two version of R2R are here HEAD master

1 files changed, 358 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/proxy/common_request_processing.py b/.venv/lib/python3.12/site-packages/litellm/proxy/common_request_processing.py
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+++ b/.venv/lib/python3.12/site-packages/litellm/proxy/common_request_processing.py
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+import asyncio
+import json
+import uuid
+from datetime import datetime
+from typing import TYPE_CHECKING, Any, Callable, Literal, Optional, Union
+
+import httpx
+from fastapi import HTTPException, Request, status
+from fastapi.responses import Response, StreamingResponse
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.proxy._types import ProxyException, UserAPIKeyAuth
+from litellm.proxy.auth.auth_utils import check_response_size_is_safe
+from litellm.proxy.common_utils.callback_utils import (
+    get_logging_caching_headers,
+    get_remaining_tokens_and_requests_from_request_data,
+)
+from litellm.proxy.route_llm_request import route_request
+from litellm.proxy.utils import ProxyLogging
+from litellm.router import Router
+
+if TYPE_CHECKING:
+    from litellm.proxy.proxy_server import ProxyConfig as _ProxyConfig
+
+    ProxyConfig = _ProxyConfig
+else:
+    ProxyConfig = Any
+from litellm.proxy.litellm_pre_call_utils import add_litellm_data_to_request
+
+
+class ProxyBaseLLMRequestProcessing:
+    def __init__(self, data: dict):
+        self.data = data
+
+    @staticmethod
+    def get_custom_headers(
+        *,
+        user_api_key_dict: UserAPIKeyAuth,
+        call_id: Optional[str] = None,
+        model_id: Optional[str] = None,
+        cache_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        version: Optional[str] = None,
+        model_region: Optional[str] = None,
+        response_cost: Optional[Union[float, str]] = None,
+        hidden_params: Optional[dict] = None,
+        fastest_response_batch_completion: Optional[bool] = None,
+        request_data: Optional[dict] = {},
+        timeout: Optional[Union[float, int, httpx.Timeout]] = None,
+        **kwargs,
+    ) -> dict:
+        exclude_values = {"", None, "None"}
+        hidden_params = hidden_params or {}
+        headers = {
+            "x-litellm-call-id": call_id,
+            "x-litellm-model-id": model_id,
+            "x-litellm-cache-key": cache_key,
+            "x-litellm-model-api-base": (
+                api_base.split("?")[0] if api_base else None
+            ),  # don't include query params, risk of leaking sensitive info
+            "x-litellm-version": version,
+            "x-litellm-model-region": model_region,
+            "x-litellm-response-cost": str(response_cost),
+            "x-litellm-key-tpm-limit": str(user_api_key_dict.tpm_limit),
+            "x-litellm-key-rpm-limit": str(user_api_key_dict.rpm_limit),
+            "x-litellm-key-max-budget": str(user_api_key_dict.max_budget),
+            "x-litellm-key-spend": str(user_api_key_dict.spend),
+            "x-litellm-response-duration-ms": str(
+                hidden_params.get("_response_ms", None)
+            ),
+            "x-litellm-overhead-duration-ms": str(
+                hidden_params.get("litellm_overhead_time_ms", None)
+            ),
+            "x-litellm-fastest_response_batch_completion": (
+                str(fastest_response_batch_completion)
+                if fastest_response_batch_completion is not None
+                else None
+            ),
+            "x-litellm-timeout": str(timeout) if timeout is not None else None,
+            **{k: str(v) for k, v in kwargs.items()},
+        }
+        if request_data:
+            remaining_tokens_header = (
+                get_remaining_tokens_and_requests_from_request_data(request_data)
+            )
+            headers.update(remaining_tokens_header)
+
+            logging_caching_headers = get_logging_caching_headers(request_data)
+            if logging_caching_headers:
+                headers.update(logging_caching_headers)
+
+        try:
+            return {
+                key: str(value)
+                for key, value in headers.items()
+                if value not in exclude_values
+            }
+        except Exception as e:
+            verbose_proxy_logger.error(f"Error setting custom headers: {e}")
+            return {}
+
+    async def base_process_llm_request(
+        self,
+        request: Request,
+        fastapi_response: Response,
+        user_api_key_dict: UserAPIKeyAuth,
+        route_type: Literal["acompletion", "aresponses"],
+        proxy_logging_obj: ProxyLogging,
+        general_settings: dict,
