mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
refactor: add black formatting
This commit is contained in:
Krrish Dholakia
parent
b87d630b0a
commit
4905929de3
156 changed files with 19723 additions and 10869 deletions
|
|
@ -14,39 +14,49 @@ from litellm import embedding, completion
|
|||
|
||||
litellm.set_verbose = False
|
||||
|
||||
|
||||
def test_openai_embedding():
|
||||
try:
|
||||
litellm.set_verbose=True
|
||||
litellm.set_verbose = True
|
||||
response = embedding(
|
||||
model="text-embedding-ada-002",
|
||||
input=["good morning from litellm", "this is another item"],
|
||||
metadata = {"anything": "good day"}
|
||||
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')
|
||||
litellm_response_keys.discard("_response_ms")
|
||||
|
||||
print(litellm_response_keys)
|
||||
print("LiteLLM Response\n")
|
||||
# print(litellm_response)
|
||||
|
||||
# same request with OpenAI 1.0+
|
||||
|
||||
# same request with OpenAI 1.0+
|
||||
import openai
|
||||
client = openai.OpenAI(api_key=os.environ['OPENAI_API_KEY'])
|
||||
|
||||
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"]
|
||||
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
|
||||
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:
|
||||
response = embedding(
|
||||
|
|
@ -55,12 +65,15 @@ def test_openai_azure_embedding_simple():
|
|||
)
|
||||
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
|
||||
response_keys.discard("_response_ms")
|
||||
assert set(["usage", "model", "object", "data"]) == set(
|
||||
response_keys
|
||||
) # assert litellm response has expected keys from OpenAI embedding response
|
||||
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
# test_openai_azure_embedding_simple()
|
||||
|
||||
|
||||
|
|
@ -69,41 +82,50 @@ def test_openai_azure_embedding_timeouts():
|
|||
response = embedding(
|
||||
model="azure/azure-embedding-model",
|
||||
input=["good morning from litellm"],
|
||||
timeout=0.00001
|
||||
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}")
|
||||
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
|
||||
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}")
|
||||
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']
|
||||
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'] = ""
|
||||
os.environ["AZURE_API_VERSION"] = ""
|
||||
os.environ["AZURE_API_BASE"] = ""
|
||||
os.environ["AZURE_API_KEY"] = ""
|
||||
|
||||
response = embedding(
|
||||
model="azure/azure-embedding-model",
|
||||
|
|
@ -114,137 +136,179 @@ def test_openai_azure_embedding():
|
|||
)
|
||||
print(response)
|
||||
|
||||
|
||||
os.environ['AZURE_API_VERSION'] = api_version
|
||||
os.environ['AZURE_API_BASE'] = api_base
|
||||
os.environ['AZURE_API_KEY'] = api_key
|
||||
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"]
|
||||
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
|
||||
litellm.set_verbose = True
|
||||
response = embedding(
|
||||
model="embed-english-v3.0",
|
||||
input=["good morning from litellm", "this is another item"],
|
||||
model="embed-english-v3.0",
|
||||
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_embedding3()
|
||||
|
||||
|
||||
def test_bedrock_embedding_titan():
|
||||
try:
|
||||
litellm.set_verbose=True
|
||||
litellm.set_verbose = True
|
||||
response = embedding(
|
||||
model="amazon.titan-embed-text-v1", input=["good morning from litellm, attempting to embed data",
|
||||
"lets test a second string for good measure"]
|
||||
model="amazon.titan-embed-text-v1",
|
||||
input=[
|
||||
"good morning from litellm, attempting to embed data",
|
||||
"lets test a second string for good measure",
|
||||
],
|
||||
)
|
||||
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"
|
||||
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"
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
test_bedrock_embedding_titan()
|
||||
|
||||
|
||||
def test_bedrock_embedding_cohere():
|
||||
try:
|
||||
litellm.set_verbose=False
|
||||
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"
|
||||
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"
|
||||
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"]
|
||||
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"]
|
||||
model="text-embedding-ada-002",
|
||||
input=["good morning from litellm", "this is another item"],
|
||||
)
|
||||
print(response)
|
||||
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()
|
||||
|
||||
|
||||
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"]
|
||||
model="azure/azure-embedding-model",
|
||||
input=["good morning from litellm", "this is another item"],
|
||||
)
|
||||
print(response)
|
||||
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"])
|
||||
|
||||
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:
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
# test_sagemaker_embeddings()
|
||||
# def local_proxy_embeddings():
|
||||
# litellm.set_verbose=True
|
||||
# response = embedding(
|
||||
# model="openai/custom_embedding",
|
||||
# model="openai/custom_embedding",
|
||||
# input=["good morning from litellm"],
|
||||
# api_base="http://0.0.0.0:8000/"
|
||||
# )
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue