import json import os import sys from datetime import datetime from unittest.mock import AsyncMock sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path import httpx import pytest from respx import MockRouter from unittest.mock import patch, MagicMock, AsyncMock import litellm from litellm import Choices, Message, ModelResponse, EmbeddingResponse, Usage from litellm import completion from base_rerank_unit_tests import BaseLLMRerankTest import litellm def test_completion_nvidia_nim(): from openai import OpenAI litellm.set_verbose = True model_name = "nvidia_nim/databricks/dbrx-instruct" client = OpenAI( api_key="fake-api-key", ) with patch.object( client.chat.completions.with_raw_response, "create" ) as mock_client: try: completion( model=model_name, messages=[ { "role": "user", "content": "What's the weather like in Boston today in Fahrenheit?", } ], presence_penalty=0.5, frequency_penalty=0.1, client=client, ) except Exception as e: print(e) # Add any assertions here to check the response mock_client.assert_called_once() request_body = mock_client.call_args.kwargs print("request_body: ", request_body) assert request_body["messages"] == [ { "role": "user", "content": "What's the weather like in Boston today in Fahrenheit?", }, ] assert request_body["model"] == "databricks/dbrx-instruct" assert request_body["frequency_penalty"] == 0.1 assert request_body["presence_penalty"] == 0.5 def test_embedding_nvidia_nim(): litellm.set_verbose = True from openai import OpenAI client = OpenAI( api_key="fake-api-key", ) with patch.object(client.embeddings.with_raw_response, "create") as mock_client: try: litellm.embedding( model="nvidia_nim/nvidia/nv-embedqa-e5-v5", input="What is the meaning of life?", input_type="passage", dimensions=1024, client=client, ) except Exception as e: print(e) mock_client.assert_called_once() request_body = mock_client.call_args.kwargs print("request_body: ", request_body) assert request_body["input"] == "What is the meaning of life?" assert request_body["model"] == "nvidia/nv-embedqa-e5-v5" assert request_body["extra_body"]["input_type"] == "passage" assert request_body["dimensions"] == 1024 def test_chat_completion_nvidia_nim_with_tools(): from openai import OpenAI litellm.set_verbose = True model_name = "nvidia_nim/meta/llama3-70b-instruct" client = OpenAI( api_key="fake-api-key", ) # Define tools tools = [ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The unit of temperature to use", }, }, "required": ["location"], }, }, }, { "type": "function", "function": { "name": "get_current_time", "description": "Get the current time in a given timezone", "parameters": { "type": "object", "properties": { "timezone": { "type": "string", "description": "The timezone, e.g. EST, PST", }, }, "required": ["timezone"], }, }, }, ] with patch.object( client.chat.completions.with_raw_response, "create" ) as mock_client: try: completion( model=model_name, messages=[ { "role": "user", "content": "What's the weather like in Boston today and what time is it in EST?", } ], tools=tools, tool_choice="auto", parallel_tool_calls=True, temperature=0.7, client=client, ) except Exception as e: print(e) # Add assertions to check the request mock_client.assert_called_once() request_body = mock_client.call_args.kwargs print("request_body: ", request_body) assert request_body["messages"] == [ { "role": "user", "content": "What's the weather like in Boston today and what time is it in EST?", }, ] assert request_body["model"] == "meta/llama3-70b-instruct" assert request_body["temperature"] == 0.7 assert request_body["tools"] == tools assert request_body["tool_choice"] == "auto" assert request_body["parallel_tool_calls"] == True @pytest.mark.asyncio() async def test_nvidia_nim_rerank_ranking_endpoint(): """ Test that using "nvidia_nim/ranking/" forces the /v1/ranking endpoint. This allows users to explicitly use the /v1/ranking endpoint for models like nvidia/llama-3.2-nv-rerankqa-1b-v2. Reference: https://build.nvidia.com/nvidia/llama-3_2-nv-rerankqa-1b-v2/deploy """ mock_response = AsyncMock() def return_val(): return { "rankings": [ {"index": 0, "logit": 0.95}, {"index": 1, "logit": 0.75}, ], } mock_response.json = return_val mock_response.headers = {"key": "value"} mock_response.status_code = 200 with patch( "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", return_value=mock_response, ) as mock_post: # Use "ranking/" prefix to force /v1/ranking endpoint response = await litellm.arerank( model="nvidia_nim/ranking/nvidia/llama-3.2-nv-rerankqa-1b-v2", query="What is the GPU memory bandwidth?", documents=["H100 delivers 3TB/s memory bandwidth", "A100 has 2TB/s memory bandwidth"], top_n=2, api_key="fake-api-key", ) mock_post.assert_called_once() args_to_api = mock_post.call_args.kwargs["data"] _url = mock_post.call_args.kwargs["url"] print("url = ", _url) # Verify URL is /v1/ranking assert _url == "https://ai.api.nvidia.com/v1/ranking" # Verify request body structure request_data = json.loads(args_to_api) print("request_data=", request_data) # Query should be an object with 'text' field assert request_data["query"] == {"text": "What is the GPU memory bandwidth?"} # Documents should be 'passages' assert request_data["passages"] == [ {"text": "H100 delivers 3TB/s memory bandwidth"}, {"text": "A100 has 2TB/s memory bandwidth"}, ] # Model name in body should NOT have "ranking/" prefix assert request_data["model"] == "nvidia/llama-3.2-nv-rerankqa-1b-v2" class TestNvidiaNim(BaseLLMRerankTest): def get_custom_llm_provider(self) -> litellm.LlmProviders: return litellm.LlmProviders.NVIDIA_NIM def get_base_rerank_call_args(self) -> dict: return { "model": "nvidia_nim/nvidia/llama-3_2-nv-rerankqa-1b-v2", } def get_expected_cost(self) -> float: """Nvidia NIM rerank models are free (cost = 0.0)""" return 0.0