+        proxy_config: ProxyConfig,
+        select_data_generator: Callable,
+        llm_router: Optional[Router] = None,
+        model: Optional[str] = None,
+        user_model: Optional[str] = None,
+        user_temperature: Optional[float] = None,
+        user_request_timeout: Optional[float] = None,
+        user_max_tokens: Optional[int] = None,
+        user_api_base: Optional[str] = None,
+        version: Optional[str] = None,
+    ) -> Any:
+        """
+        Common request processing logic for both chat completions and responses API endpoints
+        """
+        verbose_proxy_logger.debug(
+            "Request received by LiteLLM:\n{}".format(json.dumps(self.data, indent=4)),
+        )
+
+        self.data = await add_litellm_data_to_request(
+            data=self.data,
+            request=request,
+            general_settings=general_settings,
+            user_api_key_dict=user_api_key_dict,
+            version=version,
+            proxy_config=proxy_config,
+        )
+
+        self.data["model"] = (
+            general_settings.get("completion_model", None)  # server default
+            or user_model  # model name passed via cli args
+            or model  # for azure deployments
+            or self.data.get("model", None)  # default passed in http request
+        )
+
+        # override with user settings, these are params passed via cli
+        if user_temperature:
+            self.data["temperature"] = user_temperature
+        if user_request_timeout:
+            self.data["request_timeout"] = user_request_timeout
+        if user_max_tokens:
+            self.data["max_tokens"] = user_max_tokens
+        if user_api_base:
+            self.data["api_base"] = user_api_base
+
+        ### MODEL ALIAS MAPPING ###
+        # check if model name in model alias map
+        # get the actual model name
+        if (
+            isinstance(self.data["model"], str)
+            and self.data["model"] in litellm.model_alias_map
+        ):
+            self.data["model"] = litellm.model_alias_map[self.data["model"]]
+
+        ### CALL HOOKS ### - modify/reject incoming data before calling the model
+        self.data = await proxy_logging_obj.pre_call_hook(  # type: ignore
+            user_api_key_dict=user_api_key_dict, data=self.data, call_type="completion"
+        )
+
+        ## LOGGING OBJECT ## - initialize logging object for logging success/failure events for call
+        ## IMPORTANT Note: - initialize this before running pre-call checks. Ensures we log rejected requests to langfuse.
+        self.data["litellm_call_id"] = request.headers.get(
+            "x-litellm-call-id", str(uuid.uuid4())
+        )
+        logging_obj, self.data = litellm.utils.function_setup(
+            original_function=route_type,
+            rules_obj=litellm.utils.Rules(),
+            start_time=datetime.now(),
+            **self.data,
+        )
+
+        self.data["litellm_logging_obj"] = logging_obj
+
+        tasks = []
+        tasks.append(
+            proxy_logging_obj.during_call_hook(
+                data=self.data,
+                user_api_key_dict=user_api_key_dict,
+                call_type=ProxyBaseLLMRequestProcessing._get_pre_call_type(
+                    route_type=route_type
+                ),
+            )
+        )
+
+        ### ROUTE THE REQUEST ###
+        # Do not change this - it should be a constant time fetch - ALWAYS
+        llm_call = await route_request(
+            data=self.data,
+            route_type=route_type,
+            llm_router=llm_router,
+            user_model=user_model,
+        )
+        tasks.append(llm_call)
+
+        # wait for call to end
+        llm_responses = asyncio.gather(
+            *tasks
+        )  # run the moderation check in parallel to the actual llm api call
+
+        responses = await llm_responses
+
+        response = responses[1]
+
+        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 ""
+        fastest_response_batch_completion = hidden_params.get(
+            "fastest_response_batch_completion", None
+        )
+        additional_headers: dict = hidden_params.get("additional_headers", {}) or {}
+
+        # Post Call Processing
+        if llm_router is not None:
+            self.data["deployment"] = llm_router.get_deployment(model_id=model_id)
+        asyncio.create_task(
+            proxy_logging_obj.update_request_status(
+                litellm_call_id=self.data.get("litellm_call_id", ""), status="success"
+            )
+        )
+        if (
+            "stream" in self.data and self.data["stream"] is True
+        ):  # use generate_responses to stream responses
