mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
bug fixes and updates
This commit is contained in:
Krrish Dholakia
parent
74ddafb7ad
commit
104b9f21b0
17 changed files with 646 additions and 330 deletions
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@ -1,49 +1,77 @@
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import os, openai, cohere, replicate, sys
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from typing import Any
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from func_timeout import func_set_timeout, FunctionTimedOut
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from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
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import json
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import traceback
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import threading
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import dotenv
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import traceback
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import subprocess
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import litellm
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from litellm import client, logging, exception_type, timeout, success_callback, failure_callback
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import random
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####### ENVIRONMENT VARIABLES ###################
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# Loading env variables using dotenv
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dotenv.load_dotenv()
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set_verbose = False
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####### COMPLETION MODELS ###################
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open_ai_chat_completion_models = [
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'gpt-3.5-turbo',
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'gpt-4'
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]
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open_ai_text_completion_models = [
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'text-davinci-003'
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]
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cohere_models = [
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'command-nightly',
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]
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anthropic_models = [
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"claude-2",
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"claude-instant-1"
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]
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####### EMBEDDING MODELS ###################
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open_ai_embedding_models = [
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'text-embedding-ada-002'
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]
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#############################################
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dotenv.load_dotenv() # Loading env variables using dotenv
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def get_optional_params(
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# 12 optional params
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functions = [],
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function_call = "",
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temperature = 1,
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top_p = 1,
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n = 1,
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stream = False,
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stop = None,
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max_tokens = float('inf'),
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presence_penalty = 0,
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frequency_penalty = 0,
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logit_bias = {},
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user = "",
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):
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optional_params = {}
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if functions != []:
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optional_params["functions"] = functions
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if function_call != "":
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optional_params["function_call"] = function_call
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if temperature != 1:
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optional_params["temperature"] = temperature
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if top_p != 1:
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optional_params["top_p"] = top_p
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if n != 1:
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optional_params["n"] = n
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if stream:
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optional_params["stream"] = stream
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if stop != None:
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optional_params["stop"] = stop
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if max_tokens != float('inf'):
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optional_params["max_tokens"] = max_tokens
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if presence_penalty != 0:
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optional_params["presence_penalty"] = presence_penalty
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if frequency_penalty != 0:
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optional_params["frequency_penalty"] = frequency_penalty
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if logit_bias != {}:
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optional_params["logit_bias"] = logit_bias
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if user != "":
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optional_params["user"] = user
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return optional_params
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####### COMPLETION ENDPOINTS ################
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#############################################
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@func_set_timeout(10, allowOverride=True) ## https://pypi.org/project/func-timeout/ - timeouts, in case calls hang (e.g. Azure)
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def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None):
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@client
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@timeout(60) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
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def completion(
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model, messages, # required params
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# Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
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functions=[], function_call="", # optional params
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temperature=1, top_p=1, n=1, stream=False, stop=None, max_tokens=float('inf'),
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presence_penalty=0, frequency_penalty=0, logit_bias={}, user="",
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# Optional liteLLM function params
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*, force_timeout=60, azure=False, logger_fn=None, verbose=False
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):
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try:
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# check if user passed in any of the OpenAI optional params
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optional_params = get_optional_params(
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functions=functions, function_call=function_call,
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temperature=temperature, top_p=top_p, n=n, stream=stream, stop=stop, max_tokens=max_tokens,
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presence_penalty=presence_penalty, frequency_penalty=frequency_penalty, logit_bias=logit_bias, user=user
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)
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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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@ -51,21 +79,49 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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openai.api_version = os.environ.get("AZURE_API_VERSION")
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openai.api_key = os.environ.get("AZURE_API_KEY")
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## LOGGING
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logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
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## COMPLETION CALL
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response = openai.ChatCompletion.create(
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engine=model,
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messages = messages
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messages = messages,
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**optional_params
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)
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elif "replicate" in model:
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elif model in litellm.open_ai_chat_completion_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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openai.api_version = None
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openai.api_key = os.environ.get("OPENAI_API_KEY")
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## LOGGING
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logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
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## COMPLETION CALL
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response = openai.ChatCompletion.create(
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model=model,
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messages = messages,
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**optional_params
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)
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elif model in litellm.open_ai_text_completion_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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openai.api_version = None
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openai.api_key = os.environ.get("OPENAI_API_KEY")
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prompt = " ".join([message["content"] for message in messages])
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## LOGGING
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logging(model=model, input=prompt, azure=azure, logger_fn=logger_fn)
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## COMPLETION CALL
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response = openai.Completion.create(
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model=model,
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prompt = prompt
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)
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elif "replicate" in model:
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# replicate defaults to os.environ.get("REPLICATE_API_TOKEN")
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# checking in case user set it to REPLICATE_API_KEY instead
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if not os.environ.get("REPLICATE_API_TOKEN") and os.environ.get("REPLICATE_API_KEY"):
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if not os.environ.get("REPLICATE_API_TOKEN") and os.environ.get("REPLICATE_API_KEY"):
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replicate_api_token = os.environ.get("REPLICATE_API_KEY")
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os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
