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import sys, os
import traceback
import pytest
from dotenv import load_dotenv
import openai
load_dotenv()
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import litellm
from litellm import embedding, completion
litellm.set_verbose = False
def test_openai_embedding():
try:
litellm.set_verbose = True
response = embedding(
model="text-embedding-ada-002",
input=["good morning from litellm", "this is another item"],
metadata={"anything": "good day"},
)
litellm_response = dict(response)
litellm_response_keys = set(litellm_response.keys())
litellm_response_keys.discard("_response_ms")
print(litellm_response_keys)
print("LiteLLM Response\n")
# print(litellm_response)
# same request with OpenAI 1.0+
import openai
client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
response = client.embeddings.create(
model="text-embedding-ada-002",
input=["good morning from litellm", "this is another item"],
)
response = dict(response)
openai_response_keys = set(response.keys())
print(openai_response_keys)
assert (
litellm_response_keys == openai_response_keys
) # ENSURE the Keys in litellm response is exactly what the openai package returns
assert (
len(litellm_response["data"]) == 2
) # expect two embedding responses from litellm_response since input had two
print(openai_response_keys)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_openai_embedding()
def test_openai_azure_embedding_simple():
try:
litellm.set_verbose = True
response = embedding(
model="azure/azure-embedding-model",
input=["good morning from litellm"],
)
print(response)
response_keys = set(dict(response).keys())
response_keys.discard("_response_ms")
assert set(["usage", "model", "object", "data"]) == set(
response_keys
) # assert litellm response has expected keys from OpenAI embedding response
request_cost = litellm.completion_cost(completion_response=response)
print("Calculated request cost=", request_cost)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_openai_azure_embedding_simple()
def test_openai_azure_embedding_timeouts():
try:
response = embedding(
model="azure/azure-embedding-model",
input=["good morning from litellm"],
timeout=0.00001,
)
print(response)
except openai.APITimeoutError:
print("Good job got timeout error!")
pass
except Exception as e:
pytest.fail(
f"Expected timeout error, did not get the correct error. Instead got {e}"
)
# test_openai_azure_embedding_timeouts()
def test_openai_embedding_timeouts():
try:
response = embedding(
model="text-embedding-ada-002",
input=["good morning from litellm"],
timeout=0.00001,
)
print(response)
except openai.APITimeoutError:
print("Good job got OpenAI timeout error!")
pass
except Exception as e:
pytest.fail(
f"Expected timeout error, did not get the correct error. Instead got {e}"
)
# test_openai_embedding_timeouts()
def test_openai_azure_embedding():
try:
api_key = os.environ["AZURE_API_KEY"]
api_base = os.environ["AZURE_API_BASE"]
api_version = os.environ["AZURE_API_VERSION"]
os.environ["AZURE_API_VERSION"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_KEY"] = ""
response = embedding(
model="azure/azure-embedding-model",
input=["good morning from litellm", "this is another item"],
api_key=api_key,
api_base=api_base,
api_version=api_version,
)
print(response)
os.environ["AZURE_API_VERSION"] = api_version
os.environ["AZURE_API_BASE"] = api_base
os.environ["AZURE_API_KEY"] = api_key
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_openai_azure_embedding()
# test_openai_embedding()
def test_cohere_embedding():
try:
# litellm.set_verbose=True
response = embedding(
model="embed-english-v2.0",
input=["good morning from litellm", "this is another item"],
)
print(f"response:", response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_cohere_embedding()
def test_cohere_embedding3():
try:
litellm.set_verbose = True
response = embedding(
model="embed-english-v3.0",
input=["good morning from litellm", "this is another item"],
)
print(f"response:", response)
custom_llm_provider = response._hidden_params["custom_llm_provider"]
assert custom_llm_provider == "cohere"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
test_cohere_embedding3()
def test_bedrock_embedding_titan():
try:
# this tests if we support str input for bedrock embedding
litellm.set_verbose = True
litellm.enable_cache()
import time
current_time = str(time.time())
# DO NOT MAKE THE INPUT A LIST in this test
response = embedding(
model="bedrock/amazon.titan-embed-text-v1",
input=f"good morning from litellm, attempting to embed data {current_time}", # input should always be a string in this test
