forked from phoenix/litellm-mirror
Refactor proxy_server.py for readability and code consistency
This commit is contained in:
coconut49
parent
266b3b82f5
commit
4414594e7d
1 changed files with 181 additions and 153 deletions
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@ -1,11 +1,11 @@
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import sys, os, platform, time, copy
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import threading
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import shutil, random, traceback
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path - for litellm local dev
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try:
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import uvicorn
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import fastapi
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@ -22,13 +22,14 @@ except ImportError:
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import tomli as tomllib
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import appdirs
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import tomli_w
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try:
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from .llm import litellm_completion
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except ImportError as e:
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except ImportError as e:
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from llm import litellm_completion
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import random
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list_of_messages = [
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"'The thing I wish you improved is...'",
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"'A feature I really want is...'",
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@ -37,35 +38,36 @@ list_of_messages = [
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"'I don't like how this works...'",
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"'It would help me if you could add...'",
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"'This feature doesn't meet my needs because...'",
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"'I get frustrated when the product...'",
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"'I get frustrated when the product...'",
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]
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def generate_feedback_box():
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box_width = 60
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box_width = 60
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# Select a random message
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message = random.choice(list_of_messages)
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# Select a random message
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message = random.choice(list_of_messages)
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print()
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print('\033[1;37m' + '#' + '-' * box_width + '#\033[0m')
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print('\033[1;37m' + '#' + ' ' * box_width + '#\033[0m')
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print('\033[1;37m' + '# {:^59} #\033[0m'.format(message))
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print('\033[1;37m' + '# {:^59} #\033[0m'.format('https://github.com/BerriAI/litellm/issues/new'))
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print('\033[1;37m' + '#' + ' ' * box_width + '#\033[0m')
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print('\033[1;37m' + '#' + '-' * box_width + '#\033[0m')
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print()
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print(' Thank you for using LiteLLM! - Krrish & Ishaan')
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print()
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print()
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print()
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print('\033[1;37m' + '#' + '-'*box_width + '#\033[0m')
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print('\033[1;37m' + '#' + ' '*box_width + '#\033[0m')
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print('\033[1;37m' + '# {:^59} #\033[0m'.format(message))
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print('\033[1;37m' + '# {:^59} #\033[0m'.format('https://github.com/BerriAI/litellm/issues/new'))
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print('\033[1;37m' + '#' + ' '*box_width + '#\033[0m')
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print('\033[1;37m' + '#' + '-'*box_width + '#\033[0m')
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print()
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print(' Thank you for using LiteLLM! - Krrish & Ishaan')
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print()
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print()
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generate_feedback_box()
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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()
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print("\033[1;34mDocs: https://docs.litellm.ai/docs/proxy_server\033[0m")
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print()
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print()
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import litellm
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from fastapi import FastAPI, Request
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@ -100,24 +102,29 @@ config_dir = os.getcwd()
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config_dir = appdirs.user_config_dir("litellm")
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user_config_path = os.path.join(config_dir, config_filename)
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log_file = 'api_log.json'
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#### HELPER FUNCTIONS ####
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def print_verbose(print_statement):
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global user_debug
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if user_debug:
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print(print_statement)
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global user_debug
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if user_debug:
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print(print_statement)
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def usage_telemetry(feature: str): # helps us know if people are using this feature. Set `litellm --telemetry False` to your cli call to turn this off
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if user_telemetry:
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def usage_telemetry(
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feature: str): # helps us know if people are using this feature. Set `litellm --telemetry False` to your cli call to turn this off
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if user_telemetry:
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data = {
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"feature": feature # "local_proxy_server"
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"feature": feature # "local_proxy_server"
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}
