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"""Langfuse OpenTelemetry integration module.
This module implements Langfuse's core observability functionality on top of the OpenTelemetry (OTel) standard.
"""
import asyncio
import logging
import os
import re
import urllib.parse
import warnings
from datetime import datetime
from hashlib import sha256
from time import time_ns
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Type,
Union,
cast,
overload,
)
import backoff
import httpx
from opentelemetry import trace as otel_trace_api
from opentelemetry.sdk.trace import ReadableSpan, TracerProvider
from opentelemetry.sdk.trace.id_generator import RandomIdGenerator
from opentelemetry.util._decorator import (
_AgnosticContextManager,
_agnosticcontextmanager,
)
from packaging.version import Version
from typing_extensions import deprecated
from langfuse._client.attributes import LangfuseOtelSpanAttributes, _serialize
from langfuse._client.constants import (
LANGFUSE_SDK_EXPERIMENT_ENVIRONMENT,
ObservationTypeGenerationLike,
ObservationTypeLiteral,
ObservationTypeLiteralNoEvent,
ObservationTypeSpanLike,
get_observation_types_list,
)
from langfuse._client.datasets import DatasetClient
from langfuse._client.environment_variables import (
LANGFUSE_BASE_URL,
LANGFUSE_DEBUG,
LANGFUSE_HOST,
LANGFUSE_PUBLIC_KEY,
LANGFUSE_RELEASE,
LANGFUSE_SAMPLE_RATE,
LANGFUSE_SECRET_KEY,
LANGFUSE_TIMEOUT,
LANGFUSE_TRACING_ENABLED,
LANGFUSE_TRACING_ENVIRONMENT,
)
from langfuse._client.propagation import (
PropagatedExperimentAttributes,
_propagate_attributes,
)
from langfuse._client.resource_manager import LangfuseResourceManager
from langfuse._client.span import (
LangfuseAgent,
LangfuseChain,
LangfuseEmbedding,
LangfuseEvaluator,
LangfuseEvent,
LangfuseGeneration,
LangfuseGuardrail,
LangfuseRetriever,
LangfuseSpan,
LangfuseTool,
)
from langfuse._client.utils import get_sha256_hash_hex, run_async_safely
from langfuse._utils import _get_timestamp
from langfuse._utils.environment import get_common_release_envs
from langfuse._utils.parse_error import handle_fern_exception
from langfuse._utils.prompt_cache import PromptCache
from langfuse.api import (
CreateChatPromptRequest,
CreateChatPromptType,
CreateTextPromptRequest,
Dataset,
DatasetItem,
DatasetRunWithItems,
DatasetStatus,
DeleteDatasetRunResponse,
Error,
MapValue,
NotFoundError,
PaginatedDatasetRuns,
Prompt_Chat,
Prompt_Text,
ScoreBody,
TraceBody,
)
from langfuse.batch_evaluation import (
BatchEvaluationResult,
BatchEvaluationResumeToken,
BatchEvaluationRunner,
CompositeEvaluatorFunction,
MapperFunction,
)
from langfuse.experiment import (
Evaluation,
EvaluatorFunction,
ExperimentData,
ExperimentItem,
ExperimentItemResult,
ExperimentResult,
RunEvaluatorFunction,
TaskFunction,
_run_evaluator,
_run_task,
)
from langfuse.logger import langfuse_logger
from langfuse.media import LangfuseMedia
from langfuse.model import (
ChatMessageDict,
ChatMessageWithPlaceholdersDict,
ChatPromptClient,
PromptClient,
TextPromptClient,
)
from langfuse.types import MaskFunction, ScoreDataType, SpanLevel, TraceContext
class Langfuse:
"""Main client for Langfuse tracing and platform features.
This class provides an interface for creating and managing traces, spans,
and generations in Langfuse as well as interacting with the Langfuse API.
The client features a thread-safe singleton pattern for each unique public API key,
ensuring consistent trace context propagation across your application. It implements
efficient batching of spans with configurable flush settings and includes background
thread management for media uploads and score ingestion.
Configuration is flexible through either direct parameters or environment variables,
with graceful fallbacks and runtime configuration updates.
