_name == target_def_name:
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return True
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if def_name in defs:
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return _contains_recursive_ref(defs[def_name], target_def_name, defs, visited)
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for value in obj.values():
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if _contains_recursive_ref(value, target_def_name, defs, visited):
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return True
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elif isinstance(obj, list):
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for item in obj:
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if _contains_recursive_ref(item, target_def_name, defs, visited):
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return True
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finally:
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visited.discard(obj_key)
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return False
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def _check_for_circular_defs(defs: Dict[str, Any]) -> bool:
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"""
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Check if $defs contain circular references.
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Args:
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defs: Dictionary of definitions to check
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Returns:
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True if circular references are found, False otherwise
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"""
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if not defs:
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return False
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for def_name, def_value in defs.items():
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if _contains_recursive_ref(def_value, def_name, defs):
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return True
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return False
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def resolve_refs(obj: Any, defs: Dict[str, Any], visited: Optional[set] = None, depth: int = 0) -> Any:
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"""
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Resolve $ref references in a JSON schema object.
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Args:
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obj: Object to resolve references in
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defs: Dictionary of definitions
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visited: Set to track visited objects and prevent infinite recursion
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depth: Current recursion depth
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Returns:
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Object with resolved references
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"""
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if not obj or not isinstance(obj, (dict, list)) or depth > 10:
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return obj
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if visited is None:
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visited = set()
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obj_id = id(obj)
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if obj_id in visited:
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return obj
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visited.add(obj_id)
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try:
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if isinstance(obj, dict):
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if "$ref" in obj and isinstance(obj["$ref"], str):
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ref_path = obj["$ref"].split("/")
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if len(ref_path) >= 3 and ref_path[0] == "#" and ref_path[1] == "$defs":
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def_name = ref_path[-1]
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if def_name in defs:
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return resolve_refs(dict(defs[def_name]), defs, visited, depth + 1)
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return obj
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resolved = {}
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for key, value in obj.items():
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if key == "$defs":
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continue
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resolved[key] = resolve_refs(value, defs, visited, depth + 1)
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return resolved
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elif isinstance(obj, list):
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return [resolve_refs(item, defs, visited, depth + 1) for item in obj]
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finally:
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visited.discard(obj_id)
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return obj
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def detect_recursive_schema(schema: Any) -> bool:
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"""
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Detect if a schema contains recursive references.
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Args:
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schema: Schema to analyze
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Returns:
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True if schema has recursive patterns, False otherwise
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"""
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if not schema or not isinstance(schema, dict):
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return False
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import json
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schema_string = json.dumps(schema)
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has_refs = (
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'"$ref"' in schema_string or
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"#/$defs/" in schema_string or
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"#/definitions/" in schema_string
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)
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has_defs = bool(schema.get("$defs") or schema.get("definitions"))
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return has_refs or has_defs
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def select_model_for_schema(schema: Any = None) -> Dict[str, str]:
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"""
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Select appropriate model based on schema complexity.
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Args:
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schema: Schema to analyze
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Returns:
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Dict with modelName and reason
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"""
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if not schema:
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return {"modelName": "gpt-4o-mini", "reason": "no_schema"}
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if detect_recursive_schema(schema):
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return {"modelName": "gpt-4o", "reason": "recursive_schema_detected"}
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return {"modelName": "gpt-4o-mini", "reason": "simple_schema"}
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def _normalize_schema(schema: Any) -> Optional[Dict[str, Any]]:
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"""
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Normalize a schema object which may be a dict, Pydantic BaseModel subclass,
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or a Pydantic model instance into a plain dict.
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"""
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try:
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# Pydantic v2 BaseModel subclass: has "model_json_schema"
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if hasattr(schema, "model_json_schema") and callable(schema.model_json_schema):
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return schema.model_json_schema()
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# Pydantic v2 BaseModel instance: has "model_dump" or "model_json_schema"
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if hasattr(schema, "model_dump") and callable(schema.model_dump):
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# Try to get JSON schema if available on the class
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mjs = getattr(schema.__class__, "model_json_schema", None)
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if callable(mjs):
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return schema.__class__.model_json_schema()
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# Fallback to data shape (not ideal, but better than dropping)
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return schema.model_dump()
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# Pydantic v1 BaseModel subclass: has "schema"
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if hasattr(schema, "schema") and callable(schema.schema):
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return schema.schema()
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# Pydantic v1 BaseModel instance
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if hasattr(schema, "dict") and callable(schema.dict):
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# Prefer class-level schema if present
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sch = getattr(schema.__class__, "schema", None)
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if callable(sch):
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return schema.__class__.schema()
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return schema.dict()
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except Exception:
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pass
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# Already a dict or unsupported type
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return schema if isinstance(schema, dict) else None
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def _validate_json_format(format_obj: Any) -> Dict[str, Any]:
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"""
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Validate and prepare json format object.