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threading.Thread(target=litellm.utils.litellm_telemetry, args=(data,), daemon=True).start()
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def add_keys_to_config(key, value):
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# Check if file exists
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if os.path.exists(user_config_path):
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# Load existing file
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with open(user_config_path, "rb") as f:
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with open(user_config_path, "rb") as f:
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config = tomllib.load(f)
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else:
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# File doesn't exist, create empty config
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@ -130,21 +137,22 @@ def add_keys_to_config(key, value):
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with open(user_config_path, 'wb') as f:
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tomli_w.dump(config, f)
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def save_params_to_config(data: dict):
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def save_params_to_config(data: dict):
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# Check if file exists
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if os.path.exists(user_config_path):
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# Load existing file
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with open(user_config_path, "rb") as f:
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with open(user_config_path, "rb") as f:
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config = tomllib.load(f)
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else:
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# File doesn't exist, create empty config
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config = {}
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config.setdefault('general', {})
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## general config
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general_settings = data["general"]
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for key, value in general_settings.items():
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config["general"][key] = value
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@ -161,101 +169,104 @@ def save_params_to_config(data: dict):
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# Write config to file
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with open(user_config_path, 'wb') as f:
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tomli_w.dump(config, f)
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def load_config():
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try:
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global user_config, user_api_base, user_max_tokens, user_temperature, user_model
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# As the .env file is typically much simpler in structure, we use load_dotenv here directly
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with open(user_config_path, "rb") as f:
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user_config = tomllib.load(f)
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global user_config, user_api_base, user_max_tokens, user_temperature, user_model
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# As the .env file is typically much simpler in structure, we use load_dotenv here directly
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with open(user_config_path, "rb") as f:
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user_config = tomllib.load(f)
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## load keys
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if "keys" in user_config:
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for key in user_config["keys"]:
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os.environ[key] = user_config["keys"][key] # litellm can read keys from the environment
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## settings
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if "general" in user_config:
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litellm.add_function_to_prompt = user_config["general"].get("add_function_to_prompt", True) # by default add function to prompt if unsupported by provider
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litellm.drop_params = user_config["general"].get("drop_params", True) # by default drop params if unsupported by provider
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litellm.model_fallbacks = user_config["general"].get("fallbacks", None) # fallback models in case initial completion call fails
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default_model = user_config["general"].get("default_model", None) # route all requests to this model.
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## load keys
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if "keys" in user_config:
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for key in user_config["keys"]:
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os.environ[key] = user_config["keys"][key] # litellm can read keys from the environment
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## settings
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if "general" in user_config:
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litellm.add_function_to_prompt = user_config["general"].get("add_function_to_prompt",
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True) # by default add function to prompt if unsupported by provider
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litellm.drop_params = user_config["general"].get("drop_params",
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True) # by default drop params if unsupported by provider
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litellm.model_fallbacks = user_config["general"].get("fallbacks",
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None) # fallback models in case initial completion call fails
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default_model = user_config["general"].get("default_model", None) # route all requests to this model.
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if user_model is None: # `litellm --model <model-name>`` > default_model.
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user_model = default_model
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if user_model is None: # `litellm --model <model-name>`` > default_model.
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user_model = default_model
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## load model config - to set this run `litellm --config`
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model_config = None
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if "model" in user_config:
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if user_model in user_config["model"]:
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model_config = user_config["model"][user_model]
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print_verbose(f"user_config: {user_config}")
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print_verbose(f"model_config: {model_config}")
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print_verbose(f"user_model: {user_model}")
