forked from phoenix/litellm-mirror
refactor(all-files): removing all print statements; adding pre-commit + flake8 to prevent future regressions
This commit is contained in:
Krrish Dholakia
parent
38ff9f2b6f
commit
6b40546e59
9 changed files with 39 additions and 50 deletions
2
.flake8
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2
.flake8
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@ -0,0 +1,2 @@
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[flake8]
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ignore = E,F,W,B,B9,C,D,I,N,S,W503,W504,E203, TCE,TCA,EXE999,E999,TD
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8
.pre-commit-config.yaml
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8
.pre-commit-config.yaml
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@ -0,0 +1,8 @@
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repos:
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- repo: https://github.com/pycqa/flake8
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rev: 3.8.4 # The version of flake8 to use
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hooks:
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- id: flake8
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exclude: ^litellm/tests/|^litellm/proxy/|^litellm/integrations/
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additional_dependencies: [flake8-print]
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files: litellm/.*\.py
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@ -14,7 +14,8 @@ class BudgetManager:
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def print_verbose(self, print_statement):
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if litellm.set_verbose:
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print(print_statement)
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import logging
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logging.info(print_statement)
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def load_data(self):
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if self.client_type == "local":
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@ -149,8 +150,6 @@ class BudgetManager:
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'project_name' : self.project_name,
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"user_dict": self.user_dict
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}
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print(f"data: {data}")
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response = requests.post(url, headers=headers, json=data)
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print(f"response: {response.text}")
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response = response.json()
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return response
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@ -8,7 +8,7 @@
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# Thank you users! We ❤️ you! - Krrish & Ishaan
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import litellm
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import time
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import time, logging
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import json, traceback
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@ -37,7 +37,6 @@ class RedisCache(BaseCache):
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def __init__(self, host, port, password):
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import redis
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# if users don't provider one, use the default litellm cache
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print(f"HOST: {host}; PORT: {port}; PASSWORD: {password}")
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self.redis_client = redis.Redis(host=host, port=port, password=password)
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def set_cache(self, key, value, **kwargs):
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@ -46,7 +45,7 @@ class RedisCache(BaseCache):
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self.redis_client.set(name=key, value=str(value), ex=ttl)
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except Exception as e:
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# NON blocking - notify users Redis is throwing an exception
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print("LiteLLM Caching: set() - Got exception from REDIS : ", e)
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logging.debug("LiteLLM Caching: set() - Got exception from REDIS : ", e)
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def get_cache(self, key, **kwargs):
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try:
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@ -61,13 +60,13 @@ class RedisCache(BaseCache):
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except Exception as e:
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# NON blocking - notify users Redis is throwing an exception
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traceback.print_exc()
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print("LiteLLM Caching: get() - Got exception from REDIS: ", e)
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logging.debug("LiteLLM Caching: get() - Got exception from REDIS: ", e)
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class HostedCache(BaseCache):
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def set_cache(self, key, value, **kwargs):
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if "ttl" in kwargs:
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print("LiteLLM Caching: TTL is not supported for hosted cache!")
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logging.debug("LiteLLM Caching: TTL is not supported for hosted cache!")
