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litellm/tests/logging_callback_tests/test_assemble_streaming_responses.py
yuneng-jiang 6a0d03914c
test: drop the cwd-relative sys.path.insert calls from the test suite (#37802) 
* test: drop the cwd-relative sys.path.insert calls from the test suite

TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.

Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.

Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.

* test: drop the duplicate imports the sys.path sweep exposed to F811

* test(pre-call-utils): restore the os import the new bedrock tests need
2026-08-22 09:25:58 -07:00

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"""
Testing for _assemble_complete_response_from_streaming_chunks
- Test 1 - ModelResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
- Test 2 - TextCompletionResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
- Test 3 - Have multiple lists of streaming chunks, Assert that chunks are added to the correct list and that complete_streaming_response is None. After final chunk sent assert complete_streaming_response is not None
- Test 4 - build a complete response when 1 chunk is poorly formatted
"""
import json
from datetime import datetime
from unittest.mock import AsyncMock
import httpx
import pytest
from respx import MockRouter
import litellm
from litellm import (
Choices,
Message,
ModelResponse,
ModelResponseStream,
TextCompletionResponse,
TextChoices,
)
from litellm.litellm_core_utils.logging_utils import (
_assemble_complete_response_from_streaming_chunks,
)
@pytest.mark.parametrize("is_async", [True, False])
def test_assemble_complete_response_from_streaming_chunks_1(is_async):
"""
Test 1 - ModelResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
"""
request_kwargs = {
"model": "test_model",
"messages": [{"role": "user", "content": "Hello, world!"}],
}
list_streaming_chunks = []
chunk = {
"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
"choices": [
litellm.utils.StreamingChoices(
delta=litellm.utils.Delta(
content="hello in response",
function_call=None,
role=None,
tool_calls=None,
),
index=0,
logprobs=None,
)
],
"created": 1721353246,
"model": "gpt-5-mini",
"object": "chat.completion.chunk",
"system_fingerprint": None,
"usage": None,
}
chunk = ModelResponseStream(**chunk)
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
result=chunk,
start_time=datetime.now(),
end_time=datetime.now(),
request_kwargs=request_kwargs,
streaming_chunks=list_streaming_chunks,
is_async=is_async,
)
# this is the 1st chunk - complete_streaming_response should be None
print("list_streaming_chunks", list_streaming_chunks)
print("complete_streaming_response", complete_streaming_response)
assert complete_streaming_response is None
assert len(list_streaming_chunks) == 1
assert list_streaming_chunks[0] == chunk
# Add final chunk
chunk = {
"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
"choices": [
litellm.utils.StreamingChoices(
finish_reason="stop",
delta=litellm.utils.Delta(
content="end of response",
function_call=None,
role=None,
tool_calls=None,
),
index=0,
logprobs=None,
)
],
"created": 1721353246,
"model": "gpt-5-mini",
"object": "chat.completion.chunk",
"system_fingerprint": None,
"usage": None,
}
chunk = ModelResponseStream(**chunk)
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
result=chunk,
start_time=datetime.now(),
end_time=datetime.now(),
request_kwargs=request_kwargs,
streaming_chunks=list_streaming_chunks,
is_async=is_async,
)
print("list_streaming_chunks", list_streaming_chunks)
print("complete_streaming_response", complete_streaming_response)
# this is the 2nd chunk - complete_streaming_response should not be None
assert complete_streaming_response is not None
assert len(list_streaming_chunks) == 2
assert isinstance(complete_streaming_response, ModelResponse)
assert isinstance(complete_streaming_response.choices[0], Choices)
pass
@pytest.mark.parametrize("is_async", [True, False])
def test_assemble_complete_response_from_streaming_chunks_2(is_async):
"""
Test 2 - TextCompletionResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
"""
from litellm.utils import TextCompletionStreamWrapper
_text_completion_stream_wrapper = TextCompletionStreamWrapper(
completion_stream=None, model="test_model"
)
request_kwargs = {
"model": "test_model",
"messages": [{"role": "user", "content": "Hello, world!"}],
}
list_streaming_chunks = []
chunk = {
"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
"choices": [
litellm.utils.StreamingChoices(
delta=litellm.utils.Delta(
content="hello in response",
function_call=None,
role=None,
tool_calls=None,
),
index=0,
logprobs=None,
)
],
"created": 1721353246,
"model": "gpt-5-mini",
"object": "chat.completion.chunk",
"system_fingerprint": None,
"usage": None,
}
chunk = ModelResponseStream(**chunk)
chunk = _text_completion_stream_wrapper.convert_to_text_completion_object(chunk)
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
result=chunk,
start_time=datetime.now(),
end_time=datetime.now(),
request_kwargs=request_kwargs,
streaming_chunks=list_streaming_chunks,
is_async=is_async,
)
# this is the 1st chunk - complete_streaming_response should be None
print("list_streaming_chunks", list_streaming_chunks)
print("complete_streaming_response", complete_streaming_response)
assert complete_streaming_response is None
assert len(list_streaming_chunks) == 1
assert list_streaming_chunks[0] == chunk
# Add final chunk
chunk = {
"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
"choices": [
litellm.utils.StreamingChoices(
finish_reason="stop",
delta=litellm.utils.Delta(
content="end of response",
function_call=None,
role=None,
tool_calls=None,
),
index=0,
logprobs=None,
)
],
"created": 1721353246,
"model": "gpt-5-mini",
"object": "chat.completion.chunk",
"system_fingerprint": None,
"usage": None,
}
chunk = ModelResponseStream(**chunk)
chunk = _text_completion_stream_wrapper.convert_to_text_completion_object(chunk)
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
result=chunk,
start_time=datetime.now(),
end_time=datetime.now(),
request_kwargs=request_kwargs,
streaming_chunks=list_streaming_chunks,
is_async=is_async,
)
print("list_streaming_chunks", list_streaming_chunks)
print("complete_streaming_response", complete_streaming_response)
# this is the 2nd chunk - complete_streaming_response should not be None
assert complete_streaming_response is not None
assert len(list_streaming_chunks) == 2
assert isinstance(complete_streaming_response, TextCompletionResponse)
assert isinstance(complete_streaming_response.choices[0], TextChoices)
pass
@pytest.mark.parametrize("is_async", [True, False])
def test_assemble_complete_response_from_streaming_chunks_3(is_async):
request_kwargs = {
"model": "test_model",
"messages": [{"role": "user", "content": "Hello, world!"}],
}
list_streaming_chunks_1 = []
list_streaming_chunks_2 = []
chunk = {
"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
"choices": [
litellm.utils.StreamingChoices(
delta=litellm.utils.Delta(
content="hello in response",
function_call=None,
role=None,
tool_calls=None,
),
index=0,
logprobs=None,
)
],
"created": 1721353246,
"model": "gpt-5-mini",
"object": "chat.completion.chunk",
"system_fingerprint": None,
"usage": None,
}
chunk = ModelResponseStream(**chunk)
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
result=chunk,
start_time=datetime.now(),
end_time=datetime.now(),
request_kwargs=request_kwargs,
streaming_chunks=list_streaming_chunks_1,
is_async=is_async,
)
# this is the 1st chunk - complete_streaming_response should be None
print("list_streaming_chunks_1", list_streaming_chunks_1)
print("complete_streaming_response", complete_streaming_response)
assert complete_streaming_response is None
assert len(list_streaming_chunks_1) == 1
assert list_streaming_chunks_1[0] == chunk
assert len(list_streaming_chunks_2) == 0
# now add a chunk to the 2nd list
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
result=chunk,
start_time=datetime.now(),
end_time=datetime.now(),
request_kwargs=request_kwargs,
streaming_chunks=list_streaming_chunks_2,
is_async=is_async,
)
print("list_streaming_chunks_2", list_streaming_chunks_2)
print("complete_streaming_response", complete_streaming_response)
assert complete_streaming_response is None
assert len(list_streaming_chunks_2) == 1
assert list_streaming_chunks_2[0] == chunk
assert len(list_streaming_chunks_1) == 1
# now add a chunk to the 1st list
@pytest.mark.parametrize("is_async", [True, False])
def test_assemble_complete_response_from_streaming_chunks_4(is_async):
"""
Test 4 - build a complete response when 1 chunk is poorly formatted
- Assert complete_streaming_response is None
- Assert list_streaming_chunks is not empty
"""
request_kwargs = {
"model": "test_model",
"messages": [{"role": "user", "content": "Hello, world!"}],
}
list_streaming_chunks = []
chunk = {
"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
"choices": [
litellm.utils.StreamingChoices(
finish_reason="stop",
delta=litellm.utils.Delta(
content="end of response",
function_call=None,
role=None,
tool_calls=None,
),
index=0,
logprobs=None,
)
],
"created": 1721353246,
"model": "gpt-5-mini",
"object": "chat.completion.chunk",
"system_fingerprint": None,
"usage": None,
}
chunk = ModelResponseStream(**chunk)
# remove attribute id from chunk
del chunk.object
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
result=chunk,
start_time=datetime.now(),
end_time=datetime.now(),
request_kwargs=request_kwargs,
streaming_chunks=list_streaming_chunks,
is_async=is_async,
)
print("complete_streaming_response", complete_streaming_response)
assert complete_streaming_response is None
print("list_streaming_chunks", list_streaming_chunks)
assert len(list_streaming_chunks) == 1
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