litellm/proxy/proxy_cli.py

import click
import subprocess, traceback, json
import os, sys
import random
from datetime import datetime
import importlib
from dotenv import load_dotenv

sys.path.append(os.getcwd())

config_filename = "litellm.secrets"

load_dotenv()
from importlib import resources
import shutil

telemetry = None


def run_ollama_serve():
    try:
        command = ["ollama", "serve"]

        with open(os.devnull, "w") as devnull:
            process = subprocess.Popen(command, stdout=devnull, stderr=devnull)
    except Exception as e:
        print(
            f"""
            LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve`
        """
        )  # noqa


def is_port_in_use(port):
    import socket

    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        return s.connect_ex(("localhost", port)) == 0


@click.command()
@click.option("--host", default="0.0.0.0", help="Host for the server to listen on.")
@click.option("--port", default=8000, help="Port to bind the server to.")
@click.option("--num_workers", default=1, help="Number of gunicorn workers to spin up")
@click.option("--api_base", default=None, help="API base URL.")
@click.option(
    "--api_version",
    default="2023-07-01-preview",
    help="For azure - pass in the api version.",
)
@click.option(
    "--model", "-m", default=None, help="The model name to pass to litellm expects"
)
@click.option(
    "--alias",
    default=None,
    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")',
)
@click.option(
    "--add_key", default=None, help="The model name to pass to litellm expects"
)
@click.option("--headers", default=None, help="headers for the API call")
@click.option("--save", is_flag=True, type=bool, help="Save the model-specific config")
@click.option(
    "--debug", default=False, is_flag=True, type=bool, help="To debug the input"
)
@click.option(
    "--detailed_debug",
    default=False,
    is_flag=True,
    type=bool,
    help="To view detailed debug logs",
)
@click.option(
    "--use_queue",
    default=False,
    is_flag=True,
    type=bool,
    help="To use celery workers for async endpoints",
)
@click.option(
    "--temperature", default=None, type=float, help="Set temperature for the model"
)
@click.option(
    "--max_tokens", default=None, type=int, help="Set max tokens for the model"
)
@click.option(
    "--request_timeout",
    default=600,
    type=int,
    help="Set timeout in seconds for completion calls",
)
@click.option("--drop_params", is_flag=True, help="Drop any unmapped params")
@click.option(
    "--add_function_to_prompt",
    is_flag=True,
    help="If function passed but unsupported, pass it as prompt",
)
@click.option(
    "--config",
    "-c",
    default=None,
    help="Path to the proxy configuration file (e.g. config.yaml). Usage `litellm --config config.yaml`",
)
@click.option(
    "--max_budget",
    default=None,
    type=float,
    help="Set max budget for API calls - works for hosted models like OpenAI, TogetherAI, Anthropic, etc.`",
)
@click.option(
    "--telemetry",
    default=True,
    type=bool,
    help="Helps us know if people are using this feature. Turn this off by doing `--telemetry False`",
)
@click.option(
    "--version",
    "-v",
    default=False,
    is_flag=True,
    type=bool,
    help="Print LiteLLM version",
)
@click.option(
    "--health",
    flag_value=True,
    help="Make a chat/completions request to all llms in config.yaml",
)
@click.option(
    "--test",
    flag_value=True,
    help="proxy chat completions url to make a test request to",
)
@click.option(
    "--test_async",
    default=False,
    is_flag=True,
    help="Calls async endpoints /queue/requests and /queue/response",
)
@click.option(
    "--num_requests",
    default=10,
    type=int,
    help="Number of requests to hit async endpoint with",
)
@click.option("--local", is_flag=True, default=False, help="for local debugging")
def run_server(
    host,
    port,
    api_base,
    api_version,
    model,
    alias,
    add_key,
    headers,
    save,
    debug,
    detailed_debug,
    temperature,
    max_tokens,
    request_timeout,
    drop_params,
    add_function_to_prompt,
    config,
    max_budget,
    telemetry,
    test,
    local,
    num_workers,
    test_async,
    num_requests,
    use_queue,
    health,
    version,
):
    global feature_telemetry
    args = locals()
    if local:
        from proxy_server import app, save_worker_config, usage_telemetry
    else:
        try:
            from .proxy_server import app, save_worker_config, usage_telemetry
        except ImportError as e:
            if "litellm[proxy]" in str(e):
                # user is missing a proxy dependency, ask them to pip install litellm[proxy]
                raise e
            else:
                # this is just a local/relative import error, user git cloned litellm
                from proxy_server import app, save_worker_config, usage_telemetry
    feature_telemetry = usage_telemetry
    if version == True:
        pkg_version = importlib.metadata.version("litellm")
        click.echo(f"\nLiteLLM: Current Version = {pkg_version}\n")
        return
    if model and "ollama" in model and api_base is None:
        run_ollama_serve()
    if test_async is True:
        import requests, concurrent, time

