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6a0d03914c
* 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
299 lines
9.7 KiB
Python
299 lines
9.7 KiB
Python
import json
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from datetime import datetime
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from unittest.mock import AsyncMock
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import httpx
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import pytest
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from unittest.mock import patch, MagicMock
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import litellm
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from litellm import Choices, Message, ModelResponse, EmbeddingResponse, Usage
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from litellm import completion
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from base_rerank_unit_tests import BaseLLMRerankTest
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def test_completion_nvidia_nim():
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from openai import OpenAI
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litellm.set_verbose = True
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model_name = "nvidia_nim/databricks/dbrx-instruct"
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client = OpenAI(
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api_key="fake-api-key",
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)
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with patch.object(
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client.chat.completions.with_raw_response, "create"
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) as mock_client:
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try:
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completion(
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model=model_name,
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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 Boston today in Fahrenheit?",
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}
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],
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presence_penalty=0.5,
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frequency_penalty=0.1,
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client=client,
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)
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except Exception as e:
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print(e)
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# Add any assertions here to check the response
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
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print("request_body: ", request_body)
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assert request_body["messages"] == [
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{
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"role": "user",
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"content": "What's the weather like in Boston today in Fahrenheit?",
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},
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]
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assert request_body["model"] == "databricks/dbrx-instruct"
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assert request_body["frequency_penalty"] == 0.1
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assert request_body["presence_penalty"] == 0.5
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def test_embedding_nvidia_nim():
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litellm.set_verbose = True
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from openai import OpenAI
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client = OpenAI(
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api_key="fake-api-key",
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)
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with patch.object(client.embeddings.with_raw_response, "create") as mock_client:
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try:
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litellm.embedding(
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model="nvidia_nim/nvidia/nv-embedqa-e5-v5",
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input="What is the meaning of life?",
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input_type="passage",
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dimensions=1024,
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client=client,
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)
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except Exception as e:
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print(e)
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
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print("request_body: ", request_body)
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assert request_body["input"] == "What is the meaning of life?"
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assert request_body["model"] == "nvidia/nv-embedqa-e5-v5"
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assert request_body["extra_body"]["input_type"] == "passage"
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assert request_body["dimensions"] == 1024
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def test_chat_completion_nvidia_nim_with_tools():
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from openai import OpenAI
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litellm.set_verbose = True
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model_name = "nvidia_nim/meta/llama3-70b-instruct"
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client = OpenAI(
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api_key="fake-api-key",
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)
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# Define tools
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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_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": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The unit of temperature to use",
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},
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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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"type": "function",
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"function": {
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"name": "get_current_time",
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"description": "Get the current time in a given timezone",
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"parameters": {
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"type": "object",
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"properties": {
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"timezone": {
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"type": "string",
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"description": "The timezone, e.g. EST, PST",
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},
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},
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"required": ["timezone"],
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},
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},
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},
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]
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with patch.object(
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client.chat.completions.with_raw_response, "create"
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) as mock_client:
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try:
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completion(
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model=model_name,
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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 Boston today and what time is it in EST?",
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}
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],
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tools=tools,
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tool_choice="auto",
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parallel_tool_calls=True,
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temperature=0.7,
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client=client,
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)
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except Exception as e:
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print(e)
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# Add assertions to check the request
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mock_client.assert_called_once()
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request_body = mock_client.call_args.kwargs
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print("request_body: ", request_body)
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assert request_body["messages"] == [
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{
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"role": "user",
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"content": "What's the weather like in Boston today and what time is it in EST?",
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},
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]
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assert request_body["model"] == "meta/llama3-70b-instruct"
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assert request_body["temperature"] == 0.7
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assert request_body["tools"] == tools
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assert request_body["tool_choice"] == "auto"
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assert request_body["parallel_tool_calls"] == True
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@pytest.mark.asyncio()
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async def test_nvidia_nim_rerank_ranking_endpoint():
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"""
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Test that using "nvidia_nim/ranking/<model>" forces the /v1/ranking endpoint.
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This allows users to explicitly use the /v1/ranking endpoint for models like
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nvidia/llama-3.2-nv-rerankqa-1b-v2.
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Reference: https://build.nvidia.com/nvidia/llama-3_2-nv-rerankqa-1b-v2/deploy
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"""
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mock_response = AsyncMock()
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def return_val():
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return {
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"rankings": [
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{"index": 0, "logit": 0.95},
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{"index": 1, "logit": 0.75},
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],
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}
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mock_response.json = return_val
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mock_response.headers = {"key": "value"}
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mock_response.status_code = 200
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Use "ranking/" prefix to force /v1/ranking endpoint
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response = await litellm.arerank(
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model="nvidia_nim/ranking/nvidia/llama-3.2-nv-rerankqa-1b-v2",
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query="What is the GPU memory bandwidth?",
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documents=[
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"H100 delivers 3TB/s memory bandwidth",
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"A100 has 2TB/s memory bandwidth",
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],
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top_n=2,
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api_key="fake-api-key",
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)
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mock_post.assert_called_once()
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args_to_api = mock_post.call_args.kwargs["data"]
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_url = mock_post.call_args.kwargs["url"]
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print("url = ", _url)
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# Verify URL is /v1/ranking
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assert _url == "https://ai.api.nvidia.com/v1/ranking"
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# Verify request body structure
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request_data = json.loads(args_to_api)
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print("request_data=", request_data)
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# Query should be an object with 'text' field
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assert request_data["query"] == {"text": "What is the GPU memory bandwidth?"}
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# Documents should be 'passages'
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assert request_data["passages"] == [
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{"text": "H100 delivers 3TB/s memory bandwidth"},
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{"text": "A100 has 2TB/s memory bandwidth"},
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]
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# Model name in body should NOT have "ranking/" prefix
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assert request_data["model"] == "nvidia/llama-3.2-nv-rerankqa-1b-v2"
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class TestNvidiaNim(BaseLLMRerankTest):
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def get_custom_llm_provider(self) -> litellm.LlmProviders:
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return litellm.LlmProviders.NVIDIA_NIM
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def get_base_rerank_call_args(self) -> dict:
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return {
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"model": "nvidia_nim/nvidia/llama-3_2-nv-rerankqa-1b-v2",
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}
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def get_expected_cost(self) -> float:
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"""Nvidia NIM rerank models are free (cost = 0.0)"""
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return 0.0
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@pytest.mark.asyncio()
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@pytest.mark.parametrize("sync_mode", [True, False])
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async def test_basic_rerank(self, sync_mode, monkeypatch):
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"""
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Override the base live rerank test with a mocked HTTP layer.
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NVIDIA reached end-of-life for the hosted
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nvidia/llama-3.2-nv-rerankqa-1b-v2 rerank API on 2026-05-18 and
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published no replacement model, so a live call now returns HTTP 410
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("Gone"). NVIDIA's hosted catalog rotates on a schedule, so pointing
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at another live model would only defer the same failure. Mock the
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transport instead (same pattern as
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test_nvidia_nim_rerank_ranking_endpoint above) so the request/response
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transformation and cost calculation stay covered offline.
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"""
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monkeypatch.setenv("NVIDIA_NIM_API_KEY", "fake-api-key")
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mock_response = MagicMock()
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mock_response.status_code = 200
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mock_response.headers = {}
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mock_response.text = ""
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mock_response.json.return_value = {
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"rankings": [
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{"index": 0, "logit": 0.95},
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{"index": 1, "logit": 0.75},
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],
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"usage": {"total_tokens": 7},
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}
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with (
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patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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),
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patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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),
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):
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await super().test_basic_rerank(sync_mode=sync_mode)
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