Attributes:
api: Synchronous API client for Langfuse backend communication
async_api: Asynchronous API client for Langfuse backend communication
_otel_tracer: Internal LangfuseTracer instance managing OpenTelemetry components
Parameters:
public_key (Optional[str]): Your Langfuse public API key. Can also be set via LANGFUSE_PUBLIC_KEY environment variable.
secret_key (Optional[str]): Your Langfuse secret API key. Can also be set via LANGFUSE_SECRET_KEY environment variable.
base_url (Optional[str]): The Langfuse API base URL. Defaults to "https://cloud.langfuse.com". Can also be set via LANGFUSE_BASE_URL environment variable.
host (Optional[str]): Deprecated. Use base_url instead. The Langfuse API host URL. Defaults to "https://cloud.langfuse.com".
timeout (Optional[int]): Timeout in seconds for API requests. Defaults to 5 seconds.
httpx_client (Optional[httpx.Client]): Custom httpx client for making non-tracing HTTP requests. If not provided, a default client will be created.
debug (bool): Enable debug logging. Defaults to False. Can also be set via LANGFUSE_DEBUG environment variable.
tracing_enabled (Optional[bool]): Enable or disable tracing. Defaults to True. Can also be set via LANGFUSE_TRACING_ENABLED environment variable.
flush_at (Optional[int]): Number of spans to batch before sending to the API. Defaults to 512. Can also be set via LANGFUSE_FLUSH_AT environment variable.
flush_interval (Optional[float]): Time in seconds between batch flushes. Defaults to 5 seconds. Can also be set via LANGFUSE_FLUSH_INTERVAL environment variable.
environment (Optional[str]): Environment name for tracing. Default is 'default'. Can also be set via LANGFUSE_TRACING_ENVIRONMENT environment variable. Can be any lowercase alphanumeric string with hyphens and underscores that does not start with 'langfuse'.
release (Optional[str]): Release version/hash of your application. Used for grouping analytics by release.
media_upload_thread_count (Optional[int]): Number of background threads for handling media uploads. Defaults to 1. Can also be set via LANGFUSE_MEDIA_UPLOAD_THREAD_COUNT environment variable.
sample_rate (Optional[float]): Sampling rate for traces (0.0 to 1.0). Defaults to 1.0 (100% of traces are sampled). Can also be set via LANGFUSE_SAMPLE_RATE environment variable.
mask (Optional[MaskFunction]): Function to mask sensitive data in traces before sending to the API.
blocked_instrumentation_scopes (Optional[List[str]]): Deprecated. Use `should_export_span` instead. Equivalent behavior:
```python
from langfuse.span_filter import is_default_export_span
blocked = {"sqlite", "requests"}
should_export_span = lambda span: (
is_default_export_span(span)
and (
span.instrumentation_scope is None
or span.instrumentation_scope.name not in blocked
)
)
```
should_export_span (Optional[Callable[[ReadableSpan], bool]]): Callback to decide whether to export a span. If omitted, Langfuse uses the default filter (Langfuse SDK spans, spans with `gen_ai.*` attributes, and known LLM instrumentation scopes).
additional_headers (Optional[Dict[str, str]]): Additional headers to include in all API requests and OTLPSpanExporter requests. These headers will be merged with default headers. Note: If httpx_client is provided, additional_headers must be set directly on your custom httpx_client as well.
tracer_provider(Optional[TracerProvider]): OpenTelemetry TracerProvider to use for Langfuse. This can be useful to set to have disconnected tracing between Langfuse and other OpenTelemetry-span emitting libraries. Note: To track active spans, the context is still shared between TracerProviders. This may lead to broken trace trees.
Example:
```python
from langfuse.otel import Langfuse
# Initialize the client (reads from env vars if not provided)
langfuse = Langfuse(
public_key="your-public-key",
secret_key="your-secret-key",
host="https://cloud.langfuse.com", # Optional, default shown
)
# Create a trace span
with langfuse.start_as_current_observation(name="process-query") as span:
# Your application code here
# Create a nested generation span for an LLM call
with span.start_as_current_generation(
name="generate-response",
model="gpt-4",
input={"query": "Tell me about AI"},
model_parameters={"temperature": 0.7, "max_tokens": 500}
) as generation:
# Generate response here
response = "AI is a field of computer science..."