        api_base = f"http://{host}:{port}"

        def _make_openai_completion():
            data = {
                "model": "gpt-3.5-turbo",
                "messages": [
                    {"role": "user", "content": "Write a short poem about the moon"}
                ],
            }

            response = requests.post("http://0.0.0.0:8000/queue/request", json=data)

            response = response.json()

            while True:
                try:
                    url = response["url"]
                    polling_url = f"{api_base}{url}"
                    polling_response = requests.get(polling_url)
                    polling_response = polling_response.json()
                    print("\n RESPONSE FROM POLLING JOB", polling_response)
                    status = polling_response["status"]
                    if status == "finished":
                        llm_response = polling_response["result"]
                        break
                    print(
                        f"POLLING JOB{polling_url}\nSTATUS: {status}, \n Response {polling_response}"
                    )  # noqa
                    time.sleep(0.5)
                except Exception as e:
                    print("got exception in polling", e)
                    break

        # Number of concurrent calls (you can adjust this)
        concurrent_calls = num_requests

        # List to store the futures of concurrent calls
        futures = []
        start_time = time.time()
        # Make concurrent calls
        with concurrent.futures.ThreadPoolExecutor(
            max_workers=concurrent_calls
        ) as executor:
            for _ in range(concurrent_calls):
                futures.append(executor.submit(_make_openai_completion))

        # Wait for all futures to complete
        concurrent.futures.wait(futures)

        # Summarize the results
        successful_calls = 0
        failed_calls = 0

        for future in futures:
            if future.done():
                if future.result() is not None:
                    successful_calls += 1
                else:
                    failed_calls += 1
        end_time = time.time()
        print(f"Elapsed Time: {end_time-start_time}")
        print(f"Load test Summary:")
        print(f"Total Requests: {concurrent_calls}")
        print(f"Successful Calls: {successful_calls}")
        print(f"Failed Calls: {failed_calls}")
        return
    if health != False:
        import requests

        print("\nLiteLLM: Health Testing models in config")
        response = requests.get(url=f"http://{host}:{port}/health")
        print(json.dumps(response.json(), indent=4))
        return
    if test != False:
        request_model = model or "gpt-3.5-turbo"
        click.echo(
            f"\nLiteLLM: Making a test ChatCompletions request to your proxy. Model={request_model}"
        )
        import openai

        if test == True:  # flag value set
            api_base = f"http://{host}:{port}"
        else:
            api_base = test
        client = openai.OpenAI(api_key="My API Key", base_url=api_base)

        response = client.chat.completions.create(
            model=request_model,
            messages=[
                {
                    "role": "user",
                    "content": "this is a test request, write a short poem",
                }
            ],
            max_tokens=256,
        )
        click.echo(f"\nLiteLLM: response from proxy {response}")

        print(
            f"\n LiteLLM: Making a test ChatCompletions + streaming request to proxy. Model={request_model}"
        )

        response = client.chat.completions.create(
            model=request_model,
            messages=[
                {
                    "role": "user",
                    "content": "this is a test request, write a short poem",
                }
            ],
            stream=True,
        )
        for chunk in response:
            click.echo(f"LiteLLM: streaming response from proxy {chunk}")
        print("\n making completion request to proxy")
        response = client.completions.create(
            model=request_model, prompt="this is a test request, write a short poem"
        )
        print(response)

        return
    else:
        if headers:
            headers = json.loads(headers)
        save_worker_config(
            model=model,
            alias=alias,
            api_base=api_base,
            api_version=api_version,
            debug=debug,
            detailed_debug=detailed_debug,
            temperature=temperature,
            max_tokens=max_tokens,
            request_timeout=request_timeout,
            max_budget=max_budget,
            telemetry=telemetry,
            drop_params=drop_params,
            add_function_to_prompt=add_function_to_prompt,
            headers=headers,
            save=save,
            config=config,
            use_queue=use_queue,
        )
        try:
            import uvicorn

            if os.name == "nt":
                pass
            else:
                import gunicorn.app.base
        except:
            raise ImportError(
                "Uvicorn, gunicorn needs to be imported. Run - `pip 'litellm[proxy]'`"
            )

        if config is not None:
            """
            Allow user to pass in db url via config

            read from there and save it to os.env['DATABASE_URL']
            """
            try:
                import yaml
            except:
                raise ImportError(
                    "yaml needs to be imported. Run - `pip install 'litellm[proxy]'`"
                )

            if os.path.exists(config):
                with open(config, "r") as config_file:
                    config = yaml.safe_load(config_file)
            general_settings = config.get("general_settings", {})
            database_url = general_settings.get("database_url", None)
            if database_url and database_url.startswith("os.environ/"):
                original_dir = os.getcwd()
                # set the working directory to where this script is
                sys.path.insert(
                    0, os.path.abspath("../..")
                )  # Adds the parent directory to the system path - for litellm local dev
                import litellm

                database_url = litellm.get_secret(database_url)
                os.chdir(original_dir)
            if database_url is not None and isinstance(database_url, str):
                os.environ["DATABASE_URL"] = database_url

        if os.getenv("DATABASE_URL", None) is not None:
            try:
                subprocess.run(["prisma"], capture_output=True)
                is_prisma_runnable = True
            except FileNotFoundError:
                is_prisma_runnable = False

            if is_prisma_runnable:
                # run prisma db push, before starting server
                # Save the current working directory
                original_dir = os.getcwd()
                # set the working directory to where this script is
                abspath = os.path.abspath(__file__)
                dname = os.path.dirname(abspath)
                os.chdir(dname)
                try:
                    subprocess.run(
                        ["prisma", "db", "push", "--accept-data-loss"]
                    )  # this looks like a weird edge case when prisma just wont start on render. we need to have the --accept-data-loss
                finally:
                    os.chdir(original_dir)
            else:
                print(
                    f"Unable to connect to DB. DATABASE_URL found in environment, but prisma package not found."
                )
        if port == 8000 and is_port_in_use(port):
            port = random.randint(1024, 49152)
        from litellm.proxy.proxy_server import app

        if os.name == "nt":
            uvicorn.run(app, host=host, port=port)  # run uvicorn
        else:
            import gunicorn.app.base

            # Gunicorn Application Class
            class StandaloneApplication(gunicorn.app.base.BaseApplication):
                def __init__(self, app, options=None):
                    self.options = options or {}  # gunicorn options
                    self.application = app  # FastAPI app
                    super().__init__()

                    _endpoint_str = (
                        f"curl --location 'http://0.0.0.0:{port}/chat/completions' \\"
                    )
                    curl_command = (
                        _endpoint_str
                        + """
                    --header 'Content-Type: application/json' \\
                    --data ' {
                    "model": "gpt-3.5-turbo",
                    "messages": [
                        {
                        "role": "user",
                        "content": "what llm are you"
                        }
                    ]
                    }'
                    \n
                    """
                    )
                    print()  # noqa
                    print(  # noqa
                        f'\033[1;34mLiteLLM: Test your local proxy with: "litellm --test" This runs an openai.ChatCompletion request to your proxy [In a new terminal tab]\033[0m\n'
                    )
                    print(  # noqa
                        f"\033[1;34mLiteLLM: Curl Command Test for your local proxy\n {curl_command} \033[0m\n"
                    )
                    print(
                        "\033[1;34mDocs: https://docs.litellm.ai/docs/simple_proxy\033[0m\n"
                    )  # noqa
                    print(  # noqa
                        f"\033[1;34mSee all Router/Swagger docs on http://0.0.0.0:{port} \033[0m\n"
                    )  # noqa

                def load_config(self):
                    # note: This Loads the gunicorn config - has nothing to do with LiteLLM Proxy config
                    config = {
                        key: value
                        for key, value in self.options.items()
                        if key in self.cfg.settings and value is not None
                    }
                    for key, value in config.items():
                        self.cfg.set(key.lower(), value)

                def load(self):
                    # gunicorn app function
                    return self.application

            gunicorn_options = {
                "bind": f"{host}:{port}",
                "workers": num_workers,  # default is 1
                "worker_class": "uvicorn.workers.UvicornWorker",
                "preload": True,  # Add the preload flag,
                "accesslog": "-",  # Log to stdout
                "access_log_format": '%(h)s %(l)s %(u)s %(t)s "%(r)s" %(s)s %(b)s',
            }
            StandaloneApplication(
                app=app, options=gunicorn_options
            ).run()  # Run gunicorn


if __name__ == "__main__":
    run_server()