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(chore) remvoe bloat testing
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@@ -1,53 +0,0 @@
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# #### What this tests ####
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# # This tests the ability to set api key's via the params instead of as environment variables
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# import sys, os
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# import traceback
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# sys.path.insert(
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# 0, os.path.abspath("../..")
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# ) # Adds the parent directory to the system path
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# import litellm
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# from litellm import embedding, completion
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# litellm.set_verbose = False
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# def logger_fn(model_call_object: dict):
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# print(f"model call details: {model_call_object}")
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# user_message = "Hello, how are you?"
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# messages = [{"content": user_message, "role": "user"}]
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# ## Test 1: Setting key dynamically
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# temp_key = os.environ.get("ANTHROPIC_API_KEY", "")
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# os.environ["ANTHROPIC_API_KEY"] = "bad-key"
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# # test on openai completion call
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# try:
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# response = completion(
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# model="claude-instant-1",
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# messages=messages,
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# logger_fn=logger_fn,
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# api_key=temp_key,
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# )
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# print(f"response: {response}")
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# except:
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# print(f"error occurred: {traceback.format_exc()}")
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# pass
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# os.environ["ANTHROPIC_API_KEY"] = temp_key
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# ## Test 2: Setting key via __init__ params
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# litellm.anthropic_key = os.environ.get("ANTHROPIC_API_KEY", "")
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# os.environ.pop("ANTHROPIC_API_KEY")
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# # test on openai completion call
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# try:
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# response = completion(
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# model="claude-instant-1", messages=messages, logger_fn=logger_fn
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# )
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# print(f"response: {response}")
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# except:
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# print(f"error occurred: {traceback.format_exc()}")
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# pass
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# os.environ["ANTHROPIC_API_KEY"] = temp_key
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@@ -1,90 +0,0 @@
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# #### What this tests ####
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# # This tests error logging (with custom user functions) for the `completion` + `embedding` endpoints w/ callbacks
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# # This only tests posthog, sentry, and helicone
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# import sys, os
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# import traceback
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# import pytest
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# sys.path.insert(
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# 0, os.path.abspath("../..")
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# ) # Adds the parent directory to the system path
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# import litellm
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# from litellm import embedding, completion
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# litellm.success_callback = ["posthog", "helicone"]
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# litellm.failure_callback = ["sentry", "posthog"]
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# litellm.set_verbose = True
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# def logger_fn(model_call_object: dict):
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# # print(f"model call details: {model_call_object}")
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# pass
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# user_message = "Hello, how are you?"
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# messages = [{"content": user_message, "role": "user"}]
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# def test_completion_openai():
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# try:
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# print("running query")
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# response = completion(
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# model="gpt-3.5-turbo", messages=messages, logger_fn=logger_fn
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# )
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# print(f"response: {response}")
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# # Add any assertions here to check the response
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# except Exception as e:
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# traceback.print_exc()
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# pytest.fail(f"Error occurred: {e}")
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# def test_completion_claude():
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# try:
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# response = completion(
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# model="claude-instant-1", messages=messages, logger_fn=logger_fn
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# )
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# # Add any assertions here to check the response
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# def test_completion_non_openai():
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# try:
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# response = completion(
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# model="claude-instant-1", messages=messages, logger_fn=logger_fn
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# )
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# # Add any assertions here to check the response
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# def test_embedding_openai():
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# try:
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# response = embedding(
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# model="text-embedding-ada-002", input=[user_message], logger_fn=logger_fn
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# )
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# # Add any assertions here to check the response
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# print(f"response: {str(response)[:50]}")
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# def test_bad_azure_embedding():
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# try:
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# response = embedding(
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# model="chatgpt-test", input=[user_message], logger_fn=logger_fn
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# )
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# # Add any assertions here to check the response
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# print(f"response: {str(response)[:50]}")
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# except Exception as e:
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# pass
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# # def test_good_azure_embedding():
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# # try:
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# # response = embedding(model='azure/azure-embedding-model', input=[user_message], logger_fn=logger_fn)
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# # # Add any assertions here to check the response
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# # print(f"response: {str(response)[:50]}")
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# # except Exception as e:
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# # pytest.fail(f"Error occurred: {e}")
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