mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 02:34:29 +00:00
503 lines
19 KiB
Python
503 lines
19 KiB
Python
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 uuid
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####### ENVIRONMENT VARIABLES ###################
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dotenv.load_dotenv() # Loading env variables using dotenv
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set_verbose = False
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sentry_sdk_instance = None
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capture_exception = None
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add_breadcrumb = None
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posthog = None
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slack_app = None
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alerts_channel = None
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success_callback = []
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failure_callback = []
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callback_list = []
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user_logger_fn = None
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additional_details = {}
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## Set verbose to true -> ```litellm.verbose = True```
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def print_verbose(print_statement):
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if set_verbose:
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print(f"LiteLLM: {print_statement}")
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print("Get help - https://discord.com/invite/wuPM9dRgDw")
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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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####### CLIENT ################### make it easy to log completion/embedding runs
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def client(original_function):
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def function_setup(): #just run once to check if user wants to send their data anywhere
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try:
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if len(success_callback) > 0 or len(failure_callback) > 0 and len(callback_list) == 0:
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callback_list = list(set(success_callback + failure_callback))
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set_callbacks(callback_list=callback_list)
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except: # DO NOT BLOCK running the function because of this
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print_verbose(f"[Non-Blocking] {traceback.format_exc()}")
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pass
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def wrapper(*args, **kwargs):
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# Code to be executed before the embedding function
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try:
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function_setup()
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## EMBEDDING CALL
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result = original_function(*args, **kwargs)
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## LOG SUCCESS
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my_thread = threading.Thread(target=handle_success, args=(args, kwargs)) # don't interrupt execution of main thread
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my_thread.start()
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return result
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except Exception as e:
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traceback_exception = traceback.format_exc()
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my_thread = threading.Thread(target=handle_failure, args=(e, traceback.format_exc(), args, kwargs)) # don't interrupt execution of main thread
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my_thread.start()
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raise e
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return wrapper
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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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@client
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@func_set_timeout(60, allowOverride=True) ## https://pypi.org/project/func-timeout/ - timeouts, in case calls hang (e.g. Azure)
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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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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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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=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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**optional_params
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)
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elif model in 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 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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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 != 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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logging(model=model, input=input, azure=azure, additional_args={"max_tokens": max_tokens}, logger_fn=logger_fn)
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## COMPLETION CALL
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output = replicate.run(
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model,
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input=input)
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response = ""
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for item in output:
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response += item
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new_response = {
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": response,
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"role": "assistant"
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}
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}
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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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#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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if "role" in message:
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if message["role"] == "user":
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prompt += f"{HUMAN_PROMPT}{message['content']}"
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else:
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prompt += f"{AI_PROMPT}{message['content']}"
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else:
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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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# 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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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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## COMPLETION CALL
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completion = anthropic.completions.create(
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model=model,
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prompt=prompt,
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max_tokens_to_sample=max_tokens_to_sample
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)
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new_response = {
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"choices": [
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{
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"finish_reason": "stop",
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"index": 0,
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"message": {
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"content": completion.completion,
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"role": "assistant"
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}
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}
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]
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}
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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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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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## 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 = co.generate(
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model=model,
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prompt = prompt
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)
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new_response = {
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"choices": [
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{
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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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"role": "assistant"
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}
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}
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],
