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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
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
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