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test_decorators.py
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1969 lines (1521 loc) · 64.1 KB
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import asyncio
import os
import sys
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
from time import sleep
from typing import Optional
import pytest
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from opentelemetry import trace
from langfuse import Langfuse, get_client, observe
from langfuse._client.environment_variables import LANGFUSE_PUBLIC_KEY
from langfuse._client.resource_manager import LangfuseResourceManager
from langfuse.langchain import CallbackHandler
from langfuse.media import LangfuseMedia
from tests.utils import get_api
mock_metadata = {"key": "metadata"}
mock_deep_metadata = {"key": "mock_deep_metadata"}
mock_session_id = "session-id-1"
mock_args = (1, 2, 3)
mock_kwargs = {"a": 1, "b": 2, "c": 3}
def removeMockResourceManagerInstances():
with LangfuseResourceManager._lock:
for public_key in list(LangfuseResourceManager._instances.keys()):
if public_key != os.getenv(LANGFUSE_PUBLIC_KEY):
LangfuseResourceManager._instances.pop(public_key)
def test_nested_observations():
mock_name = "test_nested_observations"
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
@observe(as_type="generation", name="level_3", capture_output=False)
def level_3_function():
langfuse.update_current_generation(metadata=mock_metadata)
langfuse.update_current_generation(
metadata=mock_deep_metadata,
usage_details={"input": 150, "output": 50, "total": 300},
model="gpt-3.5-turbo",
output="mock_output",
)
langfuse.update_current_generation(version="version-1")
langfuse.update_current_trace(session_id=mock_session_id, name=mock_name)
langfuse.update_current_trace(
user_id="user_id",
)
return "level_3"
@observe(name="level_2_manually_set")
def level_2_function():
level_3_function()
langfuse.update_current_span(metadata=mock_metadata)
return "level_2"
@observe()
def level_1_function(*args, **kwargs):
level_2_function()
return "level_1"
result = level_1_function(
*mock_args, **mock_kwargs, langfuse_trace_id=mock_trace_id
)
langfuse.flush()
assert result == "level_1" # Wrapped function returns correctly
# ID setting for span or trace
trace_data = get_api().trace.get(mock_trace_id)
assert len(trace_data.observations) == 3
# trace parameters if set anywhere in the call stack
assert trace_data.session_id == mock_session_id
assert trace_data.user_id == "user_id"
assert trace_data.name == mock_name
# Check correct nesting
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id].append(o)
assert len(adjacencies) == 3
level_1_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
level_2_observation = adjacencies[level_1_observation.id][0]
level_3_observation = adjacencies[level_2_observation.id][0]
assert level_1_observation.name == "level_1_function"
assert level_1_observation.input == {"args": list(mock_args), "kwargs": mock_kwargs}
assert level_1_observation.output == "level_1"
assert level_2_observation.name == "level_2_manually_set"
assert level_2_observation.metadata["key"] == mock_metadata["key"]
assert level_3_observation.name == "level_3"
assert level_3_observation.metadata["key"] == mock_deep_metadata["key"]
assert level_3_observation.type == "GENERATION"
assert level_3_observation.calculated_total_cost > 0
assert level_3_observation.output == "mock_output"
assert level_3_observation.version == "version-1"
def test_nested_observations_with_non_parentheses_decorator():
mock_name = "test_nested_observations"
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
@observe(as_type="generation", name="level_3", capture_output=False)
def level_3_function():
langfuse.update_current_generation(metadata=mock_metadata)
langfuse.update_current_generation(
metadata=mock_deep_metadata,
usage_details={"input": 150, "output": 50, "total": 300},
model="gpt-3.5-turbo",
output="mock_output",
)
langfuse.update_current_generation(version="version-1")
langfuse.update_current_trace(session_id=mock_session_id, name=mock_name)
langfuse.update_current_trace(
user_id="user_id",
)
return "level_3"
@observe
def level_2_function():
level_3_function()
langfuse.update_current_span(metadata=mock_metadata)
return "level_2"
