import json
import os
import sys
import traceback
from dotenv import load_dotenv
load_dotenv()
import io
import os
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import os
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
import litellm
from litellm import RateLimitError, Timeout, completion, completion_cost, embedding
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.llms.prompt_templates.factory import anthropic_messages_pt
# litellm.num_retries=3
litellm.cache = None
litellm.success_callback = []
user_message = "Write a short poem about the sky"
messages = [{"content": user_message, "role": "user"}]
def logger_fn(user_model_dict):
print(f"user_model_dict: {user_model_dict}")
@pytest.fixture(autouse=True)
def reset_callbacks():
print("\npytest fixture - resetting callbacks")
litellm.success_callback = []
litellm._async_success_callback = []
litellm.failure_callback = []
litellm.callbacks = []
@pytest.mark.skip(reason="Local test")
def test_response_model_none():
"""
Addresses:https://github.com/BerriAI/litellm/issues/2972
"""
x = completion(
model="mymodel",
custom_llm_provider="openai",
messages=[{"role": "user", "content": "Hello!"}],
api_base="http://0.0.0.0:8080",
api_key="my-api-key",
)
print(f"x: {x}")
assert isinstance(x, litellm.ModelResponse)
def test_completion_custom_provider_model_name():
try:
litellm.cache = None
response = completion(
model="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
messages=messages,
logger_fn=logger_fn,
)
# Add assertions here to check the-response
print(response)
print(response["choices"][0]["finish_reason"])
except litellm.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def _openai_mock_response(*args, **kwargs) -> litellm.ModelResponse:
new_response = MagicMock()
new_response.headers = {"hello": "world"}
response_object = {
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "gpt-3.5-turbo-0125",
"system_fingerprint": "fp_44709d6fcb",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "\n\nHello there, how may I assist you today?",
},
"logprobs": None,
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 9, "completion_tokens": 12, "total_tokens": 21},
}
from openai import OpenAI
from openai.types.chat.chat_completion import ChatCompletion
pydantic_obj = ChatCompletion(**response_object) # type: ignore
pydantic_obj.choices[0].message.role = None # type: ignore
new_response.parse.return_value = pydantic_obj
return new_response
def test_null_role_response():
"""
Test if the api returns 'null' role, 'assistant' role is still returned
"""
import openai
openai_client = openai.OpenAI()
with patch.object(
openai_client.chat.completions, "create", side_effect=_openai_mock_response
) as mock_response:
response = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey! how's it going?"}],
client=openai_client,
)
print(f"response: {response}")
assert response.id == "chatcmpl-123"
assert response.choices[0].message.role == "assistant"
def test_completion_azure_ai_command_r():
try:
import os
litellm.set_verbose = True
os.environ["AZURE_AI_API_BASE"] = os.getenv("AZURE_COHERE_API_BASE", "")
os.environ["AZURE_AI_API_KEY"] = os.getenv("AZURE_COHERE_API_KEY", "")
response = completion(
model="azure_ai/command-r-plus",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What is the meaning of life?"}
],
}
],
) # type: ignore
assert "azure_ai" in response.model
except litellm.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_completion_azure_ai_mistral_invalid_params(sync_mode):
try:
import os
litellm.set_verbose = True
os.environ["AZURE_AI_API_BASE"] = os.getenv("AZURE_MISTRAL_API_BASE", "")
os.environ["AZURE_AI_API_KEY"] = os.getenv("AZURE_MISTRAL_API_KEY", "")
data = {
"model": "azure_ai/mistral",
"messages": [{"role": "user", "content": "What is the meaning of life?"}],
"frequency_penalty": 0.1,
"presence_penalty": 0.1,
"drop_params": True,
}
if sync_mode:
response: litellm.ModelResponse = completion(**data) # type: ignore
else:
response: litellm.ModelResponse = await litellm.acompletion(**data) # type: ignore
assert "azure_ai" in response.model
except litellm.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_azure_command_r():
try:
litellm.set_verbose = True
response = completion(
model="azure/command-r-plus",
api_base=os.getenv("AZURE_COHERE_API_BASE"),
api_key=os.getenv("AZURE_COHERE_API_KEY"),
messages=[{"role": "user", "content": "What is the meaning of life?"}],
)
print(response)
except litellm.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize(
"api_base",
[
"https://litellm8397336933.openai.azure.com",
"https://litellm8397336933.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview",
],
)
def test_completion_azure_ai_gpt_4o(api_base):
try:
litellm.set_verbose = True
response = completion(
model="azure_ai/gpt-4o",
api_base=api_base,
api_key=os.getenv("AZURE_AI_OPENAI_KEY"),
messages=[{"role": "user", "content": "What is the meaning of life?"}],
)
print(response)
except litellm.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_completion_databricks(sync_mode):
litellm.set_verbose = True
if sync_mode:
response: litellm.ModelResponse = completion(
model="databricks/databricks-dbrx-instruct",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
) # type: ignore
else:
response: litellm.ModelResponse = await litellm.acompletion(
model="databricks/databricks-dbrx-instruct",
messages=[{"role": "user", "content": "Hey, how's it going?"}],
) # type: ignore
print(f"response: {response}")
response_format_tests(response=response)
def predibase_mock_post(url, data=None, json=None, headers=None, timeout=None):
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.headers = {"Content-Type": "application/json"}
mock_response.json.return_value = {
"generated_text": " Is it to find happiness, to achieve success,",
"details": {
"finish_reason": "length",
"prompt_tokens": 8,
"generated_tokens": 10,
"seed": None,
"prefill": [],
"tokens": [
{"id": 2209, "text": " Is", "logprob": -1.7568359, "special": False},
{"id": 433, "text": " it", "logprob": -0.2220459, "special": False},
{"id": 311, "text": " to", "logprob": -0.6928711, "special": False},
{"id": 1505, "text": " find", "logprob": -0.6425781, "special": False},
{
"id": 23871,
"text": " happiness",
"logprob": -0.07519531,
"special": False,
},
{"id": 11, "text": ",", "logprob": -0.07110596, "special": False},
{"id": 311, "text": " to", "logprob": -0.79296875, "special": False},
{
"id": 11322,
"text": " achieve",
"logprob": -0.7602539,
"special": False,
},
{
"id": 2450,
"text": " success",
"logprob": -0.03656006,
"special": False,
},
{"id": 11, "text": ",", "logprob": -0.0011510849, "special": False},
],
},
}
return mock_response
# @pytest.mark.skip(reason="local-only test")
@pytest.mark.asyncio
async def test_completion_predibase():
try:
litellm.set_verbose = True
# with patch("requests.post", side_effect=predibase_mock_post):
response = await litellm.acompletion(
model="predibase/llama-3-8b-instruct",
tenant_id="c4768f95",
api_key=os.getenv("PREDIBASE_API_KEY"),
messages=[{"role": "user", "content": "who are u?"}],
max_tokens=10,
timeout=5,
)
print(response)
except litellm.Timeout as e:
print("got a timeout error from predibase")
pass
except litellm.ServiceUnavailableError as e:
pass
except litellm.InternalServerError:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_predibase()
# test_completion_claude()
@pytest.mark.skip(reason="No empower api key")
def test_completion_empower():
litellm.set_verbose = True
messages = [
{
"role": "user",
"content": "\nWhat is the query for `console.log` => `console.error`\n",
},
{
"role": "assistant",
"content": "\nThis is the GritQL query for the given before/after examples:\n\n`console.log` => `console.error`\n\n",
},
{
"role": "user",
"content": "\nWhat is the query for `console.info` => `consdole.heaven`\n",
},
]
try:
# test without max tokens
response = completion(
model="empower/empower-functions-small",
messages=messages,
)
# Add any assertions, here to check response args
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_github_api():
litellm.set_verbose = True
messages = [
{
"role": "user",
"content": "\nWhat is the query for `console.log` => `console.error`\n",
},
{
"role": "assistant",
"content": "\nThis is the GritQL query for the given before/after examples:\n\n`console.log` => `console.error`\n\n",
},
{
"role": "user",
"content": "\nWhat is the query for `console.info` => `consdole.heaven`\n",
},
]
try:
# test without max tokens
response = completion(
model="github/gpt-4o",
messages=messages,
)
# Add any assertions, here to check response args
print(response)
except litellm.AuthenticationError:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_claude_3_empty_response():
litellm.set_verbose = True
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are 2twNLGfqk4GMOn3ffp4p."}],
},
{"role": "user", "content": "Hi gm!", "name": "ishaan"},
{"role": "assistant", "content": "Good morning! How are you doing today?"},
{
"role": "user",
"content": "I was hoping we could chat a bit",
},
]
try:
response = litellm.completion(model="claude-3-opus-20240229", messages=messages)
print(response)
except litellm.InternalServerError as e:
pytest.skip(f"InternalServerError - {str(e)}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_claude_3():
litellm.set_verbose = True
messages = [
{
"role": "user",
"content": "\nWhat is the query for `console.log` => `console.error`\n",
},
{
"role": "assistant",
"content": "\nThis is the GritQL query for the given before/after examples:\n\n`console.log` => `console.error`\n\n",
},
{
"role": "user",
"content": "\nWhat is the query for `console.info` => `consdole.heaven`\n",
},
]
try:
# test without max tokens
response = completion(
model="anthropic/claude-3-opus-20240229",
messages=messages,
)
# Add any assertions, here to check response args
print(response)
except litellm.InternalServerError as e:
pytest.skip(f"InternalServerError - {str(e)}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize(
"model",
["anthropic/claude-3-opus-20240229", "anthropic.claude-3-sonnet-20240229-v1:0"],
)
def test_completion_claude_3_function_call(model):
litellm.set_verbose = True
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
messages = [
{
"role": "user",
"content": "What's the weather like in Boston today in Fahrenheit?",
}
]
try:
# test without max tokens
response = completion(
model=model,
messages=messages,
tools=tools,
tool_choice={
"type": "function",
"function": {"name": "get_current_weather"},
},
drop_params=True,
)
# Add any assertions here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
messages.append(
response.choices[0].message.model_dump()
) # Add assistant tool invokes
tool_result = (
'{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
)
# Add user submitted tool results in the OpenAI format
messages.append(
{
"tool_call_id": response.choices[0].message.tool_calls[0].id,
"role": "tool",
"name": response.choices[0].message.tool_calls[0].function.name,
"content": tool_result,
}
)
# In the second response, Claude should deduce answer from tool results
second_response = completion(
model=model,
messages=messages,
tools=tools,
tool_choice="auto",
drop_params=True,
)
print(second_response)
except litellm.InternalServerError:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize("sync_mode", [True])
@pytest.mark.parametrize(
"model, api_key, api_base",
[
("gpt-3.5-turbo", None, None),
("claude-3-opus-20240229", None, None),
("command-r", None, None),
("anthropic.claude-3-sonnet-20240229-v1:0", None, None),
(
"azure_ai/command-r-plus",
os.getenv("AZURE_COHERE_API_KEY"),
os.getenv("AZURE_COHERE_API_BASE"),
),
],
)
@pytest.mark.asyncio
async def test_model_function_invoke(model, sync_mode, api_key, api_base):
try:
