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This reverts commit e3729f9855.
This commit is contained in:
Krish Dholakia
GitHub
parent
e3729f9855
commit
d3e7a137ad
3 changed files with 6 additions and 198 deletions
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@ -165,18 +165,9 @@ def _check_text_in_content(parts: List[PartType]) -> bool:
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return has_text_param
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def _build_vertex_schema(parameters: dict, add_property_ordering: bool = False):
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def _build_vertex_schema(parameters: dict):
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"""
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This is a modified version of https://github.com/google-gemini/generative-ai-python/blob/8f77cc6ac99937cd3a81299ecf79608b91b06bbb/google/generativeai/types/content_types.py#L419
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Updates the input parameters, removing extraneous fields, adjusting types, unwinding $defs, and adding propertyOrdering if specified, returning the updated parameters.
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Parameters:
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parameters: dict - the json schema to build from
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add_property_ordering: bool - whether to add propertyOrdering to the schema. This is only applicable to schemas for structured outputs. See
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set_schema_property_ordering for more details.
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Returns:
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parameters: dict - the input parameters, modified in place
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"""
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# Get valid fields from Schema TypedDict
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valid_schema_fields = set(get_type_hints(Schema).keys())
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@ -195,31 +186,8 @@ def _build_vertex_schema(parameters: dict, add_property_ordering: bool = False):
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add_object_type(parameters)
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# Postprocessing
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# Filter out fields that don't exist in Schema
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parameters = filter_schema_fields(parameters, valid_schema_fields)
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if add_property_ordering:
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set_schema_property_ordering(parameters)
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return parameters
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def set_schema_property_ordering(schema: Dict[str, Any]) -> Dict[str, Any]:
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"""
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vertex ai and generativeai apis order output of fields alphabetically, unless you specify the order.
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python dicts retain order, so we just use that. Note that this field only applies to structured outputs, and not tools.
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Function tools are not afflicted by the same alphabetical ordering issue, (the order of keys returned seems to be arbitrary, up to the model)
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https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.cachedContents#Schema.FIELDS.property_ordering
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"""
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if "properties" in schema and isinstance(schema["properties"], dict):
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# retain propertyOrdering as an escape hatch if user already specifies it
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if "propertyOrdering" not in schema:
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schema["propertyOrdering"] = [k for k, v in schema["properties"].items()]
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for k, v in schema["properties"].items():
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set_schema_property_ordering(v)
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if "items" in schema:
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set_schema_property_ordering(schema["items"])
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return schema
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filtered_parameters = filter_schema_fields(parameters, valid_schema_fields)
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return filtered_parameters
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def filter_schema_fields(
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@ -207,7 +207,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
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"extra_headers",
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"seed",
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"logprobs",
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"top_logprobs",
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"top_logprobs", # Added this to list of supported openAI params
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"modalities",
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]
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@ -313,10 +313,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
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if isinstance(old_schema, list):
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for item in old_schema:
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if isinstance(item, dict):
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item = _build_vertex_schema(parameters=item, add_property_ordering=True)
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item = _build_vertex_schema(parameters=item)
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elif isinstance(old_schema, dict):
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old_schema = _build_vertex_schema(parameters=old_schema, add_property_ordering=True)
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old_schema = _build_vertex_schema(parameters=old_schema)
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return old_schema
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def apply_response_schema_transformation(self, value: dict, optional_params: dict):
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@ -9,8 +9,6 @@ from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
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VertexGeminiConfig,
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)
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from litellm.types.utils import ChoiceLogprobs
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from pydantic import BaseModel
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from typing import List, cast
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def test_top_logprobs():
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@ -64,160 +62,3 @@ def test_get_model_name_from_gemini_spec_model():
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model = "gemini/ft-uuid-123"
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result = VertexGeminiConfig._get_model_name_from_gemini_spec_model(model)
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assert result == "ft-uuid-123"
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def test_vertex_ai_response_schema_dict():
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v = VertexGeminiConfig()
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transformed_request = v.map_openai_params(
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non_default_params={
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"response_format": {
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"type": "json_schema",
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"json_schema": {
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"name": "math_reasoning",
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"schema": {
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"type": "object",
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"properties": {
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"steps": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"thought": {"type": "string"},
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"output": {"type": "string"},
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},
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"required": ["thought", "output"],
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"additionalProperties": False,
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},
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},
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"final_answer": {"type": "string"},
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},
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"required": ["steps", "final_answer"],
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"additionalProperties": False,
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},
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"strict": False,
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},
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},
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},
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optional_params={},
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model="gemini-2.0-flash-lite",
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drop_params=False,
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)
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schema = transformed_request["response_schema"]
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# should add propertyOrdering
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assert schema["propertyOrdering"] == ["steps", "final_answer"]
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# should add propertyOrdering (recursively, including array items)
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assert schema["properties"]["steps"]["items"]["propertyOrdering"] == [
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"thought",
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"output",
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]
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# should strip strict and additionalProperties
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assert "strict" not in schema
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assert "additionalProperties" not in schema
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# validate the whole thing to catch regressions
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assert transformed_request["response_schema"] == {
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"type": "object",
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"properties": {
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"steps": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"thought": {"type": "string"},
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"output": {"type": "string"},
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},
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"required": ["thought", "output"],
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"propertyOrdering": ["thought", "output"],
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},
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},
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"final_answer": {"type": "string"},
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},
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"required": ["steps", "final_answer"],
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"propertyOrdering": ["steps", "final_answer"],
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}
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class MathReasoning(BaseModel):
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steps: List["Step"]
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final_answer: str
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class Step(BaseModel):
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thought: str
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output: str
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def test_vertex_ai_response_schema_defs():
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v = VertexGeminiConfig()
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schema = cast(dict, v.get_json_schema_from_pydantic_object(MathReasoning))
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# pydantic conversion by default adds $defs to the schema, make sure this is still the case, otherwise this test isn't really testing anything
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assert "$defs" in schema["json_schema"]["schema"]
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transformed_request = v.map_openai_params(
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non_default_params={
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"response_format": schema,
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},
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optional_params={},
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model="gemini-2.0-flash-lite",
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drop_params=False,
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)
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assert "$defs" not in transformed_request["response_schema"]
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assert transformed_request["response_schema"] == {
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"title": "MathReasoning",
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"type": "object",
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"properties": {
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"steps": {
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"title": "Steps",
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"type": "array",
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"items": {
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"title": "Step",
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"type": "object",
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"properties": {
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"thought": {"title": "Thought", "type": "string"},
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"output": {"title": "Output", "type": "string"},
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},
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"required": ["thought", "output"],
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"propertyOrdering": ["thought", "output"],
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},
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},
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"final_answer": {"title": "Final Answer", "type": "string"},
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},
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"required": ["steps", "final_answer"],
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"propertyOrdering": ["steps", "final_answer"],
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}
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def test_vertex_ai_retain_property_ordering():
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v = VertexGeminiConfig()
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transformed_request = v.map_openai_params(
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non_default_params={
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"response_format": {
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"type": "json_schema",
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"json_schema": {
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"name": "math_reasoning",
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"schema": {
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"type": "object",
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"properties": {
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"output": {"type": "string"},
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"thought": {"type": "string"},
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},
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"propertyOrdering": ["thought", "output"],
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},
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},
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},
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},
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optional_params={},
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model="gemini-2.0-flash-lite",
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drop_params=False,
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)
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schema = transformed_request["response_schema"]
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# should leave existing value alone, despite dictionary ordering
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assert schema["propertyOrdering"] == ["thought", "output"]
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