@observe
def level_1_function(*args, **kwargs):
level_2_function()
return "level_1"
result = level_1_function(
*mock_args, **mock_kwargs, langfuse_trace_id=mock_trace_id
)
langfuse.flush()
assert result == "level_1" # Wrapped function returns correctly
# ID setting for span or trace
trace_data = get_api().trace.get(mock_trace_id)
assert len(trace_data.observations) == 3
# trace parameters if set anywhere in the call stack
assert trace_data.session_id == mock_session_id
assert trace_data.user_id == "user_id"
assert trace_data.name == mock_name
# Check correct nesting
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id or o.trace_id].append(o)
assert len(adjacencies) == 3
level_1_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
level_2_observation = adjacencies[level_1_observation.id][0]
level_3_observation = adjacencies[level_2_observation.id][0]
assert level_1_observation.name == "level_1_function"
assert level_1_observation.input == {"args": list(mock_args), "kwargs": mock_kwargs}
assert level_1_observation.output == "level_1"
assert level_2_observation.name == "level_2_function"
assert level_2_observation.metadata["key"] == mock_metadata["key"]
assert level_3_observation.name == "level_3"
assert level_3_observation.metadata["key"] == mock_deep_metadata["key"]
assert level_3_observation.type == "GENERATION"
assert level_3_observation.calculated_total_cost > 0
assert level_3_observation.output == "mock_output"
assert level_3_observation.version == "version-1"
# behavior on exceptions
def test_exception_in_wrapped_function():
mock_name = "test_exception_in_wrapped_function"
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
@observe(as_type="generation", capture_output=False)
def level_3_function():
langfuse.update_current_generation(metadata=mock_metadata)
langfuse.update_current_generation(
metadata=mock_deep_metadata,
usage_details={"input": 150, "output": 50, "total": 300},
model="gpt-3.5-turbo",
)
langfuse.update_current_trace(session_id=mock_session_id, name=mock_name)
raise ValueError("Mock exception")
@observe()
def level_2_function():
level_3_function()
langfuse.update_current_generation(metadata=mock_metadata)
return "level_2"
@observe()
def level_1_function(*args, **kwargs):
sleep(1)
level_2_function()
print("hello")
return "level_1"
# Check that the exception is raised
with pytest.raises(ValueError):
level_1_function(*mock_args, **mock_kwargs, langfuse_trace_id=mock_trace_id)
langfuse.flush()
trace_data = get_api().trace.get(mock_trace_id)
# trace parameters if set anywhere in the call stack
assert trace_data.session_id == mock_session_id
assert trace_data.name == mock_name
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id or o.trace_id].append(o)
assert len(adjacencies) == 3
level_1_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
level_2_observation = adjacencies[level_1_observation.id][0]
level_3_observation = adjacencies[level_2_observation.id][0]
assert level_1_observation.name == "level_1_function"
assert level_1_observation.input == {"args": list(mock_args), "kwargs": mock_kwargs}
assert level_2_observation.name == "level_2_function"
assert level_3_observation.name == "level_3_function"
assert level_3_observation.type == "GENERATION"
assert level_3_observation.status_message == "Mock exception"
assert level_3_observation.level == "ERROR"
# behavior on concurrency
def test_concurrent_decorator_executions():
mock_name = "test_concurrent_decorator_executions"
langfuse = get_client()
mock_trace_id_1 = langfuse.create_trace_id()
mock_trace_id_2 = langfuse.create_trace_id()
@observe(as_type="generation", capture_output=False)
def level_3_function():
langfuse.update_current_generation(metadata=mock_metadata)
langfuse.update_current_generation(metadata=mock_deep_metadata)
langfuse.update_current_generation(
metadata=mock_deep_metadata,
usage_details={"input": 150, "output": 50, "total": 300},
model="gpt-3.5-turbo",
)
langfuse.update_current_trace(name=mock_name, session_id=mock_session_id)
return "level_3"
@observe()
def level_2_function():
level_3_function()
langfuse.update_current_generation(metadata=mock_metadata)
return "level_2"
@observe(name=mock_name)
def level_1_function(*args, **kwargs):
sleep(1)
level_2_function()
return "level_1"
with ThreadPoolExecutor(max_workers=2) as executor:
future1 = executor.submit(
level_1_function,
*mock_args,
mock_trace_id_1,
**mock_kwargs,
langfuse_trace_id=mock_trace_id_1,
)
future2 = executor.submit(
level_1_function,
*mock_args,
mock_trace_id_2,
**mock_kwargs,
langfuse_trace_id=mock_trace_id_2,
)
future1.result()
future2.result()
langfuse.flush()
for mock_id in [mock_trace_id_1, mock_trace_id_2]:
trace_data = get_api().trace.get(mock_id)
assert len(trace_data.observations) == 3
# ID setting for span or trace
assert trace_data.session_id == mock_session_id
assert trace_data.name == mock_name
# Check correct nesting
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id].append(o)
assert len(adjacencies) == 3
level_1_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
level_2_observation = adjacencies[level_1_observation.id][0]
level_3_observation = adjacencies[level_2_observation.id][0]
assert level_1_observation.name == mock_name
assert level_1_observation.input == {
"args": list(mock_args) + [mock_id],
"kwargs": mock_kwargs,
}
assert level_1_observation.output == "level_1"
assert level_2_observation.metadata["key"] == mock_metadata["key"]
assert level_3_observation.metadata["key"] == mock_deep_metadata["key"]
assert level_3_observation.type == "GENERATION"
assert level_3_observation.calculated_total_cost > 0
def test_decorators_langchain():
mock_name = "test_decorators_langchain"
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
@observe()
def langchain_operations(*args, **kwargs):
# Get langfuse callback handler for LangChain
handler = CallbackHandler()
prompt = ChatPromptTemplate.from_template("tell me a short joke about {topic}")
model = ChatOpenAI(temperature=0)
chain = prompt | model
return chain.invoke(
{"topic": kwargs["topic"]},
config={
"callbacks": [handler],
},
)
@observe()
def level_3_function(*args, **kwargs):
langfuse.update_current_span(metadata=mock_metadata)
langfuse.update_current_span(metadata=mock_deep_metadata)
langfuse.update_current_trace(session_id=mock_session_id, name=mock_name)
return langchain_operations(*args, **kwargs)
@observe()
def level_2_function(*args, **kwargs):
langfuse.update_current_span(metadata=mock_metadata)
return level_3_function(*args, **kwargs)
@observe()
def level_1_function(*args, **kwargs):
return level_2_function(*args, **kwargs)
level_1_function(topic="socks", langfuse_trace_id=mock_trace_id)
langfuse.flush()
trace_data = get_api().trace.get(mock_trace_id)
assert len(trace_data.observations) > 2
# Check correct nesting
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id].append(o)
assert len(adjacencies) > 2
# trace parameters if set anywhere in the call stack
assert trace_data.session_id == mock_session_id
assert trace_data.name == mock_name
# Check that the langchain_operations is at the correct level
level_1_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
level_2_observation = adjacencies[level_1_observation.id][0]
level_3_observation = adjacencies[level_2_observation.id][0]
langchain_observation = adjacencies[level_3_observation.id][0]
assert level_1_observation.name == "level_1_function"
assert level_2_observation.name == "level_2_function"
assert level_2_observation.metadata["key"] == mock_metadata["key"]
assert level_3_observation.name == "level_3_function"
assert level_3_observation.metadata["key"] == mock_deep_metadata["key"]
assert langchain_observation.name == "langchain_operations"
# Check that LangChain components are captured
assert any([o.name == "ChatPromptTemplate" for o in trace_data.observations])
def test_get_current_trace_url():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
@observe()
def level_3_function():
return langfuse.get_trace_url(trace_id=langfuse.get_current_trace_id())
@observe()
def level_2_function():
return level_3_function()
@observe()
def level_1_function(*args, **kwargs):
return level_2_function()
result = level_1_function(
*mock_args, **mock_kwargs, langfuse_trace_id=mock_trace_id
)
langfuse.flush()
expected_url = f"http://localhost:3000/project/7a88fb47-b4e2-43b8-a06c-a5ce950dc53a/traces/{mock_trace_id}"
assert result == expected_url
def test_scoring_observations():
mock_name = "test_scoring_observations"
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
@observe(as_type="generation", capture_output=False)