litellm.set_verbose = True
messages = [
{
"role": "system",
"content": "Your name is Litellm Bot, you are a helpful assistant",
},
# User asks for their name and weather in San Francisco
{
"role": "user",
"content": "Hello, what is your name and can you tell me the weather?",
},
# Assistant replies with a tool call
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_123",
"type": "function",
"index": 0,
"function": {
"name": "get_weather",
"arguments": '{"location": "San Francisco, CA"}',
},
}
],
},
# The result of the tool call is added to the history
{
"role": "tool",
"tool_call_id": "call_123",
"content": "27 degrees celsius and clear in San Francisco, CA",
},
# Now the assistant can reply with the result of the tool call.
]
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",
}
},
"required": ["location"],
},
},
}
]
data = {
"model": model,
"messages": messages,
"tools": tools,
"api_key": api_key,
"api_base": api_base,
}
if sync_mode:
response = litellm.completion(**data)
else:
response = await litellm.acompletion(**data)
print(f"response: {response}")
except litellm.InternalServerError:
pass
except litellm.RateLimitError as e:
pass
except Exception as e:
if "429 Quota exceeded" in str(e):
pass
else:
pytest.fail("An unexpected exception occurred - {}".format(str(e)))
@pytest.mark.asyncio
async def test_anthropic_no_content_error():
"""
https://github.com/BerriAI/litellm/discussions/3440#discussioncomment-9323402
"""
try:
litellm.drop_params = True
response = await litellm.acompletion(
model="anthropic/claude-3-opus-20240229",
api_key=os.getenv("ANTHROPIC_API_KEY"),
messages=[
{
"role": "system",
"content": "You will be given a list of fruits. Use the submitFruit function to submit a fruit. Don't say anything after.",
},
{"role": "user", "content": "I like apples"},
{
"content": "The most relevant tool for this request is the submitFruit function.",
"role": "assistant",
"tool_calls": [
{
"function": {
"arguments": '{"name": "Apple"}',
"name": "submitFruit",
},
"id": "toolu_012ZTYKWD4VqrXGXyE7kEnAK",
"type": "function",
}
],
},
{
"role": "tool",
"content": '{"success":true}',
"tool_call_id": "toolu_012ZTYKWD4VqrXGXyE7kEnAK",
},
],
max_tokens=2000,
temperature=1,
tools=[
{
"type": "function",
"function": {
"name": "submitFruit",
"description": "Submits a fruit",
"parameters": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The name of the fruit",
}
},
"required": ["name"],
},
},
}
],
frequency_penalty=0.8,
)
pass
except litellm.InternalServerError:
pass
except litellm.APIError as e:
assert e.status_code == 500
except Exception as e:
pytest.fail(f"An unexpected error occurred - {str(e)}")
def test_gemini_completion_call_error():
try:
print("test completion + streaming")
litellm.num_retries = 3
litellm.set_verbose = True
messages = [{"role": "user", "content": "what is the capital of congo?"}]
response = completion(
model="gemini/gemini-1.5-pro-latest",
messages=messages,
stream=True,
max_tokens=10,
)
print(f"response: {response}")
for chunk in response:
print(chunk)
except litellm.RateLimitError:
pass
except litellm.InternalServerError:
pass
except Exception as e:
pytest.fail(f"error occurred: {str(e)}")
def test_completion_cohere_command_r_plus_function_call():
litellm.set_verbose = True
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
messages = [
{
"role": "user",
"content": "What's the weather like in Boston today in Fahrenheit?",
}
]
try:
# test without max tokens
response = completion(
model="command-r-plus",
messages=messages,
tools=tools,
tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
messages.append(
response.choices[0].message.model_dump()
) # Add assistant tool invokes
tool_result = (
'{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
)
# Add user submitted tool results in the OpenAI format
messages.append(
{
"tool_call_id": response.choices[0].message.tool_calls[0].id,
"role": "tool",
"name": response.choices[0].message.tool_calls[0].function.name,
"content": tool_result,
}
)
# In the second response, Cohere should deduce answer from tool results
second_response = completion(
model="command-r-plus",
messages=messages,
tools=tools,
tool_choice="auto",
force_single_step=True,
)
print(second_response)
except litellm.Timeout:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_parse_xml_params():
from litellm.llms.prompt_templates.factory import parse_xml_params
## SCENARIO 1 ## - W/ ARRAY
xml_content = """return_list_of_str\n\n\n- apple
\n- banana
\n- orange
\n\n"""
json_schema = {
"properties": {
"value": {
"items": {"type": "string"},
"title": "Value",
"type": "array",
}
},
"required": ["value"],
"type": "object",
}
response = parse_xml_params(xml_content=xml_content, json_schema=json_schema)
print(f"response: {response}")
assert response["value"] == ["apple", "banana", "orange"]
## SCENARIO 2 ## - W/OUT ARRAY
xml_content = """get_current_weather\n\nBoston, MA\nfahrenheit\n"""
json_schema = {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
}
response = parse_xml_params(xml_content=xml_content, json_schema=json_schema)
print(f"response: {response}")
assert response["location"] == "Boston, MA"
assert response["unit"] == "fahrenheit"
def test_completion_claude_3_multi_turn_conversations():
litellm.set_verbose = True
litellm.modify_params = True
messages = [
{"role": "assistant", "content": "?"}, # test first user message auto injection
{"role": "user", "content": "Hi!"},
{
"role": "user",
"content": [{"type": "text", "text": "What is the weather like today?"}],
},
{"role": "assistant", "content": "Hi! I am Claude. "},
{"role": "assistant", "content": "Today is a sunny "},
]
try:
response = completion(
model="anthropic/claude-3-opus-20240229",
messages=messages,
)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_claude_3_stream():
litellm.set_verbose = False
messages = [{"role": "user", "content": "Hello, world"}]
try:
# test without max tokens
response = completion(
model="anthropic/claude-3-opus-20240229",
messages=messages,
max_tokens=10,
stream=True,
)
# Add any assertions, here to check response args
print(response)
for chunk in response:
print(chunk)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def encode_image(image_path):
import base64
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
@pytest.mark.parametrize(
"model",
[
"gpt-4o",
"azure/gpt-4o",
"anthropic/claude-3-opus-20240229",
],
) #
def test_completion_base64(model):
try:
import base64
import requests
litellm.set_verbose = True
url = "https://dummyimage.com/100/100/fff&text=Test+image"
response = requests.get(url)
file_data = response.content
encoded_file = base64.b64encode(file_data).decode("utf-8")
base64_image = f"data:image/png;base64,{encoded_file}"
resp = litellm.completion(
model=model,
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Whats in this image?"},
{
"type": "image_url",
"image_url": {"url": base64_image},
},
],
}
],
)
print(f"\nResponse: {resp}")
prompt_tokens = resp.usage.prompt_tokens
except litellm.ServiceUnavailableError as e:
print("got service unavailable error: ", e)
pass
except litellm.InternalServerError as e:
print("got internal server error: ", e)
pass
except Exception as e:
if "500 Internal error encountered.'" in str(e):
pass
else:
pytest.fail(f"An exception occurred - {str(e)}")
@pytest.mark.parametrize("model", ["claude-3-sonnet-20240229"])
def test_completion_function_plus_image(model):
litellm.set_verbose = True
image_content = [
{"type": "text", "text": "What’s in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://litellm-listing.s3.amazonaws.com/litellm_logo.png"
},
},
]
image_message = {"role": "user", "content": image_content}
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"],
},
},
"required": ["location"],
},
},
}
]
tool_choice = {"type": "function", "function": {"name": "get_current_weather"}}
messages = [
{
"role": "user",
"content": "What's the weather like in Boston today in Fahrenheit?",
}
]
try:
response = completion(
model=model,
messages=[image_message],
tool_choice=tool_choice,
tools=tools,
stream=False,
)
print(response)
except litellm.InternalServerError:
pass
@pytest.mark.parametrize(
"provider",
["azure", "azure_ai"],
)
def test_completion_azure_mistral_large_function_calling(provider):
"""
This primarily tests if the 'Function()' pydantic object correctly handles argument param passed in as a dict vs. string
"""
litellm.set_verbose = True
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
messages = [
{
"role": "user",
"content": "What's the weather like in Boston today in Fahrenheit?",
}
]
response = completion(
model="{}/mistral-large-latest".format(provider),
api_base=os.getenv("AZURE_MISTRAL_API_BASE"),
api_key=os.getenv("AZURE_MISTRAL_API_KEY"),
messages=messages,
tools=tools,
tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(response.choices[0].message.tool_calls[0].function.arguments, str)
def test_completion_mistral_api():
try:
litellm.set_verbose = True
response = completion(
model="mistral/mistral-tiny",
max_tokens=5,
messages=[
{
"role": "user",
"content": "Hey, how's it going?",
}
],
seed=10,
)
# Add any assertions here to check the response
print(response)
cost = litellm.completion_cost(completion_response=response)
print("cost to make mistral completion=", cost)
assert cost > 0.0
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="backend api unavailable")
@pytest.mark.asyncio
async def test_completion_codestral_chat_api():
try:
litellm.set_verbose = True
response = await litellm.acompletion(
model="codestral/codestral-latest",
messages=[
{
"role": "user",
"content": "Hey, how's it going?",
}
],
temperature=0.0,
top_p=1,
max_tokens=10,
safe_prompt=False,
seed=12,
)
# Add any assertions here to-check the response
print(response)
# cost = litellm.completion_cost(completion_response=response)
# print("cost to make mistral completion=", cost)
# assert cost > 0.0
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_mistral_api_mistral_large_function_call():
litellm.set_verbose = True
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
messages = [
{
"role": "user",
"content": "What's the weather like in Boston today in Fahrenheit?",
}
]
try:
# test without max tokens
response = completion(
model="mistral/mistral-large-latest",
messages=messages,
tools=tools,
tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
messages.append(
response.choices[0].message.model_dump()
) # Add assistant tool invokes
tool_result = (
'{"location": "Boston", "temperature": "72", "unit": "fahrenheit"}'
)
# Add user submitted tool results in the OpenAI format
messages.append(
{
"tool_call_id": response.choices[0].message.tool_calls[0].id,
"role": "tool",
"name": response.choices[0].message.tool_calls[0].function.name,
"content": tool_result,
}
)
# In the second response, Mistral should deduce answer from tool results
second_response = completion(
model="mistral/mistral-large-latest",
messages=messages,
tools=tools,
tool_choice="auto",
)
print(second_response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(
reason="Since we already test mistral/mistral-tiny in test_completion_mistral_api. This is only for locally verifying azure mistral works"
)
def test_completion_mistral_azure():
try:
litellm.set_verbose = True
response = completion(
model="mistral/Mistral-large-nmefg",
api_key=os.environ["MISTRAL_AZURE_API_KEY"],
api_base=os.environ["MISTRAL_AZURE_API_BASE"],
max_tokens=5,
messages=[
{
"role": "user",
"content": "Hi from litellm",
}
],
)
# Add any assertions here to check, the response
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_mistral_api()
def test_completion_mistral_api_modified_input():
try:
litellm.set_verbose = True
response = completion(
model="mistral/mistral-tiny",
max_tokens=5,
messages=[
{
"role": "user",
"content": [{"type": "text", "text": "Hey, how's it going?"}],
}
],
)
# Add any assertions here to check the response
print(response)
cost = litellm.completion_cost(completion_response=response)
print("cost to make mistral completion=", cost)
assert cost > 0.0
except Exception as e:
if "500" in str(e):
pass
else:
pytest.fail(f"Error occurred: {e}")
def test_completion_claude2_1():
try:
litellm.set_verbose = True
print("claude2.1 test request")
messages = [
{
"role": "system",
"content": "Your goal is generate a joke on the topic user gives.",
},
{"role": "user", "content": "Generate a 3 liner joke for me"},
]
# test without max tokens
response = completion(model="claude-2.1", messages=messages)
# Add any assertions here to check the response
print(response)
print(response.usage)
print(response.usage.completion_tokens)
print(response["usage"]["completion_tokens"])
# print("new cost tracking")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_claude2_1()
@pytest.mark.asyncio
async def test_acompletion_claude2_1():
try:
litellm.set_verbose = True
print("claude2.1 test request")
messages = [
{
"role": "system",
"content": "Your goal is generate a joke on the topic user gives.",
},
{"role": "user", "content": "Generate a 3 liner joke for me"},
]
# test without max-tokens
response = await litellm.acompletion(model="claude-2.1", messages=messages)
# Add any assertions here to check the response
print(response)
print(response.usage)
print(response.usage.completion_tokens)
print(response["usage"]["completion_tokens"])
# print("new cost tracking")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# def test_completion_oobabooga():
# try:
# response = completion(
# model="oobabooga/vicuna-1.3b", messages=messages, api_base="http://127.0.0.1:5000"
# )
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_oobabooga()
# aleph alpha
# def test_completion_aleph_alpha():
# try:
# response = completion(
# model="luminous-base", messages=messages, logger_fn=logger_fn
# )
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_aleph_alpha()
# def test_completion_aleph_alpha_control_models():
# try:
# response = completion(
# model="luminous-base-control", messages=messages, logger_fn=logger_fn
# )
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_aleph_alpha_control_models()
import openai
def test_completion_gpt4_turbo():
litellm.set_verbose = True
try:
response = completion(
model="gpt-4-1106-preview",
messages=messages,
max_completion_tokens=10,
)
print(response)
except openai.RateLimitError:
print("got a rate liimt error")
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_gpt4_turbo()
def test_completion_gpt4_turbo_0125():
try:
response = completion(
model="gpt-4-0125-preview",
messages=messages,
max_tokens=10,
)
print(response)
except openai.RateLimitError:
print("got a rate liimt error")
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="this test is flaky")
def test_completion_gpt4_vision():
try:
litellm.set_verbose = True
response = completion(
model="gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Whats in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
},
],
}
],
)
print(response)
except openai.RateLimitError:
print("got a rate liimt error")