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if model_config is None:
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return
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## load model config - to set this run `litellm --config`
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model_config = None
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if "model" in user_config:
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if user_model in user_config["model"]:
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model_config = user_config["model"][user_model]
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user_max_tokens = model_config.get("max_tokens", None)
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user_temperature = model_config.get("temperature", None)
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user_api_base = model_config.get("api_base", None)
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## custom prompt template
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if "prompt_template" in model_config:
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model_prompt_template = model_config["prompt_template"]
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if len(model_prompt_template.keys()) > 0: # if user has initialized this at all
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litellm.register_prompt_template(
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model=user_model,
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initial_prompt_value=model_prompt_template.get("MODEL_PRE_PROMPT", ""),
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roles={
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"system": {
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"pre_message": model_prompt_template.get("MODEL_SYSTEM_MESSAGE_START_TOKEN", ""),
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"post_message": model_prompt_template.get("MODEL_SYSTEM_MESSAGE_END_TOKEN", ""),
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},
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"user": {
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"pre_message": model_prompt_template.get("MODEL_USER_MESSAGE_START_TOKEN", ""),
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"post_message": model_prompt_template.get("MODEL_USER_MESSAGE_END_TOKEN", ""),
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},
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"assistant": {
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"pre_message": model_prompt_template.get("MODEL_ASSISTANT_MESSAGE_START_TOKEN", ""),
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"post_message": model_prompt_template.get("MODEL_ASSISTANT_MESSAGE_END_TOKEN", ""),
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}
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},
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final_prompt_value=model_prompt_template.get("MODEL_POST_PROMPT", ""),
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)
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except Exception as e:
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pass
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print_verbose(f"user_config: {user_config}")
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print_verbose(f"model_config: {model_config}")
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print_verbose(f"user_model: {user_model}")
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if model_config is None:
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return
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def initialize(model, alias, api_base, debug, temperature, max_tokens, max_budget, telemetry, drop_params, add_function_to_prompt, headers, save):
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user_max_tokens = model_config.get("max_tokens", None)
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user_temperature = model_config.get("temperature", None)
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user_api_base = model_config.get("api_base", None)
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## custom prompt template
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if "prompt_template" in model_config:
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model_prompt_template = model_config["prompt_template"]
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if len(model_prompt_template.keys()) > 0: # if user has initialized this at all
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litellm.register_prompt_template(
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model=user_model,
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initial_prompt_value=model_prompt_template.get("MODEL_PRE_PROMPT", ""),
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roles={
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"system": {
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"pre_message": model_prompt_template.get("MODEL_SYSTEM_MESSAGE_START_TOKEN", ""),
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"post_message": model_prompt_template.get("MODEL_SYSTEM_MESSAGE_END_TOKEN", ""),
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},
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"user": {
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"pre_message": model_prompt_template.get("MODEL_USER_MESSAGE_START_TOKEN", ""),
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"post_message": model_prompt_template.get("MODEL_USER_MESSAGE_END_TOKEN", ""),
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},
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"assistant": {
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"pre_message": model_prompt_template.get("MODEL_ASSISTANT_MESSAGE_START_TOKEN", ""),
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"post_message": model_prompt_template.get("MODEL_ASSISTANT_MESSAGE_END_TOKEN", ""),
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}
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},
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final_prompt_value=model_prompt_template.get("MODEL_POST_PROMPT", ""),
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)
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def initialize(model, alias, api_base, debug, temperature, max_tokens, max_budget, telemetry, drop_params,
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add_function_to_prompt, headers, save):
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global user_model, user_api_base, user_debug, user_max_tokens, user_temperature, user_telemetry, user_headers
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user_model = model
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user_debug = debug
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load_config()
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dynamic_config = {"general": {}, user_model: {}}
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if headers: # model-specific param
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dynamic_config = {"general": {}, user_model: {}}
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if headers: # model-specific param
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user_headers = headers
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dynamic_config[user_model]["headers"] = headers
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if api_base: # model-specific param