)
print(f"response:", response)
assert isinstance(
response["data"][0]["embedding"], list
), "Expected response to be a list"
print(f"type of first embedding:", type(response["data"][0]["embedding"][0]))
assert all(
isinstance(x, float) for x in response["data"][0]["embedding"]
), "Expected response to be a list of floats"
# this also tests if we can return a cache response for this scenario
import time
start_time = time.time()
response = embedding(
model="bedrock/amazon.titan-embed-text-v1",
input=f"good morning from litellm, attempting to embed data {current_time}", # input should always be a string in this test
)
print(response)
end_time = time.time()
print(f"Embedding 2 response time: {end_time - start_time} seconds")
assert end_time - start_time < 0.1
litellm.disable_cache()
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_bedrock_embedding_titan()
def test_bedrock_embedding_cohere():
try:
litellm.set_verbose = False
response = embedding(
model="cohere.embed-multilingual-v3",
input=[
"good morning from litellm, attempting to embed data",
"lets test a second string for good measure",
],
aws_region_name="os.environ/AWS_REGION_NAME_2",
)
assert isinstance(
response["data"][0]["embedding"], list
), "Expected response to be a list"
print(f"type of first embedding:", type(response["data"][0]["embedding"][0]))
assert all(
isinstance(x, float) for x in response["data"][0]["embedding"]
), "Expected response to be a list of floats"
# print(f"response:", response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_bedrock_embedding_cohere()
# comment out hf tests - since hf endpoints are unstable
def test_hf_embedding():
try:
# huggingface/microsoft/codebert-base
# huggingface/facebook/bart-large
response = embedding(
model="huggingface/sentence-transformers/all-MiniLM-L6-v2",
input=["good morning from litellm", "this is another item"],
)
print(f"response:", response)
except Exception as e:
# Note: Huggingface inference API is unstable and fails with "model loading errors all the time"
pass
# test_hf_embedding()
# test async embeddings
def test_aembedding():
try:
import asyncio
async def embedding_call():
try:
response = await litellm.aembedding(
model="text-embedding-ada-002",
input=["good morning from litellm", "this is another item"],
)
print(response)
return response
except Exception as e:
pytest.fail(f"Error occurred: {e}")
response = asyncio.run(embedding_call())
print("Before caclulating cost, response", response)
cost = litellm.completion_cost(completion_response=response)
print("COST=", cost)
assert cost == float("1e-06")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_aembedding()
def test_aembedding_azure():
try:
import asyncio
async def embedding_call():
try:
response = await litellm.aembedding(
model="azure/azure-embedding-model",
input=["good morning from litellm", "this is another item"],
)
print(response)
print(
"hidden params - custom_llm_provider",
response._hidden_params["custom_llm_provider"],
)
assert response._hidden_params["custom_llm_provider"] == "azure"
except Exception as e:
pytest.fail(f"Error occurred: {e}")
asyncio.run(embedding_call())
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_aembedding_azure()
def test_sagemaker_embeddings():
try:
response = litellm.embedding(
model="sagemaker/berri-benchmarking-gpt-j-6b-fp16",
input=["good morning from litellm", "this is another item"],
)
print(f"response: {response}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
def test_mistral_embeddings():
try:
litellm.set_verbose = True
response = litellm.embedding(
model="mistral/mistral-embed",
input=["good morning from litellm"],
)
print(f"response: {response}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_mistral_embeddings()
def test_voyage_embeddings():
try:
litellm.set_verbose = True
response = litellm.embedding(
model="voyage/voyage-01",
input=["good morning from litellm"],
)
print(f"response: {response}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_voyage_embeddings()
# def test_xinference_embeddings():
# try:
# litellm.set_verbose = True
# response = litellm.embedding(
# model="xinference/bge-base-en",
# input=["good morning from litellm"],
# )
# print(f"response: {response}")
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# test_xinference_embeddings()
# test_sagemaker_embeddings()
# def local_proxy_embeddings():
# litellm.set_verbose=True
# response = embedding(
# model="openai/custom_embedding",
# input=["good morning from litellm"],
# api_base="http://0.0.0.0:8000/"
# )
# print(response)
# local_proxy_embeddings()
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