litellm/litellm/exceptions.py

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## LiteLLM versions of the OpenAI Exception Types

import enum
from typing import Any, Final

import httpx
import openai

from litellm.types.utils import LiteLLMCommonStrings


class RateLimitErrorCategory(str, enum.Enum):
    """
    Category of a rate limit error, allowing callers to distinguish where the rate
    limit originated. Exposed on every :class:`RateLimitError` instance via the
    ``category`` attribute.

    Use these values to switch on the rate limit source, e.g.::

        try:
            ...
        except litellm.RateLimitError as e:
            if e.category == RateLimitErrorCategory.LITELLM_RATE_LIMIT:
                ...  # litellm's own limiter (key/team/user/model RPM/TPM/budget)
            elif e.category == RateLimitErrorCategory.VENDOR_RATE_LIMIT:
                ...  # the upstream LLM provider returned 429
    """

    VENDOR_RATE_LIMIT = "vendor_rate_limit"
    """The upstream LLM provider returned a rate-limit response (e.g. OpenAI 429)."""

    VENDOR_BATCH_RATE_LIMIT = "vendor_batch_rate_limit"
    """The upstream LLM provider returned a rate-limit response on a batch endpoint."""

    LITELLM_RATE_LIMIT = "litellm_rate_limit"
    """LiteLLM's own rate limiter (key/team/user/model RPM/TPM, budget, parallel-requests, etc.) blocked the request."""

    LITELLM_BATCH_RATE_LIMIT = "litellm_batch_rate_limit"
    """LiteLLM's own batch rate limiter (token/request budget across a batch input file) blocked the request."""


class RateLimitType(str, enum.Enum):
    """
    The dimension that was exceeded when a rate-limit error fired.

    This is orthogonal to :class:`RateLimitErrorCategory` — *category* tells
    callers **who** rate-limited the request (the upstream vendor vs. one of
    litellm's own limiters), while *type* tells them **which limit dimension**
    was exceeded (an RPM ceiling, a TPM ceiling, a max-parallel-requests
    ceiling, a budget cap, or a max-iterations cap).

    Surfaced both on every :class:`RateLimitError` instance via the
    ``rate_limit_type`` attribute and on the structured
    ``StandardLoggingPayload.error_information.error_rate_limit_type`` field
    so custom callbacks / metrics consumers can split rate-limit failures by
    cause without parsing free-text error messages.
    """

    REQUESTS = "requests"
    """Requests-per-minute (RPM) or requests-per-window ceiling exceeded."""

    TOKENS = "tokens"
    """Tokens-per-minute (TPM) or tokens-per-window ceiling exceeded."""

    CONCURRENT_REQUESTS = "concurrent_requests"
    """``max_parallel_requests`` — too many in-flight requests at once."""

    BUDGET = "budget"
    """Spend budget cap reached (key, team, user, or per-session)."""

    MAX_ITERATIONS = "max_iterations"
    """Per-session max-iterations cap reached (agent-style flows)."""


_RATE_LIMIT_CATEGORY_VALUES: Final = frozenset(c.value for c in RateLimitErrorCategory)
_RATE_LIMIT_TYPE_VALUES: Final = frozenset(t.value for t in RateLimitType)


def validate_rate_limit_category(value: object) -> str | None:
    """Return ``value`` only if it matches a known :class:`RateLimitErrorCategory`.

    Used at duck-typed read sites (StandardLoggingPayload extraction, Prometheus
    labels) to reject `.category` strings set by unrelated third-party exceptions
    — otherwise those would leak into custom-callback payloads and Prometheus
    label cardinality.
    """
    if isinstance(value, RateLimitErrorCategory):
        return value.value
    if isinstance(value, str) and value in _RATE_LIMIT_CATEGORY_VALUES:
        return value
    return None


def validate_rate_limit_type(value: object) -> str | None:
    """Return ``value`` only if it matches a known :class:`RateLimitType`.