generation.update(
output=response,
usage_details={"prompt_tokens": 10, "completion_tokens": 50},
cost_details={"total_cost": 0.0023}
)
# Score the generation (supports NUMERIC, BOOLEAN, CATEGORICAL)
generation.score(name="relevance", value=0.95, data_type="NUMERIC")
```
"""
_resources: Optional[LangfuseResourceManager] = None
_mask: Optional[MaskFunction] = None
_otel_tracer: otel_trace_api.Tracer
def __init__(
self,
*,
public_key: Optional[str] = None,
secret_key: Optional[str] = None,
base_url: Optional[str] = None,
host: Optional[str] = None,
timeout: Optional[int] = None,
httpx_client: Optional[httpx.Client] = None,
debug: bool = False,
tracing_enabled: Optional[bool] = True,
flush_at: Optional[int] = None,
flush_interval: Optional[float] = None,
environment: Optional[str] = None,
release: Optional[str] = None,
media_upload_thread_count: Optional[int] = None,
sample_rate: Optional[float] = None,
mask: Optional[MaskFunction] = None,
blocked_instrumentation_scopes: Optional[List[str]] = None,
should_export_span: Optional[Callable[[ReadableSpan], bool]] = None,
additional_headers: Optional[Dict[str, str]] = None,
tracer_provider: Optional[TracerProvider] = None,
):
self._base_url = (
base_url
or os.environ.get(LANGFUSE_BASE_URL)
or host
or os.environ.get(LANGFUSE_HOST, "https://cloud.langfuse.com")
)
self._environment = environment or cast(
str, os.environ.get(LANGFUSE_TRACING_ENVIRONMENT)
)
self._release = (
release
or os.environ.get(LANGFUSE_RELEASE, None)
or get_common_release_envs()
)
self._project_id: Optional[str] = None
sample_rate = sample_rate or float(os.environ.get(LANGFUSE_SAMPLE_RATE, 1.0))
if not 0.0 <= sample_rate <= 1.0:
raise ValueError(
f"Sample rate must be between 0.0 and 1.0, got {sample_rate}"
)
timeout = timeout or int(os.environ.get(LANGFUSE_TIMEOUT, 5))
self._tracing_enabled = (
tracing_enabled
and os.environ.get(LANGFUSE_TRACING_ENABLED, "true").lower() != "false"
)
if not self._tracing_enabled:
langfuse_logger.info(
"Configuration: Langfuse tracing is explicitly disabled. No data will be sent to the Langfuse API."
)
debug = (
debug if debug else (os.getenv(LANGFUSE_DEBUG, "false").lower() == "true")
)
if debug:
logging.basicConfig(
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
langfuse_logger.setLevel(logging.DEBUG)
public_key = public_key or os.environ.get(LANGFUSE_PUBLIC_KEY)
if public_key is None:
langfuse_logger.warning(
"Authentication error: Langfuse client initialized without public_key. Client will be disabled. "
"Provide a public_key parameter or set LANGFUSE_PUBLIC_KEY environment variable. "
)
self._otel_tracer = otel_trace_api.NoOpTracer()
return
secret_key = secret_key or os.environ.get(LANGFUSE_SECRET_KEY)
if secret_key is None:
langfuse_logger.warning(
"Authentication error: Langfuse client initialized without secret_key. Client will be disabled. "
"Provide a secret_key parameter or set LANGFUSE_SECRET_KEY environment variable. "
)
self._otel_tracer = otel_trace_api.NoOpTracer()
return
if os.environ.get("OTEL_SDK_DISABLED", "false").lower() == "true":
langfuse_logger.warning(
"OTEL_SDK_DISABLED is set. Langfuse tracing will be disabled and no traces will appear in the UI."