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prompt = " ".join([message["content"] for message in messages])
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input = [{"prompt": prompt}]
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if max_tokens:
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input = {"prompt": prompt}
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if max_tokens != float('inf'):
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input["max_length"] = max_tokens # for t5 models
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input["max_new_tokens"] = max_tokens # for llama2 models
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## LOGGING
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@ -90,7 +146,7 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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]
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}
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response = new_response
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elif model in anthropic_models:
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elif model in litellm.anthropic_models:
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#anthropic defaults to os.environ.get("ANTHROPIC_API_KEY")
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prompt = f"{HUMAN_PROMPT}"
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for message in messages:
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@ -103,9 +159,10 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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prompt += f"{HUMAN_PROMPT}{message['content']}"
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prompt += f"{AI_PROMPT}"
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anthropic = Anthropic()
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if max_tokens:
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# check if user passed in max_tokens != float('inf')
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if max_tokens != float('inf'):
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max_tokens_to_sample = max_tokens
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else:
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else:
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max_tokens_to_sample = 300 # default in Anthropic docs https://docs.anthropic.com/claude/reference/client-libraries
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## LOGGING
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logging(model=model, input=prompt, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
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@ -127,9 +184,9 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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}
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]
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}
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print(f"new response: {new_response}")
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print_verbose(f"new response: {new_response}")
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response = new_response
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elif model in cohere_models:
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elif model in litellm.cohere_models:
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cohere_key = os.environ.get("COHERE_API_KEY")
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co = cohere.Client(cohere_key)
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prompt = " ".join([message["content"] for message in messages])
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@ -146,7 +203,7 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": response[0],
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"content": response[0].text,
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"role": "assistant"
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}
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}
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@ -154,7 +211,7 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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}
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response = new_response
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elif model in open_ai_chat_completion_models:
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elif model in litellm.open_ai_chat_completion_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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openai.api_version = None
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@ -166,7 +223,7 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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model=model,
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messages = messages
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)
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elif model in open_ai_text_completion_models:
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elif model in litellm.open_ai_text_completion_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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openai.api_version = None
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@ -181,249 +238,59 @@ def completion(model, messages, max_tokens=None, forceTimeout=10, azure=False, l
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)
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else:
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logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
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args = locals()
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raise ValueError(f"No valid completion model args passed in - {args}")
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return response
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except Exception as e:
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logging(model=model, input=messages, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
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raise e
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# log the original exception
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logging(model=model, input=messages, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn, exception=e)
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## Map to OpenAI Exception
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raise exception_type(model=model, original_exception=e)
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### EMBEDDING ENDPOINTS ####################
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@func_set_timeout(60, allowOverride=True) ## https://pypi.org/project/func-timeout/
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def embedding(model, input=[], azure=False, forceTimeout=60, logger_fn=None):
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response = None
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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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openai.api_base = os.environ.get("AZURE_API_BASE")
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openai.api_version = os.environ.get("AZURE_API_VERSION")
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openai.api_key = os.environ.get("AZURE_API_KEY")
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## LOGGING
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logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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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)[:50]}")
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elif model in 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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openai.api_version = None
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openai.api_key = os.environ.get("OPENAI_API_KEY")
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## LOGGING
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logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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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)[:50]}")
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else:
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logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
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return response
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### CLIENT CLASS #################### make it easy to push completion/embedding runs to different sources -> sentry/posthog/slack, etc.
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class litellm_client:
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def __init__(self, success_callback=[], failure_callback=[], verbose=False): # Constructor
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set_verbose = verbose
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self.success_callback = success_callback
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self.failure_callback = failure_callback
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self.logger_fn = None # if user passes in their own logging function
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self.callback_list = list(set(self.success_callback + self.failure_callback))
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self.set_callbacks()
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## COMPLETION CALL
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def completion(self, model, messages, max_tokens=None, forceTimeout=10, azure=False, logger_fn=None, additional_details={}) -> Any:
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try:
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self.logger_fn = logger_fn
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response = completion(model=model, messages=messages, max_tokens=max_tokens, forceTimeout=forceTimeout, azure=azure, logger_fn=self.handle_input)
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my_thread = threading.Thread(target=self.handle_success, args=(model, messages, additional_details)) # don't interrupt execution of main thread
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my_thread.start()
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return response
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except Exception as e:
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args = locals() # get all the param values
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self.handle_failure(e, args)
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raise e
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## EMBEDDING CALL
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def embedding(self, model, input=[], azure=False, logger_fn=None, forceTimeout=60, additional_details={}) -> Any:
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try:
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self.logger_fn = logger_fn
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response = embedding(model, input, azure=azure, logger_fn=self.handle_input)
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my_thread = threading.Thread(target=self.handle_success, args=(model, input, additional_details)) # don't interrupt execution of main thread
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my_thread.start()
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return response
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except Exception as e:
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args = locals() # get all the param values
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self.handle_failure(e, args)
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raise e
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def set_callbacks(self): #instantiate any external packages
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for callback in self.callback_list: # only install what's required
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if callback == "sentry":
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try:
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import sentry_sdk
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except ImportError:
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print_verbose("Package 'sentry_sdk' is missing. Installing it...")