    See :func:`validate_rate_limit_category` for the rationale.
    """
    if isinstance(value, RateLimitType):
        return value.value
    if isinstance(value, str) and value in _RATE_LIMIT_TYPE_VALUES:
        return value
    return None


_MINIMAL_ERROR_RESPONSE: httpx.Response | None = None


def _get_minimal_error_response() -> httpx.Response:
    """Get a cached minimal httpx.Response object for error cases."""
    global _MINIMAL_ERROR_RESPONSE
    if _MINIMAL_ERROR_RESPONSE is None:
        _MINIMAL_ERROR_RESPONSE = httpx.Response(
            status_code=400,
            request=httpx.Request(method="GET", url="https://litellm.ai"),
        )
    return _MINIMAL_ERROR_RESPONSE


class AuthenticationError(openai.AuthenticationError):
    def __init__(
        self,
        message,
        llm_provider,
        model,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 401
        self.message = f"litellm.AuthenticationError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        self.response = response or httpx.Response(
            status_code=self.status_code,
            request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
        )
        super().__init__(
            self.message, response=self.response, body=None
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


# raise when invalid models passed, example gpt-8
class NotFoundError(openai.NotFoundError):
    def __init__(
        self,
        message,
        model,
        llm_provider,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 404
        self.message = f"litellm.NotFoundError: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        self.response = response or httpx.Response(
            status_code=self.status_code,
            request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
        )
        super().__init__(
            self.message, response=self.response, body=None
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class BadRequestError(openai.BadRequestError):
    def __init__(
        self,
        message,
        model,
        llm_provider,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
        body: dict | None = None,
    ):
        self.status_code = 400
        self.message = f"litellm.BadRequestError: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        # Use response if it's a valid httpx.Response with a request, otherwise use minimal error response
        # Note: We check _request (not .request property) to avoid RuntimeError when _request is None
        if (
            response is not None
            and isinstance(response, httpx.Response)
            and hasattr(response, "_request")
            and getattr(response, "_request", None) is not None
        ):
            self.response = response
        else:
            self.response = _get_minimal_error_response()
        super().__init__(
            self.message, response=self.response, body=body
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class ImageFetchError(BadRequestError):
    def __init__(
        self,
        message,
        model=None,
        llm_provider=None,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
        body: dict | None = None,
    ):
        super().__init__(
            message=message,
            model=model,
            llm_provider=llm_provider,
            response=response,
            litellm_debug_info=litellm_debug_info,
            max_retries=max_retries,
            num_retries=num_retries,
            body=body,
        )


class UnprocessableEntityError(openai.UnprocessableEntityError):
    def __init__(
        self,
        message,
        model,
        llm_provider,
        response: httpx.Response,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 422
        self.message = f"litellm.UnprocessableEntityError: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        super().__init__(
            self.message, response=response, body=None
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class Timeout(openai.APITimeoutError):
    def __init__(
        self,
        message,
        model,
        llm_provider,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
        headers: dict | None = None,
        exception_status_code: int | None = None,
        response: httpx.Response | None = None,
    ):
        request: Final = httpx.Request(
            method="POST",
            url="https://api.openai.com/v1",
        )
        super().__init__(request=request)  # Call the base class constructor with the parameters it needs
        self.status_code = exception_status_code or 408
        self.message = f"litellm.Timeout: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        self.headers = headers
        if response is not None:
            self.response = response

    # custom function to convert to str
    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class PermissionDeniedError(openai.PermissionDeniedError):
    def __init__(
        self,
        message,
        llm_provider,
        model,
        response: httpx.Response,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 403
        self.message = f"litellm.PermissionDeniedError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        super().__init__(
            self.message, response=response, body=None
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class RateLimitError(openai.RateLimitError):
    """
    Unified rate-limit error.

    Every rate-limit condition surfaced by litellm — whether it originated from
    an upstream LLM provider, a vendor batch endpoint, or one of litellm's own
    proxy-side limiters (parallel-requests, dynamic-rate, batch-rate, budget,
    max-iterations, etc.) — is raised as an instance of this class.

    The :attr:`category` attribute lets callers distinguish the source. See
    :class:`RateLimitErrorCategory` for the available values.
    """

