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from __future__ import annotations
from collections.abc import Mapping, Sequence
from typing import TYPE_CHECKING, Any
from ..logger import logger
from .setup import get_trace_provider
from .span_data import (
AgentSpanData,
CustomSpanData,
FunctionSpanData,
GenerationSpanData,
GuardrailSpanData,
HandoffSpanData,
MCPListToolsSpanData,
ResponseSpanData,
SpeechGroupSpanData,
SpeechSpanData,
TranscriptionSpanData,
)
from .spans import Span
from .traces import Trace
if TYPE_CHECKING:
from openai.types.responses import Response
def trace(
workflow_name: str,
trace_id: str | None = None,
group_id: str | None = None,
metadata: dict[str, Any] | None = None,
disabled: bool = False,
) -> Trace:
"""
Create a new trace. The trace will not be started automatically; you should either use
it as a context manager (`with trace(...):`) or call `trace.start()` + `trace.finish()`
manually.
In addition to the workflow name and optional grouping identifier, you can provide
an arbitrary metadata dictionary to attach additional user-defined information to
the trace.
Args:
workflow_name: The name of the logical app or workflow. For example, you might provide
"code_bot" for a coding agent, or "customer_support_agent" for a customer support agent.
trace_id: The ID of the trace. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_trace_id()` to generate a trace ID, to guarantee that IDs are
correctly formatted.
group_id: Optional grouping identifier to link multiple traces from the same conversation
or process. For instance, you might use a chat thread ID.
metadata: Optional dictionary of additional metadata to attach to the trace.
disabled: If True, we will return a Trace but the Trace will not be recorded. This will
not be checked if there's an existing trace and `even_if_trace_running` is True.
Returns:
The newly created trace object.
"""
current_trace = get_trace_provider().get_current_trace()
if current_trace:
logger.warning(
"Trace already exists. Creating a new trace, but this is probably a mistake."
)
return get_trace_provider().create_trace(
name=workflow_name,
trace_id=trace_id,
group_id=group_id,
metadata=metadata,
disabled=disabled,
)
def get_current_trace() -> Trace | None:
"""Returns the currently active trace, if present."""
return get_trace_provider().get_current_trace()
def get_current_span() -> Span[Any] | None:
"""Returns the currently active span, if present."""
return get_trace_provider().get_current_span()
def agent_span(
name: str,
handoffs: list[str] | None = None,
tools: list[str] | None = None,
output_type: str | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[AgentSpanData]:
"""Create a new agent span. The span will not be started automatically, you should either do
`with agent_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
name: The name of the agent.
handoffs: Optional list of agent names to which this agent could hand off control.
tools: Optional list of tool names available to this agent.
output_type: Optional name of the output type produced by the agent.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
Returns:
The newly created agent span.
"""
return get_trace_provider().create_span(
span_data=AgentSpanData(name=name, handoffs=handoffs, tools=tools, output_type=output_type),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def function_span(
name: str,
input: str | None = None,
output: str | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[FunctionSpanData]:
"""Create a new function span. The span will not be started automatically, you should either do
`with function_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
name: The name of the function.
input: The input to the function.
output: The output of the function.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
Returns:
The newly created function span.
"""
return get_trace_provider().create_span(
span_data=FunctionSpanData(name=name, input=input, output=output),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def generation_span(
input: Sequence[Mapping[str, Any]] | None = None,
output: Sequence[Mapping[str, Any]] | None = None,
model: str | None = None,
model_config: Mapping[str, Any] | None = None,
usage: dict[str, Any] | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[GenerationSpanData]:
"""Create a new generation span. The span will not be started automatically, you should either
do `with generation_span() ...` or call `span.start()` + `span.finish()` manually.
This span captures the details of a model generation, including the
input message sequence, any generated outputs, the model name and
configuration, and usage data. If you only need to capture a model
response identifier, use `response_span()` instead.
Args:
input: The sequence of input messages sent to the model.
output: The sequence of output messages received from the model.
model: The model identifier used for the generation.
model_config: The model configuration (hyperparameters) used.
usage: A dictionary of usage information (input tokens, output tokens, etc.).
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
Returns:
The newly created generation span.