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# make a post request to api.litellm.ai/set_cache
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import requests
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url = f"https://api.litellm.ai/set_cache?key={key}&value={str(value)}"
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@ -200,12 +199,10 @@ class Cache:
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cached_result = self.cache.get_cache(cache_key)
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if cached_result != None and 'stream' in kwargs and kwargs['stream'] == True:
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# if streaming is true and we got a cache hit, return a generator
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# print("cache hit and stream=True")
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# print(cached_result)
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return self.generate_streaming_content(cached_result["choices"][0]['message']['content'])
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return cached_result
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except Exception as e:
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print(f"An exception occurred: {traceback.format_exc()}")
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logging.debug(f"An exception occurred: {traceback.format_exc()}")
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return None
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def add_cache(self, result, *args, **kwargs):
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@ -224,10 +221,7 @@ class Cache:
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cache_key = kwargs["cache_key"]
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else:
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cache_key = self.get_cache_key(*args, **kwargs)
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# print("adding to cache", cache_key, result)
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# print(cache_key)
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if cache_key is not None:
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# print("adding to cache", cache_key, result)
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self.cache.set_cache(cache_key, result, **kwargs)
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except:
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pass
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@ -36,10 +36,6 @@ class LangFuseLogger:
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print_verbose(
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f"Langfuse Logging - Enters logging function for model {kwargs}"
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)
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# print(response_obj)
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# print(response_obj['choices'][0]['message']['content'])
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# print(response_obj['usage']['prompt_tokens'])
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# print(response_obj['usage']['completion_tokens'])
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metadata = kwargs.get("metadata", {})
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prompt = [kwargs['messages']]
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@ -397,7 +397,6 @@ def completion(
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outputText = response_body.get('results')[0].get('outputText')
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response_metadata = response.get("ResponseMetadata", {})
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print(f"response_metadata: {response_metadata}")
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if response_metadata.get("HTTPStatusCode", 500) >= 400:
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raise BedrockError(
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message=outputText,
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@ -147,7 +147,6 @@ def get_ollama_response_stream(
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yield completion_obj
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except Exception as e:
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traceback.print_exc()
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print(f"Error decoding JSON: {e}")
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session.close()
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if async_generator_imported:
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@ -198,5 +197,6 @@ if async_generator_imported:
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completion_obj["content"] = j["response"]
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await yield_({"choices": [{"delta": completion_obj}]})
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except Exception as e:
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print(f"Error decoding JSON: {e}")
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import logging
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logging.debug(f"Error decoding JSON: {e}")
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session.close()
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@ -1624,13 +1624,9 @@ def batch_completion_models_all_responses(*args, **kwargs):
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with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor:
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for idx, model in enumerate(models):
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print(f"{GREEN}LiteLLM: Making request to model: {model}{RESET}")
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future = executor.submit(completion, *args, model=model, **kwargs)
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if future.result() is not None:
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responses.append(future.result())
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print(f"{GREEN}LiteLLM: Model {model} returned response{RESET}")
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else:
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print(f"{RED}LiteLLM: Model {model } did not return a response{RESET}")
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return responses
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@ -1863,6 +1859,7 @@ def embedding(
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###### Text Completion ################
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def text_completion(*args, **kwargs):
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global print_verbose
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import copy
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"""
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This maps to the Openai.Completion.create format, which has a different I/O (accepts prompt, returning ["choices"]["text"].
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@ -1930,7 +1927,7 @@ def text_completion(*args, **kwargs):
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raw_response = response._hidden_params.get("original_response", None)
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transformed_logprobs = litellm.utils.transform_logprobs(raw_response)
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except Exception as e:
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print("LiteLLM non blocking exception", e)
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print_verbose("LiteLLM non blocking exception", e)
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text_completion_response["id"] = response["id"]
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text_completion_response["object"] = "text_completion"
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text_completion_response["created"] = response["created"]
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@ -1964,7 +1961,8 @@ def moderation(input: str, api_key: Optional[str]=None):
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## Set verbose to true -> ```litellm.set_verbose = True```
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def print_verbose(print_statement):
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if litellm.set_verbose:
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print(f"LiteLLM: {print_statement}")
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import logging
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logging.info(f"LiteLLM: {print_statement}")
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def config_completion(**kwargs):
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if litellm.config_path != None:
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@ -285,7 +285,8 @@ class TextCompletionResponse(OpenAIObject):
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############################################################
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def print_verbose(print_statement):
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if litellm.set_verbose:
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print(f"LiteLLM: {print_statement}")
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import logging
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logging.info(f"LiteLLM: {print_statement}")
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####### LOGGING ###################
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from enum import Enum
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@ -538,8 +539,6 @@ class Logging:
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print_verbose("reaches api manager for updating model cost")
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litellm.apiManager.update_cost(completion_obj=result, user=self.user)
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if callback == "cache":
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# print("entering logger first time")
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# print(self.litellm_params["stream_response"])
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if litellm.cache != None and self.model_call_details.get('optional_params', {}).get('stream', False) == True:
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litellm_call_id = self.litellm_params["litellm_call_id"]
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if litellm_call_id in self.litellm_params["stream_response"]:
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@ -550,10 +549,7 @@ class Logging:
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self.litellm_params["stream_response"][litellm_call_id]["choices"][0]["message"]["content"] += result["content"]
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else: # init a streaming response for this call id
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new_model_response = ModelResponse(choices=[Choices(message=Message(content="default"))])
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#print("creating new model response")
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#print(new_model_response)
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self.litellm_params["stream_response"][litellm_call_id] = new_model_response
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#print("adding to cache for", litellm_call_id)
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litellm.cache.add_cache(self.litellm_params["stream_response"][litellm_call_id], **self.model_call_details)
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if callback == "promptlayer":
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print_verbose("reaches promptlayer for logging!")
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@ -576,7 +572,6 @@ class Logging:
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print_verbose("reaches supabase for streaming logging!")
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result = kwargs["complete_streaming_response"]
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# print(kwargs)
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model = kwargs["model"]
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messages = kwargs["messages"]
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optional_params = kwargs.get("optional_params", {})
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@ -732,11 +727,11 @@ def exception_logging(
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model_call_details
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) # Expectation: any logger function passed in by the user should accept a dict object
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except Exception as e:
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print(
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
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)
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except Exception as e:
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print(
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
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)
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pass
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@ -799,7 +794,6 @@ def client(original_function):
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return logging_obj
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except Exception as e: # DO NOT BLOCK running the function because of this
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print_verbose(f"[Non-Blocking] {traceback.format_exc()}; args - {args}; kwargs - {kwargs}")
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print(e)
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pass
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def crash_reporting(*args, **kwargs):
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@ -1776,9 +1770,9 @@ def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None, api_
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custom_llm_provider = "bedrock"
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if custom_llm_provider is None or custom_llm_provider=="":
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print()
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print("\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m")
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print()
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print() # noqa
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print("\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m") # noqa
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print() # noqa
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raise ValueError(f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/{model}',..)` Learn more: https://docs.litellm.ai/docs/providers")
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return model, custom_llm_provider, dynamic_api_key, api_base
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except Exception as e:
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@ -2772,7 +2766,7 @@ def get_all_keys(llm_provider=None):
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def get_model_list():
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global last_fetched_at
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global last_fetched_at, print_verbose
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try:
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# if user is using hosted product -> get their updated model list
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user_email = (
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@ -2784,7 +2778,7 @@ def get_model_list():
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if user_email:
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# make the api call
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last_fetched_at = time.time()
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print(f"last_fetched_at: {last_fetched_at}")
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print_verbose(f"last_fetched_at: {last_fetched_at}")
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response = requests.post(
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url="http://api.litellm.ai/get_model_list",
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headers={"content-type": "application/json"},
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@ -2820,10 +2814,10 @@ def exception_type(
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global user_logger_fn, liteDebuggerClient
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exception_mapping_worked = False
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if litellm.suppress_debug_info is False:
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print()
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print("\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m")
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print("LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'.")
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print()
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print() # noqa
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print("\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m") # noqa
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print("LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'.") # noqa
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print() # noqa
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try:
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if isinstance(original_exception, OriginalError):
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# Handle the OpenAIError
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@ -3401,7 +3395,7 @@ def exception_type(
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model=model
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)
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elif hasattr(original_exception, "status_code"):
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print(f"status code: {original_exception.status_code}")
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print_verbose(f"status code: {original_exception.status_code}")
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if original_exception.status_code == 401:
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exception_mapping_worked = True
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raise AuthenticationError(
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@ -4267,12 +4261,11 @@ def completion_with_fallbacks(**kwargs):
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return response
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except Exception as e:
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print(e)
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print_verbose(e)
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rate_limited_models.add(model)
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model_expiration_times[model] = (
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time.time() + 60
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) # cool down this selected model
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# print(f"rate_limited_models {rate_limited_models}")
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pass
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return response
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@ -4417,7 +4410,7 @@ def trim_messages(
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return final_messages
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except Exception as e: # [NON-Blocking, if error occurs just return final_messages
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print("Got exception while token trimming", e)
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print_verbose("Got exception while token trimming", e)
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return messages
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def get_valid_models():
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