mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
temp comment out test_exceptions.py
This commit is contained in:
Ishaan Jaff
Krrish Dholakia
parent
c318ae13c7
commit
4c67fe6d6e
1 changed files with 102 additions and 102 deletions
|
|
@ -1,12 +1,12 @@
|
|||
#### What this tests ####
|
||||
# This tests exception mapping -> trigger an exception from an llm provider -> assert if output is of the expected type
|
||||
# #### What this tests ####
|
||||
# # This tests exception mapping -> trigger an exception from an llm provider -> assert if output is of the expected type
|
||||
|
||||
|
||||
# 5 providers -> OpenAI, Azure, Anthropic, Cohere, Replicate
|
||||
# # 5 providers -> OpenAI, Azure, Anthropic, Cohere, Replicate
|
||||
|
||||
# 3 main types of exceptions -> - Rate Limit Errors, Context Window Errors, Auth errors (incorrect/rotated key, etc.)
|
||||
# # 3 main types of exceptions -> - Rate Limit Errors, Context Window Errors, Auth errors (incorrect/rotated key, etc.)
|
||||
|
||||
# Approach: Run each model through the test -> assert if the correct error (always the same one) is triggered
|
||||
# # Approach: Run each model through the test -> assert if the correct error (always the same one) is triggered
|
||||
|
||||
from openai.error import AuthenticationError, InvalidRequestError, RateLimitError, OpenAIError
|
||||
import os
|
||||
|
|
@ -19,112 +19,112 @@ from concurrent.futures import ThreadPoolExecutor
|
|||
|
||||
models = ["gpt-3.5-turbo", "chatgpt-test", "claude-instant-1", "command-nightly", "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1"]
|
||||
|
||||
# Test 1: Rate Limit Errors
|
||||
def test_model(model):
|
||||
try:
|
||||
sample_text = "how does a court case get to the Supreme Court?" * 50000
|
||||
messages = [{ "content": sample_text,"role": "user"}]
|
||||
azure = False
|
||||
if model == "chatgpt-test":
|
||||
azure = True
|
||||
print(f"model: {model}")
|
||||
response = completion(model=model, messages=messages, azure=azure)
|
||||
except RateLimitError:
|
||||
return True
|
||||
except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Uncaught Exception {model}: {type(e).__name__} - {e}")
|
||||
pass
|
||||
return False
|
||||
# # Test 1: Rate Limit Errors
|
||||
# def test_model(model):
|
||||
# try:
|
||||
# sample_text = "how does a court case get to the Supreme Court?" * 50000
|
||||
# messages = [{ "content": sample_text,"role": "user"}]
|
||||
# azure = False
|
||||
# if model == "chatgpt-test":
|
||||
# azure = True
|
||||
# print(f"model: {model}")
|
||||
# response = completion(model=model, messages=messages, azure=azure)
|
||||
# except RateLimitError:
|
||||
# return True
|
||||
# except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
|
||||
# return True
|
||||
# except Exception as e:
|
||||
# print(f"Uncaught Exception {model}: {type(e).__name__} - {e}")
|
||||
# pass
|
||||
# return False
|
||||
|
||||
# Repeat each model 500 times
|
||||
extended_models = [model for model in models for _ in range(250)]
|
||||
# # Repeat each model 500 times
|
||||
# extended_models = [model for model in models for _ in range(250)]
|
||||
|
||||
def worker(model):
|
||||
return test_model(model)
|
||||
# def worker(model):
|
||||
# return test_model(model)
|
||||
|
||||
# Create a dictionary to store the results
|
||||
counts = {True: 0, False: 0}
|
||||
# # Create a dictionary to store the results
|
||||
# counts = {True: 0, False: 0}
|
||||
|
||||
# Use Thread Pool Executor
|
||||
with ThreadPoolExecutor(max_workers=500) as executor:
|
||||
# Use map to start the operation in thread pool
|
||||
results = executor.map(worker, extended_models)
|
||||
# # Use Thread Pool Executor
|
||||
# with ThreadPoolExecutor(max_workers=500) as executor:
|
||||
# # Use map to start the operation in thread pool
|
||||
# results = executor.map(worker, extended_models)
|
||||
|
||||
# Iterate over results and count True/False
|
||||
for result in results:
|
||||
counts[result] += 1
|
||||
# # Iterate over results and count True/False
|
||||
# for result in results:
|
||||
# counts[result] += 1
|
||||
|
||||
accuracy_score = counts[True]/(counts[True] + counts[False])
|
||||
print(f"accuracy_score: {accuracy_score}")
|
||||
# accuracy_score = counts[True]/(counts[True] + counts[False])
|
||||
# print(f"accuracy_score: {accuracy_score}")
|
||||
|
||||
# Test 2: Context Window Errors
|
||||
print("Testing Context Window Errors")
|
||||
def test_model(model): # pass extremely long input
|
||||
sample_text = "how does a court case get to the Supreme Court?" * 100000
|
||||
messages = [{ "content": sample_text,"role": "user"}]
|
||||
try:
|
||||
azure = False
|
||||
if model == "chatgpt-test":
|
||||
azure = True
|
||||
print(f"model: {model}")
|
||||
