openai-agents-python/tests/fake_model.py
Rohan Mehta 2b9b8f7e73
Prompts support (#876)
Add support for the new openai prompts feature.
2025-06-16 15:47:48 -04:00

160 lines
5.3 KiB
Python

from __future__ import annotations
from collections.abc import AsyncIterator
from typing import Any
from openai.types.responses import Response, ResponseCompletedEvent, ResponseUsage
from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails
from agents.agent_output import AgentOutputSchemaBase
from agents.handoffs import Handoff
from agents.items import (
ModelResponse,
TResponseInputItem,
TResponseOutputItem,
TResponseStreamEvent,
)
from agents.model_settings import ModelSettings
from agents.models.interface import Model, ModelTracing
from agents.tool import Tool
from agents.tracing import SpanError, generation_span
from agents.usage import Usage
class FakeModel(Model):
def __init__(
self,
tracing_enabled: bool = False,
initial_output: list[TResponseOutputItem] | Exception | None = None,
):
if initial_output is None:
initial_output = []
self.turn_outputs: list[list[TResponseOutputItem] | Exception] = (
[initial_output] if initial_output else []
)
self.tracing_enabled = tracing_enabled
self.last_turn_args: dict[str, Any] = {}
self.hardcoded_usage: Usage | None = None
def set_hardcoded_usage(self, usage: Usage):
self.hardcoded_usage = usage
def set_next_output(self, output: list[TResponseOutputItem] | Exception):
self.turn_outputs.append(output)
def add_multiple_turn_outputs(self, outputs: list[list[TResponseOutputItem] | Exception]):
self.turn_outputs.extend(outputs)
def get_next_output(self) -> list[TResponseOutputItem] | Exception:
if not self.turn_outputs:
return []
return self.turn_outputs.pop(0)
async def get_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
tracing: ModelTracing,
*,
previous_response_id: str | None,
prompt: Any | None,
) -> ModelResponse:
self.last_turn_args = {
"system_instructions": system_instructions,
"input": input,
"model_settings": model_settings,
"tools": tools,
"output_schema": output_schema,
"previous_response_id": previous_response_id,
}
with generation_span(disabled=not self.tracing_enabled) as span:
output = self.get_next_output()
if isinstance(output, Exception):
span.set_error(
SpanError(
message="Error",
data={
"name": output.__class__.__name__,
"message": str(output),
},
)
)
raise output
return ModelResponse(
output=output,
usage=self.hardcoded_usage or Usage(),
response_id=None,
)
async def stream_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
tracing: ModelTracing,
*,
previous_response_id: str | None,
prompt: Any | None,
) -> AsyncIterator[TResponseStreamEvent]:
self.last_turn_args = {
"system_instructions": system_instructions,
"input": input,
"model_settings": model_settings,
"tools": tools,
"output_schema": output_schema,
"previous_response_id": previous_response_id,
}
with generation_span(disabled=not self.tracing_enabled) as span:
output = self.get_next_output()
if isinstance(output, Exception):
span.set_error(
SpanError(
message="Error",
data={
"name": output.__class__.__name__,
"message": str(output),
},
)
)
raise output
yield ResponseCompletedEvent(
type="response.completed",
response=get_response_obj(output, usage=self.hardcoded_usage),
sequence_number=0,
)
def get_response_obj(
output: list[TResponseOutputItem],
response_id: str | None = None,
usage: Usage | None = None,
) -> Response:
return Response(
id=response_id or "123",
created_at=123,
model="test_model",
object="response",
output=output,
tool_choice="none",
tools=[],
top_p=None,
parallel_tool_calls=False,
usage=ResponseUsage(
input_tokens=usage.input_tokens if usage else 0,
output_tokens=usage.output_tokens if usage else 0,
total_tokens=usage.total_tokens if usage else 0,
input_tokens_details=InputTokensDetails(cached_tokens=0),
output_tokens_details=OutputTokensDetails(reasoning_tokens=0),
),
)