"""
return get_trace_provider().create_span(
span_data=GenerationSpanData(
input=input,
output=output,
model=model,
model_config=model_config,
usage=usage,
),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def response_span(
response: Response | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[ResponseSpanData]:
"""Create a new response span. The span will not be started automatically, you should either do
`with response_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
response: The OpenAI Response object.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
"""
return get_trace_provider().create_span(
span_data=ResponseSpanData(response=response),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def handoff_span(
from_agent: str | None = None,
to_agent: str | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[HandoffSpanData]:
"""Create a new handoff span. The span will not be started automatically, you should either do
`with handoff_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
from_agent: The name of the agent that is handing off.
to_agent: The name of the agent that is receiving the handoff.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
Returns:
The newly created handoff span.
"""
return get_trace_provider().create_span(
span_data=HandoffSpanData(from_agent=from_agent, to_agent=to_agent),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def custom_span(
name: str,
data: dict[str, Any] | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[CustomSpanData]:
"""Create a new custom span, to which you can add your own metadata. The span will not be
started automatically, you should either do `with custom_span() ...` or call
`span.start()` + `span.finish()` manually.
Args:
name: The name of the custom span.
data: Arbitrary structured data to associate with the span.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
Returns:
The newly created custom span.
"""
return get_trace_provider().create_span(
span_data=CustomSpanData(name=name, data=data or {}),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def guardrail_span(
name: str,
triggered: bool = False,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[GuardrailSpanData]:
"""Create a new guardrail span. The span will not be started automatically, you should either
do `with guardrail_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
name: The name of the guardrail.
triggered: Whether the guardrail was triggered.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
"""
return get_trace_provider().create_span(
span_data=GuardrailSpanData(name=name, triggered=triggered),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def transcription_span(
model: str | None = None,
input: str | None = None,
input_format: str | None = "pcm",
output: str | None = None,
model_config: Mapping[str, Any] | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[TranscriptionSpanData]:
"""Create a new transcription span. The span will not be started automatically, you should
either do `with transcription_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
model: The name of the model used for the speech-to-text.
input: The audio input of the speech-to-text transcription, as a base64 encoded string of
audio bytes.
input_format: The format of the audio input (defaults to "pcm").
output: The output of the speech-to-text transcription.
model_config: The model configuration (hyperparameters) used.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
Returns:
The newly created speech-to-text span.
"""
return get_trace_provider().create_span(
span_data=TranscriptionSpanData(
input=input,
input_format=input_format,
output=output,
model=model,
model_config=model_config,
),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def speech_span(
model: str | None = None,
input: str | None = None,
output: str | None = None,
output_format: str | None = "pcm",
model_config: Mapping[str, Any] | None = None,
first_content_at: str | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[SpeechSpanData]:
"""Create a new speech span. The span will not be started automatically, you should either do
`with speech_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
model: The name of the model used for the text-to-speech.
input: The text input of the text-to-speech.
output: The audio output of the text-to-speech as base64 encoded string of PCM audio bytes.
output_format: The format of the audio output (defaults to "pcm").
model_config: The model configuration (hyperparameters) used.
first_content_at: The time of the first byte of the audio output.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
"""
return get_trace_provider().create_span(
span_data=SpeechSpanData(
model=model,
input=input,
output=output,
output_format=output_format,
model_config=model_config,
first_content_at=first_content_at,
),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def speech_group_span(
input: str | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[SpeechGroupSpanData]:
"""Create a new speech group span. The span will not be started automatically, you should
either do `with speech_group_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
input: The input text used for the speech request.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
"""
return get_trace_provider().create_span(
span_data=SpeechGroupSpanData(input=input),
span_id=span_id,
parent=parent,
disabled=disabled,
)
def mcp_tools_span(
server: str | None = None,
result: list[str] | None = None,
span_id: str | None = None,
parent: Trace | Span[Any] | None = None,
disabled: bool = False,
) -> Span[MCPListToolsSpanData]:
"""Create a new MCP list tools span. The span will not be started automatically, you should
either do `with mcp_tools_span() ...` or call `span.start()` + `span.finish()` manually.
Args:
server: The name of the MCP server.
result: The result of the MCP list tools call.
span_id: The ID of the span. Optional. If not provided, we will generate an ID. We
recommend using `util.gen_span_id()` to generate a span ID, to guarantee that IDs are
correctly formatted.
parent: The parent span or trace. If not provided, we will automatically use the current
trace/span as the parent.
disabled: If True, we will return a Span but the Span will not be recorded.
"""
return get_trace_provider().create_span(
span_data=MCPListToolsSpanData(server=server, result=result),
span_id=span_id,
parent=parent,
disabled=disabled,
)
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