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Args:
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format_obj: Format object that should be json type
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Returns:
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Validated json format dict
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Raises:
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ValueError: If json format is missing required fields
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"""
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if not isinstance(format_obj, dict):
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raise ValueError("json format must be an object with 'type', 'prompt', and 'schema' fields")
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if format_obj.get('type') != 'json':
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raise ValueError("json format must have type='json'")
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# prompt is optional in v2; only normalize when present
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# schema is recommended; if provided, normalize Pydantic forms
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schema = format_obj.get('schema')
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normalized = dict(format_obj)
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if schema is not None:
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normalized_schema = _normalize_schema(schema)
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if normalized_schema is not None:
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# Handle schema reference resolution similar to TypeScript implementation
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if isinstance(normalized_schema, dict):
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defs = normalized_schema.get("$defs", {})
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import json
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schema_string = json.dumps(normalized_schema)
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has_any_refs = (
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normalized_schema.get("$defs") or
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'"$ref"' in schema_string or
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"#/$defs/" in schema_string
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)
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if has_any_refs:
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try:
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resolved_schema = resolve_refs(normalized_schema, defs)
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resolved_string = json.dumps(resolved_schema)
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has_remaining_refs = '"$ref"' in resolved_string or "#/$defs/" in resolved_string
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if not has_remaining_refs:
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normalized_schema = resolved_schema
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# Remove $defs after successful resolution
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if isinstance(normalized_schema, dict) and "$defs" in normalized_schema:
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del normalized_schema["$defs"]
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# If refs remain, preserve original schema
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except Exception:
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# Failed to resolve refs, preserve original schema
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pass
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else:
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# No recursive references detected, resolve refs anyway
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try:
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normalized_schema = resolve_refs(normalized_schema, defs)
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if isinstance(normalized_schema, dict) and "$defs" in normalized_schema:
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del normalized_schema["$defs"]
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except Exception:
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pass
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# Apply OpenAI normalization and validation
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openai_normalized_schema = normalize_schema_for_openai(normalized_schema)
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if not validate_schema_for_openai(openai_normalized_schema):
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raise ValueError(OPENAI_SCHEMA_ERROR_MESSAGE)
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normalized['schema'] = openai_normalized_schema
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return normalized
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def _validate_query_format(format_obj: Any) -> Dict[str, Any]:
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"""
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Validate and prepare query format object.
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|
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Args:
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format_obj: Format object that should be query type
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||||
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||||
Returns:
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Validated query format dict
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||||
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||||
Raises:
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||||
ValueError: If query format is missing required 'prompt' field
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||||
"""
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||||
if not isinstance(format_obj, dict):
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raise ValueError("query format must be an object with 'type' and 'prompt' fields")
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if not isinstance(format_obj.get('prompt'), str) or not format_obj['prompt'].strip():
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raise ValueError("query format requires a non-empty 'prompt' string")
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if "directQuote" in format_obj:
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||||
raise ValueError("query format uses 'mode' instead of 'directQuote'")
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||||
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||||
mode = format_obj.get("mode")
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if mode is not None and mode not in ("freeform", "directQuote"):
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||||
raise ValueError("query format mode must be 'freeform' or 'directQuote'")
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||||
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||||
return format_obj
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||||
|
||||
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||||
def _validate_question_format(format_obj: Any) -> Dict[str, Any]:
|
||||
"""Validate and prepare question format object."""
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if not isinstance(format_obj, dict):
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||||
raise ValueError("question format must be an object with 'type' and 'question' fields")
|
||||
|
||||
if not isinstance(format_obj.get('question'), str) or not format_obj['question'].strip():
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||||
raise ValueError("question format requires a non-empty 'question' string")
|
||||
|
||||
return format_obj
|
||||
|
||||
|
||||
def _validate_highlights_format(format_obj: Any) -> Dict[str, Any]:
|
||||
"""Validate and prepare highlights format object."""
|
||||
if not isinstance(format_obj, dict):
|
||||
raise ValueError("highlights format must be an object with 'type' and 'query' fields")
|
||||
|
||||
if not isinstance(format_obj.get('query'), str) or not format_obj['query'].strip():
|
||||
raise ValueError("highlights format requires a non-empty 'query' string")
|
||||
|
||||
return format_obj
|
||||
|
||||
|
||||
def validate_scrape_options(options: Optional[ScrapeOptions]) -> Optional[ScrapeOptions]:
|
||||
"""
|
||||
Validate and normalize scrape options.
|
||||
|
||||
Args:
|
||||
options: Scraping options to validate
|
||||
|
||||
Returns:
|
||||
Validated options or None
|
||||
|
||||
Raises:
|
||||
ValueError: If options are invalid
|
||||
"""
|
||||
if options is None:
|
||||
return None
|
||||
|
||||
# Validate timeout
|
||||
if options.timeout is not None and options.timeout <= 0:
|
||||
raise ValueError("Timeout must be positive")
|
||||
|
||||
# Validate wait_for
|
||||
if options.wait_for is not None and options.wait_for < 0:
|
||||
raise ValueError("wait_for must be non-negative")
|
||||
|
||||
return options
|
||||
|
||||
|
||||
def prepare_scrape_options(options: Optional[ScrapeOptions]) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Prepare ScrapeOptions for API submission with manual snake_case to camelCase conversion.
|
||||
|
||||
Args:
|
||||
options: ScrapeOptions to prepare
|
||||
|
||||
Returns:
|
||||
Dictionary ready for API submission or None if options is None
|
||||
"""
|
||||
if options is None:
|
||||
return None
|
||||
|
||||
# Validate options first
|
||||
validated_options = validate_scrape_options(options)
|
||||
if validated_options is None:
|
||||
return None
|
||||
|
||||
# Apply default values for None fields
|
||||
default_values = {
|
||||
"only_main_content": True,
|
||||
"mobile": False,
|
||||
"skip_tls_verification": True,
|
||||
"remove_base64_images": True,
|
||||
"fast_mode": False,
|
||||
"block_ads": True,
|
||||
"max_age": 14400000,
|
||||
"store_in_cache": True
|
||||
}
|
||||
|
||||
# Convert to dict and handle manual snake_case to camelCase conversion
|
||||
options_data = validated_options.model_dump(exclude_none=True)
|
||||
|
||||
# Apply defaults for None fields
|
||||
for field, default_value in default_values.items():
|
||||
if field not in options_data:
|
||||
options_data[field] = default_value
|
||||
|
||||
scrape_data = {}
|
||||
|
||||
# Manual field mapping for snake_case to camelCase conversion
|
||||
field_mappings = {
|
||||
"include_tags": "includeTags",
|
||||
"exclude_tags": "excludeTags",
|
||||
"only_main_content": "onlyMainContent",
|
||||
"wait_for": "waitFor",
|
||||
"skip_tls_verification": "skipTlsVerification",
|
||||
"remove_base64_images": "removeBase64Images",
|
||||
"fast_mode": "fastMode",
|
||||
"use_mock": "useMock",
|
||||
"block_ads": "blockAds",
|
||||
"store_in_cache": "storeInCache",
|
||||
"max_age": "maxAge"
|
||||
}
|
||||
|
||||
# Apply field mappings
|
||||
for snake_case, camel_case in field_mappings.items():
|
||||
if snake_case in options_data:
|
||||
scrape_data[camel_case] = options_data.pop(snake_case)
|
||||
|
||||
# Handle special cases
|
||||
for key, value in options_data.items():
|
||||
if value is not None:
|
||||
if key == "integration":
|
||||
scrape_data["integration"] = (str(value).strip() or None)
|
||||
continue
|
||||
if key == "formats":
|
||||
# Handle formats conversion
|
||||
converted_formats: List[Any] = []
|
||||
|
||||
# Prefer using original object to detect ScrapeFormats vs list
|
||||
original_formats = getattr(options, 'formats', None)
|
||||
|
||||
if isinstance(original_formats, ScrapeFormats):
|
||||
# Include explicit list first
|
||||
if original_formats.formats:
|
||||
for fmt in original_formats.formats:
|
||||
if isinstance(fmt, str):
|
||||
if fmt == "json":
|
||||
raise ValueError("json format must be an object with 'type', 'prompt', and 'schema' fields")
|
||||
if fmt == "query":
|
||||
raise ValueError("query format must be an object with 'type' and 'prompt' fields")
|
||||
if fmt == "question":
|
||||
raise ValueError("question format must be an object with 'type' and 'question' fields")
|
||||
if fmt == "highlights":
|
||||
raise ValueError("highlights format must be an object with 'type' and 'query' fields")
|
||||
converted_formats.append(_convert_format_string(fmt))
|
||||
elif isinstance(fmt, dict):
|
||||
fmt_type = _convert_format_string(fmt.get('type')) if fmt.get('type') else None
|
||||
if fmt_type == 'json':
|
||||
