ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
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"""
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Utilities for creating optimized Pydantic schemas for LLM usage.
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"""
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from typing import Any
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from pydantic import BaseModel
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class SchemaOptimizer:
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@staticmethod
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def create_optimized_json_schema(model: type[BaseModel]) -> dict[str, Any]:
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"""
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Create the most optimized schema by flattening all $ref/$defs while preserving
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FULL descriptions and ALL action definitions. Also ensures OpenAI strict mode compatibility.
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Args:
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model: The Pydantic model to optimize
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Returns:
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Optimized schema with all $refs resolved and strict mode compatibility
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"""
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# Generate original schema
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original_schema = model.model_json_schema()
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# Extract $defs for reference resolution, then flatten everything
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defs_lookup = original_schema.get('$defs', {})
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def optimize_schema(
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obj: Any,
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defs_lookup: dict[str, Any] | None = None,
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*,
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in_properties: bool = False, # NEW: track context
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) -> Any:
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"""Apply all optimization techniques including flattening all $ref/$defs"""
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if isinstance(obj, dict):
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optimized: dict[str, Any] = {}
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flattened_ref: dict[str, Any] | None = None
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# Skip unnecessary fields AND $defs (we'll inline everything)
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skip_fields = ['additionalProperties', '$defs']
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for key, value in obj.items():
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if key in skip_fields:
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continue
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# Skip metadata "title" unless we're iterating inside an actual `properties` map
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if key == 'title' and not in_properties:
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continue
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# Preserve FULL descriptions without truncation
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elif key == 'description':
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optimized[key] = value
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# Handle type field
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elif key == 'type':
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optimized[key] = value
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# FLATTEN: Resolve $ref by inlining the actual definition
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elif key == '$ref' and defs_lookup:
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ref_path = value.split('/')[-1] # Get the definition name from "#/$defs/SomeName"
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if ref_path in defs_lookup:
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# Get the referenced definition and flatten it
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referenced_def = defs_lookup[ref_path]
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flattened_ref = optimize_schema(referenced_def, defs_lookup)
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# Keep all anyOf structures (action unions) and resolve any $refs within
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elif key == 'anyOf' and isinstance(value, list):
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optimized[key] = [optimize_schema(item, defs_lookup) for item in value]
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# Recursively optimize nested structures
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elif key in ['properties', 'items']:
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optimized[key] = optimize_schema(
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value,
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defs_lookup,
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in_properties=(key == 'properties'),
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)
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# Keep essential validation fields
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elif key in ['type', 'required', 'minimum', 'maximum', 'minItems', 'maxItems', 'pattern', 'default']:
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optimized[key] = value if not isinstance(value, (dict, list)) else optimize_schema(value, defs_lookup)
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# Recursively process all other fields
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else:
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optimized[key] = optimize_schema(value, defs_lookup) if isinstance(value, (dict, list)) else value
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# If we have a flattened reference, merge it with the optimized properties
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if flattened_ref is not None and isinstance(flattened_ref, dict):
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# Start with the flattened reference as the base
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result = flattened_ref.copy()
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# Merge in any sibling properties that were processed
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for key, value in optimized.items():
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# Preserve descriptions from the original object if they exist
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if key == 'description' and 'description' not in result:
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result[key] = value
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elif key != 'description': # Don't overwrite description from flattened ref
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result[key] = value
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return result
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else:
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# No $ref, just return the optimized object
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# CRITICAL: Add additionalProperties: false to ALL objects for OpenAI strict mode
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if optimized.get('type') == 'object':
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optimized['additionalProperties'] = False
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return optimized
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elif isinstance(obj, list):
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return [optimize_schema(item, defs_lookup, in_properties=in_properties) for item in obj]
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return obj
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# Create optimized schema with flattening
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optimized_result = optimize_schema(original_schema, defs_lookup)
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# Ensure we have a dictionary (should always be the case for schema root)
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if not isinstance(optimized_result, dict):
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raise ValueError('Optimized schema result is not a dictionary')
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optimized_schema: dict[str, Any] = optimized_result
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# Additional pass to ensure ALL objects have additionalProperties: false
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def ensure_additional_properties_false(obj: Any) -> None:
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"""Ensure all objects have additionalProperties: false"""
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if isinstance(obj, dict):
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# If it's an object type, ensure additionalProperties is false
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if obj.get('type') == 'object':
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obj['additionalProperties'] = False
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# Recursively apply to all values
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for value in obj.values():
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if isinstance(value, (dict, list)):
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ensure_additional_properties_false(value)
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elif isinstance(obj, list):
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for item in obj:
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if isinstance(item, (dict, list)):
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ensure_additional_properties_false(item)
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ensure_additional_properties_false(optimized_schema)
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SchemaOptimizer._make_strict_compatible(optimized_schema)
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return optimized_schema
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@staticmethod
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def _make_strict_compatible(schema: dict[str, Any] | list[Any]) -> None:
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"""Ensure all properties are required for OpenAI strict mode"""
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if isinstance(schema, dict):
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# First recursively apply to nested objects
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for key, value in schema.items():
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if isinstance(value, (dict, list)) and key != 'required':
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SchemaOptimizer._make_strict_compatible(value)
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# Then update required for this level
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if 'properties' in schema and 'type' in schema and schema['type'] == 'object':
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# Add all properties to required array
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all_props = list(schema['properties'].keys())
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schema['required'] = all_props # Set all properties as required
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elif isinstance(schema, list):
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for item in schema:
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SchemaOptimizer._make_strict_compatible(item)
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