forked from phoenix/litellm-mirror
406 lines
12 KiB
Python
406 lines
12 KiB
Python
import click
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import subprocess, traceback, json
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import os, sys
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import random
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from datetime import datetime
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import importlib
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from dotenv import load_dotenv
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import operator
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sys.path.append(os.getcwd())
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config_filename = "litellm.secrets"
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load_dotenv()
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from importlib import resources
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import shutil
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telemetry = None
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def run_ollama_serve():
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try:
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command = ["ollama", "serve"]
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with open(os.devnull, "w") as devnull:
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process = subprocess.Popen(command, stdout=devnull, stderr=devnull)
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except Exception as e:
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print(
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f"""
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LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve`
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"""
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) # noqa
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def clone_subfolder(repo_url, subfolder, destination):
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# Clone the full repo
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repo_name = repo_url.split("/")[-1]
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repo_master = os.path.join(destination, "repo_master")
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subprocess.run(["git", "clone", repo_url, repo_master])
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# Move into the subfolder
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subfolder_path = os.path.join(repo_master, subfolder)
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# Copy subfolder to destination
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for file_name in os.listdir(subfolder_path):
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source = os.path.join(subfolder_path, file_name)
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if os.path.isfile(source):
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shutil.copy(source, destination)
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else:
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dest_path = os.path.join(destination, file_name)
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shutil.copytree(source, dest_path)
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# Remove cloned repo folder
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subprocess.run(["rm", "-rf", os.path.join(destination, "repo_master")])
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feature_telemetry(feature="create-proxy")
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def is_port_in_use(port):
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import socket
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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return s.connect_ex(("localhost", port)) == 0
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@click.command()
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@click.option("--host", default="0.0.0.0", help="Host for the server to listen on.")
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@click.option("--port", default=8000, help="Port to bind the server to.")
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@click.option("--num_workers", default=1, help="Number of uvicorn workers to spin up")
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@click.option("--api_base", default=None, help="API base URL.")
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@click.option(
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"--api_version",
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default="2023-07-01-preview",
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help="For azure - pass in the api version.",
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)
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@click.option(
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"--model", "-m", default=None, help="The model name to pass to litellm expects"
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)
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@click.option(
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"--alias",
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default=None,
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help='The alias for the model - use this to give a litellm model name (e.g. "huggingface/codellama/CodeLlama-7b-Instruct-hf") a more user-friendly name ("codellama")',
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)
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@click.option(
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"--add_key", default=None, help="The model name to pass to litellm expects"
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)
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@click.option("--headers", default=None, help="headers for the API call")
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@click.option("--save", is_flag=True, type=bool, help="Save the model-specific config")
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@click.option(
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"--debug", default=False, is_flag=True, type=bool, help="To debug the input"
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)
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@click.option(
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"--use_queue",
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default=False,
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is_flag=True,
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type=bool,
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help="To use celery workers for async endpoints",
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)
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@click.option(
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"--temperature", default=None, type=float, help="Set temperature for the model"
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)
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@click.option(
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"--max_tokens", default=None, type=int, help="Set max tokens for the model"
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)
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@click.option(
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"--request_timeout",
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default=600,
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type=int,
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help="Set timeout in seconds for completion calls",
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)
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@click.option("--drop_params", is_flag=True, help="Drop any unmapped params")
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@click.option(
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"--add_function_to_prompt",
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is_flag=True,
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help="If function passed but unsupported, pass it as prompt",
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)
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@click.option(
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"--config",
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"-c",
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default=None,
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help="Path to the proxy configuration file (e.g. config.yaml). Usage `litellm --config config.yaml`",
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)
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@click.option(
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"--max_budget",
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default=None,
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type=float,
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help="Set max budget for API calls - works for hosted models like OpenAI, TogetherAI, Anthropic, etc.`",
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)
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@click.option(
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"--telemetry",
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default=True,
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type=bool,
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help="Helps us know if people are using this feature. Turn this off by doing `--telemetry False`",
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)
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@click.option(
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"--version",
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"-v",
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default=False,
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is_flag=True,
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type=bool,
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help="Print LiteLLM version",
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)
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@click.option(
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"--logs",
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flag_value=False,
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type=int,
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help='Gets the "n" most recent logs. By default gets most recent log.',
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)
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@click.option(
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"--health",
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flag_value=True,
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help="Make a chat/completions request to all llms in config.yaml",
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)
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@click.option(
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"--test",
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flag_value=True,
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help="proxy chat completions url to make a test request to",
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)
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@click.option(
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"--test_async",
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default=False,
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is_flag=True,
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help="Calls async endpoints /queue/requests and /queue/response",
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)
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@click.option(
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"--num_requests",
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default=10,
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type=int,
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help="Number of requests to hit async endpoint with",
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)
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@click.option("--local", is_flag=True, default=False, help="for local debugging")
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def run_server(
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host,
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port,
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api_base,
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api_version,
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model,
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alias,
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add_key,
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headers,
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save,
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debug,
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temperature,
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max_tokens,
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request_timeout,
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drop_params,
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add_function_to_prompt,
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config,
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max_budget,
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telemetry,
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logs,
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test,
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local,
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num_workers,
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test_async,
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num_requests,
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use_queue,
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health,
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version,
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):
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global feature_telemetry
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args = locals()
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if local:
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from proxy_server import app, save_worker_config, usage_telemetry
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else:
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try:
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from .proxy_server import app, save_worker_config, usage_telemetry
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except ImportError as e:
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if "litellm[proxy]" in str(e):
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# user is missing a proxy dependency, ask them to pip install litellm[proxy]
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raise e
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else:
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# this is just a local/relative import error, user git cloned litellm
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from proxy_server import app, save_worker_config, usage_telemetry
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feature_telemetry = usage_telemetry
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if logs is not None:
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if logs == 0: # default to 1
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logs = 1
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try:
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with open("api_log.json") as f:
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data = json.load(f)
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# convert keys to datetime objects
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log_times = {
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datetime.strptime(k, "%Y%m%d%H%M%S%f"): v for k, v in data.items()
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}
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# sort by timestamp
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sorted_times = sorted(
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log_times.items(), key=operator.itemgetter(0), reverse=True
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)
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# get n recent logs
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recent_logs = {
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k.strftime("%Y%m%d%H%M%S%f"): v for k, v in sorted_times[:logs]
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}
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print(json.dumps(recent_logs, indent=4)) # noqa
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except:
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raise Exception("LiteLLM: No logs saved!")