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subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'sentry_sdk'])
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import sentry_sdk
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self.sentry_sdk = sentry_sdk
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self.sentry_sdk.init(dsn=os.environ.get("SENTRY_API_URL"), traces_sample_rate=float(os.environ.get("SENTRY_API_TRACE_RATE")))
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self.capture_exception = self.sentry_sdk.capture_exception
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self.add_breadcrumb = self.sentry_sdk.add_breadcrumb
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elif callback == "posthog":
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try:
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from posthog import Posthog
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except:
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print_verbose("Package 'posthog' is missing. Installing it...")
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subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'posthog'])
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from posthog import Posthog
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self.posthog = Posthog(
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project_api_key=os.environ.get("POSTHOG_API_KEY"),
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host=os.environ.get("POSTHOG_API_URL"))
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elif callback == "slack":
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try:
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from slack_bolt import App
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except ImportError:
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print_verbose("Package 'slack_bolt' is missing. Installing it...")
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subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'slack_bolt'])
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from slack_bolt import App
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self.slack_app = App(
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token=os.environ.get("SLACK_API_TOKEN"),
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signing_secret=os.environ.get("SLACK_API_SECRET")
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)
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self.alerts_channel = os.environ["SLACK_API_CHANNEL"]
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def handle_input(self, model_call_details={}):
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if len(model_call_details.keys()) > 0:
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model = model_call_details["model"] if "model" in model_call_details else None
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if model:
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for callback in self.callback_list:
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if callback == "sentry": # add a sentry breadcrumb if user passed in sentry integration
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self.add_breadcrumb(
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category=f'{model}',
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message='Trying request model {} input {}'.format(model, json.dumps(model_call_details)),
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level='info',
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)
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if self.logger_fn and callable(self.logger_fn):
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self.logger_fn(model_call_details)
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pass
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def handle_success(self, model, messages, additional_details):
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success_handler = additional_details.pop("success_handler", None)
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failure_handler = additional_details.pop("failure_handler", None)
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additional_details["litellm_model"] = str(model)
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additional_details["litellm_messages"] = str(messages)
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for callback in self.success_callback:
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try:
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if callback == "posthog":
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ph_obj = {}
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for detail in additional_details:
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ph_obj[detail] = additional_details[detail]
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event_name = additional_details["successful_event"] if "successful_event" in additional_details else "litellm.succes_query"
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if "user_id" in additional_details:
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self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
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else:
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self.posthog.capture(event_name, ph_obj)
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pass
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elif callback == "slack":
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slack_msg = ""
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if len(additional_details.keys()) > 0:
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for detail in additional_details:
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slack_msg += f"{detail}: {additional_details[detail]}\n"
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slack_msg += f"Successful call"
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self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
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except:
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pass
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|
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if success_handler and callable(success_handler):
