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79 lines
1.9 KiB
Python
79 lines
1.9 KiB
Python
#### What this tests ####
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# This tests streaming for the completion endpoint
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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 completion
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litellm.set_verbose = False
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score = 0
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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 on openai completion call
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try:
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response = completion(
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model="gpt-3.5-turbo", messages=messages, stream=True, logger_fn=logger_fn
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)
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for chunk in response:
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print(chunk["choices"][0]["delta"])
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score += 1
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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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# test on azure completion call
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try:
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response = completion(
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model="azure/chatgpt-test", messages=messages, stream=True, logger_fn=logger_fn
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)
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for chunk in response:
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print(chunk["choices"][0]["delta"])
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score += 1
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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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# test on anthropic completion call
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try:
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response = completion(
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model="claude-instant-1", messages=messages, stream=True, logger_fn=logger_fn
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)
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for chunk in response:
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print(chunk["choices"][0]["delta"])
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score += 1
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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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# # test on huggingface completion call
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# try:
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# response = completion(
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# model="meta-llama/Llama-2-7b-chat-hf",
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# messages=messages,
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# custom_llm_provider="huggingface",
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# custom_api_base="https://s7c7gytn18vnu4tw.us-east-1.aws.endpoints.huggingface.cloud",
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# stream=True,
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# logger_fn=logger_fn,
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# )
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# for chunk in response:
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# print(chunk["choices"][0]["delta"])
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# score += 1
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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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