forked from phoenix/litellm-mirror
new logger client
This commit is contained in:
Krrish Dholakia
parent
f47faa1139
commit
e8eb92c108
9 changed files with 235 additions and 195 deletions
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@ -92,6 +92,7 @@ def completion(
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custom_llm_provider=None,
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custom_api_base=None,
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litellm_call_id=None,
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litellm_logging_obj=None,
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# model specific optional params
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# used by text-bison only
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top_k=40,
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@ -100,6 +101,7 @@ def completion(
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) -> ModelResponse:
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args = locals()
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try:
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logging = litellm_logging_obj
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if fallbacks != []:
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return completion_with_fallbacks(**args)
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if litellm.model_alias_map and model in litellm.model_alias_map:
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@ -151,12 +153,7 @@ def completion(
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litellm_call_id=litellm_call_id,
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model_alias_map=litellm.model_alias_map,
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)
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logging = Logging(
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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)
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logging.update_environment_variables(optional_params=optional_params, litellm_params=litellm_params)
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if custom_llm_provider == "azure":
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# azure configs
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openai.api_type = "azure"
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@ -306,7 +303,7 @@ def completion(
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response = openai.Completion.create(model=model, prompt=prompt, **optional_params)
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if "stream" in optional_params and optional_params["stream"] == True:
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response = CustomStreamWrapper(response, model)
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response = CustomStreamWrapper(response, model, logging_obj=logging)
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return response
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## LOGGING
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logging.post_call(
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@ -363,7 +360,7 @@ def completion(
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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# let the stream handler know this is replicate
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response = CustomStreamWrapper(output, "replicate")
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response = CustomStreamWrapper(output, "replicate", logging_obj=logging)
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return response
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response = ""
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for item in output:
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@ -413,7 +410,7 @@ def completion(
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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response = CustomStreamWrapper(model_response, model)
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response = CustomStreamWrapper(model_response, model, logging_obj=logging)
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return response
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response = model_response
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elif model in litellm.openrouter_models or custom_llm_provider == "openrouter":
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@ -486,7 +483,7 @@ def completion(
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response = co.generate(model=model, prompt=prompt, **optional_params)
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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response = CustomStreamWrapper(response, model)
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response = CustomStreamWrapper(response, model, logging_obj=logging)
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return response
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## LOGGING
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logging.post_call(
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@ -532,7 +529,7 @@ def completion(
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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response = CustomStreamWrapper(
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model_response, model, custom_llm_provider="huggingface"
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model_response, model, custom_llm_provider="huggingface", logging_obj=logging
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)
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return response
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response = model_response
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@ -572,7 +569,7 @@ def completion(
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headers=headers,
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)
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response = CustomStreamWrapper(
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res.iter_lines(), model, custom_llm_provider="together_ai"
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res.iter_lines(), model, custom_llm_provider="together_ai", logging_obj=logging
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)
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return response
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else:
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@ -689,7 +686,7 @@ def completion(
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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response = CustomStreamWrapper(
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model_response, model, custom_llm_provider="ai21"
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model_response, model, custom_llm_provider="ai21", logging_obj=logging
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)
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return response
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@ -732,7 +729,7 @@ def completion(
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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response = CustomStreamWrapper(
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model_response, model, custom_llm_provider="baseten"
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model_response, model, custom_llm_provider="baseten", logging_obj=logging
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)
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return response
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response = model_response
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@ -775,8 +772,6 @@ def completion(
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)
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return response
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except Exception as e:
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## LOGGING
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logging.post_call(input=messages, api_key=api_key, original_response=e)
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## Map to OpenAI Exception
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raise exception_type(
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model=model, custom_llm_provider=custom_llm_provider, original_exception=e
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@ -816,21 +811,12 @@ def batch_completion(*args, **kwargs):
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60
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) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
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def embedding(
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model, input=[], azure=False, force_timeout=60, litellm_call_id=None, logger_fn=None
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model, input=[], azure=False, force_timeout=60, litellm_call_id=None, litellm_logging_obj=None, logger_fn=None
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):
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try:
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response = None
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logging = Logging(
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model=model,
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messages=input,
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optional_params={},
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litellm_params={
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"azure": azure,
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"force_timeout": force_timeout,
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"logger_fn": logger_fn,
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"litellm_call_id": litellm_call_id,
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},
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)
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logging = litellm_logging_obj
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logging.update_environment_variables(optional_params={}, litellm_params={"force_timeout": force_timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
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if azure == True:
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# azure configs
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openai.api_type = "azure"
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@ -849,7 +835,6 @@ def embedding(
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)
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## EMBEDDING CALL
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response = openai.Embedding.create(input=input, engine=model)
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print_verbose(f"response_value: {str(response)[:100]}")
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elif model in litellm.open_ai_embedding_models:
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openai.api_type = "openai"
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openai.api_base = "https://api.openai.com/v1"
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@ -867,15 +852,13 @@ def embedding(
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)
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## EMBEDDING CALL
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response = openai.Embedding.create(input=input, model=model)
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print_verbose(f"response_value: {str(response)[:100]}")
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else:
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args = locals()
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raise ValueError(f"No valid embedding model args passed in - {args}")
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## LOGGING
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logging.post_call(input=input, api_key=openai.api_key, original_response=response)
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return response
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except Exception as e:
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## LOGGING
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logging.post_call(input=input, api_key=openai.api_key, original_response=e)
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## Map to OpenAI Exception
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raise exception_type(
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model=model,
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