class OpenAIChatCompletionsModel(Model):
def __init__(
self,
model: str | ChatModel,
openai_client: AsyncOpenAI,
) -> None:
self.model = model
self._client = openai_client
def _non_null_or_not_given(self, value: Any) -> Any:
return value if value is not None else NOT_GIVEN
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,
) -> ModelResponse:
with generation_span(
model=str(self.model),
model_config=model_settings.to_json_dict() | {"base_url": str(self._client.base_url)},
disabled=tracing.is_disabled(),
) as span_generation:
response = await self._fetch_response(
system_instructions,
input,
model_settings,
tools,
output_schema,
handoffs,
span_generation,
tracing,
stream=False,
)
if _debug.DONT_LOG_MODEL_DATA:
logger.debug("Received model response")
else:
logger.debug(
f"LLM resp:\n{json.dumps(response.choices[0].message.model_dump(), indent=2)}\n"
)
usage = (
Usage(
requests=1,
input_tokens=response.usage.prompt_tokens,
output_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
)
if response.usage
else Usage()
)
if tracing.include_data():
span_generation.span_data.output = [response.choices[0].message.model_dump()]
span_generation.span_data.usage = {
"input_tokens": usage.input_tokens,
"output_tokens": usage.output_tokens,
}
items = Converter.message_to_output_items(response.choices[0].message)
return ModelResponse(
output=items,
usage=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,
) -> AsyncIterator[TResponseStreamEvent]:
"""
Yields a partial message as it is generated, as well as the usage information.
"""
with generation_span(
model=str(self.model),
model_config=model_settings.to_json_dict() | {"base_url": str(self._client.base_url)},
disabled=tracing.is_disabled(),
) as span_generation:
response, stream = await self._fetch_response(
system_instructions,
input,
model_settings,
tools,
output_schema,
handoffs,
span_generation,
tracing,
stream=True,
)
final_response: Response | None = None
async for chunk in ChatCmplStreamHandler.handle_stream(response, stream):
yield chunk
if chunk.type == "response.completed":
final_response = chunk.response
if tracing.include_data() and final_response:
span_generation.span_data.output = [final_response.model_dump()]
if final_response and final_response.usage:
span_generation.span_data.usage = {
"input_tokens": final_response.usage.input_tokens,
"output_tokens": final_response.usage.output_tokens,
}
@overload
async def _fetch_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
span: Span[GenerationSpanData],
tracing: ModelTracing,
stream: Literal[True],
) -> tuple[Response, AsyncStream[ChatCompletionChunk]]: ...
@overload
async def _fetch_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
span: Span[GenerationSpanData],
tracing: ModelTracing,
stream: Literal[False],
) -> ChatCompletion: ...
async def _fetch_response(
self,
system_instructions: str | None,
input: str | list[TResponseInputItem],
model_settings: ModelSettings,
tools: list[Tool],
output_schema: AgentOutputSchemaBase | None,
handoffs: list[Handoff],
span: Span[GenerationSpanData],
tracing: ModelTracing,
stream: bool = False,
) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]:
converted_messages = Converter.items_to_messages(input)
if system_instructions:
converted_messages.insert(
0,
{
"content": system_instructions,
"role": "system",
},
)
if tracing.include_data():
span.span_data.input = converted_messages
parallel_tool_calls = (
True
if model_settings.parallel_tool_calls and tools and len(tools) > 0
else False
if model_settings.parallel_tool_calls is False
else NOT_GIVEN
)
tool_choice = Converter.convert_tool_choice(model_settings.tool_choice)
response_format = Converter.convert_response_format(output_schema)
converted_tools = [Converter.tool_to_openai(tool) for tool in tools] if tools else []
for handoff in handoffs:
converted_tools.append(Converter.convert_handoff_tool(handoff))
if _debug.DONT_LOG_MODEL_DATA:
logger.debug("Calling LLM")
else:
logger.debug(
f"{json.dumps(converted_messages, indent=2)}\n"
f"Tools:\n{json.dumps(converted_tools, indent=2)}\n"
f"Stream: {stream}\n"
f"Tool choice: {tool_choice}\n"
f"Response format: {response_format}\n"
)
reasoning_effort = model_settings.reasoning.effort if model_settings.reasoning else None
store = ChatCmplHelpers.get_store_param(self._get_client(), model_settings)
stream_options = ChatCmplHelpers.get_stream_options_param(
self._get_client(), model_settings, stream=stream
)
ret = await self._get_client().chat.completions.create(
model=self.model,
messages=converted_messages,
tools=converted_tools or NOT_GIVEN,
temperature=self._non_null_or_not_given(model_settings.temperature),
top_p=self._non_null_or_not_given(model_settings.top_p),
frequency_penalty=self._non_null_or_not_given(model_settings.frequency_penalty),
presence_penalty=self._non_null_or_not_given(model_settings.presence_penalty),
max_tokens=self._non_null_or_not_given(model_settings.max_tokens),
tool_choice=tool_choice,
response_format=response_format,
parallel_tool_calls=parallel_tool_calls,
stream=stream,
stream_options=self._non_null_or_not_given(stream_options),
store=self._non_null_or_not_given(store),
reasoning_effort=self._non_null_or_not_given(reasoning_effort),
extra_headers={ **HEADERS, **(model_settings.extra_headers or {}) },
extra_query=model_settings.extra_query,
extra_body=model_settings.extra_body,
metadata=self._non_null_or_not_given(model_settings.metadata),
)
if isinstance(ret, ChatCompletion):
return ret
response = Response(
id=FAKE_RESPONSES_ID,
created_at=time.time(),
model=self.model,
object="response",
output=[],
tool_choice=cast(Literal["auto", "required", "none"], tool_choice)
if tool_choice != NOT_GIVEN
else "auto",
top_p=model_settings.top_p,
temperature=model_settings.temperature,
tools=[],
parallel_tool_calls=parallel_tool_calls or False,
reasoning=model_settings.reasoning,
)
return response, ret
def _get_client(self) -> AsyncOpenAI:
if self._client is None:
self._client = AsyncOpenAI()
return self._client