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Code Issues Projects Releases Packages Wiki Activity Actions 57
litellm/tests/local_testing/test_text_completion.py
devin-ai-integration[bot] fa2c8984ba
test: move the unit half of 126 mixed legacy files into tests/unit (#45090) 
* test: move the unit half of 126 mixed legacy files into tests/unit

* test: restore litellm globals that moved tests set

* test: finalize migration test cleanup

* test: restore original bodies of moved legacy tests

The move into tests/unit had rewritten 612 test bodies, and some of the rewrites dropped assertions. Each moved test now carries its original body from the legacy file, with only the imports, helpers, fake provider credentials and monkeypatched env it needs to run under tests/unit

test_timeout_streaming goes back to tests/local_testing because it needs the fake OpenAI endpoint server. The image payload fixture moves with its only user, and two tests that leaked global state (a registered model cost entry and queued logging tasks) are now isolated

* test: drop module imports shadowed by restored local imports

* test: assert on LiteLLM output in no-assertion moved tests and isolate leaks

Twenty no-assertion candidates get one assertion on the value LiteLLM returns, with the original lines unchanged. Four tests go back to their legacy files because they only check types or imports, write into the working directory, or cannot assert without a body change

Two moved tests leaked globals into later tests in the same worker, so monkeypatch fixtures now restore the retry-after header parser and the end user cost tracking flags

* test: drain queued logging tasks before the Phoenix span test

The moved Phoenix test counted spans from logging tasks that earlier tests had queued, so the drain fixture moves to tests/unit/conftest.py and both it and the Datadog batch test use it. test_factory_function goes back to its legacy file because its returned wrapper calls the real Assistants API and cannot be asserted on without a body change

---------

Co-authored-by: yuneng <yuneng@berri.ai>
2026-10-07 14:07:43 -07:00

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import asyncio
import json
import os
from types import MappingProxyType
from typing import Final
from dotenv import load_dotenv
from unittest.mock import patch
load_dotenv()
import pytest
import litellm
from litellm import (
TextCompletionResponse,
text_completion,
)
litellm.num_retries = 3
FIREWORKS_TEXT_COMPLETION: Final = MappingProxyType(
{
"model": "text-completion-openai/accounts/fireworks/models/glm-5p3-flash",
"api_base": "https://api.fireworks.ai/inference/v1",
"api_key": os.environ.get("FIREWORKS_AI_API_KEY"),
}
)
token_prompt = [
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def test_completion_openai_prompt():
try:
print("\n text 003 test\n")
response = text_completion(
prompt=["What's the weather in SF?", "How is Manchester?"],
max_tokens=5,
**FIREWORKS_TEXT_COMPLETION,
)
print(response)
assert len(response.choices) == 2
response_str = response["choices"][0]["text"]
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_openai_prompt()
def test_completion_openai_engine_and_model() -> None:
response: Final = text_completion(
model="gpt-6-luna",
engine="anything",
reasoning_effort="none",
prompt="What's the weather in SF?",
max_tokens=5,
)
assert response.model == "gpt-6-luna"
assert response.choices[0].text
# test_completion_openai_engine_and_model()
def test_completion_openai_engine() -> None:
response: Final = text_completion(
engine="gpt-6-luna",
reasoning_effort="none",
prompt="What's the weather in SF?",
max_tokens=5,
)
assert response.model == "gpt-6-luna"
assert response.choices[0].text
# test_completion_openai_engine()
def test_completion_chatgpt_prompt():
try:
print("\n gpt3.5 test\n")
response = text_completion(model="openai/gpt-3.5-turbo", prompt="What's the weather in SF?")
print(response)
response_str = response["choices"][0]["text"]
print("\n", response.choices)
print("\n", response.choices[0])
# print(response.choices[0].text)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_chatgpt_prompt()
def test_completion_gpt_instruct():
try:
response = text_completion(
model="gpt-5.4-nano",
prompt="What's the weather in SF?",
custom_llm_provider="text-completion-openai",
)
print(response)
response_str = response["choices"][0]["text"]
print("\n", response.choices)
print("\n", response.choices[0])
# print(response.choices[0].text)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_chatgpt_prompt()
def test_text_completion_basic():
try:
print("\n test 003 with logprobs \n")
litellm.set_verbose = False
response = text_completion(
model="text-completion-openai/gpt-5.4-nano",
prompt="good morning",
max_tokens=10,
logprobs=10,
)
print(response)
print(response.choices)
print(response.choices[0])
# print(response.choices[0].text)
response_str = response["choices"][0]["text"]
except Exception as e:
if "502: Bad gateway" in str(e):
print("502: Bad gateway error occurred... passing")
return
pytest.fail(f"Error occurred: {e}")
# test_text_completion_basic()
def test_completion_text_003_prompt_array():
try:
litellm.set_verbose = False
response = text_completion(
prompt=token_prompt, # token prompt is a 2d list
max_tokens=5,
**FIREWORKS_TEXT_COMPLETION,
)
assert len(response.choices) == len(token_prompt)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_text_003_prompt_array()
# not including this in our ci cd pipeline, since we don't want to fail tests due to an unstable replit
# def test_text_completion_with_proxy():
# try:
# litellm.set_verbose=True
# response = text_completion(
# model="facebook/opt-125m",
# prompt='Write a tagline for a traditional bavarian tavern',
# api_base="https://openai-proxy.berriai.repl.co/v1",
# custom_llm_provider="openai",
# temperature=0,
# max_tokens=10,
# )
# print("\n\n response")
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_text_completion_with_proxy()
##### hugging face tests
# test_completion_hf_prompt_array()
# test_text_completion_stream()
# async def test_text_completion_async_stream():
# try:
# response = await atext_completion(
# model="text-completion-openai/gpt-3.5-turbo-instruct",
# prompt="good morning",
# stream=True,
# max_tokens=10,
# )
# async for chunk in response:
# print(f"chunk: {chunk}")
# except Exception as e:
# pytest.fail(f"GOT exception for HF In streaming{e}")
# asyncio.run(test_text_completion_async_stream())
def test_async_text_completion():
litellm.set_verbose = True
print("test_async_text_completion")
async def test_get_response():
try:
response = await litellm.atext_completion(
model="gpt-3.5-turbo-instruct",
prompt="good morning",
stream=False,
max_tokens=10,
)
print(f"response: {response}")
except litellm.Timeout as e:
print(e)
except Exception as e:
print(e)
asyncio.run(test_get_response())
# test_async_text_completion()
@pytest.mark.asyncio
async def test_async_text_completion_stream() -> None:
response: Final = await litellm.atext_completion(
model="gpt-6-luna",
reasoning_effort="none",
prompt="good morning",
stream=True,
max_tokens=32,
)
chunks: Final = [chunk async for chunk in response]
assert sum(chunk.choices[0].finish_reason is not None for chunk in chunks) == 1
assert any(chunk.choices[0].text for chunk in chunks)
# test_async_text_completion_stream()
@pytest.mark.asyncio
async def test_async_text_completion_chat_model_stream():
try:
response = await litellm.atext_completion(
model="gpt-3.5-turbo",
prompt="good morning",
stream=True,
max_tokens=10,
)
num_finish_reason = 0
chunks = []
async for chunk in response:
print(chunk)
chunks.append(chunk)
if chunk["choices"][0].get("finish_reason") is not None:
num_finish_reason += 1
assert num_finish_reason == 1, f"expected only one finish reason. Got {num_finish_reason}"
response_obj = litellm.stream_chunk_builder(chunks=chunks)
cost = litellm.completion_cost(completion_response=response_obj)
assert cost > 0
except Exception as e:
pytest.fail(f"GOT exception for gpt-3.5 In streaming{e}")
# asyncio.run(test_async_text_completion_chat_model_stream())
@pytest.mark.parametrize("stream", [True, False])
def test_text_completion_with_echo(stream):
litellm.set_verbose = True
response = litellm.text_completion(
prompt="hello",
**FIREWORKS_TEXT_COMPLETION,
max_tokens=1, # only see the first token
stop="\n", # stop at the first newline
logprobs=1, # return log prob
echo=True, # if True, return the prompt as well
stream=stream,
)
print(response)
if stream:
for chunk in response:
print(chunk)
else:
assert isinstance(response, TextCompletionResponse)
assert response.choices[0].text.startswith("hello")
assert response.choices[0].logprobs.token_logprobs
def test_text_completion_ollama():
from litellm.llms.custom_httpx.http_handler import HTTPHandler
client = HTTPHandler()
with patch.object(client, "post") as mock_call:
try:
response = litellm.text_completion(
model="ollama/llama3.1:8b",
prompt="hello",
client=client,
)
print(response)
except Exception as e:
print(e)
mock_call.assert_called_once()
print(mock_call.call_args.kwargs)
json_data = json.loads(mock_call.call_args.kwargs["data"])
assert json_data["prompt"] == "hello"
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