)
if blocked_instrumentation_scopes is not None:
warnings.warn(
"`blocked_instrumentation_scopes` is deprecated and will be removed in a future release. "
"Use `should_export_span` instead. Example: "
"from langfuse.span_filter import is_default_export_span; "
'blocked={"scope"}; should_export_span=lambda span: '
"is_default_export_span(span) and (span.instrumentation_scope is None or "
"span.instrumentation_scope.name not in blocked).",
DeprecationWarning,
stacklevel=2,
)
# Initialize api and tracer if requirements are met
self._resources = LangfuseResourceManager(
public_key=public_key,
secret_key=secret_key,
base_url=self._base_url,
timeout=timeout,
environment=self._environment,
release=release,
flush_at=flush_at,
flush_interval=flush_interval,
httpx_client=httpx_client,
media_upload_thread_count=media_upload_thread_count,
sample_rate=sample_rate,
mask=mask,
tracing_enabled=self._tracing_enabled,
blocked_instrumentation_scopes=blocked_instrumentation_scopes,
should_export_span=should_export_span,
additional_headers=additional_headers,
tracer_provider=tracer_provider,
)
self._mask = self._resources.mask
self._otel_tracer = (
self._resources.tracer
if self._tracing_enabled and self._resources.tracer is not None
else otel_trace_api.NoOpTracer()
)
self.api = self._resources.api
self.async_api = self._resources.async_api
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["generation"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
completion_start_time: Optional[datetime] = None,
model: Optional[str] = None,
model_parameters: Optional[Dict[str, MapValue]] = None,
usage_details: Optional[Dict[str, int]] = None,
cost_details: Optional[Dict[str, float]] = None,
prompt: Optional[PromptClient] = None,
) -> LangfuseGeneration: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["span"] = "span",
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
) -> LangfuseSpan: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["agent"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
) -> LangfuseAgent: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["tool"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
) -> LangfuseTool: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["chain"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
) -> LangfuseChain: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["retriever"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
) -> LangfuseRetriever: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["evaluator"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
) -> LangfuseEvaluator: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["embedding"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
completion_start_time: Optional[datetime] = None,
model: Optional[str] = None,
model_parameters: Optional[Dict[str, MapValue]] = None,
usage_details: Optional[Dict[str, int]] = None,
cost_details: Optional[Dict[str, float]] = None,
prompt: Optional[PromptClient] = None,
) -> LangfuseEmbedding: ...
@overload
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["guardrail"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
) -> LangfuseGuardrail: ...
def start_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: ObservationTypeLiteralNoEvent = "span",
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
completion_start_time: Optional[datetime] = None,
model: Optional[str] = None,
model_parameters: Optional[Dict[str, MapValue]] = None,
usage_details: Optional[Dict[str, int]] = None,
cost_details: Optional[Dict[str, float]] = None,
prompt: Optional[PromptClient] = None,
) -> Union[
LangfuseSpan,
LangfuseGeneration,
LangfuseAgent,
LangfuseTool,
LangfuseChain,
LangfuseRetriever,
LangfuseEvaluator,
LangfuseEmbedding,
LangfuseGuardrail,
]:
"""Create a new observation of the specified type.
This method creates a new observation but does not set it as the current span in the
context. To create and use an observation within a context, use start_as_current_observation().
Args:
trace_context: Optional context for connecting to an existing trace
name: Name of the observation
as_type: Type of observation to create (defaults to "span")
input: Input data for the operation
output: Output data from the operation
metadata: Additional metadata to associate with the observation
version: Version identifier for the code or component
level: Importance level of the observation
status_message: Optional status message for the observation
completion_start_time: When the model started generating (for generation types)
model: Name/identifier of the AI model used (for generation types)
model_parameters: Parameters used for the model (for generation types)
usage_details: Token usage information (for generation types)
cost_details: Cost information (for generation types)
prompt: Associated prompt template (for generation types)
Returns:
An observation object of the appropriate type that must be ended with .end()
"""
if trace_context:
trace_id = trace_context.get("trace_id", None)
parent_span_id = trace_context.get("parent_span_id", None)
if trace_id:
remote_parent_span = self._create_remote_parent_span(
trace_id=trace_id, parent_span_id=parent_span_id
)
with otel_trace_api.use_span(
cast(otel_trace_api.Span, remote_parent_span)
):
otel_span = self._otel_tracer.start_span(name=name)
otel_span.set_attribute(LangfuseOtelSpanAttributes.AS_ROOT, True)
return self._create_observation_from_otel_span(
otel_span=otel_span,
as_type=as_type,
input=input,
output=output,
metadata=metadata,
version=version,
level=level,
status_message=status_message,
completion_start_time=completion_start_time,
model=model,
model_parameters=model_parameters,
usage_details=usage_details,
cost_details=cost_details,
prompt=prompt,
)
otel_span = self._otel_tracer.start_span(name=name)
return self._create_observation_from_otel_span(
otel_span=otel_span,
as_type=as_type,
input=input,
output=output,
metadata=metadata,
version=version,
level=level,
status_message=status_message,
completion_start_time=completion_start_time,
model=model,
model_parameters=model_parameters,
usage_details=usage_details,
cost_details=cost_details,
prompt=prompt,
)
def _create_observation_from_otel_span(
self,
*,
otel_span: otel_trace_api.Span,
as_type: ObservationTypeLiteralNoEvent,
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
completion_start_time: Optional[datetime] = None,
model: Optional[str] = None,
model_parameters: Optional[Dict[str, MapValue]] = None,
usage_details: Optional[Dict[str, int]] = None,
cost_details: Optional[Dict[str, float]] = None,
prompt: Optional[PromptClient] = None,
) -> Union[
LangfuseSpan,
LangfuseGeneration,
LangfuseAgent,
LangfuseTool,
LangfuseChain,
LangfuseRetriever,
LangfuseEvaluator,
LangfuseEmbedding,
LangfuseGuardrail,
]:
"""Create the appropriate observation type from an OTEL span."""