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if api_base: # model-specific param
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user_api_base = api_base
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dynamic_config[user_model]["api_base"] = api_base
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if max_tokens: # model-specific param
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if max_tokens: # model-specific param
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user_max_tokens = max_tokens
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dynamic_config[user_model]["max_tokens"] = max_tokens
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if temperature: # model-specific param
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if temperature: # model-specific param
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user_temperature = temperature
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dynamic_config[user_model]["temperature"] = temperature
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if alias: # model-specific param
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if alias: # model-specific param
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dynamic_config[user_model]["alias"] = alias
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if drop_params == True: # litellm-specific param
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if drop_params == True: # litellm-specific param
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litellm.drop_params = True
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dynamic_config["general"]["drop_params"] = True
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if add_function_to_prompt == True: # litellm-specific param
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if add_function_to_prompt == True: # litellm-specific param
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litellm.add_function_to_prompt = True
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dynamic_config["general"]["add_function_to_prompt"] = True
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if max_budget: # litellm-specific param
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if max_budget: # litellm-specific param
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litellm.max_budget = max_budget
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dynamic_config["general"]["max_budget"] = max_budget
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if save:
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if save:
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save_params_to_config(dynamic_config)
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with open(user_config_path) as f:
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print(f.read())
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@ -263,6 +274,7 @@ def initialize(model, alias, api_base, debug, temperature, max_tokens, max_budge
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user_telemetry = telemetry
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usage_telemetry(feature="local_proxy_server")
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def deploy_proxy(model, api_base, debug, temperature, max_tokens, telemetry, deploy):
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import requests
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# Load .env file
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@ -293,8 +305,6 @@ def deploy_proxy(model, api_base, debug, temperature, max_tokens, telemetry, dep
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files = {"file": open(".env", "rb")}
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# print(files)
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response = requests.post(url, data=data, files=files)
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# print(response)
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# Check the status of the request
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@ -309,10 +319,11 @@ def deploy_proxy(model, api_base, debug, temperature, max_tokens, telemetry, dep
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return url
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def track_cost_callback(
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kwargs, # kwargs to completion
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completion_response, # response from completion
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start_time, end_time # start/end time
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kwargs, # kwargs to completion
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completion_response, # response from completion
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start_time, end_time # start/end time
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):
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# track cost like this
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# {
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@ -330,12 +341,12 @@ def track_cost_callback(
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# for streaming responses
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if "complete_streaming_response" in kwargs:
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# for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
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completion_response=kwargs["complete_streaming_response"]
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completion_response = kwargs["complete_streaming_response"]
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input_text = kwargs["messages"]
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output_text = completion_response["choices"][0]["message"]["content"]
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response_cost = litellm.completion_cost(
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model = kwargs["model"],
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messages = input_text,
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model=kwargs["model"],
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messages=input_text,
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completion=output_text
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)
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model = kwargs['model']
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@ -353,7 +364,7 @@ def track_cost_callback(
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with open("costs.json") as f:
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cost_data = json.load(f)
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except FileNotFoundError:
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cost_data = {}
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cost_data = {}
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import datetime
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date = datetime.datetime.now().strftime("%b-%d-%Y")
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if date not in cost_data:
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@ -374,47 +385,32 @@ def track_cost_callback(
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except:
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pass
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def logger(
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kwargs, # kwargs to completion
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completion_response=None, # response from completion