validated_json = _validate_json_format({**fmt, 'type': 'json'})
|
||||
converted_formats.append(validated_json)
|
||||
elif fmt_type == 'question':
|
||||
converted_formats.append(_validate_question_format(fmt))
|
||||
elif fmt_type == 'highlights':
|
||||
converted_formats.append(_validate_highlights_format(fmt))
|
||||
elif fmt_type == 'query':
|
||||
converted_formats.append(_validate_query_format(fmt))
|
||||
elif fmt_type == 'screenshot':
|
||||
# Normalize screenshot options
|
||||
normalized = {**fmt, 'type': 'screenshot'}
|
||||
if 'full_page' in normalized:
|
||||
normalized['fullPage'] = normalized.pop('full_page')
|
||||
# Normalize viewport if it's a model instance
|
||||
vp = normalized.get('viewport')
|
||||
if hasattr(vp, 'model_dump'):
|
||||
normalized['viewport'] = vp.model_dump(exclude_none=True)
|
||||
converted_formats.append(normalized)
|
||||
else:
|
||||
if 'type' in fmt:
|
||||
fmt['type'] = fmt_type or fmt['type']
|
||||
converted_formats.append(fmt)
|
||||
elif hasattr(fmt, 'type'):
|
||||
if fmt.type == 'json':
|
||||
converted_formats.append(_validate_json_format(fmt.model_dump()))
|
||||
elif fmt.type == 'question':
|
||||
converted_formats.append(_validate_question_format(fmt.model_dump(exclude_none=True)))
|
||||
elif fmt.type == 'highlights':
|
||||
converted_formats.append(_validate_highlights_format(fmt.model_dump(exclude_none=True)))
|
||||
elif fmt.type == 'query':
|
||||
converted_formats.append(_validate_query_format(fmt.model_dump(exclude_none=True)))
|
||||
else:
|
||||
converted_formats.append(_convert_format_string(fmt.type))
|
||||
else:
|
||||
converted_formats.append(fmt)
|
||||
|
||||
# Add booleans from ScrapeFormats
|
||||
if original_formats.markdown:
|
||||
converted_formats.append("markdown")
|
||||
if original_formats.html:
|
||||
converted_formats.append("html")
|
||||
if original_formats.raw_html:
|
||||
converted_formats.append("rawHtml")
|
||||
if original_formats.summary:
|
||||
converted_formats.append("summary")
|
||||
if original_formats.links:
|
||||
converted_formats.append("links")
|
||||
if original_formats.screenshot:
|
||||
converted_formats.append("screenshot")
|
||||
if original_formats.change_tracking:
|
||||
converted_formats.append("changeTracking")
|
||||
# Note: We intentionally do not auto-include 'json' when boolean is set,
|
||||
# because JSON requires an object with schema/prompt. The caller must
|
||||
# supply the full json format object explicitly.
|
||||
elif isinstance(original_formats, list):
|
||||
for fmt in original_formats:
|
||||
if isinstance(fmt, str):
|
||||
if fmt == "json":
|
||||
raise ValueError("json format must be an object with 'type', 'prompt', and 'schema' fields")
|
||||
if fmt == "query":
|
||||
raise ValueError("query format must be an object with 'type' and 'prompt' fields")
|
||||
if fmt == "question":
|
||||
raise ValueError("question format must be an object with 'type' and 'question' fields")
|
||||
if fmt == "highlights":
|
||||
raise ValueError("highlights format must be an object with 'type' and 'query' fields")
|
||||
converted_formats.append(_convert_format_string(fmt))
|
||||
elif isinstance(fmt, dict):
|
||||
fmt_type = _convert_format_string(fmt.get('type')) if fmt.get('type') else None
|
||||
if fmt_type == 'json':
|
||||
validated_json = _validate_json_format({**fmt, 'type': 'json'})
|
||||
converted_formats.append(validated_json)
|
||||
elif fmt_type == 'question':
|
||||
converted_formats.append(_validate_question_format(fmt))
|
||||
elif fmt_type == 'highlights':
|
||||
converted_formats.append(_validate_highlights_format(fmt))
|
||||
elif fmt_type == 'query':
|
||||
converted_formats.append(_validate_query_format(fmt))
|
||||
elif fmt_type == 'screenshot':
|
||||
normalized = {**fmt, 'type': 'screenshot'}
|
||||
if 'full_page' in normalized:
|
||||
normalized['fullPage'] = normalized.pop('full_page')
|
||||
vp = normalized.get('viewport')
|
||||
if hasattr(vp, 'model_dump'):
|
||||
normalized['viewport'] = vp.model_dump(exclude_none=True)
|
||||
converted_formats.append(normalized)
|
||||
else:
|
||||
if 'type' in fmt:
|
||||
fmt['type'] = fmt_type or fmt['type']
|
||||
converted_formats.append(fmt)
|
||||
elif hasattr(fmt, 'type'):
|
||||
if fmt.type == 'json':
|
||||
converted_formats.append(_validate_json_format(fmt.model_dump()))
|
||||
elif fmt.type == 'question':
|
||||
converted_formats.append(_validate_question_format(fmt.model_dump(exclude_none=True)))
|
||||
elif fmt.type == 'highlights':
|
||||
converted_formats.append(_validate_highlights_format(fmt.model_dump(exclude_none=True)))
|
||||
elif fmt.type == 'screenshot':
|
||||
normalized = {'type': 'screenshot'}
|
||||
if getattr(fmt, 'full_page', None) is not None:
|
||||
normalized['fullPage'] = fmt.full_page
|
||||
if getattr(fmt, 'quality', None) is not None:
|
||||
normalized['quality'] = fmt.quality
|
||||
vp = getattr(fmt, 'viewport', None)
|
||||
if vp is not None:
|
||||
normalized['viewport'] = vp.model_dump(exclude_none=True) if hasattr(vp, 'model_dump') else vp
|
||||
converted_formats.append(normalized)
|
||||
elif fmt.type == 'query':
|
||||
converted_formats.append(_validate_query_format(fmt.model_dump(exclude_none=True)))
|
||||
else:
|
||||
converted_formats.append(_convert_format_string(fmt.type))
|
||||
else:
|
||||
converted_formats.append(fmt)
|
||||
else:
|
||||
# Fallback: try to iterate over value if it's a list-like
|
||||
try:
|
||||
for fmt in value:
|
||||
converted_formats.append(fmt)
|
||||
except TypeError:
|
||||
pass
|
||||
|
||||
if converted_formats:
|
||||
scrape_data["formats"] = converted_formats
|
||||
elif key == "actions":
|
||||
# Handle actions conversion
|
||||
converted_actions = []
|
||||
for action in value:
|
||||
if isinstance(action, dict):
|
||||
# Convert action dict
|
||||
converted_action = {}
|
||||
for action_key, action_value in action.items():
|
||||
if action_key == "full_page":
|
||||
converted_action["fullPage"] = action_value
|
||||
else:
|
||||
converted_action[action_key] = action_value
|
||||
converted_actions.append(converted_action)
|
||||
else:
|
||||
# Handle action objects
|
||||
action_data = action.model_dump(exclude_none=True)
|
||||
converted_action = {}
|
||||
for action_key, action_value in action_data.items():
|
||||
if action_key == "full_page":
|
||||
converted_action["fullPage"] = action_value
|
||||
else:
|
||||
converted_action[action_key] = action_value
|
||||
converted_actions.append(converted_action)
|
||||
scrape_data["actions"] = converted_actions
|
||||
elif key == "parsers":
|
||||
converted_parsers = []
|
||||
for parser in value:
|
||||
if isinstance(parser, str):
|
||||
converted_parsers.append(parser)
|
||||
elif isinstance(parser, dict):
|
||||
parser_data = dict(parser)
|
||||
if "max_pages" in parser_data:
|
||||
parser_data["maxPages"] = parser_data.pop("max_pages")
|
||||
converted_parsers.append(parser_data)
|
||||
else:
|
||||
parser_data = parser.model_dump(exclude_none=True)
|
||||
# Convert snake_case to camelCase for API
|
||||
if "max_pages" in parser_data:
|
||||
parser_data["maxPages"] = parser_data.pop("max_pages")
|
||||
converted_parsers.append(parser_data)
|
||||
scrape_data["parsers"] = converted_parsers
|
||||
elif key == "location":
|
||||
if isinstance(value, dict):
|
||||
scrape_data["location"] = value
|
||||
else:
|
||||
scrape_data["location"] = value.model_dump(exclude_none=True)
|
||||
elif key == "profile":
|
||||
if isinstance(value, dict):
|
||||
profile_data = {
|
||||
"name": value["name"],
|
||||
"saveChanges": value.get("save_changes", value.get("saveChanges", True)),
|
||||
}
|
||||
else:
|
||||
profile_data = {
|
||||
"name": value.name,
|
||||
"saveChanges": getattr(value, "save_changes", getattr(value, "saveChanges", True)),
|
||||
}
|
||||
scrape_data["profile"] = profile_data
|
||||
else:
|
||||
# For fields that don't need conversion, use as-is
|
||||
scrape_data[key] = value
|
||||
|
||||
return scrape_data
|
||||
Reference in New Issue
Block a user