mirror of
https://github.com/tiennm99/litellm.git
synced 2026-07-12 21:04:10 +00:00
Mateo Wang
c1602587c1
* fix(tests): drop module-level test calls that break local_testing collection Several files in tests/local_testing invoked their test functions at module scope (e.g. test_register_model.py ran test_update_model_cost_via_completion() at the bottom of the file). Those calls execute during pytest collection, so they fire real network requests at import time. test_register_model.py's call hit an OpenAI 429 and raised, turning into a collection error. A collection error aborts the whole session for every job that globs tests/local_testing/**/test_*.py, which is why unrelated jobs like langfuse_logging_unit_tests (-k langfuse) and litellm_assistants_api_testing (-k assistants) both failed even though neither touches register_model; the -k filter only applies after collection. pytest discovers and runs these test_* functions on its own, so the top-level calls were dead and harmful. Removes them from test_register_model.py, test_wandb.py, test_lunary.py, and test_multiple_deployments.py, and adds a regression test that scans the directory for module-level test invocations. * test(local_testing): skip unparseable files in module-scope invocation guardrail A syntax error in any tests/local_testing file would make ast.parse raise an unhandled SyntaxError, so the guardrail itself would crash with a confusing traceback instead of its assertion message. Such a file already fails pytest collection on its own, which is the clearer signal, so the guardrail now skips files it cannot parse and stays focused on detecting module-scope test calls. Reads files as utf-8 for deterministic behavior across platforms.
116 lines
3.0 KiB
Python
116 lines
3.0 KiB
Python
import io
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import os
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import sys
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sys.path.insert(0, os.path.abspath("../.."))
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import litellm
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from litellm import completion
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litellm.failure_callback = ["lunary"]
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litellm.success_callback = ["lunary"]
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litellm.set_verbose = True
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def test_lunary_logging():
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try:
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response = completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "what llm are u"}],
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max_tokens=10,
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temperature=0.2,
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user="test-user",
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)
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print(response)
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except Exception as e:
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print(e)
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def test_lunary_template():
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import lunary
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try:
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template = lunary.render_template("test-template", {"question": "Hello!"})
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response = completion(**template)
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print(response)
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except Exception as e:
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print(e)
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def test_lunary_logging_with_metadata():
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try:
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response = completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "what llm are u"}],
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max_tokens=10,
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temperature=0.2,
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metadata={
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"run_name": "litellmRUN",
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"project_name": "litellm-completion",
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"tags": ["tag1", "tag2"],
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},
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)
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print(response)
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except Exception as e:
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print(e)
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def test_lunary_with_tools():
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import litellm
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messages = [
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{
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"role": "user",
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"content": "What's the weather like in San Francisco, Tokyo, and Paris?",
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}
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]
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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},
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}
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]
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response = litellm.completion(
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model="gpt-3.5-turbo-1106",
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messages=messages,
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tools=tools,
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tool_choice="auto", # auto is default, but we'll be explicit
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)
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response_message = response.choices[0].message
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print("\nLLM Response:\n", response.choices[0].message)
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def test_lunary_logging_with_streaming_and_metadata():
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try:
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response = completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "what llm are u"}],
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max_tokens=10,
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temperature=0.2,
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metadata={
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"run_name": "litellmRUN",
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"project_name": "litellm-completion",
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},
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stream=True,
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)
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for chunk in response:
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continue
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except Exception as e:
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print(e)
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