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start_time=None,
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end_time=None # start/end time
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):
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log_event_type = kwargs['log_event_type']
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try:
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if log_event_type == 'pre_api_call':
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inference_params = copy.deepcopy(kwargs)
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timestamp = inference_params.pop('start_time')
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dt_key = timestamp.strftime("%Y%m%d%H%M%S%f")[:23]
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log_data = {
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dt_key: {
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'pre_api_call': inference_params
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}
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}
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try:
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with open(log_file, 'r') as f:
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existing_data = json.load(f)
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except FileNotFoundError:
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existing_data = {}
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existing_data.update(log_data)
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def write_to_log():
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with open(log_file, 'w') as f:
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json.dump(existing_data, f, indent=2)
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thread = threading.Thread(target=write_to_log, daemon=True)
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thread.start()
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elif log_event_type == 'post_api_call':
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if "stream" not in kwargs["optional_params"] or kwargs["optional_params"]["stream"] is False or kwargs.get("complete_streaming_response", False):
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def logger(
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kwargs, # kwargs to completion
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completion_response=None, # response from completion
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start_time=None,
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end_time=None # start/end time
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||||
):
|
||||
log_event_type = kwargs['log_event_type']
|
||||
try:
|
||||
if log_event_type == 'pre_api_call':
|
||||
inference_params = copy.deepcopy(kwargs)
|
||||
timestamp = inference_params.pop('start_time')
|
||||
dt_key = timestamp.strftime("%Y%m%d%H%M%S%f")[:23]
|
||||
|
||||
with open(log_file, 'r') as f:
|
||||
existing_data = json.load(f)
|
||||
|
||||
existing_data[dt_key]['post_api_call'] = inference_params
|
||||
log_data = {
|
||||
dt_key: {
|
||||
'pre_api_call': inference_params
|
||||
}
|
||||
}
|
||||
|
||||
try:
|
||||
with open(log_file, 'r') as f:
|
||||
existing_data = json.load(f)
|
||||
except FileNotFoundError:
|
||||
existing_data = {}
|
||||
|
||||
existing_data.update(log_data)
|
||||
|
||||
def write_to_log():
|
||||
with open(log_file, 'w') as f:
|
||||
|
|
@ -422,15 +418,35 @@ def logger(
|
|||
|
||||
thread = threading.Thread(target=write_to_log, daemon=True)
|
||||
thread.start()
|
||||
except:
|
||||
pass
|
||||
elif log_event_type == 'post_api_call':
|
||||
if "stream" not in kwargs["optional_params"] or kwargs["optional_params"]["stream"] is False or kwargs.get(
|
||||
"complete_streaming_response", False):
|
||||
inference_params = copy.deepcopy(kwargs)
|
||||
timestamp = inference_params.pop('start_time')
|
||||
dt_key = timestamp.strftime("%Y%m%d%H%M%S%f")[:23]
|
||||
|
||||
with open(log_file, 'r') as f:
|
||||
existing_data = json.load(f)
|
||||
|
||||
existing_data[dt_key]['post_api_call'] = inference_params
|
||||
|
||||
def write_to_log():
|
||||
with open(log_file, 'w') as f:
|
||||
json.dump(existing_data, f, indent=2)
|
||||
|
||||
thread = threading.Thread(target=write_to_log, daemon=True)
|
||||
thread.start()
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
litellm.input_callback = [logger]
|
||||
litellm.success_callback = [logger]
|
||||
litellm.failure_callback = [logger]
|
||||
|
||||
|
||||
#### API ENDPOINTS ####
|
||||
@router.get("/models") # if project requires model list
|
||||
@router.get("/models") # if project requires model list
|
||||
def model_list():
|
||||
if user_model != None:
|
||||
return dict(
|
||||
|
|
@ -440,19 +456,26 @@ def model_list():
|
|||
else:
|
||||
all_models = litellm.utils.get_valid_models()
|
||||
return dict(
|
||||
data = [{"id": model, "object": "model", "created": 1677610602, "owned_by": "openai"} for model in all_models],
|
||||
data=[{"id": model, "object": "model", "created": 1677610602, "owned_by": "openai"} for model in
|
||||
all_models],
|
||||
object="list",
|
||||
)
|
||||
|
||||
|
||||
@router.post("/completions")
|
||||
async def completion(request: Request):
|
||||
data = await request.json()
|
||||
return litellm_completion(data=data, type="completion", user_model=user_model, user_temperature=user_temperature, user_max_tokens=user_max_tokens, user_api_base=user_api_base, user_headers=user_headers, user_debug=user_debug)
|
||||
return litellm_completion(data=data, type="completion", user_model=user_model, user_temperature=user_temperature,
|
||||
user_max_tokens=user_max_tokens, user_api_base=user_api_base, user_headers=user_headers,
|
||||
user_debug=user_debug)
|
||||
|
||||
|
||||
@router.post("/chat/completions")
|
||||
async def chat_completion(request: Request):
|
||||
data = await request.json()
|
||||
response = litellm_completion(data, type="chat_completion", user_model=user_model, user_temperature=user_temperature, user_max_tokens=user_max_tokens, user_api_base=user_api_base, user_headers=user_headers, user_debug=user_debug)
|
||||
response = litellm_completion(data, type="chat_completion", user_model=user_model,
|
||||
user_temperature=user_temperature, user_max_tokens=user_max_tokens,
|
||||
user_api_base=user_api_base, user_headers=user_headers, user_debug=user_debug)
|
||||
return response
|
||||
|
||||
|
||||
|
|
@ -462,6 +485,7 @@ async def v1_completion(request: Request):
|
|||
data = await request.json()
|
||||
return litellm_completion(data=data, type="completion")
|
||||
|
||||
|
||||
@router.post("/v1/chat/completions")
|
||||
async def v1_chat_completion(request: Request):
|
||||
data = await request.json()
|
||||
|
|
@ -469,6 +493,7 @@ async def v1_chat_completion(request: Request):
|
|||
response = litellm_completion(data, type="chat_completion")
|
||||
return response
|
||||
|
||||
|
||||
def print_cost_logs():
|
||||
with open('costs.json', 'r') as f:
|
||||
# print this in green
|
||||
|
|
@ -477,13 +502,16 @@ def print_cost_logs():
|
|||
print("\033[0m")
|
||||
return
|
||||
|
||||
|
||||
@router.get("/ollama_logs")
|
||||
async def retrieve_server_log(request: Request):
|
||||
filepath = os.path.expanduser('~/.ollama/logs/server.log')
|
||||
return FileResponse(filepath)
|
||||
|
||||
|
||||
@router.get("/")
|
||||
async def home(request: Request):
|
||||
return "LiteLLM: RUNNING"
|
||||
|
||||
app.include_router(router)
|
||||
|
||||
app.include_router(router)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue