참고소스 수정본

참고/instructor-main/instructor/processing/schema.py

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+"""
+Standalone schema generation utilities for different LLM providers.
+
+This module provides provider-agnostic functions to generate schemas from Pydantic models
+without requiring inheritance from OpenAISchema or use of decorators.
+"""
+
+from __future__ import annotations
+
+import functools
+import warnings
+from typing import Any, cast
+
+from docstring_parser import parse
+from pydantic import BaseModel
+
+from ..providers.gemini.utils import map_to_gemini_function_schema
+
+__all__ = [
+    "generate_openai_schema",
+    "generate_anthropic_schema",
+    "generate_gemini_schema",
+]
+
+
+@functools.lru_cache(maxsize=256)
+def generate_openai_schema(model: type[BaseModel]) -> dict[str, Any]:
+    """
+    Generate OpenAI function schema from a Pydantic model.
+
+    Args:
+        model: A Pydantic BaseModel subclass
+
+    Returns:
+        A dictionary in the format of OpenAI's function schema
+
+    Note:
+        The model's docstring will be used for the function description.
+        Parameter descriptions from the docstring will enrich field descriptions.
+    """
+    schema = model.model_json_schema()
+    docstring = parse(model.__doc__ or "")
+    parameters = {k: v for k, v in schema.items() if k not in ("title", "description")}
+
+    # Enrich parameter descriptions from docstring
+    for param in docstring.params:
+        if (name := param.arg_name) in parameters["properties"] and (
+            description := param.description
+        ):
+            if "description" not in parameters["properties"][name]:
+                parameters["properties"][name]["description"] = description
+
+    parameters["required"] = sorted(
+        k for k, v in parameters["properties"].items() if "default" not in v
+    )
+
+    if "description" not in schema:
+        if docstring.short_description:
+            schema["description"] = docstring.short_description
+        else:
+            schema["description"] = (
+                f"Correctly extracted `{model.__name__}` with all "
+                f"the required parameters with correct types"
+            )
+
+    return {
+        "name": schema["title"],
+        "description": schema["description"],
+        "parameters": parameters,
+    }
+
+
+@functools.lru_cache(maxsize=256)
+def generate_anthropic_schema(model: type[BaseModel]) -> dict[str, Any]:
+    """
+    Generate Anthropic tool schema from a Pydantic model.
+
+    Args:
+        model: A Pydantic BaseModel subclass
+
+    Returns:
+        A dictionary in the format of Anthropic's tool schema
+    """
+    # Generate the Anthropic schema based on the OpenAI schema to avoid redundant schema generation
+    openai_schema = generate_openai_schema(model)
+    return {
+        "name": openai_schema["name"],
+        "description": openai_schema["description"],
+        "input_schema": model.model_json_schema(),
+    }
+
+
+@functools.lru_cache(maxsize=256)
+def generate_gemini_schema(model: type[BaseModel]) -> Any:
+    """
+    Generate Gemini function schema from a Pydantic model.
+
+    Args:
+        model: A Pydantic BaseModel subclass
+
+    Returns:
+        A Gemini FunctionDeclaration object
+
+    Note:
+        This function is deprecated. The google-generativeai library is being replaced by google-genai.
+    """
+    # This is kept for backward compatibility but deprecated
+    warnings.warn(
+        "generate_gemini_schema is deprecated. The google-generativeai library is being replaced by google-genai.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    try:
+        import importlib
+
+        genai_types = cast(Any, importlib.import_module("google.generativeai.types"))
+
+        # Use OpenAI schema
+        openai_schema = generate_openai_schema(model)
+
+        # Transform to Gemini format
+        function = genai_types.FunctionDeclaration(
+            name=openai_schema["name"],
+            description=openai_schema["description"],
+            parameters=map_to_gemini_function_schema(openai_schema["parameters"]),
+        )
+
+        return function
+    except ImportError as e:
+        raise ImportError(
+            "google-generativeai is deprecated. Please install google-genai instead: pip install google-genai"
+        ) from e