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https://github.com/BerriAI/litellm.git
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fix(main.py): route openai calls to /completion when text_completion is True
This commit is contained in:
Krrish Dholakia
parent
343d956c6e
commit
7966266d4f
3 changed files with 149 additions and 93 deletions
168
litellm/main.py
168
litellm/main.py
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@ -1078,6 +1078,91 @@ def completion(
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"api_base": api_base,
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},
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)
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elif (
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custom_llm_provider == "text-completion-openai"
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or (text_completion is True and custom_llm_provider == "openai")
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or "ft:babbage-002" in model
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or "ft:davinci-002" in model # support for finetuned completion models
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):
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openai.api_type = "openai"
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api_base = (
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api_base
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or litellm.api_base
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or get_secret("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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openai.api_version = None
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# set API KEY
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api_key = (
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api_key
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or litellm.api_key
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or litellm.openai_key
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or get_secret("OPENAI_API_KEY")
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)
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headers = headers or litellm.headers
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## LOAD CONFIG - if set
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config = litellm.OpenAITextCompletionConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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if litellm.organization:
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openai.organization = litellm.organization
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if (
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len(messages) > 0
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and "content" in messages[0]
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and type(messages[0]["content"]) == list
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):
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# text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content']
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# https://platform.openai.com/docs/api-reference/completions/create
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prompt = messages[0]["content"]
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else:
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prompt = " ".join([message["content"] for message in messages]) # type: ignore
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## COMPLETION CALL
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_response = openai_text_completions.completion(
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model=model,
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messages=messages,
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model_response=model_response,
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print_verbose=print_verbose,
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api_key=api_key,
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api_base=api_base,
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acompletion=acompletion,
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client=client, # pass AsyncOpenAI, OpenAI client
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logging_obj=logging,
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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timeout=timeout, # type: ignore
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)
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if (
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optional_params.get("stream", False) == False
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and acompletion == False
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and text_completion == False
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):
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# convert to chat completion response
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_response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
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response_object=_response, model_response_object=model_response
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)
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if optional_params.get("stream", False) or acompletion == True:
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## LOGGING
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logging.post_call(
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input=messages,
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api_key=api_key,
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original_response=_response,
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additional_args={"headers": headers},
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)
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response = _response
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elif (
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model in litellm.open_ai_chat_completion_models
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or custom_llm_provider == "custom_openai"
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@ -1164,89 +1249,6 @@ def completion(
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original_response=response,
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additional_args={"headers": headers},
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)
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elif (
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custom_llm_provider == "text-completion-openai"
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or "ft:babbage-002" in model
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or "ft:davinci-002" in model # support for finetuned completion models
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):
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openai.api_type = "openai"
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api_base = (
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api_base
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or litellm.api_base
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or get_secret("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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openai.api_version = None
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# set API KEY
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api_key = (
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api_key
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or litellm.api_key
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or litellm.openai_key
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or get_secret("OPENAI_API_KEY")
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)
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headers = headers or litellm.headers
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## LOAD CONFIG - if set
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config = litellm.OpenAITextCompletionConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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if litellm.organization:
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openai.organization = litellm.organization
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if (
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len(messages) > 0
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and "content" in messages[0]
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and type(messages[0]["content"]) == list
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):
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# text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content']
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# https://platform.openai.com/docs/api-reference/completions/create
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prompt = messages[0]["content"]
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else:
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prompt = " ".join([message["content"] for message in messages]) # type: ignore
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## COMPLETION CALL
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_response = openai_text_completions.completion(
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model=model,
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messages=messages,
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model_response=model_response,
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print_verbose=print_verbose,
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api_key=api_key,
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api_base=api_base,
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acompletion=acompletion,
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client=client, # pass AsyncOpenAI, OpenAI client
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logging_obj=logging,
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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timeout=timeout, # type: ignore
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)
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if (
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optional_params.get("stream", False) == False
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and acompletion == False
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and text_completion == False
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):
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# convert to chat completion response
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_response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
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response_object=_response, model_response_object=model_response
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)
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if optional_params.get("stream", False) or acompletion == True:
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## LOGGING
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logging.post_call(
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input=messages,
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api_key=api_key,
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original_response=_response,
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additional_args={"headers": headers},
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)
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response = _response
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elif (
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"replicate" in model
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or custom_llm_provider == "replicate"
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|
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