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fix(lint): annotate type-array list construction as mutable-ok
The repo's type_discipline_gate (LIT002) flags the new list comprehension as a mutable-collection construction. A JSON Schema type array must round-trip as a JSON list, so a tuple would be wrong; annotate it with the sanctioned # mutable-ok reason instead. Verified with scripts/check_type_discipline.py (delta 0 vs base) and ruff format.
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eeshsaxena
parent
affd76499e
commit
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1 changed files with 127 additions and 125 deletions
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@ -1,125 +1,127 @@
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import json
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from typing import Any
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from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH
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def normalize_json_schema_types(
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schema: dict[str, Any] | list[Any] | Any,
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depth: int = 0,
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max_depth: int = DEFAULT_MAX_RECURSE_DEPTH,
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) -> dict[str, Any] | list[Any] | Any:
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"""
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Normalize JSON schema types from uppercase to lowercase format.
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Some providers (like certain Google services) use uppercase types like 'BOOLEAN', 'STRING', 'ARRAY', 'OBJECT'
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but standard JSON Schema requires lowercase: 'boolean', 'string', 'array', 'object'
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This function recursively normalizes all type fields in a schema to lowercase.
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Args:
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schema: The schema to normalize (dict, list, or other)
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depth: Current recursion depth
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max_depth: Maximum recursion depth to prevent infinite loops
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Returns:
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The normalized schema with lowercase types
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"""
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# Prevent infinite recursion
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if depth >= max_depth:
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return schema
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if not isinstance(schema, (dict, list)):
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return schema
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# Type mapping from uppercase to lowercase
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type_mapping = {
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"BOOLEAN": "boolean",
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"STRING": "string",
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"ARRAY": "array",
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"OBJECT": "object",
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"NUMBER": "number",
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"INTEGER": "integer",
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"NULL": "null",
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}
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if isinstance(schema, list):
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return [normalize_json_schema_types(item, depth + 1, max_depth) for item in schema]
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if isinstance(schema, dict):
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normalized_schema: dict[str, Any] = {}
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for key, value in schema.items():
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if key == "type" and isinstance(value, str) and value in type_mapping:
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normalized_schema[key] = type_mapping[value]
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elif key == "type" and isinstance(value, list):
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# JSON Schema also allows a list of types, which is the usual way
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# to mark a field nullable (e.g. ["STRING", "NULL"]). Without this
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# branch those entries fall through to the generic list recursion,
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# which leaves the bare strings uppercase.
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normalized_schema[key] = [type_mapping.get(entry, entry) for entry in value]
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elif key == "properties" and isinstance(value, dict):
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# Recursively normalize properties
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normalized_schema[key] = {
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prop_key: normalize_json_schema_types(prop_value, depth + 1, max_depth)
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for prop_key, prop_value in value.items()
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}
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elif key == "items" and isinstance(value, (dict, list)):
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# Recursively normalize array items
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normalized_schema[key] = normalize_json_schema_types(value, depth + 1, max_depth)
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elif isinstance(value, (dict, list)):
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# Recursively normalize any nested dict or list
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normalized_schema[key] = normalize_json_schema_types(value, depth + 1, max_depth)
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else:
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normalized_schema[key] = value
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return normalized_schema
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return schema
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def normalize_tool_schema(tool: dict[str, Any]) -> dict[str, Any]:
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"""
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Normalize a tool's parameter schema to use standard JSON Schema lowercase types.
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Args:
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tool: The tool definition containing function parameters
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Returns:
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The tool with normalized schema types
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"""
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if not isinstance(tool, dict):
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return tool
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normalized_tool = tool.copy()
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# Normalize function parameters if present
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if "function" in tool and isinstance(tool["function"], dict):
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normalized_tool["function"] = tool["function"].copy()
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if "parameters" in tool["function"]:
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normalized_tool["function"]["parameters"] = normalize_json_schema_types(tool["function"]["parameters"])
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return normalized_tool
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def validate_schema(schema: dict, response: str):
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"""
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Validate if the returned json response follows the schema.
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Params:
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- schema - dict: JSON schema
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- response - str: Received json response as string.