response = completion(model=model, messages=messages, azure=azure)
|
||||
except InvalidRequestError:
|
||||
return True
|
||||
except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error Type: {type(e).__name__}")
|
||||
print(f"Uncaught Exception - {e}")
|
||||
pass
|
||||
return False
|
||||
# # Test 2: Context Window Errors
|
||||
# print("Testing Context Window Errors")
|
||||
# def test_model(model): # pass extremely long input
|
||||
# sample_text = "how does a court case get to the Supreme Court?" * 100000
|
||||
# messages = [{ "content": sample_text,"role": "user"}]
|
||||
# try:
|
||||
# azure = False
|
||||
# if model == "chatgpt-test":
|
||||
# azure = True
|
||||
# print(f"model: {model}")
|
||||
# response = completion(model=model, messages=messages, azure=azure)
|
||||
# except InvalidRequestError:
|
||||
# return True
|
||||
# except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
|
||||
# return True
|
||||
# except Exception as e:
|
||||
# print(f"Error Type: {type(e).__name__}")
|
||||
# print(f"Uncaught Exception - {e}")
|
||||
# pass
|
||||
# return False
|
||||
|
||||
## TEST SCORE
|
||||
true_val = 0
|
||||
for model in models:
|
||||
if test_model(model=model) == True:
|
||||
true_val += 1
|
||||
accuracy_score = true_val/len(models)
|
||||
print(f"CTX WINDOW accuracy_score: {accuracy_score}")
|
||||
# ## TEST SCORE
|
||||
# true_val = 0
|
||||
# for model in models:
|
||||
# if test_model(model=model) == True:
|
||||
# true_val += 1
|
||||
# accuracy_score = true_val/len(models)
|
||||
# print(f"CTX WINDOW accuracy_score: {accuracy_score}")
|
||||
|
||||
# Test 3: InvalidAuth Errors
|
||||
def logger_fn(model_call_object: dict):
|
||||
print(f"model call details: {model_call_object}")
|
||||
# # Test 3: InvalidAuth Errors
|
||||
# def logger_fn(model_call_object: dict):
|
||||
# print(f"model call details: {model_call_object}")
|
||||
|
||||
|
||||
def test_model(model): # set the model key to an invalid key, depending on the model
|
||||
messages = [{ "content": "Hello, how are you?","role": "user"}]
|
||||
try:
|
||||
azure = False
|
||||
if model == "gpt-3.5-turbo":
|
||||
os.environ["OPENAI_API_KEY"] = "bad-key"
|
||||
elif model == "chatgpt-test":
|
||||
os.environ["AZURE_API_KEY"] = "bad-key"
|
||||
azure = True
|
||||
elif model == "claude-instant-1":
|
||||
os.environ["ANTHROPIC_API_KEY"] = "bad-key"
|
||||
elif model == "command-nightly":
|
||||
os.environ["COHERE_API_KEY"] = "bad-key"
|
||||
elif model == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1":
|
||||
os.environ["REPLICATE_API_KEY"] = "bad-key"
|
||||
os.environ["REPLICATE_API_TOKEN"] = "bad-key"
|
||||
print(f"model: {model}")
|
||||
response = completion(model=model, messages=messages, azure=azure, logger_fn=logger_fn)
|
||||
print(f"response: {response}")
|
||||
except AuthenticationError as e:
|
||||
return True
|
||||
except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Uncaught Exception - {e}")
|
||||
pass
|
||||
return False
|
||||
# def test_model(model): # set the model key to an invalid key, depending on the model
|
||||
# messages = [{ "content": "Hello, how are you?","role": "user"}]
|
||||
# try:
|
||||
# azure = False
|
||||
# if model == "gpt-3.5-turbo":
|
||||
# os.environ["OPENAI_API_KEY"] = "bad-key"
|
||||
# elif model == "chatgpt-test":
|
||||
# os.environ["AZURE_API_KEY"] = "bad-key"
|
||||
# azure = True
|
||||
# elif model == "claude-instant-1":
|
||||
# os.environ["ANTHROPIC_API_KEY"] = "bad-key"
|
||||
# elif model == "command-nightly":
|
||||
# os.environ["COHERE_API_KEY"] = "bad-key"
|
||||
# elif model == "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1":
|
||||
# os.environ["REPLICATE_API_KEY"] = "bad-key"
|
||||
# os.environ["REPLICATE_API_TOKEN"] = "bad-key"
|
||||
# print(f"model: {model}")
|
||||
# response = completion(model=model, messages=messages, azure=azure, logger_fn=logger_fn)
|
||||
# print(f"response: {response}")
|
||||
# except AuthenticationError as e:
|
||||
# return True
|
||||
# except OpenAIError: # is at least an openai error -> in case of random model errors - e.g. overloaded server
|
||||
# return True
|
||||
# except Exception as e:
|
||||
# print(f"Uncaught Exception - {e}")
|
||||
# pass
|
||||
# return False
|
||||
|
||||
## TEST SCORE
|
||||
true_val = 0
|
||||
for model in models:
|
||||
if test_model(model=model) == True:
|
||||
true_val += 1
|
||||
accuracy_score = true_val/len(models)
|
||||
print(f"INVALID AUTH accuracy_score: {accuracy_score}")
|
||||
# ## TEST SCORE
|
||||
# true_val = 0
|
||||
# for model in models:
|
||||
# if test_model(model=model) == True:
|
||||
# true_val += 1
|
||||
# accuracy_score = true_val/len(models)
|
||||
# print(f"INVALID AUTH accuracy_score: {accuracy_score}")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue