Upgrade Langfuse to v2

Python pattern

Upgrade the Langfuse SDK to v2 following this guide.

Apply with the Grit CLI
grit apply langfuse_v2

Rewrites Pydantic interface argument

BEFORE 
from langfuse.model import InitialSpan

langfuse.span(
    InitialSpan(
        name="span",
        startTime=timestamp,
        endTime=timestamp,
        input={"key": "value"},
        output={"key": "value"},
    )
)
AFTER 
langfuse.span(
    name="span",
    start_time=timestamp,
    end_time=timestamp,
    input={"key": "value"},
    output={"key": "value"},
)

Renames prompt and completion to input and output

BEFORE 
from langfuse import *

client.generation(InitialGeneration(
        name="some_generation",
        startTime=start_time,
        endTime=end_time,
        prompt=prompt,
        completion=completion,
        metadata=metadata
    )
)
AFTER 
from langfuse import *

client.generation(
    name="some_generation",
    start_time=start_time,
    end_time=end_time,
    input=prompt,
    output=completion,
    metadata=metadata
)

Snake cases parameters without Pydantic

BEFORE 
import langfuse

generation = observation.generation(
    name='name',
    prompt=kwargs['messages'],
    startTime=dt.datetime.utcnow(),
)
AFTER 
import langfuse

generation = observation.generation(
    name='name',
    input=kwargs['messages'],
    start_time=dt.datetime.utcnow(),
)

Converts Pydantic enum

BEFORE 
from langfuse.model import InitialGeneration
from langfuse.api.resources.commons.types.observation_level import ObservationLevel
import langfuse

langfuse.generation(InitialGeneration(level=ObservationLevel.ERROR))
AFTER 
import langfuse

langfuse.generation(level="ERROR")

Rewrites nested Pydantic interface

BEFORE 
from langfuse.model import InitialGeneration, Usage
import langfuse as lf

generation = lf.generation(
    InitialGeneration(
        name="chatgpt-completion",
        startTime=generationStartTime,
        endTime=datetime.now(),
        model=self.model,
        modelParameters={"temperature": str(temperature)},
        prompt=history,
        completion=response["choices"][0]["message"]["content"],
        usage=Usage(
            promptTokens=50,
            completionTokens=50,
        ),
    )
)
AFTER 
import langfuse as lf

generation = lf.generation(name="chatgpt-completion",
    start_time=generationStartTime,
    end_time=datetime.now(),
    model=self.model,
    model_parameters={"temperature": str(temperature)},
    input=history,
    output=response["choices"][0]["message"]["content"],
    usage={"promptTokens": 50, "completionTokens": 50},
)

Does nothing without langfuse import

PYTHON 
model.event(
    CreateEvent(
        name="span",
        startTime=timestamp,
        endTime=timestamp,
    )
)