if as_type in get_observation_types_list(ObservationTypeGenerationLike):
observation_class = self._get_span_class(as_type)
# Type ignore to prevent overloads of internal _get_span_class function,
# issue is that LangfuseEvent could be returned and that classes have diff. args
return observation_class( # type: ignore[return-value,call-arg]
otel_span=otel_span,
langfuse_client=self,
environment=self._environment,
release=self._release,
input=input,
output=output,
metadata=metadata,
version=version,
level=level,
status_message=status_message,
completion_start_time=completion_start_time,
model=model,
model_parameters=model_parameters,
usage_details=usage_details,
cost_details=cost_details,
prompt=prompt,
)
else:
# For other types (e.g. span, guardrail), create appropriate class without generation properties
observation_class = self._get_span_class(as_type)
# Type ignore to prevent overloads of internal _get_span_class function,
# issue is that LangfuseEvent could be returned and that classes have diff. args
return observation_class( # type: ignore[return-value,call-arg]
otel_span=otel_span,
langfuse_client=self,
environment=self._environment,
release=self._release,
input=input,
output=output,
metadata=metadata,
version=version,
level=level,
status_message=status_message,
)
# span._observation_type = as_type
# span._otel_span.set_attribute("langfuse.observation.type", as_type)
# return span
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["generation"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
completion_start_time: Optional[datetime] = None,
model: Optional[str] = None,
model_parameters: Optional[Dict[str, MapValue]] = None,
usage_details: Optional[Dict[str, int]] = None,
cost_details: Optional[Dict[str, float]] = None,
prompt: Optional[PromptClient] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseGeneration]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["span"] = "span",
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseSpan]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["agent"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseAgent]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["tool"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseTool]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["chain"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseChain]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["retriever"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseRetriever]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["evaluator"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseEvaluator]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["embedding"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
completion_start_time: Optional[datetime] = None,
model: Optional[str] = None,
model_parameters: Optional[Dict[str, MapValue]] = None,
usage_details: Optional[Dict[str, int]] = None,
cost_details: Optional[Dict[str, float]] = None,
prompt: Optional[PromptClient] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseEmbedding]: ...
@overload
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: Literal["guardrail"],
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
end_on_exit: Optional[bool] = None,
) -> _AgnosticContextManager[LangfuseGuardrail]: ...
def start_as_current_observation(
self,
*,
trace_context: Optional[TraceContext] = None,
name: str,
as_type: ObservationTypeLiteralNoEvent = "span",
input: Optional[Any] = None,
output: Optional[Any] = None,
metadata: Optional[Any] = None,
version: Optional[str] = None,
level: Optional[SpanLevel] = None,
status_message: Optional[str] = None,
completion_start_time: Optional[datetime] = None,
model: Optional[str] = None,
model_parameters: Optional[Dict[str, MapValue]] = None,
usage_details: Optional[Dict[str, int]] = None,
cost_details: Optional[Dict[str, float]] = None,
prompt: Optional[PromptClient] = None,
end_on_exit: Optional[bool] = None,
) -> Union[
_AgnosticContextManager[LangfuseGeneration],
_AgnosticContextManager[LangfuseSpan],
_AgnosticContextManager[LangfuseAgent],
_AgnosticContextManager[LangfuseTool],
_AgnosticContextManager[LangfuseChain],
_AgnosticContextManager[LangfuseRetriever],
_AgnosticContextManager[LangfuseEvaluator],
_AgnosticContextManager[LangfuseEmbedding],
_AgnosticContextManager[LangfuseGuardrail],
]:
"""Create a new observation and set it as the current span in a context manager.