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return
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if version == True:
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pkg_version = importlib.metadata.version("litellm")
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click.echo(f"\nLiteLLM: Current Version = {pkg_version}\n")
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return
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if model and "ollama" in model and api_base is None:
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run_ollama_serve()
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if test_async is True:
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import requests, concurrent, time
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api_base = f"http://{host}:{port}"
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def _make_openai_completion():
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data = {
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"model": "gpt-3.5-turbo",
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"messages": [
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{"role": "user", "content": "Write a short poem about the moon"}
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],
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}
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response = requests.post("http://0.0.0.0:8000/queue/request", json=data)
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response = response.json()
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while True:
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try:
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url = response["url"]
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polling_url = f"{api_base}{url}"
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polling_response = requests.get(polling_url)
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polling_response = polling_response.json()
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print("\n RESPONSE FROM POLLING JOB", polling_response)
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status = polling_response["status"]
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if status == "finished":
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llm_response = polling_response["result"]
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break
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print(
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f"POLLING JOB{polling_url}\nSTATUS: {status}, \n Response {polling_response}"
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) # noqa
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time.sleep(0.5)
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except Exception as e:
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print("got exception in polling", e)
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break
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# Number of concurrent calls (you can adjust this)
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concurrent_calls = num_requests
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# List to store the futures of concurrent calls
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futures = []
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start_time = time.time()
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# Make concurrent calls
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with concurrent.futures.ThreadPoolExecutor(
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max_workers=concurrent_calls
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) as executor:
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for _ in range(concurrent_calls):
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futures.append(executor.submit(_make_openai_completion))
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# Wait for all futures to complete
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concurrent.futures.wait(futures)
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# Summarize the results
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successful_calls = 0
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failed_calls = 0
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for future in futures:
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if future.done():
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if future.result() is not None:
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successful_calls += 1
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else:
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failed_calls += 1
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end_time = time.time()
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print(f"Elapsed Time: {end_time-start_time}")
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print(f"Load test Summary:")
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print(f"Total Requests: {concurrent_calls}")
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print(f"Successful Calls: {successful_calls}")
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print(f"Failed Calls: {failed_calls}")
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return
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if health != False:
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import requests
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print("\nLiteLLM: Health Testing models in config")
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response = requests.get(url=f"http://{host}:{port}/health")
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print(json.dumps(response.json(), indent=4))
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return
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if test != False:
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request_model = model or "gpt-3.5-turbo"
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click.echo(
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f"\nLiteLLM: Making a test ChatCompletions request to your proxy. Model={request_model}"
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)
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import openai
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if test == True: # flag value set
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api_base = f"http://{host}:{port}"
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else:
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api_base = test
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client = openai.OpenAI(api_key="My API Key", base_url=api_base)
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response = client.chat.completions.create(
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model=request_model,
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messages=[
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{
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"role": "user",
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"content": "this is a test request, write a short poem",
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}
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],
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max_tokens=256,
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)
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click.echo(f"\nLiteLLM: response from proxy {response}")
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print(
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f"\n LiteLLM: Making a test ChatCompletions + streaming request to proxy. Model={request_model}"
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)
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response = client.chat.completions.create(
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model=request_model,
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messages=[
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{
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"role": "user",
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"content": "this is a test request, write a short poem",
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}
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],
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stream=True,
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)
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for chunk in response:
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click.echo(f"LiteLLM: streaming response from proxy {chunk}")
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print("\n making completion request to proxy")
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response = client.completions.create(
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model=request_model, prompt="this is a test request, write a short poem"
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)
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print(response)
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return
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else:
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if headers:
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headers = json.loads(headers)
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save_worker_config(
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model=model,
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alias=alias,
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api_base=api_base,
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api_version=api_version,
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debug=debug,
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temperature=temperature,
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max_tokens=max_tokens,
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request_timeout=request_timeout,
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max_budget=max_budget,
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telemetry=telemetry,
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drop_params=drop_params,
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add_function_to_prompt=add_function_to_prompt,
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headers=headers,
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save=save,
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config=config,
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use_queue=use_queue,
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)
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try:
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import uvicorn
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except:
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raise ImportError(
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"Uvicorn needs to be imported. Run - `pip install uvicorn`"
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)
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if port == 8000 and is_port_in_use(port):
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port = random.randint(1024, 49152)
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uvicorn.run(
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"litellm.proxy.proxy_server:app", host=host, port=port, workers=num_workers
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)
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if __name__ == "__main__":
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run_server()
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