def level_3_function():
langfuse.score_current_span(name="test-observation-score", value=1)
langfuse.score_current_trace(name="another-test-trace-score", value="my_value")
return "level_3"
@observe()
def level_2_function():
return level_3_function()
@observe()
def level_1_function(*args, **kwargs):
langfuse.score_current_trace(name="test-trace-score", value=3)
langfuse.update_current_trace(name=mock_name)
return level_2_function()
result = level_1_function(
*mock_args, **mock_kwargs, langfuse_trace_id=mock_trace_id
)
langfuse.flush()
sleep(1)
assert result == "level_3" # Wrapped function returns correctly
# ID setting for span or trace
trace_data = get_api().trace.get(mock_trace_id)
assert (
len(trace_data.observations) == 3
) # Top-most function is trace, so it's not an observations
assert trace_data.name == mock_name
# Check for correct scoring
scores = trace_data.scores
assert len(scores) == 3
trace_scores = [
s for s in scores if s.trace_id == mock_trace_id and s.observation_id is None
]
observation_score = [s for s in scores if s.observation_id is not None][0]
assert any(
[
score.name == "another-test-trace-score"
and score.string_value == "my_value"
and score.data_type == "CATEGORICAL"
for score in trace_scores
]
)
assert any(
[
score.name == "test-trace-score"
and score.value == 3
and score.data_type == "NUMERIC"
for score in trace_scores
]
)
assert observation_score.name == "test-observation-score"
assert observation_score.value == 1
assert observation_score.data_type == "NUMERIC"
def test_circular_reference_handling():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
# Define a class that will contain a circular reference
class CircularRefObject:
def __init__(self):
self.reference: Optional[CircularRefObject] = None
@observe()
def function_with_circular_arg(circular_obj, *args, **kwargs):
# This function doesn't need to do anything with circular_obj,
# the test is simply to see if it can be called without error.
return "function response"
# Create an instance of the object and establish a circular reference
circular_obj = CircularRefObject()
circular_obj.reference = circular_obj
# Call the decorated function, passing the circularly-referenced object
result = function_with_circular_arg(circular_obj, langfuse_trace_id=mock_trace_id)
langfuse.flush()
# Validate that the function executed as expected
assert result == "function response"
trace_data = get_api().trace.get(mock_trace_id)
assert (
trace_data.observations[0].input["args"][0]["reference"] == "CircularRefObject"
)
def test_disabled_io_capture():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
class Node:
def __init__(self, value: tuple):
self.value = value
@observe(capture_input=False, capture_output=False)
def nested(*args, **kwargs):
langfuse.update_current_span(
input=Node(("manually set tuple", 1)), output="manually set output"
)
return "nested response"
@observe(capture_output=False)
def main(*args, **kwargs):
nested(*args, **kwargs)
return "function response"
result = main("Hello, World!", name="John", langfuse_trace_id=mock_trace_id)
langfuse.flush()
assert result == "function response"
trace_data = get_api().trace.get(mock_trace_id)
# Check that disabled capture_io doesn't capture manually set input/output
assert len(trace_data.observations) == 2
# Only one of the observations must satisfy this
found_match = False
for observation in trace_data.observations:
if (
observation.input
and isinstance(observation.input, dict)
and "value" in observation.input
and observation.input["value"] == ["manually set tuple", 1]
and observation.output == "manually set output"
):
found_match = True
break
assert found_match, "No observation found with expected input and output"
def test_decorated_class_and_instance_methods():
mock_name = "test_decorated_class_and_instance_methods"
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
class TestClass:
@classmethod
@observe(name="class-method")
def class_method(cls, *args, **kwargs):
langfuse.update_current_span()
return "class_method"
@observe(as_type="generation", capture_output=False)
def level_3_function(self):
langfuse.update_current_generation(metadata=mock_metadata)
langfuse.update_current_generation(
metadata=mock_deep_metadata,
usage_details={"input": 150, "output": 50, "total": 300},
model="gpt-3.5-turbo",
output="mock_output",
)
langfuse.update_current_trace(session_id=mock_session_id, name=mock_name)
return "level_3"
@observe()
def level_2_function(self):
TestClass.class_method()
self.level_3_function()
langfuse.update_current_span(metadata=mock_metadata)
return "level_2"
@observe()
def level_1_function(self, *args, **kwargs):
self.level_2_function()
return "level_1"
result = TestClass().level_1_function(
*mock_args, **mock_kwargs, langfuse_trace_id=mock_trace_id
)
langfuse.flush()
assert result == "level_1" # Wrapped function returns correctly
# ID setting for span or trace
trace_data = get_api().trace.get(mock_trace_id)
assert len(trace_data.observations) == 4
# trace parameters if set anywhere in the call stack
assert trace_data.session_id == mock_session_id
assert trace_data.name == mock_name
# Check correct nesting
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id].append(o)
assert len(adjacencies) == 3
level_1_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
level_2_observation = adjacencies[level_1_observation.id][0]
# Find level_3_observation and class_method_observation in level_2's children
level_2_children = adjacencies[level_2_observation.id]
level_3_observation = next(o for o in level_2_children if o.name != "class-method")
class_method_observation = next(
o for o in level_2_children if o.name == "class-method"
)
assert level_1_observation.name == "level_1_function"
assert level_1_observation.input == {"args": list(mock_args), "kwargs": mock_kwargs}
assert level_1_observation.output == "level_1"
assert level_2_observation.name == "level_2_function"
assert level_2_observation.metadata["key"] == mock_metadata["key"]
assert class_method_observation.name == "class-method"
assert class_method_observation.output == "class_method"
assert level_3_observation.name == "level_3_function"
assert level_3_observation.metadata["key"] == mock_deep_metadata["key"]
assert level_3_observation.type == "GENERATION"
assert level_3_observation.calculated_total_cost > 0
assert level_3_observation.output == "mock_output"
def test_generator_as_return_value():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
mock_output = "Hello, World!"
def custom_transform_to_string(x):
return "--".join(x)
def generator_function():
yield "Hello"
yield ", "
yield "World!"
@observe(transform_to_string=custom_transform_to_string)
def nested():
return generator_function()
@observe()
def main(**kwargs):
gen = nested()
result = ""
for item in gen:
result += item
return result
result = main(langfuse_trace_id=mock_trace_id)
langfuse.flush()
assert result == mock_output
trace_data = get_api().trace.get(mock_trace_id)
# Find the main and nested observations
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id].append(o)
main_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
nested_observation = adjacencies[main_observation.id][0]
assert main_observation.name == "main"
assert main_observation.output == mock_output
assert nested_observation.name == "nested"
assert nested_observation.output == "Hello--, --World!"
@pytest.mark.asyncio
async def test_async_generator_as_return_value():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
mock_output = "Hello, async World!"
def custom_transform_to_string(x):
return "--".join(x)
@observe(transform_to_string=custom_transform_to_string)
async def async_generator_function():
await asyncio.sleep(0.1) # Simulate async operation
yield "Hello"
await asyncio.sleep(0.1)
yield ", async "
await asyncio.sleep(0.1)
yield "World!"
@observe()
async def main_async(**kwargs):
gen = async_generator_function()
result = ""
async for item in gen:
result += item
return result
result = await main_async(langfuse_trace_id=mock_trace_id)
langfuse.flush()
assert result == mock_output
trace_data = get_api().trace.get(mock_trace_id)
# Check correct nesting
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id].append(o)
main_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
nested_observation = adjacencies[main_observation.id][0]
assert main_observation.name == "main_async"
assert main_observation.output == mock_output
assert nested_observation.name == "async_generator_function"
assert nested_observation.output == "Hello--, async --World!"
@pytest.mark.asyncio
async def test_async_nested_openai_chat_stream():
from langfuse.openai import AsyncOpenAI
mock_name = "test_async_nested_openai_chat_stream"
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
mock_tags = ["tag1", "tag2"]
mock_session_id = "session-id-1"
mock_user_id = "user-id-1"
@observe(capture_output=False)
async def level_2_function():
gen = await AsyncOpenAI().chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "1 + 1 = "}],
temperature=0,
metadata={"someKey": "someResponse"},
stream=True,
)
langfuse.update_current_trace(
session_id=mock_session_id,
user_id=mock_user_id,
tags=mock_tags,
)
async for c in gen:
print(c)
langfuse.update_current_span(metadata=mock_metadata)
langfuse.update_current_trace(name=mock_name)
return "level_2"
@observe()
async def level_1_function(*args, **kwargs):
await level_2_function()
return "level_1"
result = await level_1_function(
*mock_args, **mock_kwargs, langfuse_trace_id=mock_trace_id
)
langfuse.flush()
assert result == "level_1" # Wrapped function returns correctly
# ID setting for span or trace
trace_data = get_api().trace.get(mock_trace_id)
assert len(trace_data.observations) == 3
# trace parameters if set anywhere in the call stack
assert trace_data.session_id == mock_session_id
assert trace_data.name == mock_name
# Check correct nesting
adjacencies = defaultdict(list)
for o in trace_data.observations:
adjacencies[o.parent_observation_id or o.trace_id].append(o)
assert len(adjacencies) == 3
level_1_observation = next(
o
for o in trace_data.observations
if o.parent_observation_id not in [o.id for o in trace_data.observations]
)
level_2_observation = adjacencies[level_1_observation.id][0]
level_3_observation = adjacencies[level_2_observation.id][0]
assert level_2_observation.metadata["key"] == mock_metadata["key"]
generation = level_3_observation
assert generation.name == "OpenAI-generation"
assert generation.metadata["someKey"] == "someResponse"
assert generation.input == [{"content": "1 + 1 = ", "role": "user"}]
assert generation.type == "GENERATION"
assert "gpt-3.5-turbo" in generation.model
assert generation.start_time is not None
assert generation.end_time is not None
assert generation.start_time < generation.end_time
assert generation.model_parameters == {
"temperature": 0,
"top_p": 1,
"frequency_penalty": 0,
"max_tokens": "Infinity",
"presence_penalty": 0,
}
assert generation.usage.input is not None
assert generation.usage.output is not None
assert generation.usage.total is not None
print(generation)
assert generation.output == 2
def test_generator_as_function_input():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
mock_output = "Hello, World!"
def generator_function():
yield "Hello"
yield ", "
yield "World!"
@observe()
def nested(gen):
result = ""
for item in gen:
result += item
return result
@observe()
def main(**kwargs):
gen = generator_function()
return nested(gen)
result = main(langfuse_trace_id=mock_trace_id)
langfuse.flush()
assert result == mock_output
trace_data = get_api().trace.get(mock_trace_id)
nested_obs = next(o for o in trace_data.observations if o.name == "nested")
assert nested_obs.input["args"][0] == "<generator>"
assert nested_obs.output == "Hello, World!"
observation_start_time = nested_obs.start_time
observation_end_time = nested_obs.end_time
assert observation_start_time is not None
assert observation_end_time is not None
assert observation_start_time <= observation_end_time
def test_nest_list_of_generator_as_function_IO():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()
def generator_function():
yield "Hello"
yield ", "
yield "World!"
@observe()
def nested(list_of_gens):
return list_of_gens
@observe()
def main(**kwargs):
gen = generator_function()
return nested([(gen, gen)])
main(langfuse_trace_id=mock_trace_id)
langfuse.flush()
trace_data = get_api().trace.get(mock_trace_id)
# Find the observation with name 'nested'
nested_observation = next(o for o in trace_data.observations if o.name == "nested")
assert [[["<generator>", "<generator>"]]] == nested_observation.input["args"]
assert all(
["generator" in arg for arg in nested_observation.output[0]],
)
observation_start_time = nested_observation.start_time
observation_end_time = nested_observation.end_time
assert observation_start_time is not None
assert observation_end_time is not None
assert observation_start_time <= observation_end_time
def test_return_dict_for_output():
langfuse = get_client()
mock_trace_id = langfuse.create_trace_id()