+            custom_headers = ProxyBaseLLMRequestProcessing.get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                call_id=logging_obj.litellm_call_id,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                response_cost=response_cost,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+                fastest_response_batch_completion=fastest_response_batch_completion,
+                request_data=self.data,
+                hidden_params=hidden_params,
+                **additional_headers,
+            )
+            selected_data_generator = select_data_generator(
+                response=response,
+                user_api_key_dict=user_api_key_dict,
+                request_data=self.data,
+            )
+            return StreamingResponse(
+                selected_data_generator,
+                media_type="text/event-stream",
+                headers=custom_headers,
+            )
+
+        ### CALL HOOKS ### - modify outgoing data
+        response = await proxy_logging_obj.post_call_success_hook(
+            data=self.data, user_api_key_dict=user_api_key_dict, response=response
+        )
+
+        hidden_params = (
+            getattr(response, "_hidden_params", {}) or {}
+        )  # get any updated response headers
+        additional_headers = hidden_params.get("additional_headers", {}) or {}
+
+        fastapi_response.headers.update(
+            ProxyBaseLLMRequestProcessing.get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                call_id=logging_obj.litellm_call_id,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                response_cost=response_cost,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+                fastest_response_batch_completion=fastest_response_batch_completion,
+                request_data=self.data,
+                hidden_params=hidden_params,
+                **additional_headers,
+            )
+        )
+        await check_response_size_is_safe(response=response)
+
+        return response
+
+    async def _handle_llm_api_exception(
+        self,
+        e: Exception,
+        user_api_key_dict: UserAPIKeyAuth,
+        proxy_logging_obj: ProxyLogging,
+        version: Optional[str] = None,
+    ):
+        """Raises ProxyException (OpenAI API compatible) if an exception is raised"""
+        verbose_proxy_logger.exception(
+            f"litellm.proxy.proxy_server._handle_llm_api_exception(): Exception occured - {str(e)}"
+        )
+        await proxy_logging_obj.post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict,
+            original_exception=e,
+            request_data=self.data,
+        )
+        litellm_debug_info = getattr(e, "litellm_debug_info", "")
+        verbose_proxy_logger.debug(
+            "\033[1;31mAn error occurred: %s %s\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`",
+            e,
+            litellm_debug_info,
+        )
+
+        timeout = getattr(
+            e, "timeout", None
+        )  # returns the timeout set by the wrapper. Used for testing if model-specific timeout are set correctly
+        _litellm_logging_obj: Optional[LiteLLMLoggingObj] = self.data.get(
+            "litellm_logging_obj", None
+        )
+        custom_headers = ProxyBaseLLMRequestProcessing.get_custom_headers(
+            user_api_key_dict=user_api_key_dict,
+            call_id=(
+                _litellm_logging_obj.litellm_call_id if _litellm_logging_obj else None
+            ),
+            version=version,
+            response_cost=0,
+            model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+            request_data=self.data,
+            timeout=timeout,
+        )
+        headers = getattr(e, "headers", {}) or {}
+        headers.update(custom_headers)
+
+        if isinstance(e, HTTPException):
+            raise ProxyException(
+                message=getattr(e, "detail", str(e)),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+                headers=headers,
+            )
+        error_msg = f"{str(e)}"
+        raise ProxyException(
+            message=getattr(e, "message", error_msg),
+            type=getattr(e, "type", "None"),
+            param=getattr(e, "param", "None"),
+            openai_code=getattr(e, "code", None),
+            code=getattr(e, "status_code", 500),
+            headers=headers,
+        )
+
+    @staticmethod
+    def _get_pre_call_type(
+        route_type: Literal["acompletion", "aresponses"]
+    ) -> Literal["completion", "responses"]:
+        if route_type == "acompletion":
+            return "completion"
+        elif route_type == "aresponses":
+            return "responses"