litellm-mirror/litellm/proxy/anthropic_endpoints/endpoints.py

"""
Unified /v1/messages endpoint - (Anthropic Spec)
"""

import asyncio
import json
import time
import traceback

from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
from fastapi.responses import StreamingResponse

import litellm
from litellm._logging import verbose_proxy_logger
from litellm.proxy._types import *
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
from litellm.proxy.litellm_pre_call_utils import add_litellm_data_to_request
from litellm.proxy.utils import ProxyLogging

router = APIRouter()


async def async_data_generator_anthropic(
    response,
    user_api_key_dict: UserAPIKeyAuth,
    request_data: dict,
    proxy_logging_obj: ProxyLogging,
):
    verbose_proxy_logger.debug("inside generator")
    try:
        time.time()
        async for chunk in response:
            verbose_proxy_logger.debug(
                "async_data_generator: received streaming chunk - {}".format(chunk)
            )
            ### CALL HOOKS ### - modify outgoing data
            chunk = await proxy_logging_obj.async_post_call_streaming_hook(
                user_api_key_dict=user_api_key_dict, response=chunk
            )

            yield chunk
    except Exception as e:
        verbose_proxy_logger.exception(
            "litellm.proxy.proxy_server.async_data_generator(): Exception occured - {}".format(
                str(e)
            )
        )
        await proxy_logging_obj.post_call_failure_hook(
            user_api_key_dict=user_api_key_dict,
            original_exception=e,
            request_data=request_data,
        )
        verbose_proxy_logger.debug(
            f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`"
        )

        if isinstance(e, HTTPException):
            raise e
        else:
            error_traceback = traceback.format_exc()
            error_msg = f"{str(e)}\n\n{error_traceback}"

        proxy_exception = ProxyException(
            message=getattr(e, "message", error_msg),
            type=getattr(e, "type", "None"),
            param=getattr(e, "param", "None"),
            code=getattr(e, "status_code", 500),
        )
        error_returned = json.dumps({"error": proxy_exception.to_dict()})
        yield f"data: {error_returned}\n\n"


@router.post(
    "/v1/messages",
    tags=["[beta] Anthropic `/v1/messages`"],
    dependencies=[Depends(user_api_key_auth)],
    include_in_schema=False,
)
async def anthropic_response(  # noqa: PLR0915
    fastapi_response: Response,
    request: Request,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
    """
    Use `{PROXY_BASE_URL}/anthropic/v1/messages` instead - [Docs](https://docs.litellm.ai/docs/anthropic_completion).

    This was a BETA endpoint that calls 100+ LLMs in the anthropic format.
    """
    from litellm.proxy.proxy_server import (
        general_settings,
        llm_router,
        proxy_config,
        proxy_logging_obj,
        user_api_base,
        user_max_tokens,
        user_model,
        user_request_timeout,
        user_temperature,
        version,
    )

    request_data = await _read_request_body(request=request)
    data: dict = {**request_data}
    try:
        data["model"] = (
            general_settings.get("completion_model", None)  # server default
            or user_model  # model name passed via cli args
            or data.get("model", None)  # default passed in http request
        )
        if user_model:
            data["model"] = user_model

        data = await add_litellm_data_to_request(
            data=data,  # type: ignore
            request=request,
            general_settings=general_settings,
            user_api_key_dict=user_api_key_dict,
            version=version,
            proxy_config=proxy_config,
        )

        # override with user settings, these are params passed via cli
        if user_temperature:
            data["temperature"] = user_temperature
        if user_request_timeout:
            data["request_timeout"] = user_request_timeout
        if user_max_tokens:
            data["max_tokens"] = user_max_tokens
        if user_api_base:
            data["api_base"] = user_api_base

        ### MODEL ALIAS MAPPING ###
        # check if model name in model alias map
        # get the actual model name
        if data["model"] in litellm.model_alias_map:
            data["model"] = litellm.model_alias_map[data["model"]]

        ### CALL HOOKS ### - modify incoming data before calling the model
        data = await proxy_logging_obj.pre_call_hook(  # type: ignore
            user_api_key_dict=user_api_key_dict, data=data, call_type="text_completion"
        )

        ### ROUTE THE REQUESTs ###
        router_model_names = llm_router.model_names if llm_router is not None else []

        # skip router if user passed their key
        if (
            llm_router is not None and data["model"] in router_model_names
        ):  # model in router model list
            llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data))
        elif (
            llm_router is not None
            and llm_router.model_group_alias is not None
            and data["model"] in llm_router.model_group_alias
        ):  # model set in model_group_alias
            llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data))
        elif (
            llm_router is not None and data["model"] in llm_router.deployment_names
        ):  # model in router deployments, calling a specific deployment on the router
            llm_response = asyncio.create_task(
                llm_router.aanthropic_messages(**data, specific_deployment=True)
            )
        elif (
            llm_router is not None and data["model"] in llm_router.get_model_ids()
        ):  # model in router model list
            llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data))
        elif (
            llm_router is not None
            and data["model"] not in router_model_names
            and (
                llm_router.default_deployment is not None
                or len(llm_router.pattern_router.patterns) > 0
            )
        ):  # model in router deployments, calling a specific deployment on the router
            llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data))
        elif user_model is not None:  # `litellm --model <your-model-name>`
            llm_response = asyncio.create_task(litellm.anthropic_messages(**data))
        else:
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail={
                    "error": "completion: Invalid model name passed in model="
                    + data.get("model", "")
                },
            )

        # Await the llm_response task
        response = await llm_response

        hidden_params = getattr(response, "_hidden_params", {}) or {}
        model_id = hidden_params.get("model_id", None) or ""
        cache_key = hidden_params.get("cache_key", None) or ""
        api_base = hidden_params.get("api_base", None) or ""
        response_cost = hidden_params.get("response_cost", None) or ""

        ### ALERTING ###
        asyncio.create_task(
            proxy_logging_obj.update_request_status(
                litellm_call_id=data.get("litellm_call_id", ""), status="success"
            )
        )

        verbose_proxy_logger.debug("final response: %s", response)

        fastapi_response.headers.update(
            ProxyBaseLLMRequestProcessing.get_custom_headers(
                user_api_key_dict=user_api_key_dict,
                model_id=model_id,
                cache_key=cache_key,
                api_base=api_base,
                version=version,
                response_cost=response_cost,
                request_data=data,
                hidden_params=hidden_params,
            )
        )

        if (
            "stream" in data and data["stream"] is True
        ):  # use generate_responses to stream responses
            selected_data_generator = async_data_generator_anthropic(
                response=response,
                user_api_key_dict=user_api_key_dict,
                request_data=data,
                proxy_logging_obj=proxy_logging_obj,
            )

            return StreamingResponse(
                selected_data_generator,  # type: ignore
                media_type="text/event-stream",
            )

        verbose_proxy_logger.info("\nResponse from Litellm:\n{}".format(response))
        return response
    except Exception as e:
        await proxy_logging_obj.post_call_failure_hook(
            user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
        )
        verbose_proxy_logger.exception(
            "litellm.proxy.proxy_server.anthropic_response(): Exception occured - {}".format(
                str(e)
            )
        )
        error_msg = f"{str(e)}"
        raise ProxyException(
            message=getattr(e, "message", error_msg),
            type=getattr(e, "type", "None"),
            param=getattr(e, "param", "None"),
            code=getattr(e, "status_code", 500),
        )