This method creates a new observation of the specified type and sets it as the
current span within a context manager. Use this method with a 'with' statement to
automatically handle the observation lifecycle within a code block.
The created observation will be the child of the current span in the context.
Args:
trace_context: Optional context for connecting to an existing trace
name: Name of the observation (e.g., function or operation name)
as_type: Type of observation to create (defaults to "span")
input: Input data for the operation (can be any JSON-serializable object)
output: Output data from the operation (can be any JSON-serializable object)
metadata: Additional metadata to associate with the observation
version: Version identifier for the code or component
level: Importance level of the observation (info, warning, error)
status_message: Optional status message for the observation
end_on_exit (default: True): Whether to end the span automatically when leaving the context manager. If False, the span must be manually ended to avoid memory leaks.
The following parameters are available when as_type is: "generation" or "embedding".
completion_start_time: When the model started generating the response
model: Name/identifier of the AI model used (e.g., "gpt-4")
model_parameters: Parameters used for the model (e.g., temperature, max_tokens)
usage_details: Token usage information (e.g., prompt_tokens, completion_tokens)
cost_details: Cost information for the model call
prompt: Associated prompt template from Langfuse prompt management
Returns:
A context manager that yields the appropriate observation type based on as_type
Example:
```python
# Create a span
with langfuse.start_as_current_observation(name="process-query", as_type="span") as span:
# Do work
result = process_data()
span.update(output=result)
# Create a child span automatically
with span.start_as_current_observation(name="sub-operation") as child_span:
# Do sub-operation work
child_span.update(output="sub-result")
# Create a tool observation
with langfuse.start_as_current_observation(name="web-search", as_type="tool") as tool:
# Do tool work
results = search_web(query)
tool.update(output=results)
# Create a generation observation
with langfuse.start_as_current_observation(
name="answer-generation",
as_type="generation",
model="gpt-4"
) as generation:
# Generate answer
response = llm.generate(...)
generation.update(output=response)
```
"""
if as_type in get_observation_types_list(ObservationTypeGenerationLike):
if trace_context:
trace_id = trace_context.get("trace_id", None)
parent_span_id = trace_context.get("parent_span_id", None)
if trace_id:
remote_parent_span = self._create_remote_parent_span(
trace_id=trace_id, parent_span_id=parent_span_id
)
return cast(
Union[
_AgnosticContextManager[LangfuseGeneration],
_AgnosticContextManager[LangfuseEmbedding],
],
self._create_span_with_parent_context(
as_type=as_type,
name=name,
remote_parent_span=remote_parent_span,
parent=None,
end_on_exit=end_on_exit,
input=input,
output=output,
metadata=metadata,
version=version,
level=level,
status_message=status_message,
completion_start_time=completion_start_time,
model=model,
model_parameters=model_parameters,
usage_details=usage_details,
cost_details=cost_details,
prompt=prompt,
),
)
return cast(
Union[
_AgnosticContextManager[LangfuseGeneration],
_AgnosticContextManager[LangfuseEmbedding],
],
self._start_as_current_otel_span_with_processed_media(
as_type=as_type,
name=name,
end_on_exit=end_on_exit,
input=input,
output=output,
metadata=metadata,
version=version,
level=level,
status_message=status_message,
completion_start_time=completion_start_time,
model=model,
model_parameters=model_parameters,
usage_details=usage_details,
cost_details=cost_details,
prompt=prompt,
),
)
if as_type in get_observation_types_list(ObservationTypeSpanLike):
if trace_context:
trace_id = trace_context.get("trace_id", None)
parent_span_id = trace_context.get("parent_span_id", None)
if trace_id:
remote_parent_span = self._create_remote_parent_span(
trace_id=trace_id, parent_span_id=parent_span_id
)
return cast(
Union[
_AgnosticContextManager[LangfuseSpan],
_AgnosticContextManager[LangfuseAgent],
_AgnosticContextManager[LangfuseTool],
_AgnosticContextManager[LangfuseChain],