    def __init__(
        self,
        message,
        llm_provider,
        model,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
        category: str | RateLimitErrorCategory = (RateLimitErrorCategory.VENDOR_RATE_LIMIT),
        rate_limit_type: str | RateLimitType | None = None,
        headers: dict[str, str] | None = None,
        detail: Any = None,
    ):
        self.status_code = 429
        self.message = f"litellm.RateLimitError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        self.category = category.value if isinstance(category, RateLimitErrorCategory) else category
        # Which dimension was exceeded — request count, token count, parallel
        # requests, budget, max iterations. None when the source didn't
        # classify the failure (e.g. legacy vendor 429 with no header hints).
        self.rate_limit_type: str | None = (
            rate_limit_type.value if isinstance(rate_limit_type, RateLimitType) else rate_limit_type
        )
        # Headers explicitly attached to the error (e.g. retry-after,
        # rate_limit_type, reset_at). Preserved across the proxy boundary so
        # clients can react appropriately.
        #
        # IMPORTANT: we deliberately do NOT auto-populate self.headers from
        # response.headers when only `response` is provided. A vendor 429 can
        # set arbitrary response headers (Set-Cookie, CORS overrides, …); if
        # those leaked into e.headers and a downstream proxy serializer
        # forwarded them to the client, a malicious upstream could inject
        # browser-interpreted headers for the proxy origin. Vendor response
        # headers stay reachable on `e.response.headers` for callers that
        # explicitly want them; only the proxy-supplied `headers=` kwarg
        # makes it onto `self.headers`.
        _response_headers: Final = getattr(response, "headers", None) if response is not None else None
        self.headers: dict[str, str] | None = {k: str(v) for k, v in headers.items()} if headers else None
        # Mirrors FastAPI HTTPException.detail so the same instance can be
        # serialized through both the ProxyException and HTTPException paths.
        self.detail = detail if detail is not None else self.message
        self.response = httpx.Response(
            status_code=429,
            headers=_response_headers,
            request=httpx.Request(
                method="POST",
                url=" https://cloud.google.com/vertex-ai/",
            ),
        )
        super().__init__(
            self.message, response=self.response, body=None
        )  # Call the base class constructor with the parameters it needs
        self.code = "429"
        self.type = "throttling_error"

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


# sub class of rate limit error - meant to give more granularity for error handling context window exceeded errors
class ContextWindowExceededError(BadRequestError):
    def __init__(
        self,
        message,
        model,
        llm_provider,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
    ):
        self.status_code = 400
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        super().__init__(
            message=message,
            model=self.model,
            llm_provider=self.llm_provider,
            response=response,
            litellm_debug_info=self.litellm_debug_info,
        )  # Call the base class constructor with the parameters it needs

        # set after, to make it clear the raised error is a context window exceeded error
        self.message = f"litellm.ContextWindowExceededError: {self.message}"

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


# sub class of bad request error - meant to help us catch guardrails-related errors on proxy.
class RejectedRequestError(BadRequestError):
    def __init__(
        self,
        message,
        model,
        llm_provider,
        request_data: dict,
        litellm_debug_info: str | None = None,
    ):
        self.status_code = 400
        self.message = f"litellm.RejectedRequestError: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        self.request_data = request_data
        request: Final = httpx.Request(method="POST", url="https://api.openai.com/v1")
        response: Final = httpx.Response(status_code=400, request=request)
        super().__init__(
            message=self.message,
            model=self.model,
            llm_provider=self.llm_provider,
            response=response,
            litellm_debug_info=self.litellm_debug_info,
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class ContentPolicyViolationError(BadRequestError):
    #  Error code: 400 - {'error': {'code': 'content_policy_violation', 'message': 'Your request was rejected as a result of our safety system. Image descriptions generated from your prompt may contain text that is not allowed by our safety system. If you believe this was done in error, your request may succeed if retried, or by adjusting your prompt.', 'param': None, 'type': 'invalid_request_error'}}
    def __init__(
        self,
        message,
        model,
        llm_provider,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        provider_specific_fields: dict | None = None,
        body: dict | None = None,
    ):
        self.status_code = 400
        self.message = f"litellm.ContentPolicyViolationError: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        self.provider_specific_fields = provider_specific_fields
        super().__init__(
            message=self.message,
            model=self.model,
            llm_provider=self.llm_provider,
            response=response,
            litellm_debug_info=self.litellm_debug_info,
            body=body,
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        return self._transform_error_to_string()

    def __repr__(self):
        return self._transform_error_to_string()

    def _transform_error_to_string(self) -> str:
        """
        Transform the error to a string
        """
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class ServiceUnavailableError(openai.APIStatusError):
    def __init__(
        self,
        message,
        llm_provider,
        model,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 503
        self.message = f"litellm.ServiceUnavailableError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        _response_headers: Final = getattr(response, "headers", None) if response is not None else None
        self.response = httpx.Response(
            status_code=self.status_code,
            headers=_response_headers,
            request=httpx.Request(
                method="POST",
                url=" https://cloud.google.com/vertex-ai/",
            ),
        )
        super().__init__(
            self.message, response=self.response, body=None
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class BadGatewayError(openai.APIStatusError):
    def __init__(
        self,
        message,
        llm_provider,
        model,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 502
        self.message = f"litellm.BadGatewayError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        _response_headers: Final = getattr(response, "headers", None) if response is not None else None
        self.response = httpx.Response(
            status_code=self.status_code,
            headers=_response_headers,
            request=httpx.Request(
                method="POST",
                url=" https://cloud.google.com/vertex-ai/",
            ),
        )
        super().__init__(
            self.message, response=self.response, body=None
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class InternalServerError(openai.InternalServerError):
    def __init__(
        self,
        message,
        llm_provider,
        model,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 500
        self.message = f"litellm.InternalServerError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        _response_headers: Final = getattr(response, "headers", None) if response is not None else None
        self.response = httpx.Response(
            status_code=self.status_code,
            headers=_response_headers,
            request=httpx.Request(
                method="POST",
                url=" https://cloud.google.com/vertex-ai/",
            ),
        )
        super().__init__(
            self.message, response=self.response, body=None
        )  # Call the base class constructor with the parameters it needs

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


# raise this when the API returns an invalid response object - https://github.com/openai/openai-python/blob/1be14ee34a0f8e42d3f9aa5451aa4cb161f1781f/openai/api_requestor.py#L401
class APIError(openai.APIError):
    def __init__(
        self,
        status_code: int,
        message,
        llm_provider,
        model,
        request: httpx.Request | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = status_code
        self.message = f"litellm.APIError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        if request is None:
            request = httpx.Request(method="POST", url="https://api.openai.com/v1")
        super().__init__(self.message, request=request, body=None)

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


# raised if an invalid request (not get, delete, put, post) is made
class APIConnectionError(openai.APIConnectionError):
    def __init__(
        self,
        message,
        llm_provider,
        model,
        request: httpx.Request | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.message = f"litellm.APIConnectionError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.status_code = 500
        self.litellm_debug_info = litellm_debug_info
        self.request = httpx.Request(method="POST", url="https://api.openai.com/v1")
        self.max_retries = max_retries
        self.num_retries = num_retries
        super().__init__(message=self.message, request=self.request)

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


# raised if an invalid request (not get, delete, put, post) is made
class APIResponseValidationError(openai.APIResponseValidationError):
    def __init__(
        self,
        message,
        llm_provider,
        model,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.message = f"litellm.APIResponseValidationError: {message}"
        self.llm_provider = llm_provider
        self.model = model
        request: Final = httpx.Request(method="POST", url="https://api.openai.com/v1")
        response: Final = httpx.Response(status_code=500, request=request)
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        super().__init__(response=response, body=None, message=message)

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message

    def __repr__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        return _message


class JSONSchemaValidationError(APIResponseValidationError):
    def __init__(self, model: str, llm_provider: str, raw_response: str, schema: str) -> None:
        self.raw_response = raw_response
        self.schema = schema
        self.model = model
        message = f"litellm.JSONSchemaValidationError: model={model}, returned an invalid response={raw_response}, for schema={schema}.\nAccess raw response with `e.raw_response`"
        self.message = message
        super().__init__(model=model, message=message, llm_provider=llm_provider)


class OpenAIError(openai.OpenAIError):
    def __init__(self, original_exception=None):
        super().__init__()
        self.llm_provider = "openai"


class UnsupportedParamsError(BadRequestError):
    def __init__(
        self,
        message,
        llm_provider: str | None = None,
        model: str | None = None,
        status_code: int = 400,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = 400
        self.message = f"litellm.UnsupportedParamsError: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.litellm_debug_info = litellm_debug_info
        response = response or httpx.Response(
            status_code=self.status_code,
            request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
        )
        self.max_retries = max_retries
        self.num_retries = num_retries


LITELLM_EXCEPTION_TYPES: Final = [
    AuthenticationError,
    NotFoundError,
    BadRequestError,
    UnprocessableEntityError,
    UnsupportedParamsError,
    Timeout,
    PermissionDeniedError,
    RateLimitError,
    ContextWindowExceededError,
    RejectedRequestError,
    ContentPolicyViolationError,
    InternalServerError,
    ServiceUnavailableError,
    BadGatewayError,
    APIError,
    APIConnectionError,
    APIResponseValidationError,
    OpenAIError,
    InternalServerError,
    JSONSchemaValidationError,
]


class BudgetExceededError(Exception):
    def __init__(
        self,
        current_cost: float,
        max_budget: float,
        message: str | None = None,
        llm_provider: str | None = None,
        entity_type: str | None = None,
        entity_id: str | None = None,
    ):
        self.current_cost = current_cost
        self.max_budget = max_budget
        self.status_code = 429
        self.llm_provider = llm_provider or ""
        self.entity_type = entity_type
        self.entity_id = entity_id
        # Surface unified rate-limit fields without joining the RateLimitError
        # hierarchy so existing `except BudgetExceededError:` handlers keep
        # working; custom callbacks reading StandardLoggingPayload pick these
        # up via the same `category` / `rate_limit_type` attributes the rest
        # of the unified rate-limit error path uses. Stored as plain strings
        # to match the normalization RateLimitError.__init__ performs.
        self.category: str = RateLimitErrorCategory.LITELLM_RATE_LIMIT.value
        self.rate_limit_type: str = RateLimitType.BUDGET.value
        message = message or f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}"
        self.message = message
        super().__init__(message)


## DEPRECATED ##
class InvalidRequestError(openai.BadRequestError):
    def __init__(self, message, model, llm_provider):
        self.status_code = 400
        self.message = message
        self.model = model
        self.llm_provider = llm_provider
        self.response = httpx.Response(
            status_code=400,
            request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
        )
        super().__init__(
            message=self.message, response=self.response, body=None
        )  # Call the base class constructor with the parameters it needs


class MockException(openai.APIError):
    # used for testing
    def __init__(
        self,
        status_code: int,
        message,
        llm_provider,
        model,
        request: httpx.Request | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
    ):
        self.status_code = status_code
        self.message = f"litellm.MockException: {message}"
        self.llm_provider = llm_provider
        self.model = model
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        if request is None:
            request = httpx.Request(method="POST", url="https://api.openai.com/v1")
        super().__init__(self.message, request=request, body=None)


class LiteLLMUnknownProvider(BadRequestError):
    def __init__(self, model: str, custom_llm_provider: str | None = None):
        self.message = LiteLLMCommonStrings.llm_provider_not_provided.value.format(
            model=model, custom_llm_provider=custom_llm_provider
        )
        super().__init__(self.message, model=model, llm_provider=custom_llm_provider, response=None)

    def __str__(self):
        return self.message


class GuardrailRaisedException(Exception):
    """
    Raised both when a guardrail judged content and when it could not judge it at all, since a
    guardrail that fails closed refuses the request the same way a policy violation does.

    ``blocked_content`` separates the two. Set it only where the guardrail actually reached a
    verdict on the payload; leave it alone for an unreachable backend, a timeout, or a response
    the integration could not parse. Callers that treat a block as something other than a plain
    failure, such as the batch path dropping one record and submitting the rest, must gate on it,
    because dropping a record no guardrail ever inspected is a silent loss of enforcement.
    """

    def __init__(
        self,
        guardrail_name: str | None = None,
        message: str = "",
        should_wrap_with_default_message: bool = True,
        status_code: int = 400,
        blocked_content: bool = False,
    ):
        default_message: Final = f"Guardrail raised an exception, Guardrail: {guardrail_name}, Message: {message}"
        self.guardrail_name = guardrail_name
        self.status_code = status_code
        self.blocked_content = blocked_content
        self.message = default_message if should_wrap_with_default_message else message
        super().__init__(self.message)


class BlockedPiiEntityError(Exception):
    def __init__(
        self,
        entity_type: str,
        guardrail_name: str | None = None,
        status_code: int = 400,
    ):
        """
        Raised when a blocked entity is detected by a guardrail.
        """
        self.entity_type = entity_type
        self.guardrail_name = guardrail_name
        self.status_code = status_code
        self.message = f"Blocked entity detected: {entity_type} by Guardrail: {guardrail_name}. This entity is not allowed to be used in this request."
        super().__init__(self.message)


class MidStreamFallbackError(ServiceUnavailableError):
    def __init__(
        self,
        message: str,
        model: str,
        llm_provider: str,
        original_exception: Exception | None = None,
        response: httpx.Response | None = None,
        litellm_debug_info: str | None = None,
        max_retries: int | None = None,
        num_retries: int | None = None,
        generated_content: str = "",
        is_pre_first_chunk: bool = False,
    ):
        original_status: Final = getattr(original_exception, "status_code", None)
        self.status_code = int(original_status) if original_status is not None else 503
        self.message = f"litellm.MidStreamFallbackError: {message}"
        self.model = model
        self.llm_provider = llm_provider
        self.original_exception = original_exception
        self.litellm_debug_info = litellm_debug_info
        self.max_retries = max_retries
        self.num_retries = num_retries
        self.generated_content = generated_content
        self.is_pre_first_chunk = is_pre_first_chunk

        # Create a response if one wasn't provided
        if response is None:
            self.response = httpx.Response(
                status_code=self.status_code,
                request=httpx.Request(
                    method="POST",
                    url=f"https://{llm_provider}.com/v1/",
                ),
            )
        else:
            self.response = response

        # Save the original attributes before they are overridden by ServiceUnavailableError
        _saved_response: Final = self.response
        _saved_request: Final = getattr(self.response, "request", None) or httpx.Request(
            method="POST", url=f"https://{llm_provider}.com/v1/"
        )
        _saved_message: Final = self.message

        # Call the parent constructor (which hardcodes status_code=503 and modifies the response object)
        super().__init__(
            message=self.message,
            llm_provider=llm_provider,
            model=model,
            response=self.response,
            litellm_debug_info=self.litellm_debug_info,
            max_retries=self.max_retries,
            num_retries=self.num_retries,
        )

        # Restore the propagated status and original response/request objects
        self.status_code = int(original_status) if original_status is not None else 503
        self.response = _saved_response
        self.request = _saved_request
        self.message = _saved_message
        self.args = (_saved_message,)

    def __str__(self):
        _message = self.message
        if self.num_retries:
            _message += f" LiteLLM Retried: {self.num_retries} times"
        if self.max_retries:
            _message += f", LiteLLM Max Retries: {self.max_retries}"
        if self.original_exception:
            _message += f" Original exception: {type(self.original_exception).__name__}: {self.original_exception}"
        return _message

    def __repr__(self):
        return self.__str__()


class ModifyResponseException(Exception):
    """
    Exception raised when a guardrail wants to modify the response.

    This exception carries the synthetic response that should be returned
    to the user instead of calling the LLM or instead of the LLM's response.
    It should be caught by the proxy and returned with a 200 status code.

    This is a base exception that all guardrails can use to replace responses,
    allowing violation messages to be returned as successful responses
    rather than errors.
    """

    def __init__(
        self,
        message: str,
        model: str,
        request_data: dict[str, Any],
        guardrail_name: str | None = None,
        detection_info: dict[str, Any] | None = None,
        original_response: Any | None = None,
    ):
        self.message = message
        self.model = model
        self.request_data = request_data
        self.guardrail_name = guardrail_name
        self.detection_info = detection_info or {}
        # The LLM response that was blocked (post-call). Carries the real token
        # usage the upstream call consumed, so the synthetic block response can
        # report it instead of discarding it. None for pre-call blocks (the LLM
        # was never invoked).
        self.original_response = original_response
        super().__init__(message)


class SensitiveDataRouteException(Exception):
    """
    Exception raised when a guardrail detects sensitive data and wants to reroute the request.

    Instead of blocking the request, this exception signals that the request should be
    routed to a different model (typically an on-premise model for data privacy).

    The proxy catches this exception and:
    1. Reroutes the current request to the specified model
    2. When sticky_session_routing is True, stores the routing decision in session
       cache so all subsequent requests in the same session are routed to the same model
    """

    def __init__(
        self,
        route_to_model: str,
        session_id: str,
        guardrail_name: str | None = None,
        detection_info: dict[str, Any] | None = None,
        message: str | None = None,
        sticky_session_routing: bool = True,
    ):
        self.route_to_model = route_to_model
        self.session_id = session_id
        self.guardrail_name = guardrail_name
        self.detection_info = detection_info or {}
        self.sticky_session_routing = sticky_session_routing
        self.message = message or f"Sensitive data detected by {guardrail_name}. Routing to model: {route_to_model}"
        super().__init__(self.message)