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"""
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from jsonschema import ValidationError, validate
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from litellm import JSONSchemaValidationError
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try:
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response_dict = json.loads(response)
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except json.JSONDecodeError:
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raise JSONSchemaValidationError(model="", llm_provider="", raw_response=response, schema=json.dumps(schema))
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try:
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validate(response_dict, schema=schema)
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except ValidationError:
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raise JSONSchemaValidationError(model="", llm_provider="", raw_response=response, schema=json.dumps(schema))
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import json
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from typing import Any
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from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH
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def normalize_json_schema_types(
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schema: dict[str, Any] | list[Any] | Any,
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depth: int = 0,
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max_depth: int = DEFAULT_MAX_RECURSE_DEPTH,
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) -> dict[str, Any] | list[Any] | Any:
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"""
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Normalize JSON schema types from uppercase to lowercase format.
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Some providers (like certain Google services) use uppercase types like 'BOOLEAN', 'STRING', 'ARRAY', 'OBJECT'
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but standard JSON Schema requires lowercase: 'boolean', 'string', 'array', 'object'
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This function recursively normalizes all type fields in a schema to lowercase.
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Args:
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schema: The schema to normalize (dict, list, or other)
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depth: Current recursion depth
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max_depth: Maximum recursion depth to prevent infinite loops
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Returns:
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The normalized schema with lowercase types
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"""
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# Prevent infinite recursion
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if depth >= max_depth:
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return schema
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if not isinstance(schema, (dict, list)):
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return schema
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# Type mapping from uppercase to lowercase
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type_mapping = {
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"BOOLEAN": "boolean",
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"STRING": "string",
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"ARRAY": "array",
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"OBJECT": "object",
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"NUMBER": "number",
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"INTEGER": "integer",
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"NULL": "null",
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}
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if isinstance(schema, list):
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return [normalize_json_schema_types(item, depth + 1, max_depth) for item in schema]
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if isinstance(schema, dict):
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normalized_schema: dict[str, Any] = {}
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for key, value in schema.items():
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if key == "type" and isinstance(value, str) and value in type_mapping:
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normalized_schema[key] = type_mapping[value]
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elif key == "type" and isinstance(value, list):
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# JSON Schema also allows a list of types, which is the usual way
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# to mark a field nullable (e.g. ["STRING", "NULL"]). Without this
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# branch those entries fall through to the generic list recursion,
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# which leaves the bare strings uppercase.
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normalized_schema[key] = [ # mutable-ok: a JSON Schema type array must round-trip as a JSON list
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type_mapping.get(entry, entry) for entry in value
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]
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elif key == "properties" and isinstance(value, dict):
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# Recursively normalize properties
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normalized_schema[key] = {
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prop_key: normalize_json_schema_types(prop_value, depth + 1, max_depth)
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for prop_key, prop_value in value.items()
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}
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elif key == "items" and isinstance(value, (dict, list)):
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# Recursively normalize array items
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normalized_schema[key] = normalize_json_schema_types(value, depth + 1, max_depth)
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elif isinstance(value, (dict, list)):
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# Recursively normalize any nested dict or list
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normalized_schema[key] = normalize_json_schema_types(value, depth + 1, max_depth)
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else:
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normalized_schema[key] = value
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return normalized_schema
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return schema
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def normalize_tool_schema(tool: dict[str, Any]) -> dict[str, Any]:
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"""
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Normalize a tool's parameter schema to use standard JSON Schema lowercase types.
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Args:
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tool: The tool definition containing function parameters
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Returns:
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The tool with normalized schema types
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"""
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if not isinstance(tool, dict):
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return tool
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normalized_tool = tool.copy()
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# Normalize function parameters if present
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if "function" in tool and isinstance(tool["function"], dict):
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normalized_tool["function"] = tool["function"].copy()
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if "parameters" in tool["function"]:
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normalized_tool["function"]["parameters"] = normalize_json_schema_types(tool["function"]["parameters"])
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return normalized_tool
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def validate_schema(schema: dict, response: str):
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"""
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Validate if the returned json response follows the schema.
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Params:
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- schema - dict: JSON schema
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- response - str: Received json response as string.
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"""
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from jsonschema import ValidationError, validate
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from litellm import JSONSchemaValidationError
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try:
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response_dict = json.loads(response)
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except json.JSONDecodeError:
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raise JSONSchemaValidationError(model="", llm_provider="", raw_response=response, schema=json.dumps(schema))
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try:
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validate(response_dict, schema=schema)
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except ValidationError:
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raise JSONSchemaValidationError(model="", llm_provider="", raw_response=response, schema=json.dumps(schema))
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