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}
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response = new_response
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else:
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logging(model=model, input=messages, azure=azure, logger_fn=logger_fn)
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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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### EMBEDDING ENDPOINTS ####################
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@client
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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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args = locals()
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raise ValueError(f"No valid embedding model args passed in - {args}")
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return response
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####### HELPER FUNCTIONS ################
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def set_callbacks(callback_list):
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global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel
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for callback in callback_list:
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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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sentry_sdk_instance = sentry_sdk
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sentry_sdk_instance.init(dsn=os.environ.get("SENTRY_API_URL"), traces_sample_rate=float(os.environ.get("SENTRY_API_TRACE_RATE")))
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capture_exception = sentry_sdk_instance.capture_exception
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add_breadcrumb = sentry_sdk_instance.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 ImportError:
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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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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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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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alerts_channel = os.environ["SLACK_API_CHANNEL"]
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print_verbose(f"Initialized Slack App: {slack_app}")
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def handle_failure(exception, traceback_exception, args, kwargs):
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print_verbose(f"handle_failure args: {args}")
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print_verbose(f"handle_failure kwargs: {kwargs}")
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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["Event_Name"] = additional_details.pop("failed_event_name", "litellm.failed_query")
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print_verbose(f"self.failure_callback: {failure_callback}")
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print_verbose(f"additional_details: {additional_details}")
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for callback in failure_callback:
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try:
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if callback == "slack":
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slack_msg = ""
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if len(kwargs) > 0:
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for key in kwargs:
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slack_msg += f"{key}: {kwargs[key]}\n"
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if len(args) > 0:
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for i, arg in enumerate(args):
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slack_msg += f"LiteLLM_Args_{str(i)}: {arg}"
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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"Traceback: {traceback_exception}"
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print_verbose(f"This is the slack message: {slack_msg}")
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slack_app.client.chat_postMessage(channel=alerts_channel, text=slack_msg)
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elif callback == "sentry":
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capture_exception(exception)
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elif callback == "posthog":
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print_verbose(f"inside posthog, additional_details: {len(additional_details.keys())}")
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ph_obj = {}
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if len(kwargs) > 0:
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ph_obj = kwargs
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if len(args) > 0:
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for i, arg in enumerate(args):
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ph_obj["litellm_args_" + str(i)] = arg
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print_verbose(f"ph_obj: {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["Event_Name"]
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print_verbose(f"PostHog Event Name: {event_name}")
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if "user_id" in additional_details:
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posthog.capture(additional_details["user_id"], event_name, ph_obj)
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else: # PostHog calls require a unique id to identify a user - https://posthog.com/docs/libraries/python
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print(f"ph_obj: {ph_obj})")
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unique_id = str(uuid.uuid4())
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posthog.capture(unique_id, event_name)
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print_verbose(f"successfully logged to PostHog!")
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except:
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print_verbose(f"Error Occurred while logging failure: {traceback.format_exc()}")
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pass
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if failure_handler and callable(failure_handler):
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call_details = {
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"exception": exception,
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"additional_details": additional_details
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}
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failure_handler(call_details)
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pass
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def handle_input(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 callback_list:
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if callback == "sentry": # add a sentry breadcrumb if user passed in sentry integration
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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 user_logger_fn and callable(user_logger_fn):
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user_logger_fn(model_call_details)
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pass
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def handle_success(*args, **kwargs):
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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["Event_Name"] = additional_details.pop("successful_event_name", "litellm.succes_query")
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for callback in 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["Event_Name"]
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if "user_id" in additional_details:
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posthog.capture(additional_details["user_id"], event_name, ph_obj)
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else: # PostHog calls require a unique id to identify a user - https://posthog.com/docs/libraries/python
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unique_id = str(uuid.uuid4())
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posthog.capture(unique_id, 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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for detail in additional_details:
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|
slack_msg += f"{detail}: {additional_details[detail]}\n"
|
|
slack_app.client.chat_postMessage(channel=alerts_channel, text=slack_msg)
|
|
except:
|
|
pass
|
|
|
|
if success_handler and callable(success_handler):
|
|
success_handler(args, kwargs)
|
|
pass
|
|
|
|
#Logging function -> log the exact model details + what's being sent | Non-Blocking
|
|
def logging(model, input, azure=False, additional_args={}, logger_fn=None):
|
|
try:
|
|
model_call_details = {}
|
|
model_call_details["model"] = model
|
|
model_call_details["input"] = input
|
|
model_call_details["azure"] = azure
|
|
# 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")
|
|
model_call_details["additional_args"] = additional_args
|
|
## Logging
|
|
print_verbose(f"Basic model call details: {model_call_details}")
|
|
if logger_fn and callable(logger_fn):
|
|
try:
|
|
logger_fn(model_call_details) # Expectation: any logger function passed in by the user should accept a dict object
|
|
except:
|
|
print_verbose(f"[Non-Blocking] Exception occurred while logging {traceback.format_exc()}")
|
|
pass
|
|
except:
|
|
pass
|