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_gpt4_vision()
def test_completion_azure_gpt4_vision():
# azure/gpt-4, vision takes 5-seconds to respond
try:
litellm.set_verbose = True
response = completion(
model="azure/gpt-4-vision",
timeout=5,
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Whats in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://avatars.githubusercontent.com/u/29436595?v=4"
},
},
],
}
],
base_url="https://gpt-4-vision-resource.openai.azure.com/openai/deployments/gpt-4-vision/extensions",
api_key=os.getenv("AZURE_VISION_API_KEY"),
enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}},
dataSources=[
{
"type": "AzureComputerVision",
"parameters": {
"endpoint": "https://gpt-4-vision-enhancement.cognitiveservices.azure.com/",
"key": os.environ["AZURE_VISION_ENHANCE_KEY"],
},
}
],
)
print(response)
except openai.APIError as e:
pass
except openai.APITimeoutError:
print("got a timeout error")
pass
except openai.RateLimitError as e:
print("got a rate liimt error", e)
pass
except openai.APIStatusError as e:
print("got an api status error", e)
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_azure_gpt4_vision()
def test_completion_openai_response_headers():
"""
Tests if LiteLLM reurns response hea
"""
litellm.return_response_headers = True
# /chat/completion
messages = [
{
"role": "user",
"content": "hi",
}
]
response = completion(
model="gpt-4o-mini",
messages=messages,
)
print(f"response: {response}")
print("response_headers=", response._response_headers)
assert response._response_headers is not None
assert "x-ratelimit-remaining-tokens" in response._response_headers
assert isinstance(
response._hidden_params["additional_headers"][
"llm_provider-x-ratelimit-remaining-requests"
],
str,
)
# /chat/completion - with streaming
streaming_response = litellm.completion(
model="gpt-4o-mini",
messages=messages,
stream=True,
)
response_headers = streaming_response._response_headers
print("streaming response_headers=", response_headers)
assert response_headers is not None
assert "x-ratelimit-remaining-tokens" in response_headers
assert isinstance(
response._hidden_params["additional_headers"][
"llm_provider-x-ratelimit-remaining-requests"
],
str,
)
for chunk in streaming_response:
print("chunk=", chunk)
# embedding
embedding_response = litellm.embedding(
model="text-embedding-ada-002",
input="hello",
)
embedding_response_headers = embedding_response._response_headers
print("embedding_response_headers=", embedding_response_headers)
assert embedding_response_headers is not None
assert "x-ratelimit-remaining-tokens" in embedding_response_headers
assert isinstance(
response._hidden_params["additional_headers"][
"llm_provider-x-ratelimit-remaining-requests"
],
str,
)
litellm.return_response_headers = False
@pytest.mark.asyncio()
async def test_async_completion_openai_response_headers():
"""
Tests if LiteLLM reurns response hea
"""
litellm.return_response_headers = True
# /chat/completion
messages = [
{
"role": "user",
"content": "hi",
}
]
response = await litellm.acompletion(
model="gpt-4o-mini",
messages=messages,
)
print(f"response: {response}")
print("response_headers=", response._response_headers)
assert response._response_headers is not None
assert "x-ratelimit-remaining-tokens" in response._response_headers
# /chat/completion with streaming
streaming_response = await litellm.acompletion(
model="gpt-4o-mini",
messages=messages,
stream=True,
)
response_headers = streaming_response._response_headers
print("streaming response_headers=", response_headers)
assert response_headers is not None
assert "x-ratelimit-remaining-tokens" in response_headers
async for chunk in streaming_response:
print("chunk=", chunk)
# embedding
embedding_response = await litellm.aembedding(
model="text-embedding-ada-002",
input="hello",
)
embedding_response_headers = embedding_response._response_headers
print("embedding_response_headers=", embedding_response_headers)
assert embedding_response_headers is not None
assert "x-ratelimit-remaining-tokens" in embedding_response_headers
litellm.return_response_headers = False
@pytest.mark.parametrize("model", ["gpt-3.5-turbo", "gpt-4", "gpt-4o"])
def test_completion_openai_params(model):
litellm.drop_params = True
messages = [
{
"role": "user",
"content": """Generate JSON about Bill Gates: { "full_name": "", "title": "" }""",
}
]
response = completion(
model=model,
messages=messages,
response_format={"type": "json_object"},
)
print(f"response: {response}")
def test_completion_fireworks_ai():
try:
litellm.set_verbose = True
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="fireworks_ai/accounts/fireworks/models/mixtral-8x7b-instruct",
messages=messages,
)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize(
"api_key, api_base", [(None, "my-bad-api-base"), ("my-bad-api-key", None)]
)
def test_completion_fireworks_ai_dynamic_params(api_key, api_base):
try:
litellm.set_verbose = True
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="fireworks_ai/accounts/fireworks/models/mixtral-8x7b-instruct",
messages=messages,
api_base=api_base,
api_key=api_key,
)
pytest.fail(f"This call should have failed!")
except Exception as e:
pass
# @pytest.mark.skip(reason="this test is flaky")
def test_completion_perplexity_api():
try:
response_object = {
"id": "a8f37485-026e-45da-81a9-cf0184896840",
"model": "llama-3-sonar-small-32k-online",
"created": 1722186391,
"usage": {"prompt_tokens": 17, "completion_tokens": 65, "total_tokens": 82},
"citations": [
"https://www.sciencedirect.com/science/article/pii/S007961232200156X",
"https://www.britannica.com/event/World-War-II",
"https://www.loc.gov/classroom-materials/united-states-history-primary-source-timeline/great-depression-and-world-war-ii-1929-1945/world-war-ii/",
"https://www.nationalww2museum.org/war/topics/end-world-war-ii-1945",
"https://en.wikipedia.org/wiki/World_War_II",
],
"object": "chat.completion",
"choices": [
{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": "World War II was won by the Allied powers, which included the United States, the Soviet Union, Great Britain, France, China, and other countries. The war concluded with the surrender of Germany on May 8, 1945, and Japan on September 2, 1945[2][3][4].",
},
"delta": {"role": "assistant", "content": ""},
}
],
}
from openai import OpenAI
from openai.types.chat.chat_completion import ChatCompletion
pydantic_obj = ChatCompletion(**response_object)
def _return_pydantic_obj(*args, **kwargs):
new_response = MagicMock()
new_response.headers = {"hello": "world"}
new_response.parse.return_value = pydantic_obj
return new_response
openai_client = OpenAI()
with patch.object(
openai_client.chat.completions.with_raw_response,
"create",
side_effect=_return_pydantic_obj,
) as mock_client:
# litellm.set_verbose= True
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="mistral-7b-instruct",
messages=messages,
api_base="https://api.perplexity.ai",
client=openai_client,
)
print(response)
assert hasattr(response, "citations")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_perplexity_api()
@pytest.mark.skip(reason="this test is flaky")
def test_completion_perplexity_api_2():
try:
# litellm.set_verbose=True
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
{
"role": "user",
"content": "Hey",
},
]
response = completion(model="perplexity/mistral-7b-instruct", messages=messages)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_perplexity_api_2()
# commenting out as this is a flaky test on circle-ci
# def test_completion_nlp_cloud():
# try:
# messages = [
# {"role": "system", "content": "You are a helpful assistant."},
# {
# "role": "user",
# "content": "how does a court case get to the Supreme Court?",
# },
# ]
# response = completion(model="dolphin", messages=messages, logger_fn=logger_fn)
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_nlp_cloud()
######### HUGGING FACE TESTS ########################
#####################################################
"""
HF Tests we should pass
- TGI:
- Pro Inference API
- Deployed Endpoint
- Coversational
- Free Inference API
- Deployed Endpoint
- Neither TGI or Coversational
- Free Inference API
- Deployed Endpoint
"""
#####################################################
#####################################################
# Test util to sort models to TGI, conv, None
def test_get_hf_task_for_model():
model = "glaiveai/glaive-coder-7b"
model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
print(f"model:{model}, model type: {model_type}")
assert model_type == "text-generation-inference"
model = "meta-llama/Llama-2-7b-hf"
model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
print(f"model:{model}, model type: {model_type}")
assert model_type == "text-generation-inference"
model = "facebook/blenderbot-400M-distill"
model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
print(f"model:{model}, model type: {model_type}")
assert model_type == "conversational"
model = "facebook/blenderbot-3B"
model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
print(f"model:{model}, model type: {model_type}")
assert model_type == "conversational"
# neither Conv or None
model = "roneneldan/TinyStories-3M"
model_type, _ = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
print(f"model:{model}, model type: {model_type}")
assert model_type == "text-generation"
# test_get_hf_task_for_model()
# litellm.set_verbose=False
# ################### Hugging Face TGI models ########################
# # TGI model
# # this is a TGI model https://huggingface.co/glaiveai/glaive-coder-7b
def tgi_mock_post(url, **kwargs):
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.headers = {"Content-Type": "application/json"}
mock_response.json.return_value = [
{
"generated_text": "<|assistant|>\nI'm",
"details": {
"finish_reason": "length",
"generated_tokens": 10,
"seed": None,
"prefill": [],
"tokens": [
{
"id": 28789,
"text": "<",
"logprob": -0.025222778,
"special": False,
},
{
"id": 28766,
"text": "|",
"logprob": -0.000003695488,
"special": False,
},
{
"id": 489,
"text": "ass",
"logprob": -0.0000019073486,
"special": False,
},
{
"id": 11143,
"text": "istant",
"logprob": -0.000002026558,
"special": False,
},
{
"id": 28766,
"text": "|",
"logprob": -0.0000015497208,
"special": False,
},
{
"id": 28767,
"text": ">",
"logprob": -0.0000011920929,
"special": False,
},
{
"id": 13,
"text": "\n",
"logprob": -0.00009703636,
"special": False,
},
{"id": 28737, "text": "I", "logprob": -0.1953125, "special": False},
{
"id": 28742,
"text": "'",
"logprob": -0.88183594,
"special": False,
},
{
"id": 28719,
"text": "m",
"logprob": -0.00032639503,
"special": False,
},
],
},
}
]
return mock_response
def test_hf_test_completion_tgi():
litellm.set_verbose = True
try:
with patch("requests.post", side_effect=tgi_mock_post) as mock_client:
response = completion(
model="huggingface/HuggingFaceH4/zephyr-7b-beta",
messages=[{"content": "Hello, how are you?", "role": "user"}],
max_tokens=10,
wait_for_model=True,
)
# Add any assertions-here to check the response
print(response)
assert "options" in mock_client.call_args.kwargs["data"]
json_data = json.loads(mock_client.call_args.kwargs["data"])
assert "wait_for_model" in json_data["options"]
assert json_data["options"]["wait_for_model"] is True
except litellm.ServiceUnavailableError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# hf_test_completion_tgi()
@pytest.mark.parametrize(
"provider", ["openai", "hosted_vllm", "lm_studio"]
) # "vertex_ai",
@pytest.mark.asyncio
async def test_openai_compatible_custom_api_base(provider):
litellm.set_verbose = True
messages = [
{
"role": "user",
"content": "Hello world",
}
]
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
with patch.object(
openai_client.chat.completions, "create", new=MagicMock()
) as mock_call:
try:
completion(
model="{provider}/my-vllm-model".format(provider=provider),
messages=messages,
response_format={"type": "json_object"},
client=openai_client,
api_base="my-custom-api-base",
hello="world",
)
except Exception as e:
print(e)
mock_call.assert_called_once()
print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
assert "hello" in mock_call.call_args.kwargs["extra_body"]
@pytest.mark.asyncio
async def test_litellm_gateway_from_sdk():
litellm.set_verbose = True
messages = [
{
"role": "user",
"content": "Hello world",
}
]
from openai import OpenAI
openai_client = OpenAI(api_key="fake-key")
with patch.object(
openai_client.chat.completions, "create", new=MagicMock()
) as mock_call:
try:
completion(
model="litellm_proxy/my-vllm-model",
messages=messages,
response_format={"type": "json_object"},
client=openai_client,
api_base="my-custom-api-base",
hello="world",
)
except Exception as e:
print(e)
mock_call.assert_called_once()
print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
assert "hello" in mock_call.call_args.kwargs["extra_body"]
# ################### Hugging Face Conversational models ########################
# def hf_test_completion_conv():
# try:
# response = litellm.completion(
# model="huggingface/facebook/blenderbot-3B",
# messages=[{ "content": "Hello, how are you?","role": "user"}],
# )
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# hf_test_completion_conv()
# ################### Hugging Face Neither TGI or Conversational models ########################
# # Neither TGI or Conversational task
# def hf_test_completion_none_task():
# try:
# user_message = "My name is Merve and my favorite"
# messages = [{ "content": user_message,"role": "user"}]
# response = completion(
# model="huggingface/roneneldan/TinyStories-3M",
# messages=messages,
# api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud",
# )
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# hf_test_completion_none_task()
def mock_post(url, **kwargs):
print(f"url={url}")
if "text-classification" in url:
raise Exception("Model not found")
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.headers = {"Content-Type": "application/json"}
mock_response.json.return_value = [
[
{"label": "LABEL_0", "score": 0.9990691542625427},
{"label": "LABEL_1", "score": 0.0009308889275416732},
]
]
return mock_response
def test_hf_classifier_task():
try:
with patch("requests.post", side_effect=mock_post):
litellm.set_verbose = True
user_message = "I like you. I love you"
messages = [{"content": user_message, "role": "user"}]
response = completion(
model="huggingface/text-classification/shahrukhx01/question-vs-statement-classifier",
messages=messages,
)
print(f"response: {response}")
assert isinstance(response, litellm.ModelResponse)
assert isinstance(response.choices[0], litellm.Choices)
assert response.choices[0].message.content is not None
assert isinstance(response.choices[0].message.content, str)
except Exception as e:
pytest.fail(f"Error occurred: {str(e)}")
def test_ollama_image():
"""
Test that datauri prefixes are removed, JPEG/PNG images are passed
through, and other image formats are converted to JPEG. Non-image
data is untouched.
"""
import base64
import io
from PIL import Image
def mock_post(url, **kwargs):
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.headers = {"Content-Type": "application/json"}
mock_response.json.return_value = {
# return the image in the response so that it can be tested
# against the original
"response": kwargs["json"]["images"]
}
return mock_response
def make_b64image(format):
image = Image.new(mode="RGB", size=(1, 1))
image_buffer = io.BytesIO()
image.save(image_buffer, format)
return base64.b64encode(image_buffer.getvalue()).decode("utf-8")
jpeg_image = make_b64image("JPEG")
webp_image = make_b64image("WEBP")
png_image = make_b64image("PNG")
base64_data = base64.b64encode(b"some random data")
datauri_base64_data = f"data:text/plain;base64,{base64_data}"
tests = [
# input expected
[jpeg_image, jpeg_image],
[webp_image, None],
[png_image, png_image],
[f"data:image/jpeg;base64,{jpeg_image}", jpeg_image],
[f"data:image/webp;base64,{webp_image}", None],
[f"data:image/png;base64,{png_image}", png_image],
[datauri_base64_data, datauri_base64_data],
]
for test in tests:
try:
with patch("requests.post", side_effect=mock_post):
response = completion(
model="ollama/llava",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Whats in this image?"},
{
"type": "image_url",
"image_url": {"url": test[0]},
},
],
}
],
)
if not test[1]:
# the conversion process may not always generate the same image,
# so just check for a JPEG image when a conversion was done.
image_data = response["choices"][0]["message"]["content"][0]
image = Image.open(io.BytesIO(base64.b64decode(image_data)))
assert image.format == "JPEG"
else:
assert response["choices"][0]["message"]["content"][0] == test[1]
except Exception as e:
pytest.fail(f"Error occurred: {e}")
########################### End of Hugging Face Tests ##############################################
# def test_completion_hf_api():
# # failing on circle-ci commenting out
# try:
# user_message = "write some code to find the sum of two numbers"
# messages = [{ "content": user_message,"role": "user"}]
# api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud"
# response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# if "loading" in str(e):
# pass
# pytest.fail(f"Error occurred: {e}")
# test_completion_hf_api()
# def test_completion_hf_api_best_of():
# # failing on circle ci commenting out
# try:
# user_message = "write some code to find the sum of two numbers"
# messages = [{ "content": user_message,"role": "user"}]
# api_base = "https://a8l9e3ucxinyl3oj.us-east-1.aws.endpoints.huggingface.cloud"
# response = completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", messages=messages, api_base=api_base, n=2)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# if "loading" in str(e):
# pass
# pytest.fail(f"Error occurred: {e}")
# test_completion_hf_api_best_of()
# def test_completion_hf_deployed_api():
# try:
# user_message = "There's a llama in my garden 😱 What should I do?"
# messages = [{ "content": user_message,"role": "user"}]
# response = completion(model="huggingface/https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud", messages=messages, logger_fn=logger_fn)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# this should throw an exception, to trigger https://logs.litellm.ai/
# def hf_test_error_logs():
# try:
# litellm.set_verbose=True
# user_message = "My name is Merve and my favorite"
# messages = [{ "content": user_message,"role": "user"}]
# response = completion(
# model="huggingface/roneneldan/TinyStories-3M",
# messages=messages,
# api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud",
# )
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# hf_test_error_logs()
# def test_completion_cohere(): # commenting out,for now as the cohere endpoint is being flaky
# try:
# litellm.CohereConfig(max_tokens=10, stop_sequences=["a"])
# response = completion(
# model="command-nightly", messages=messages, logger_fn=logger_fn
# )
# # Add any assertions here to check the response
# print(response)
# response_str = response["choices"][0]["message"]["content"]
# response_str_2 = response.choices[0].message.content
# if type(response_str) != str:
# pytest.fail(f"Error occurred: {e}")
# if type(response_str_2) != str:
# pytest.fail(f"Error occurred: {e}")
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_cohere()
def test_completion_openai():
try:
litellm.set_verbose = True
litellm.drop_params = True
print(f"api key: {os.environ['OPENAI_API_KEY']}")
litellm.api_key = os.environ["OPENAI_API_KEY"]
response = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey"}],
max_tokens=10,
metadata={"hi": "bye"},
)
print("This is the response object\n", response)
response_str = response["choices"][0]["message"]["content"]
response_str_2 = response.choices[0].message.content
cost = completion_cost(completion_response=response)
print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")
assert response_str == response_str_2
assert type(response_str) == str
assert len(response_str) > 1
litellm.api_key = None
except Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize(
"model, api_version",
[
("gpt-4o-2024-08-06", None),
("azure/chatgpt-v-2", None),
("bedrock/anthropic.claude-3-sonnet-20240229-v1:0", None),
("azure/gpt-4o", "2024-08-01-preview"),
],
)
@pytest.mark.flaky(retries=3, delay=1)
def test_completion_openai_pydantic(model, api_version):
try:
litellm.set_verbose = True
from pydantic import BaseModel
messages = [
{"role": "user", "content": "List 5 important events in the XIX century"}
]
class CalendarEvent(BaseModel):
name: str
date: str
participants: list[str]
class EventsList(BaseModel):
events: list[CalendarEvent]
litellm.enable_json_schema_validation = True
for _ in range(3):
try:
response = completion(
model=model,
messages=messages,
metadata={"hi": "bye"},
response_format=EventsList,
api_version=api_version,
)
break
except litellm.JSONSchemaValidationError:
pytest.fail("ERROR OCCURRED! INVALID JSON")
print("This is the response object\n", response)
response_str = response["choices"][0]["message"]["content"]
print(f"response_str: {response_str}")
json.loads(response_str) # check valid json is returned
except Timeout:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_openai_organization():
try:
litellm.set_verbose = True
try:
response = completion(
model="gpt-3.5-turbo", messages=messages, organization="org-ikDc4ex8NB"
)
pytest.fail("Request should have failed - This organization does not exist")
except Exception as e:
assert "No such organization: org-ikDc4ex8NB" in str(e)
except Exception as e:
print(e)
pytest.fail(f"Error occurred: {e}")
def test_completion_text_openai():
try:
# litellm.set_verbose =True
response = completion(model="gpt-3.5-turbo-instruct", messages=messages)
print(response["choices"][0]["message"]["content"])
except Exception as e:
print(e)
pytest.fail(f"Error occurred: {e}")
@pytest.mark.asyncio
async def test_completion_text_openai_async():
try:
# litellm.set_verbose =True
response = await litellm.acompletion(
model="gpt-3.5-turbo-instruct", messages=messages
)
print(response["choices"][0]["message"]["content"])
except Exception as e:
print(e)
pytest.fail(f"Error occurred: {e}")
def custom_callback(
kwargs, # kwargs to completion
completion_response, # response from completion
start_time,
end_time, # start/end time
):
# Your custom code here
try:
print("LITELLM: in custom callback function")
print("\nkwargs\n", kwargs)
model = kwargs["model"]
messages = kwargs["messages"]
user = kwargs.get("user")
#################################################
print(
f"""
Model: {model},
Messages: {messages},
User: {user},
Seed: {kwargs["seed"]},
temperature: {kwargs["temperature"]},
"""
)
assert kwargs["user"] == "ishaans app"
assert kwargs["model"] == "gpt-3.5-turbo-1106"
assert kwargs["seed"] == 12
assert kwargs["temperature"] == 0.5
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_openai_with_optional_params():
# [Proxy PROD TEST] WARNING: DO NOT DELETE THIS TEST
# assert that `user` gets passed to the completion call
# Note: This tests that we actually send the optional params to the completion call
# We use custom callbacks to test this
try:
litellm.set_verbose = True
litellm.success_callback = [custom_callback]
response = completion(
model="gpt-3.5-turbo-1106",
messages=[
{"role": "user", "content": "respond in valid, json - what is the day"}
],
temperature=0.5,
top_p=0.1,
seed=12,
response_format={"type": "json_object"},
logit_bias=None,
user="ishaans app",
)
# Add any assertions here to check the response
print(response)
litellm.success_callback = [] # unset callbacks
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_openai_with_optional_params()
def test_completion_logprobs():
"""
This function is used to test the litellm.completion logprobs functionality.
Parameters:
None
Returns:
None
"""
try:
litellm.set_verbose = True
response = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "what is the time"}],
temperature=0.5,
top_p=0.1,
seed=12,
logit_bias=None,
user="ishaans app",
logprobs=True,
top_logprobs=3,
)
# Add any assertions here to check the response
print(response)
print(len(response.choices[0].logprobs["content"][0]["top_logprobs"]))
assert "logprobs" in response.choices[0]
assert "content" in response.choices[0]["logprobs"]
assert len(response.choices[0].logprobs["content"][0]["top_logprobs"]) == 3
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_logprobs()
def test_completion_logprobs_stream():
"""
This function is used to test the litellm.completion logprobs functionality.
Parameters:
None
Returns:
None
"""
try:
litellm.set_verbose = False
response = completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "what is the time"}],
temperature=0.5,
top_p=0.1,
seed=12,
max_tokens=5,
logit_bias=None,
user="ishaans app",
logprobs=True,
top_logprobs=3,
stream=True,
)
# Add any assertions here to check the response
print(response)
found_logprob = False
for chunk in response:
# check if atleast one chunk has log probs
print(chunk)
print(f"chunk.choices[0]: {chunk.choices[0]}")
if "logprobs" in chunk.choices[0]:
# assert we got a valid logprob in the choices
assert len(chunk.choices[0].logprobs.content[0].top_logprobs) == 3
found_logprob = True
break
print(chunk)
assert found_logprob == True
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_logprobs_stream()
def test_completion_openai_litellm_key():
try:
litellm.set_verbose = True
litellm.num_retries = 0
litellm.api_key = os.environ["OPENAI_API_KEY"]
# ensure key is set to None in .env and in openai.api_key
os.environ["OPENAI_API_KEY"] = ""
import openai
openai.api_key = ""
##########################################################
response = completion(
model="gpt-3.5-turbo",
messages=messages,
temperature=0.5,
top_p=0.1,
max_tokens=10,
user="ishaan_dev@berri.ai",
)
# Add any assertions here to check the response
print(response)
###### reset environ key
os.environ["OPENAI_API_KEY"] = litellm.api_key
##### unset litellm var
litellm.api_key = None
except Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_ completion_openai_litellm_key()
@pytest.mark.skip(reason="Unresponsive endpoint.[TODO] Rehost this somewhere else")
def test_completion_ollama_hosted():
try:
litellm.request_timeout = 20 # give ollama 20 seconds to response
litellm.set_verbose = True
response = completion(
model="ollama/phi",
messages=messages,
max_tokens=2,
api_base="https://test-ollama-endpoint.onrender.com",
)
# Add any assertions here to check the response
print(response)
except openai.APITimeoutError as e:
print("got a timeout error. Passed ! ")
litellm.request_timeout = None
pass
except Exception as e:
if "try pulling it first" in str(e):
return
pytest.fail(f"Error occurred: {e}")
# test_completion_ollama_hosted()
@pytest.mark.skip(reason="Local test")
@pytest.mark.parametrize(
("model"),
[
"ollama/llama2",
"ollama_chat/llama2",
],
)
def test_completion_ollama_function_call(model):
messages = [
{"role": "user", "content": "What's the weather like in San Francisco?"}
]
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
try:
litellm.set_verbose = True
response = litellm.completion(model=model, messages=messages, tools=tools)
print(response)
assert response.choices[0].message.tool_calls
assert (
response.choices[0].message.tool_calls[0].function.name
== "get_current_weather"
)
assert response.choices[0].finish_reason == "tool_calls"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="Local test")
@pytest.mark.parametrize(
("model"),
[
"ollama/llama2",
"ollama_chat/llama2",
],
)
def test_completion_ollama_function_call_stream(model):
messages = [
{"role": "user", "content": "What's the weather like in San Francisco?"}
]
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
try:
litellm.set_verbose = True
response = litellm.completion(
model=model, messages=messages, tools=tools, stream=True
)
print(response)
first_chunk = next(response)
assert first_chunk.choices[0].delta.tool_calls
assert (
first_chunk.choices[0].delta.tool_calls[0].function.name
== "get_current_weather"
)
assert first_chunk.choices[0].finish_reason == "tool_calls"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="local test")
@pytest.mark.parametrize(
("model"),
[
"ollama/llama2",
"ollama_chat/llama2",
],
)
@pytest.mark.asyncio
async def test_acompletion_ollama_function_call(model):
messages = [
{"role": "user", "content": "What's the weather like in San Francisco?"}
]
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
try:
litellm.set_verbose = True
response = await litellm.acompletion(
model=model, messages=messages, tools=tools
)
print(response)
assert response.choices[0].message.tool_calls
assert (
response.choices[0].message.tool_calls[0].function.name
== "get_current_weather"
)
assert response.choices[0].finish_reason == "tool_calls"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="local test")
@pytest.mark.parametrize(
("model"),
[
"ollama/llama2",
"ollama_chat/llama2",
],
)
@pytest.mark.asyncio
async def test_acompletion_ollama_function_call_stream(model):
messages = [
{"role": "user", "content": "What's the weather like in San Francisco?"}
]
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
try:
litellm.set_verbose = True
response = await litellm.acompletion(
model=model, messages=messages, tools=tools, stream=True
)
print(response)
first_chunk = await anext(response)
assert first_chunk.choices[0].delta.tool_calls
assert (
first_chunk.choices[0].delta.tool_calls[0].function.name
== "get_current_weather"
)
assert first_chunk.choices[0].finish_reason == "tool_calls"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_completion_openrouter1():
try:
litellm.set_verbose = True
response = completion(
model="openrouter/mistralai/mistral-tiny",
messages=messages,
max_tokens=5,
)
# Add any assertions here to check the response
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_openrouter1()
def test_completion_hf_model_no_provider():
try:
response = completion(
model="WizardLM/WizardLM-70B-V1.0",
messages=messages,
max_tokens=5,
)
# Add any assertions here to check the response
print(response)
pytest.fail(f"Error occurred: {e}")
except Exception as e:
pass
# test_completion_hf_model_no_provider()
def gemini_mock_post(*args, **kwargs):
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.headers = {"Content-Type": "application/json"}
mock_response.json = MagicMock(
return_value={
"candidates": [
{
"content": {
"parts": [
{
"functionCall": {
"name": "get_current_weather",
"args": {"location": "Boston, MA"},
}
}
],
"role": "model",
},
"finishReason": "STOP",
"index": 0,
"safetyRatings": [
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"probability": "NEGLIGIBLE",
},
{
"category": "HARM_CATEGORY_HARASSMENT",
"probability": "NEGLIGIBLE",
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"probability": "NEGLIGIBLE",
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"probability": "NEGLIGIBLE",
},
],
}
],
"usageMetadata": {
"promptTokenCount": 86,
"candidatesTokenCount": 19,
"totalTokenCount": 105,
},
}
)
return mock_response
@pytest.mark.asyncio
async def test_completion_functions_param():
litellm.set_verbose = True
function1 = [
{
"name": "get_current_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"]},
},
"required": ["location"],
},
}
]
try:
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
messages = [{"role": "user", "content": "What is the weather like in Boston?"}]
client = AsyncHTTPHandler(concurrent_limit=1)
with patch.object(client, "post", side_effect=gemini_mock_post) as mock_client:
response: litellm.ModelResponse = await litellm.acompletion(
model="gemini/gemini-1.5-pro",
messages=messages,
functions=function1,
client=client,
)
print(response)
# Add any assertions here to check the response
mock_client.assert_called()
print(f"mock_client.call_args.kwargs: {mock_client.call_args.kwargs}")
assert "tools" in mock_client.call_args.kwargs["json"]
assert (
"litellm_param_is_function_call"
not in mock_client.call_args.kwargs["json"]
)
assert (
"litellm_param_is_function_call"
not in mock_client.call_args.kwargs["json"]["generationConfig"]
)
assert response.choices[0].message.function_call is not None
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_anyscale_with_functions()
def test_completion_azure_extra_headers():
# this tests if we can pass api_key to completion, when it's not in the env.
# DO NOT REMOVE THIS TEST. No MATTER WHAT Happens!
# If you want to remove it, speak to Ishaan!
# Ishaan will be very disappointed if this test is removed -> this is a standard way to pass api_key + the router + proxy use this
from httpx import Client
from openai import AzureOpenAI
from litellm.llms.custom_httpx.httpx_handler import HTTPHandler
http_client = Client()
with patch.object(http_client, "send", new=MagicMock()) as mock_client:
litellm.client_session = http_client
try:
response = completion(
model="azure/chatgpt-v-2",
messages=messages,
api_base=os.getenv("AZURE_API_BASE"),
api_version="2023-07-01-preview",
api_key=os.getenv("AZURE_API_KEY"),
extra_headers={
"Authorization": "my-bad-key",
"Ocp-Apim-Subscription-Key": "hello-world-testing",
},
)
print(response)
pytest.fail("Expected this to fail")
except Exception as e:
pass
mock_client.assert_called()
print(f"mock_client.call_args: {mock_client.call_args}")
request = mock_client.call_args[0][0]
print(request.method) # This will print 'POST'
print(request.url) # This will print the full URL
print(request.headers) # This will print the full URL
auth_header = request.headers.get("Authorization")
apim_key = request.headers.get("Ocp-Apim-Subscription-Key")
print(auth_header)
assert auth_header == "my-bad-key"
assert apim_key == "hello-world-testing"
def test_completion_azure_ad_token():
# this tests if we can pass api_key to completion, when it's not in the env.
# DO NOT REMOVE THIS TEST. No MATTER WHAT Happens!
# If you want to remove it, speak to Ishaan!
# Ishaan will be very disappointed if this test is removed -> this is a standard way to pass api_key + the router + proxy use this
from httpx import Client
from litellm import completion
litellm.set_verbose = True
old_key = os.environ["AZURE_API_KEY"]
os.environ.pop("AZURE_API_KEY", None)
http_client = Client()
with patch.object(http_client, "send", new=MagicMock()) as mock_client:
litellm.client_session = http_client
try:
response = completion(
model="azure/chatgpt-v-2",
messages=messages,
azure_ad_token="my-special-token",
)
print(response)
except Exception as e:
pass
finally:
os.environ["AZURE_API_KEY"] = old_key
mock_client.assert_called_once()
request = mock_client.call_args[0][0]
print(request.method) # This will print 'POST'
print(request.url) # This will print the full URL
print(request.headers) # This will print the full URL
auth_header = request.headers.get("Authorization")
assert auth_header == "Bearer my-special-token"
def test_completion_azure_key_completion_arg():
# this tests if we can pass api_key to completion, when it's not in the env.
# DO NOT REMOVE THIS TEST. No MATTER WHAT Happens!
# If you want to remove it, speak to Ishaan!
# Ishaan will be very disappointed if this test is removed -> this is a standard way to pass api_key + the router + proxy use this
old_key = os.environ["AZURE_API_KEY"]
os.environ.pop("AZURE_API_KEY", None)
try:
print("azure gpt-3.5 test\n\n")
litellm.set_verbose = True
## Test azure call
response = completion(
model="azure/chatgpt-v-2",
messages=messages,
api_key=old_key,
logprobs=True,
max_tokens=10,
)
print(f"response: {response}")
print("Hidden Params", response._hidden_params)
assert response._hidden_params["custom_llm_provider"] == "azure"
os.environ["AZURE_API_KEY"] = old_key
except Exception as e:
os.environ["AZURE_API_KEY"] = old_key
pytest.fail(f"Error occurred: {e}")
# test_completion_azure_key_completion_arg()
def test_azure_instruct():
litellm.set_verbose = True
response = completion(
model="azure_text/instruct-model",
messages=[{"role": "user", "content": "What is the weather like in Boston?"}],
max_tokens=10,
)
print("response", response)
@pytest.mark.asyncio
async def test_azure_instruct_stream():
litellm.set_verbose = False
response = await litellm.acompletion(
model="azure_text/instruct-model",
messages=[{"role": "user", "content": "What is the weather like in Boston?"}],
max_tokens=10,
stream=True,
)
print("response", response)
async for chunk in response:
print(chunk)
async def test_re_use_azure_async_client():
try:
print("azure gpt-3.5 ASYNC with clie nttest\n\n")
litellm.set_verbose = True
import openai
client = openai.AsyncAzureOpenAI(
azure_endpoint=os.environ["AZURE_API_BASE"],
api_key=os.environ["AZURE_API_KEY"],
api_version="2023-07-01-preview",
)
## Test azure call
for _ in range(3):
response = await litellm.acompletion(
model="azure/chatgpt-v-2", messages=messages, client=client
)
print(f"response: {response}")
except Exception as e:
pytest.fail("got Exception", e)
def test_re_use_openaiClient():
try:
print("gpt-3.5 with client test\n\n")
litellm.set_verbose = True
import openai
client = openai.OpenAI(
api_key=os.environ["OPENAI_API_KEY"],
)
## Test OpenAI call
for _ in range(2):
response = litellm.completion(
model="gpt-3.5-turbo", messages=messages, client=client
)
print(f"response: {response}")
except Exception as e:
pytest.fail("got Exception", e)
def test_completion_azure():
try:
print("azure gpt-3.5 test\n\n")
litellm.set_verbose = False
## Test azure call
response = completion(
model="azure/chatgpt-v-2",
messages=messages,
api_key="os.environ/AZURE_API_KEY",
)
print(f"response: {response}")
print(f"response hidden params: {response._hidden_params}")
## Test azure flag for backwards-compat
# response = completion(
# model="chatgpt-v-2",
# messages=messages,
# azure=True,
# max_tokens=10
# )
# Add any assertions here to check the response
print(response)
cost = completion_cost(completion_response=response)
assert cost > 0.0
print("Cost for azure completion request", cost)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_azure()
def test_azure_openai_ad_token():
# this tests if the azure ad token is set in the request header
# the request can fail since azure ad tokens expire after 30 mins, but the header MUST have the azure ad token
# we use litellm.input_callbacks for this test
def tester(
kwargs, # kwargs to completion
):
print(kwargs["additional_args"])
if kwargs["additional_args"]["headers"]["Authorization"] != "Bearer gm":
pytest.fail("AZURE AD TOKEN Passed but not set in request header")
return
litellm.input_callback = [tester]
try:
response = litellm.completion(
model="azure/chatgpt-v-2", # e.g. gpt-35-instant
messages=[
{
"role": "user",
"content": "what is your name",
},
],
azure_ad_token="gm",
)
print("azure ad token respoonse\n")
print(response)
litellm.input_callback = []
except Exception as e:
litellm.input_callback = []
pytest.fail(f"An exception occurs - {str(e)}")
# test_azure_openai_ad_token()
# test_completion_azure()
def test_completion_azure2():
# test if we can pass api_base, api_version and api_key in compleition()
try:
print("azure gpt-3.5 test\n\n")
litellm.set_verbose = False
api_base = os.environ["AZURE_API_BASE"]
api_key = os.environ["AZURE_API_KEY"]
api_version = os.environ["AZURE_API_VERSION"]
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
os.environ["AZURE_API_KEY"] = ""
## Test azure call
response = completion(
model="azure/chatgpt-v-2",
messages=messages,
api_base=api_base,
api_key=api_key,
api_version=api_version,
max_tokens=10,
)
# Add any assertions here to check the response
print(response)
os.environ["AZURE_API_BASE"] = api_base
os.environ["AZURE_API_VERSION"] = api_version
os.environ["AZURE_API_KEY"] = api_key
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_azure2()
def test_completion_azure3():
# test if we can pass api_base, api_version and api_key in compleition()
try:
print("azure gpt-3.5 test\n\n")
litellm.set_verbose = True
litellm.api_base = os.environ["AZURE_API_BASE"]
litellm.api_key = os.environ["AZURE_API_KEY"]
litellm.api_version = os.environ["AZURE_API_VERSION"]
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
os.environ["AZURE_API_KEY"] = ""
## Test azure call
response = completion(
model="azure/chatgpt-v-2",
messages=messages,
max_tokens=10,
)
# Add any assertions here to check the response
print(response)
os.environ["AZURE_API_BASE"] = litellm.api_base
os.environ["AZURE_API_VERSION"] = litellm.api_version
os.environ["AZURE_API_KEY"] = litellm.api_key
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_azure3()
# new azure test for using litellm. vars,
# use the following vars in this test and make an azure_api_call
# litellm.api_type = self.azure_api_type
# litellm.api_base = self.azure_api_base
# litellm.api_version = self.azure_api_version
# litellm.api_key = self.api_key
def test_completion_azure_with_litellm_key():
try:
print("azure gpt-3.5 test\n\n")
import openai
#### set litellm vars
litellm.api_type = "azure"
litellm.api_base = os.environ["AZURE_API_BASE"]
litellm.api_version = os.environ["AZURE_API_VERSION"]
litellm.api_key = os.environ["AZURE_API_KEY"]
######### UNSET ENV VARs for this ################
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
os.environ["AZURE_API_KEY"] = ""
######### UNSET OpenAI vars for this ##############
openai.api_type = ""
openai.api_base = "gm"
openai.api_version = "333"
openai.api_key = "ymca"
response = completion(
model="azure/chatgpt-v-2",
messages=messages,
)
# Add any assertions here to check the response
print(response)
######### RESET ENV VARs for this ################
os.environ["AZURE_API_BASE"] = litellm.api_base
os.environ["AZURE_API_VERSION"] = litellm.api_version
os.environ["AZURE_API_KEY"] = litellm.api_key
######### UNSET litellm vars
litellm.api_type = None
litellm.api_base = None
litellm.api_version = None
litellm.api_key = None
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_azure()
def test_completion_azure_deployment_id():
try:
litellm.set_verbose = True
response = completion(
deployment_id="chatgpt-v-2",
model="gpt-3.5-turbo",
messages=messages,
)
# Add any assertions here to check the response
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_azure_deployment_id()
import asyncio
@pytest.mark.parametrize("sync_mode", [False, True])
@pytest.mark.asyncio
async def test_completion_replicate_llama3(sync_mode):
litellm.set_verbose = True
model_name = "replicate/meta/meta-llama-3-8b-instruct"
try:
if sync_mode:
response = completion(
model=model_name,
messages=messages,
)
else:
response = await litellm.acompletion(
model=model_name,
messages=messages,
)
print(f"ASYNC REPLICATE RESPONSE - {response}")
print(response)
# Add any assertions here to check the response
assert isinstance(response, litellm.ModelResponse)
response_format_tests(response=response)
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="replicate endpoints take +2 mins just for this request")
def test_completion_replicate_vicuna():
print("TESTING REPLICATE")
litellm.set_verbose = True
model_name = "replicate/meta/llama-2-7b-chat:f1d50bb24186c52daae319ca8366e53debdaa9e0ae7ff976e918df752732ccc4"
try:
response = completion(
model=model_name,
messages=messages,
temperature=0.5,
top_k=20,
repetition_penalty=1,
min_tokens=1,
seed=-1,
max_tokens=2,
)
print(response)
# Add any assertions here to check the response
response_str = response["choices"][0]["message"]["content"]
print("RESPONSE STRING\n", response_str)
if type(response_str) != str:
pytest.fail(f"Error occurred: {e}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_replicate_vicuna()
def test_replicate_custom_prompt_dict():
litellm.set_verbose = True
model_name = "replicate/meta/llama-2-7b"
litellm.register_prompt_template(
model="replicate/meta/llama-2-7b",
initial_prompt_value="You are a good assistant", # [OPTIONAL]
roles={
"system": {
"pre_message": "[INST] <>\n", # [OPTIONAL]
"post_message": "\n<>\n [/INST]\n", # [OPTIONAL]
},
"user": {
"pre_message": "[INST] ", # [OPTIONAL]
"post_message": " [/INST]", # [OPTIONAL]
},
"assistant": {
"pre_message": "\n", # [OPTIONAL]
"post_message": "\n", # [OPTIONAL]
},
},
final_prompt_value="Now answer as best you can:", # [OPTIONAL]
)
try:
response = completion(
model=model_name,
messages=[
{
"role": "user",
"content": "what is yc write 1 paragraph",
}
],
mock_response="Hello world",
repetition_penalty=0.1,
num_retries=3,
)
except litellm.APIError as e:
pass
except litellm.APIConnectionError as e:
pass
except Exception as e:
pytest.fail(f"An exception occurred - {str(e)}")
print(f"response: {response}")
litellm.custom_prompt_dict = {} # reset
# test_replicate_custom_prompt_dict()
# commenthing this out since we won't be always testing a custom, replicate deployment
# def test_completion_replicate_deployments():
# print("TESTING REPLICATE")
# litellm.set_verbose=False
# model_name = "replicate/deployments/ishaan-jaff/ishaan-mistral"
# try:
# response = completion(
# model=model_name,
# messages=messages,
# temperature=0.5,
# seed=-1,
# )
# print(response)
# # Add any assertions here to check the response
# response_str = response["choices"][0]["message"]["content"]
# print("RESPONSE STRING\n", response_str)
# if type(response_str) != str:
# pytest.fail(f"Error occurred: {e}")
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_replicate_deployments()
######## Test TogetherAI ########
@pytest.mark.skip(reason="Skip flaky test")
def test_completion_together_ai_mixtral():
model_name = "together_ai/DiscoResearch/DiscoLM-mixtral-8x7b-v2"
try:
messages = [
{"role": "user", "content": "Who are you"},
{"role": "assistant", "content": "I am your helpful assistant."},
{"role": "user", "content": "Tell me a joke"},
]
response = completion(
model=model_name,
messages=messages,
max_tokens=256,
n=1,
logger_fn=logger_fn,
)
# Add any assertions here to check the response
print(response)
cost = completion_cost(completion_response=response)
assert cost > 0.0
print(
"Cost for completion call together-computer/llama-2-70b: ",
f"${float(cost):.10f}",
)
except litellm.Timeout as e:
pass
except litellm.ServiceUnavailableError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_together_ai_mixtral()
def test_completion_together_ai_llama():
litellm.set_verbose = True
model_name = "together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
try:
messages = [
{"role": "user", "content": "What llm are you?"},
]
response = completion(model=model_name, messages=messages, max_tokens=5)
# Add any assertions here to check the response
print(response)
cost = completion_cost(completion_response=response)
assert cost > 0.0
print(
"Cost for completion call together-computer/llama-2-70b: ",
f"${float(cost):.10f}",
)
except litellm.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_together_ai_yi_chat()
# test_completion_together_ai()
def test_customprompt_together_ai():
try:
litellm.set_verbose = False
litellm.num_retries = 0
print("in test_customprompt_together_ai")
print(litellm.success_callback)
print(litellm._async_success_callback)
response = completion(
model="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
messages=messages,
roles={
"system": {
"pre_message": "<|im_start|>system\n",
"post_message": "<|im_end|>",
},
"assistant": {
"pre_message": "<|im_start|>assistant\n",
"post_message": "<|im_end|>",
},
"user": {
"pre_message": "<|im_start|>user\n",
"post_message": "<|im_end|>",
},
},
)
print(response)
except litellm.exceptions.Timeout as e:
print(f"Timeout Error")
pass
except Exception as e:
print(f"ERROR TYPE {type(e)}")
pytest.fail(f"Error occurred: {e}")
# test_customprompt_together_ai()
def response_format_tests(response: litellm.ModelResponse):
assert isinstance(response.id, str)
assert response.id != ""
assert isinstance(response.object, str)
assert response.object != ""
assert isinstance(response.created, int)
assert isinstance(response.model, str)
assert response.model != ""
assert isinstance(response.choices, list)
assert len(response.choices) == 1
choice = response.choices[0]
assert isinstance(choice, litellm.Choices)
assert isinstance(choice.get("index"), int)
message = choice.get("message")
assert isinstance(message, litellm.Message)
assert isinstance(message.get("role"), str)
assert message.get("role") != ""
assert isinstance(message.get("content"), str)
assert message.get("content") != ""
assert choice.get("logprobs") is None
assert isinstance(choice.get("finish_reason"), str)
assert choice.get("finish_reason") != ""
assert isinstance(response.usage, litellm.Usage) # type: ignore
assert isinstance(response.usage.prompt_tokens, int) # type: ignore
assert isinstance(response.usage.completion_tokens, int) # type: ignore
assert isinstance(response.usage.total_tokens, int) # type: ignore
@pytest.mark.parametrize(
"model",
[
"bedrock/mistral.mistral-large-2407-v1:0",
"bedrock/cohere.command-r-plus-v1:0",
"anthropic.claude-3-sonnet-20240229-v1:0",
"anthropic.claude-instant-v1",
"mistral.mistral-7b-instruct-v0:2",
# "bedrock/amazon.titan-tg1-large",
"meta.llama3-8b-instruct-v1:0",
],
)
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_completion_bedrock_httpx_models(sync_mode, model):
litellm.set_verbose = True
try:
if sync_mode:
response = completion(
model=model,
messages=[{"role": "user", "content": "Hey! how's it going?"}],
temperature=0.2,
max_tokens=200,
stop=["stop sequence"],
)
assert isinstance(response, litellm.ModelResponse)
response_format_tests(response=response)
else:
response = await litellm.acompletion(
model=model,
messages=[{"role": "user", "content": "Hey! how's it going?"}],
temperature=0.2,
max_tokens=100,
stop=["stop sequence"],
)
assert isinstance(response, litellm.ModelResponse)
print(f"response: {response}")
response_format_tests(response=response)
print(f"response: {response}")
except litellm.RateLimitError as e:
print("got rate limit error=", e)
pass
except Exception as e:
pytest.fail(f"An error occurred - {str(e)}")
def test_completion_bedrock_titan_null_response():
try:
response = completion(
model="bedrock/amazon.titan-text-lite-v1",
messages=[
{
"role": "user",
"content": "Hello!",
},
{
"role": "assistant",
"content": "Hello! How can I help you?",
},
{
"role": "user",
"content": "What model are you?",
},
],
)
# Add any assertions here to check the response
print(f"response: {response}")
except Exception as e:
pytest.fail(f"An error occurred - {str(e)}")
# test_completion_bedrock_titan()
# test_completion_bedrock_claude()
# test_completion_bedrock_cohere()
# def test_completion_bedrock_claude_stream():
# print("calling claude")
# litellm.set_verbose = False
# try:
# response = completion(
# model="bedrock/anthropic.claude-instant-v1",
# messages=messages,
# stream=True
# )
# # Add any assertions here to check the response
# print(response)
# for chunk in response:
# print(chunk)
# except RateLimitError:
# pass
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_bedrock_claude_stream()
######## Test VLLM ########
# def test_completion_vllm():
# try:
# response = completion(
# model="vllm/facebook/opt-125m",
# messages=messages,
# temperature=0.2,
# max_tokens=80,
# )
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_vllm()
# def test_completion_hosted_chatCompletion():
# # this tests calling a server where vllm is hosted
# # this should make an openai.Completion() call to the specified api_base
# # send a request to this proxy server: https://replit.com/@BerriAI/openai-proxy#main.py
# # it checks if model == facebook/opt-125m and returns test passed
# try:
# litellm.set_verbose = True
# response = completion(
# model="facebook/opt-125m",
# messages=messages,
# temperature=0.2,
# max_tokens=80,
# api_base="https://openai-proxy.berriai.repl.co",
# custom_llm_provider="openai"
# )
# print(response)
# if response['choices'][0]['message']['content'] != "passed":
# # see https://replit.com/@BerriAI/openai-proxy#main.py
# pytest.fail(f"Error occurred: proxy server did not respond")
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_hosted_chatCompletion()
# def test_completion_custom_api_base():
# try:
# response = completion(
# model="custom/meta-llama/Llama-2-13b-hf",
# messages=messages,
# temperature=0.2,
# max_tokens=10,
# api_base="https://api.autoai.dev/inference",
# request_timeout=300,
# )
# # Add any assertions here to check the response
# print("got response\n", response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_completion_custom_api_base()
def test_completion_with_fallbacks():
print(f"RUNNING TEST COMPLETION WITH FALLBACKS - test_completion_with_fallbacks")
fallbacks = ["gpt-3.5-turbo", "gpt-3.5-turbo", "command-nightly"]
try:
response = completion(
model="bad-model", messages=messages, force_timeout=120, fallbacks=fallbacks
)
# Add any assertions here to check the response
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_with_fallbacks()
# @pytest.mark.parametrize(
# "function_call",
# [
# [{"role": "function", "name": "get_capital", "content": "Kokoko"}],
# [
# {"role": "function", "name": "get_capital", "content": "Kokoko"},
# {"role": "function", "name": "get_capital", "content": "Kokoko"},
# ],
# ],
# )
# @pytest.mark.parametrize(
# "tool_call",
# [
# [{"role": "tool", "tool_call_id": "1234", "content": "Kokoko"}],
# [
# {"role": "tool", "tool_call_id": "12344", "content": "Kokoko"},
# {"role": "tool", "tool_call_id": "1214", "content": "Kokoko"},
# ],
# ],
# )
def test_completion_anthropic_hanging():
litellm.set_verbose = True
litellm.modify_params = True
messages = [
{
"role": "user",
"content": "What's the capital of fictional country Ubabababababaaba? Use your tools.",
},
{
"role": "assistant",
"function_call": {
"name": "get_capital",
"arguments": '{"country": "Ubabababababaaba"}',
},
},
{"role": "function", "name": "get_capital", "content": "Kokoko"},
]
converted_messages = anthropic_messages_pt(
messages, model="claude-3-sonnet-20240229", llm_provider="anthropic"
)
print(f"converted_messages: {converted_messages}")
## ENSURE USER / ASSISTANT ALTERNATING
for i, msg in enumerate(converted_messages):
if i < len(converted_messages) - 1:
assert msg["role"] != converted_messages[i + 1]["role"]
@pytest.mark.skip(reason="anyscale stopped serving public api endpoints")
def test_completion_anyscale_api():
try:
# litellm.set_verbose = True
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="anyscale/meta-llama/Llama-2-7b-chat-hf",
messages=messages,
)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_anyscale_api()
# @pytest.mark.skip(reason="flaky test, times out frequently")
@pytest.mark.flaky(retries=6, delay=1)
def test_completion_cohere():
try:
# litellm.set_verbose=True
messages = [
{"role": "system", "content": "You're a good bot"},
{"role": "assistant", "content": [{"text": "2", "type": "text"}]},
{"role": "assistant", "content": [{"text": "3", "type": "text"}]},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="command-r",
messages=messages,
extra_headers={"Helicone-Property-Locale": "ko"},
)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# FYI - cohere_chat looks quite unstable, even when testing locally
def test_chat_completion_cohere():
try:
litellm.set_verbose = True
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="cohere_chat/command-r",
messages=messages,
max_tokens=10,
)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_chat_completion_cohere_stream():
try:
litellm.set_verbose = False
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="cohere_chat/command-r",
messages=messages,
max_tokens=10,
stream=True,
)
print(response)
for chunk in response:
print(chunk)
except litellm.APIConnectionError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_azure_cloudflare_api():
litellm.set_verbose = True
try:
messages = [
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
]
response = completion(
model="azure/gpt-turbo",
messages=messages,
base_url=os.getenv("CLOUDFLARE_AZURE_BASE_URL"),
api_key=os.getenv("AZURE_FRANCE_API_KEY"),
)
print(f"response: {response}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
traceback.print_exc()
pass
# test_azure_cloudflare_api()
@pytest.mark.skip(reason="anyscale stopped serving public api endpoints")
def test_completion_anyscale_2():
try:
# litellm.set_verbose = True
messages = [
{"role": "system", "content": "You're a good bot"},
{
"role": "user",
"content": "Hey",
},
{
"role": "user",
"content": "Hey",
},
]
response = completion(
model="anyscale/meta-llama/Llama-2-7b-chat-hf", messages=messages
)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="anyscale stopped serving public api endpoints")
def test_mistral_anyscale_stream():
litellm.set_verbose = False
response = completion(
model="anyscale/mistralai/Mistral-7B-Instruct-v0.1",
messages=[{"content": "hello, good morning", "role": "user"}],
stream=True,
)
for chunk in response:
# print(chunk)
print(chunk["choices"][0]["delta"].get("content", ""), end="")
# test_completion_anyscale_2()
# def test_completion_with_fallbacks_multiple_keys():
# print(f"backup key 1: {os.getenv('BACKUP_OPENAI_API_KEY_1')}")
# print(f"backup key 2: {os.getenv('BACKUP_OPENAI_API_KEY_2')}")
# backup_keys = [{"api_key": os.getenv("BACKUP_OPENAI_API_KEY_1")}, {"api_key": os.getenv("BACKUP_OPENAI_API_KEY_2")}]
# try:
# api_key = "bad-key"
# response = completion(
# model="gpt-3.5-turbo", messages=messages, force_timeout=120, fallbacks=backup_keys, api_key=api_key
# )
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# error_str = traceback.format_exc()
# pytest.fail(f"Error occurred: {error_str}")
# test_completion_with_fallbacks_multiple_keys()
# def test_petals():
# try:
# response = completion(model="petals-team/StableBeluga2", messages=messages)
# # Add any assertions here to check the response
# print(response)
# response = completion(model="petals-team/StableBeluga2", messages=messages)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# def test_baseten():
# try:
# response = completion(model="baseten/7qQNLDB", messages=messages, logger_fn=logger_fn)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_baseten()
# def test_baseten_falcon_7bcompletion():
# model_name = "qvv0xeq"
# try:
# response = completion(model=model_name, messages=messages, custom_llm_provider="baseten")
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# def test_baseten_falcon_7bcompletion_withbase():
# model_name = "qvv0xeq"
# litellm.api_base = "https://app.baseten.co"
# try:
# response = completion(model=model_name, messages=messages)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# litellm.api_base = None
# test_baseten_falcon_7bcompletion_withbase()
# def test_baseten_wizardLMcompletion_withbase():
# model_name = "q841o8w"
# litellm.api_base = "https://app.baseten.co"
# try:
# response = completion(model=model_name, messages=messages)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_baseten_wizardLMcompletion_withbase()
# def test_baseten_mosaic_ML_completion_withbase():
# model_name = "31dxrj3",
# litellm.api_base = "https://app.baseten.co"
# try:
# response = completion(model=model_name, messages=messages)
# # Add any assertions here to check the response
# print(response)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
#### Test A121 ###################
@pytest.mark.skip(reason="Local test")
def test_completion_ai21():
print("running ai21 j2light test")
litellm.set_verbose = True
model_name = "j2-light"
try:
response = completion(
model=model_name, messages=messages, max_tokens=100, temperature=0.8
)
# Add any assertions here to check the response
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_ai21()
# test_completion_ai21()
## test deep infra
@pytest.mark.parametrize("drop_params", [True, False])
def test_completion_deep_infra(drop_params):
litellm.set_verbose = False
model_name = "deepinfra/meta-llama/Llama-2-70b-chat-hf"
tools = [
{
"type": "function",
"function": {
"name": "get_current_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"]},
},
"required": ["location"],
},
},
}
]
messages = [
{
"role": "user",
"content": "What's the weather like in Boston today in Fahrenheit?",
}
]
try:
response = completion(
model=model_name,
messages=messages,
temperature=0,
max_tokens=10,
tools=tools,
tool_choice={
"type": "function",
"function": {"name": "get_current_weather"},
},
drop_params=drop_params,
)
# Add any assertions here to check the response
print(response)
except Exception as e:
if drop_params is True:
pytest.fail(f"Error occurred: {e}")
# test_completion_deep_infra()
def test_completion_deep_infra_mistral():
print("deep infra test with temp=0")
model_name = "deepinfra/mistralai/Mistral-7B-Instruct-v0.1"
try:
response = completion(
model=model_name,
messages=messages,
temperature=0.01, # mistrail fails with temperature=0
max_tokens=10,
)
# Add any assertions here to check the response
print(response)
except litellm.exceptions.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_deep_infra_mistral()
@pytest.mark.skip(reason="Local test - don't have a volcengine account as yet")
def test_completion_volcengine():
litellm.set_verbose = True
model_name = "volcengine/"
try:
response = completion(
model=model_name,
messages=[
{
"role": "user",
"content": "What's the weather like in Boston today in Fahrenheit?",
}
],
api_key="",
)
# Add any assertions here to check the response
print(response)
except litellm.exceptions.Timeout as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# Gemini tests
@pytest.mark.parametrize(
"model",
[
# "gemini-1.0-pro",
"gemini-1.5-pro",
# "gemini-1.5-flash",
],
)
@pytest.mark.flaky(retries=3, delay=1)
def test_completion_gemini(model):
litellm.set_verbose = True
model_name = "gemini/{}".format(model)
messages = [
{"role": "system", "content": "Be a good bot!"},
{"role": "user", "content": "Hey, how's it going?"},
]
try:
response = completion(
model=model_name,
messages=messages,
safety_settings=[
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_NONE",
},
],
)
# Add any assertions,here to check the response
print(response)
assert response.choices[0]["index"] == 0
except litellm.RateLimitError:
pass
except litellm.APIError:
pass
except Exception as e:
if "InternalServerError" in str(e):
pass
else:
pytest.fail(f"Error occurred:{e}")
# test_completion_gemini()
@pytest.mark.asyncio
async def test_acompletion_gemini():
litellm.set_verbose = True
model_name = "gemini/gemini-pro"
messages = [{"role": "user", "content": "Hey, how's it going?"}]
try:
response = await litellm.acompletion(model=model_name, messages=messages)
# Add any assertions here to check the response
print(f"response: {response}")
except litellm.Timeout as e:
pass
except litellm.APIError as e:
pass
except Exception as e:
if "InternalServerError" in str(e):
pass
else:
pytest.fail(f"Error occurred: {e}")
# Deepseek tests
def test_completion_deepseek():
litellm.set_verbose = True
model_name = "deepseek/deepseek-chat"
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather of an location, the user shoud supply a location first",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
}
},
"required": ["location"],
},
},
},
]
messages = [{"role": "user", "content": "How's the weather in Hangzhou?"}]
try:
response = completion(model=model_name, messages=messages, tools=tools)
# Add any assertions here to check the response
print(response)
except litellm.APIError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="Account deleted by IBM.")
def test_completion_watsonx():
litellm.set_verbose = True
model_name = "watsonx/ibm/granite-13b-chat-v2"
try:
response = completion(
model=model_name,
messages=messages,
stop=["stop"],
max_tokens=20,
)
# Add any assertions here to check the response
print(response)
except litellm.APIError as e:
pass
except litellm.RateLimitError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="Skip test. account deleted.")
def test_completion_stream_watsonx():
litellm.set_verbose = True
model_name = "watsonx/ibm/granite-13b-chat-v2"
try:
response = completion(
model=model_name,
messages=messages,
stop=["stop"],
max_tokens=20,
stream=True,
)
for chunk in response:
print(chunk)
except litellm.APIError as e:
pass
except litellm.RateLimitError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.parametrize(
"provider, model, project, region_name, token",
[
("azure", "chatgpt-v-2", None, None, "test-token"),
("vertex_ai", "anthropic-claude-3", "adroit-crow-1", "us-east1", None),
("watsonx", "ibm/granite", "96946574", "dallas", "1234"),
("bedrock", "anthropic.claude-3", None, "us-east-1", None),
],
)
def test_unified_auth_params(provider, model, project, region_name, token):
"""
Check if params = ["project", "region_name", "token"]
are correctly translated for = ["azure", "vertex_ai", "watsonx", "aws"]
tests get_optional_params
"""
data = {
"project": project,
"region_name": region_name,
"token": token,
"custom_llm_provider": provider,
"model": model,
}
translated_optional_params = litellm.utils.get_optional_params(**data)
if provider == "azure":
special_auth_params = (
litellm.AzureOpenAIConfig().get_mapped_special_auth_params()
)
elif provider == "bedrock":
special_auth_params = (
litellm.AmazonBedrockGlobalConfig().get_mapped_special_auth_params()
)
elif provider == "vertex_ai":
special_auth_params = litellm.VertexAIConfig().get_mapped_special_auth_params()
elif provider == "watsonx":
special_auth_params = (
litellm.IBMWatsonXAIConfig().get_mapped_special_auth_params()
)
for param, value in special_auth_params.items():
assert param in data
assert value in translated_optional_params
@pytest.mark.skip(reason="Local test")
@pytest.mark.asyncio
async def test_acompletion_watsonx():
litellm.set_verbose = True
model_name = "watsonx/ibm/granite-13b-chat-v2"
print("testing watsonx")
try:
response = await litellm.acompletion(
model=model_name,
messages=messages,
temperature=0.2,
max_tokens=80,
)
# Add any assertions here to check the response
print(response)
except litellm.RateLimitError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@pytest.mark.skip(reason="Local test")
@pytest.mark.asyncio
async def test_acompletion_stream_watsonx():
litellm.set_verbose = True
model_name = "watsonx/ibm/granite-13b-chat-v2"
print("testing watsonx")
try:
response = await litellm.acompletion(
model=model_name,
messages=messages,
temperature=0.2,
max_tokens=80,
stream=True,
)
# Add any assertions here to check the response
async for chunk in response:
print(chunk)
except litellm.RateLimitError as e:
pass
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_palm_stream()
# test_completion_deep_infra()
# test_completion_ai21()
# test config file with completion #
# def test_completion_openai_config():
# try:
# litellm.config_path = "../config.json"
# litellm.set_verbose = True
# response = litellm.config_completion(messages=messages)
# # Add any assertions here to check the response
# print(response)
# litellm.config_path = None
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# def test_maritalk():
# messages = [{"role": "user", "content": "Hey"}]
# try:
# response = completion("maritalk", messages=messages)
# print(f"response: {response}")
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_maritalk()
def test_completion_together_ai_stream():
litellm.set_verbose = True
user_message = "Write 1pg about YC & litellm"
messages = [{"content": user_message, "role": "user"}]
try:
response = completion(
model="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
messages=messages,
stream=True,
max_tokens=5,
)
print(response)
for chunk in response:
print(chunk)
# print(string_response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_together_ai_stream()
# Cloud flare AI tests
@pytest.mark.skip(reason="Flaky test-cloudflare is very unstable")
def test_completion_cloudflare():
try:
litellm.set_verbose = True
response = completion(
model="cloudflare/@cf/meta/llama-2-7b-chat-int8",
messages=[{"content": "what llm are you", "role": "user"}],
max_tokens=15,
num_retries=3,
)
print(response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_cloudflare()
def test_moderation():
response = litellm.moderation(input="i'm ishaan cto of litellm")
print(response)
output = response.results[0]
print(output)
return output
@pytest.mark.parametrize("stream", [False, True])
@pytest.mark.parametrize("sync_mode", [False, True])
@pytest.mark.asyncio
async def test_dynamic_azure_params(stream, sync_mode):
"""
If dynamic params are given, which are different from the initialized client, use a new client
"""
from openai import AsyncAzureOpenAI, AzureOpenAI
if sync_mode:
client = AzureOpenAI(
api_key="my-test-key",
base_url="my-test-base",
api_version="my-test-version",
)
mock_client = MagicMock(return_value="Hello world!")
else:
client = AsyncAzureOpenAI(
api_key="my-test-key",
base_url="my-test-base",
api_version="my-test-version",
)
mock_client = AsyncMock(return_value="Hello world!")
## CHECK IF CLIENT IS USED (NO PARAM CHANGE)
with patch.object(
client.chat.completions.with_raw_response, "create", new=mock_client
) as mock_client:
try:
# client.chat.completions.with_raw_response.create = mock_client
if sync_mode:
_ = completion(
model="azure/chatgpt-v2",
messages=[{"role": "user", "content": "Hello world"}],
client=client,
stream=stream,
)
else:
_ = await litellm.acompletion(
model="azure/chatgpt-v2",
messages=[{"role": "user", "content": "Hello world"}],
client=client,
stream=stream,
)
except Exception:
pass
mock_client.assert_called()
## recreate mock client
if sync_mode:
mock_client = MagicMock(return_value="Hello world!")
else:
mock_client = AsyncMock(return_value="Hello world!")
## CHECK IF NEW CLIENT IS USED (PARAM CHANGE)
with patch.object(
client.chat.completions.with_raw_response, "create", new=mock_client
) as mock_client:
try:
if sync_mode:
_ = completion(
model="azure/chatgpt-v2",
messages=[{"role": "user", "content": "Hello world"}],
client=client,
api_version="my-new-version",
stream=stream,
)
else:
_ = await litellm.acompletion(
model="azure/chatgpt-v2",
messages=[{"role": "user", "content": "Hello world"}],
client=client,
api_version="my-new-version",
stream=stream,
)
except Exception:
pass
try:
mock_client.assert_not_called()
except Exception as e:
raise e
@pytest.mark.asyncio()
@pytest.mark.flaky(retries=3, delay=1)
async def test_completion_ai21_chat():
litellm.set_verbose = True
response = await litellm.acompletion(
model="jamba-1.5-large",
user="ishaan",
tool_choice="auto",
seed=123,
messages=[{"role": "user", "content": "what does the document say"}],
documents=[
{
"content": "hello world",
"metadata": {"source": "google", "author": "ishaan"},
}
],
)
@pytest.mark.parametrize(
"model",
["gpt-4o", "azure/chatgpt-v-2", "claude-3-sonnet-20240229"],
)
@pytest.mark.parametrize(
"stream",
[False, True],
)
@pytest.mark.flaky(retries=3, delay=1)
def test_completion_response_ratelimit_headers(model, stream):
response = completion(
model=model,
messages=[{"role": "user", "content": "Hello world"}],
stream=stream,
)
hidden_params = response._hidden_params
additional_headers = hidden_params.get("additional_headers", {})
print(additional_headers)
for k, v in additional_headers.items():
assert v != "None" and v is not None
assert "x-ratelimit-remaining-requests" in additional_headers
assert "x-ratelimit-remaining-tokens" in additional_headers
if model == "azure/chatgpt-v-2":
# Azure OpenAI header
assert "llm_provider-azureml-model-session" in additional_headers
if model == "claude-3-sonnet-20240229":
# anthropic header
assert "llm_provider-anthropic-ratelimit-requests-reset" in additional_headers
def _openai_hallucinated_tool_call_mock_response(
*args, **kwargs
) -> litellm.ModelResponse:
new_response = MagicMock()
new_response.headers = {"hello": "world"}
response_object = {
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "gpt-3.5-turbo-0125",
"system_fingerprint": "fp_44709d6fcb",
"choices": [
{
"index": 0,
"message": {
"content": None,
"role": "assistant",
"tool_calls": [
{
"function": {
"arguments": '{"tool_uses":[{"recipient_name":"product_title","parameters":{"content":"Story Scribe"}},{"recipient_name":"one_liner","parameters":{"content":"Transform interview transcripts into actionable user stories"}}]}',
"name": "multi_tool_use.parallel",
},
"id": "call_IzGXwVa5OfBd9XcCJOkt2q0s",
"type": "function",
}
],
},
"logprobs": None,
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 9, "completion_tokens": 12, "total_tokens": 21},
}
from openai import OpenAI
from openai.types.chat.chat_completion import ChatCompletion
pydantic_obj = ChatCompletion(**response_object) # type: ignore
pydantic_obj.choices[0].message.role = None # type: ignore
new_response.parse.return_value = pydantic_obj
return new_response
def test_openai_hallucinated_tool_call():
"""
Patch for this issue: https://community.openai.com/t/model-tries-to-call-unknown-function-multi-tool-use-parallel/490653
Handle openai invalid tool calling response.
OpenAI assistant will sometimes return an invalid tool calling response, which needs to be parsed
- "arguments": "{\"tool_uses\":[{\"recipient_name\":\"product_title\",\"parameters\":{\"content\":\"Story Scribe\"}},{\"recipient_name\":\"one_liner\",\"parameters\":{\"content\":\"Transform interview transcripts into actionable user stories\"}}]}",
To extract actual tool calls:
1. Parse arguments JSON object
2. Iterate over tool_uses array to call functions:
- get function name from recipient_name value
- parameters will be JSON object for function arguments
"""
import openai
openai_client = openai.OpenAI()
with patch.object(
openai_client.chat.completions,
"create",
side_effect=_openai_hallucinated_tool_call_mock_response,
) as mock_response:
response = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hey! how's it going?"}],
client=openai_client,
)
print(f"response: {response}")
response_dict = response.model_dump()
tool_calls = response_dict["choices"][0]["message"]["tool_calls"]
print(f"tool_calls: {tool_calls}")
for idx, tc in enumerate(tool_calls):
if idx == 0:
print(f"tc in test_openai_hallucinated_tool_call: {tc}")
assert tc == {
"function": {
"arguments": '{"content": "Story Scribe"}',
"name": "product_title",
},
"id": "call_IzGXwVa5OfBd9XcCJOkt2q0s_0",
"type": "function",
}
elif idx == 1:
assert tc == {
"function": {
"arguments": '{"content": "Transform interview transcripts into actionable user stories"}',
"name": "one_liner",
},
"id": "call_IzGXwVa5OfBd9XcCJOkt2q0s_1",
"type": "function",
}
@pytest.mark.parametrize(
"function_name, expect_modification",
[
("multi_tool_use.parallel", True),
("my-fake-function", False),
],
)
def test_openai_hallucinated_tool_call_util(function_name, expect_modification):
"""
Patch for this issue: https://community.openai.com/t/model-tries-to-call-unknown-function-multi-tool-use-parallel/490653
Handle openai invalid tool calling response.
OpenAI assistant will sometimes return an invalid tool calling response, which needs to be parsed
- "arguments": "{\"tool_uses\":[{\"recipient_name\":\"product_title\",\"parameters\":{\"content\":\"Story Scribe\"}},{\"recipient_name\":\"one_liner\",\"parameters\":{\"content\":\"Transform interview transcripts into actionable user stories\"}}]}",
To extract actual tool calls:
1. Parse arguments JSON object
2. Iterate over tool_uses array to call functions:
- get function name from recipient_name value
- parameters will be JSON object for function arguments
"""
from litellm.utils import _handle_invalid_parallel_tool_calls
from litellm.types.utils import ChatCompletionMessageToolCall
response = _handle_invalid_parallel_tool_calls(
tool_calls=[
ChatCompletionMessageToolCall(
**{
"function": {
"arguments": '{"tool_uses":[{"recipient_name":"product_title","parameters":{"content":"Story Scribe"}},{"recipient_name":"one_liner","parameters":{"content":"Transform interview transcripts into actionable user stories"}}]}',
"name": function_name,
},
"id": "call_IzGXwVa5OfBd9XcCJOkt2q0s",
"type": "function",
}
)
]
)
print(f"response: {response}")
if expect_modification:
for idx, tc in enumerate(response):
if idx == 0:
assert tc.model_dump() == {
"function": {
"arguments": '{"content": "Story Scribe"}',
"name": "product_title",
},
"id": "call_IzGXwVa5OfBd9XcCJOkt2q0s_0",
"type": "function",
}
elif idx == 1:
assert tc.model_dump() == {
"function": {
"arguments": '{"content": "Transform interview transcripts into actionable user stories"}',
"name": "one_liner",
},
"id": "call_IzGXwVa5OfBd9XcCJOkt2q0s_1",
"type": "function",
}
else:
assert len(response) == 1
assert response[0].function.name == function_name