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test(test_custom_callback_unit.py): adding unit tests for custom callbacks + fixing related bugs

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Krrish Dholakia 2023-12-11 11:38:28 -08:00
parent 1d2f5ce975
commit ea89a8a938
8 changed files with 501 additions and 122 deletions
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44
litellm/main.py
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@ -319,7 +319,6 @@ def completion(
######### unpacking kwargs #####################
args = locals()
api_base = kwargs.get('api_base', None)
return_async = kwargs.get('return_async', False)
mock_response = kwargs.get('mock_response', None)
force_timeout= kwargs.get('force_timeout', 600) ## deprecated
logger_fn = kwargs.get('logger_fn', None)
@ -351,7 +350,7 @@ def completion(
client = kwargs.get("client", None)
######## end of unpacking kwargs ###########
openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries"]
litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "model_info", "proxy_server_request", "preset_cache_key"]
litellm_params = ["metadata", "acompletion", "caching", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "model_info", "proxy_server_request", "preset_cache_key"]
default_params = openai_params + litellm_params
non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
if mock_response:
@ -449,7 +448,6 @@ def completion(
# For logging - save the values of the litellm-specific params passed in
litellm_params = get_litellm_params(
acompletion=acompletion,
return_async=return_async,
api_key=api_key,
force_timeout=force_timeout,
logger_fn=logger_fn,
@ -526,17 +524,18 @@ def completion(
client=client # pass AsyncAzureOpenAI, AzureOpenAI client
)
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={
"headers": headers,
"api_version": api_version,
"api_base": api_base,
},
)
if optional_params.get("stream", False) or acompletion == True:
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={
"headers": headers,
"api_version": api_version,
"api_base": api_base,
},
)
elif (
model in litellm.open_ai_chat_completion_models
or custom_llm_provider == "custom_openai"
@ -606,13 +605,14 @@ def completion(
)
raise e
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={"headers": headers},
)
if optional_params.get("stream", False) or acompletion == True:
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={"headers": headers},
)
elif (
custom_llm_provider == "text-completion-openai"
or "ft:babbage-002" in model
@ -1787,7 +1787,7 @@ def embedding(
proxy_server_request = kwargs.get("proxy_server_request", None)
aembedding = kwargs.pop("aembedding", None)
openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries", "encoding_format"]
litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "proxy_server_request", "model_info", "preset_cache_key"]
litellm_params = ["metadata", "acompletion", "caching", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "proxy_server_request", "model_info", "preset_cache_key"]
default_params = openai_params + litellm_params
non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
optional_params = {}