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call_details = {
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"model": model,
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"messages": messages,
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"additional_details": additional_details
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}
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success_handler(call_details)
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pass
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def handle_failure(self, exception, args):
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args.pop("self")
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additional_details = args.pop("additional_details", {})
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success_handler = additional_details.pop("success_handler", None)
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failure_handler = additional_details.pop("failure_handler", None)
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for callback in self.failure_callback:
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||||
try:
|
||||
if callback == "slack":
|
||||
slack_msg = ""
|
||||
for param in args:
|
||||
slack_msg += f"{param}: {args[param]}\n"
|
||||
if len(additional_details.keys()) > 0:
|
||||
for detail in additional_details:
|
||||
slack_msg += f"{detail}: {additional_details[detail]}\n"
|
||||
slack_msg += f"Traceback: {traceback.format_exc()}"
|
||||
self.slack_app.client.chat_postMessage(channel=self.alerts_channel, text=slack_msg)
|
||||
elif callback == "sentry":
|
||||
self.capture_exception(exception)
|
||||
elif callback == "posthog":
|
||||
if len(additional_details.keys()) > 0:
|
||||
ph_obj = {}
|
||||
for param in args:
|
||||
ph_obj[param] += args[param]
|
||||
for detail in additional_details:
|
||||
ph_obj[detail] = additional_details[detail]
|
||||
event_name = additional_details["failed_event"] if "failed_event" in additional_details else "litellm.failed_query"
|
||||
if "user_id" in additional_details:
|
||||
self.posthog.capture(additional_details["user_id"], event_name, ph_obj)
|
||||
else:
|
||||
self.posthog.capture(event_name, ph_obj)
|
||||
else:
|
||||
pass
|
||||
except:
|
||||
print(f"got an error calling {callback} - {traceback.format_exc()}")
|
||||
|
||||
if failure_handler and callable(failure_handler):
|
||||
call_details = {
|
||||
"exception": exception,
|
||||
"additional_details": additional_details
|
||||
}
|
||||
failure_handler(call_details)
|
||||
pass
|
||||
####### HELPER FUNCTIONS ################
|
||||
|
||||
#Logging function -> log the exact model details + what's being sent | Non-Blocking
|
||||
def logging(model, input, azure=False, additional_args={}, logger_fn=None):
|
||||
@client
|
||||
@timeout(60) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
|
||||
def embedding(model, input=[], azure=False, force_timeout=60, logger_fn=None):
|
||||
try:
|
||||
model_call_details = {}
|
||||
model_call_details["model"] = model
|
||||
model_call_details["input"] = input
|
||||
model_call_details["azure"] = azure
|
||||
model_call_details["additional_args"] = additional_args
|
||||
if logger_fn and callable(logger_fn):
|
||||
try:
|
||||
# log additional call details -> api key, etc.
|
||||
if azure == True or model in open_ai_chat_completion_models or model in open_ai_chat_completion_models or model in open_ai_embedding_models:
|
||||
model_call_details["api_type"] = openai.api_type
|
||||
model_call_details["api_base"] = openai.api_base
|
||||
model_call_details["api_version"] = openai.api_version
|
||||
model_call_details["api_key"] = openai.api_key
|
||||
elif "replicate" in model:
|
||||
model_call_details["api_key"] = os.environ.get("REPLICATE_API_TOKEN")
|
||||
elif model in anthropic_models:
|
||||
model_call_details["api_key"] = os.environ.get("ANTHROPIC_API_KEY")
|
||||
elif model in cohere_models:
|
||||
model_call_details["api_key"] = os.environ.get("COHERE_API_KEY")
|
||||
|
||||
logger_fn(model_call_details) # Expectation: any logger function passed in by the user should accept a dict object
|
||||
except:
|
||||
print_verbose(f"Basic model call details: {model_call_details}")
|
||||
print_verbose(f"[Non-Blocking] Exception occurred while logging {traceback.format_exc()}")
|
||||
pass
|
||||
else:
|
||||
print_verbose(f"Basic model call details: {model_call_details}")
|
||||
pass
|
||||
except:
|
||||
pass
|
||||
|
||||
## Set verbose to true -> ```litellm.verbose = True```
|
||||
response = None
|
||||
if azure == True:
|
||||
# azure configs
|
||||
openai.api_type = "azure"
|
||||
openai.api_base = os.environ.get("AZURE_API_BASE")
|
||||
openai.api_version = os.environ.get("AZURE_API_VERSION")
|
||||
openai.api_key = os.environ.get("AZURE_API_KEY")
|
||||
## LOGGING
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
|
||||
## EMBEDDING CALL
|
||||
response = openai.Embedding.create(input=input, engine=model)
|
||||
print_verbose(f"response_value: {str(response)[:50]}")
|
||||
elif model in litellm.open_ai_embedding_models:
|
||||
openai.api_type = "openai"
|
||||
openai.api_base = "https://api.openai.com/v1"
|
||||
openai.api_version = None
|
||||
openai.api_key = os.environ.get("OPENAI_API_KEY")
|
||||
## LOGGING
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
|
||||
## EMBEDDING CALL
|
||||
response = openai.Embedding.create(input=input, model=model)
|
||||
print_verbose(f"response_value: {str(response)[:50]}")
|
||||
else:
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn)
|
||||
args = locals()
|
||||
raise ValueError(f"No valid embedding model args passed in - {args}")
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
# log the original exception
|
||||
logging(model=model, input=input, azure=azure, logger_fn=logger_fn, exception=e)
|
||||
## Map to OpenAI Exception
|
||||
raise exception_type(model=model, original_exception=e)
|
||||
####### HELPER FUNCTIONS ################
|
||||
## Set verbose to true -> ```litellm.set_verbose = True```
|
||||
def print_verbose(print_statement):
|
||||
if set_verbose:
|
||||
if litellm.set_verbose:
|
||||
print(f"LiteLLM: {print_statement}")
|
||||
print("Get help - https://discord.com/invite/wuPM9dRgDw")
|
||||
if random.random() <= 0.3:
|
||||
print("Get help - https://discord.com/invite/wuPM9dRgDw")
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue