知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
1256 lines
45 KiB
Python
1256 lines
45 KiB
Python
# +-----------------------------------------------+
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# | |
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# | Give Feedback / Get Help |
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# | https://github.com/BerriAI/litellm/issues/new |
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# | |
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# +-----------------------------------------------+
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#
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# Thank you users! We ❤️ you! - Krrish & Ishaan
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## LiteLLM versions of the OpenAI Exception Types
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import enum
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from typing import Any, Dict, Optional, Union
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import httpx
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import openai
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from litellm.types.utils import LiteLLMCommonStrings
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class RateLimitErrorCategory(str, enum.Enum):
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"""
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Category of a rate limit error, allowing callers to distinguish where the rate
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limit originated. Exposed on every :class:`RateLimitError` instance via the
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``category`` attribute.
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Use these values to switch on the rate limit source, e.g.::
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try:
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...
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except litellm.RateLimitError as e:
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if e.category == RateLimitErrorCategory.LITELLM_RATE_LIMIT:
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... # litellm's own limiter (key/team/user/model RPM/TPM/budget)
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elif e.category == RateLimitErrorCategory.VENDOR_RATE_LIMIT:
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... # the upstream LLM provider returned 429
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"""
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VENDOR_RATE_LIMIT = "vendor_rate_limit"
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"""The upstream LLM provider returned a rate-limit response (e.g. OpenAI 429)."""
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VENDOR_BATCH_RATE_LIMIT = "vendor_batch_rate_limit"
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"""The upstream LLM provider returned a rate-limit response on a batch endpoint."""
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LITELLM_RATE_LIMIT = "litellm_rate_limit"
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"""LiteLLM's own rate limiter (key/team/user/model RPM/TPM, budget, parallel-requests, etc.) blocked the request."""
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LITELLM_BATCH_RATE_LIMIT = "litellm_batch_rate_limit"
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"""LiteLLM's own batch rate limiter (token/request budget across a batch input file) blocked the request."""
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class RateLimitType(str, enum.Enum):
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"""
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The dimension that was exceeded when a rate-limit error fired.
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This is orthogonal to :class:`RateLimitErrorCategory` — *category* tells
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callers **who** rate-limited the request (the upstream vendor vs. one of
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litellm's own limiters), while *type* tells them **which limit dimension**
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was exceeded (an RPM ceiling, a TPM ceiling, a max-parallel-requests
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ceiling, a budget cap, or a max-iterations cap).
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Surfaced both on every :class:`RateLimitError` instance via the
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``rate_limit_type`` attribute and on the structured
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``StandardLoggingPayload.error_information.error_rate_limit_type`` field
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so custom callbacks / metrics consumers can split rate-limit failures by
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cause without parsing free-text error messages.
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"""
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REQUESTS = "requests"
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"""Requests-per-minute (RPM) or requests-per-window ceiling exceeded."""
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TOKENS = "tokens"
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"""Tokens-per-minute (TPM) or tokens-per-window ceiling exceeded."""
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CONCURRENT_REQUESTS = "concurrent_requests"
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"""``max_parallel_requests`` — too many in-flight requests at once."""
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BUDGET = "budget"
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"""Spend budget cap reached (key, team, user, or per-session)."""
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MAX_ITERATIONS = "max_iterations"
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"""Per-session max-iterations cap reached (agent-style flows)."""
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_RATE_LIMIT_CATEGORY_VALUES = frozenset(c.value for c in RateLimitErrorCategory)
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_RATE_LIMIT_TYPE_VALUES = frozenset(t.value for t in RateLimitType)
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def validate_rate_limit_category(value: Any) -> Optional[str]:
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"""Return ``value`` only if it matches a known :class:`RateLimitErrorCategory`.
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Used at duck-typed read sites (StandardLoggingPayload extraction, Prometheus
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labels) to reject `.category` strings set by unrelated third-party exceptions
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— otherwise those would leak into custom-callback payloads and Prometheus
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label cardinality.
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"""
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if isinstance(value, RateLimitErrorCategory):
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return value.value
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if isinstance(value, str) and value in _RATE_LIMIT_CATEGORY_VALUES:
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return value
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return None
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def validate_rate_limit_type(value: Any) -> Optional[str]:
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"""Return ``value`` only if it matches a known :class:`RateLimitType`.
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See :func:`validate_rate_limit_category` for the rationale.
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"""
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if isinstance(value, RateLimitType):
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return value.value
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if isinstance(value, str) and value in _RATE_LIMIT_TYPE_VALUES:
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return value
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return None
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_MINIMAL_ERROR_RESPONSE: Optional[httpx.Response] = None
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def _get_minimal_error_response() -> httpx.Response:
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"""Get a cached minimal httpx.Response object for error cases."""
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global _MINIMAL_ERROR_RESPONSE
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if _MINIMAL_ERROR_RESPONSE is None:
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_MINIMAL_ERROR_RESPONSE = httpx.Response(
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status_code=400,
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request=httpx.Request(method="GET", url="https://litellm.ai"),
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)
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return _MINIMAL_ERROR_RESPONSE
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class AuthenticationError(openai.AuthenticationError): # type: ignore
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def __init__(
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self,
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message,
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llm_provider,
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model,
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response: Optional[httpx.Response] = None,
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litellm_debug_info: Optional[str] = None,
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max_retries: Optional[int] = None,
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num_retries: Optional[int] = None,
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):
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self.status_code = 401
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self.message = "litellm.AuthenticationError: {}".format(message)
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self.llm_provider = llm_provider
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self.model = model
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self.litellm_debug_info = litellm_debug_info
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self.max_retries = max_retries
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self.num_retries = num_retries
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self.response = response or httpx.Response(
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status_code=self.status_code,
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request=httpx.Request(
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method="GET", url="https://litellm.ai"
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), # mock request object
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)
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super().__init__(
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self.message, response=self.response, body=None
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) # Call the base class constructor with the parameters it needs
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def __str__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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def __repr__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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# raise when invalid models passed, example gpt-8
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class NotFoundError(openai.NotFoundError): # type: ignore
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def __init__(
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self,
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message,
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model,
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llm_provider,
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response: Optional[httpx.Response] = None,
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litellm_debug_info: Optional[str] = None,
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max_retries: Optional[int] = None,
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num_retries: Optional[int] = None,
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):
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self.status_code = 404
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self.message = "litellm.NotFoundError: {}".format(message)
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self.model = model
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self.llm_provider = llm_provider
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self.litellm_debug_info = litellm_debug_info
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self.max_retries = max_retries
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self.num_retries = num_retries
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self.response = response or httpx.Response(
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status_code=self.status_code,
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request=httpx.Request(
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method="GET", url="https://litellm.ai"
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), # mock request object
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)
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super().__init__(
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self.message, response=self.response, body=None
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) # Call the base class constructor with the parameters it needs
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def __str__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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def __repr__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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class BadRequestError(openai.BadRequestError): # type: ignore
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def __init__(
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self,
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message,
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model,
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llm_provider,
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response: Optional[httpx.Response] = None,
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litellm_debug_info: Optional[str] = None,
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max_retries: Optional[int] = None,
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num_retries: Optional[int] = None,
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body: Optional[dict] = None,
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):
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self.status_code = 400
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self.message = "litellm.BadRequestError: {}".format(message)
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self.model = model
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self.llm_provider = llm_provider
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self.litellm_debug_info = litellm_debug_info
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self.max_retries = max_retries
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self.num_retries = num_retries
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# Use response if it's a valid httpx.Response with a request, otherwise use minimal error response
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# Note: We check _request (not .request property) to avoid RuntimeError when _request is None
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if (
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response is not None
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and isinstance(response, httpx.Response)
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and hasattr(response, "_request")
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and getattr(response, "_request", None) is not None
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):
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self.response = response
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else:
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self.response = _get_minimal_error_response()
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super().__init__(
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self.message, response=self.response, body=body
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) # Call the base class constructor with the parameters it needs
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def __str__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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def __repr__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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class ImageFetchError(BadRequestError):
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def __init__(
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self,
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message,
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model=None,
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llm_provider=None,
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response: Optional[httpx.Response] = None,
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litellm_debug_info: Optional[str] = None,
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max_retries: Optional[int] = None,
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num_retries: Optional[int] = None,
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body: Optional[dict] = None,
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):
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super().__init__(
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message=message,
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model=model,
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llm_provider=llm_provider,
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response=response,
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litellm_debug_info=litellm_debug_info,
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max_retries=max_retries,
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num_retries=num_retries,
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body=body,
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)
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class UnprocessableEntityError(openai.UnprocessableEntityError): # type: ignore
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def __init__(
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self,
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message,
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model,
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llm_provider,
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response: httpx.Response,
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litellm_debug_info: Optional[str] = None,
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max_retries: Optional[int] = None,
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num_retries: Optional[int] = None,
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):
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self.status_code = 422
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self.message = "litellm.UnprocessableEntityError: {}".format(message)
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self.model = model
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self.llm_provider = llm_provider
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self.litellm_debug_info = litellm_debug_info
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self.max_retries = max_retries
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self.num_retries = num_retries
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super().__init__(
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self.message, response=response, body=None
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) # Call the base class constructor with the parameters it needs
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def __str__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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def __repr__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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class Timeout(openai.APITimeoutError): # type: ignore
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def __init__(
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self,
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message,
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model,
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llm_provider,
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litellm_debug_info: Optional[str] = None,
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max_retries: Optional[int] = None,
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num_retries: Optional[int] = None,
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headers: Optional[dict] = None,
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exception_status_code: Optional[int] = None,
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):
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request = httpx.Request(
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method="POST",
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url="https://api.openai.com/v1",
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)
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super().__init__(
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request=request
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) # Call the base class constructor with the parameters it needs
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self.status_code = exception_status_code or 408
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self.message = "litellm.Timeout: {}".format(message)
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self.model = model
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self.llm_provider = llm_provider
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self.litellm_debug_info = litellm_debug_info
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self.max_retries = max_retries
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self.num_retries = num_retries
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self.headers = headers
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# custom function to convert to str
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def __str__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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def __repr__(self):
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_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
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class PermissionDeniedError(openai.PermissionDeniedError): # type: ignore
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def __init__(
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self,
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message,
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llm_provider,
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model,
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response: httpx.Response,
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litellm_debug_info: Optional[str] = None,
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max_retries: Optional[int] = None,
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num_retries: Optional[int] = None,
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):
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self.status_code = 403
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self.message = "litellm.PermissionDeniedError: {}".format(message)
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self.llm_provider = llm_provider
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self.model = model
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self.litellm_debug_info = litellm_debug_info
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self.max_retries = max_retries
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self.num_retries = num_retries
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super().__init__(
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self.message, response=response, body=None
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) # Call the base class constructor with the parameters it needs
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|
|
|
def __str__(self):
|
|
_message = self.message
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if self.num_retries:
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_message += f" LiteLLM Retried: {self.num_retries} times"
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|
if self.max_retries:
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
|
|
|
|
def __repr__(self):
|
|
_message = self.message
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|
if self.num_retries:
|
|
_message += f" LiteLLM Retried: {self.num_retries} times"
|
|
if self.max_retries:
|
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_message += f", LiteLLM Max Retries: {self.max_retries}"
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return _message
|
|
|
|
|
|
class RateLimitError(openai.RateLimitError): # type: ignore
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|
"""
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Unified rate-limit error.
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|
|
|
Every rate-limit condition surfaced by litellm — whether it originated from
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an upstream LLM provider, a vendor batch endpoint, or one of litellm's own
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proxy-side limiters (parallel-requests, dynamic-rate, batch-rate, budget,
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max-iterations, etc.) — is raised as an instance of this class.
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The :attr:`category` attribute lets callers distinguish the source. See
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:class:`RateLimitErrorCategory` for the available values.
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"""
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|
|
def __init__(
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|
self,
|
|
message,
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|
llm_provider,
|
|
model,
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|
response: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = None,
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|
max_retries: Optional[int] = None,
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|
num_retries: Optional[int] = None,
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category: Union[str, RateLimitErrorCategory] = (
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RateLimitErrorCategory.VENDOR_RATE_LIMIT
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),
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rate_limit_type: Optional[Union[str, RateLimitType]] = None,
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headers: Optional[Dict[str, str]] = None,
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|
detail: Any = None,
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):
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self.status_code = 429
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self.message = "litellm.RateLimitError: {}".format(message)
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self.llm_provider = llm_provider
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self.model = model
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self.litellm_debug_info = litellm_debug_info
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self.max_retries = max_retries
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|
self.num_retries = num_retries
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self.category = (
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category.value if isinstance(category, RateLimitErrorCategory) else category
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)
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# Which dimension was exceeded — request count, token count, parallel
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# requests, budget, max iterations. None when the source didn't
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# classify the failure (e.g. legacy vendor 429 with no header hints).
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self.rate_limit_type: Optional[str] = (
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rate_limit_type.value
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if isinstance(rate_limit_type, RateLimitType)
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else rate_limit_type
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)
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# Headers explicitly attached to the error (e.g. retry-after,
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# rate_limit_type, reset_at). Preserved across the proxy boundary so
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# clients can react appropriately.
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#
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# IMPORTANT: we deliberately do NOT auto-populate self.headers from
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# response.headers when only `response` is provided. A vendor 429 can
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# set arbitrary response headers (Set-Cookie, CORS overrides, …); if
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# those leaked into e.headers and a downstream proxy serializer
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# forwarded them to the client, a malicious upstream could inject
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# browser-interpreted headers for the proxy origin. Vendor response
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# headers stay reachable on `e.response.headers` for callers that
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# explicitly want them; only the proxy-supplied `headers=` kwarg
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# makes it onto `self.headers`.
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_response_headers = (
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getattr(response, "headers", None) if response is not None else None
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)
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self.headers: Optional[Dict[str, str]] = (
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|
{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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message,
|
|
model,
|
|
llm_provider,
|
|
response: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = 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, # type: ignore
|
|
llm_provider=self.llm_provider, # type: ignore
|
|
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 = "litellm.ContextWindowExceededError: {}".format(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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message,
|
|
model,
|
|
llm_provider,
|
|
request_data: dict,
|
|
litellm_debug_info: Optional[str] = None,
|
|
):
|
|
self.status_code = 400
|
|
self.message = "litellm.RejectedRequestError: {}".format(message)
|
|
self.model = model
|
|
self.llm_provider = llm_provider
|
|
self.litellm_debug_info = litellm_debug_info
|
|
self.request_data = request_data
|
|
request = httpx.Request(method="POST", url="https://api.openai.com/v1")
|
|
response = httpx.Response(status_code=400, request=request)
|
|
super().__init__(
|
|
message=self.message,
|
|
model=self.model, # type: ignore
|
|
llm_provider=self.llm_provider, # type: ignore
|
|
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): # type: ignore
|
|
# 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: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
provider_specific_fields: Optional[dict] = None,
|
|
body: Optional[dict] = None,
|
|
):
|
|
self.status_code = 400
|
|
self.message = "litellm.ContentPolicyViolationError: {}".format(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, # type: ignore
|
|
llm_provider=self.llm_provider, # type: ignore
|
|
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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message,
|
|
llm_provider,
|
|
model,
|
|
response: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.status_code = 503
|
|
self.message = "litellm.ServiceUnavailableError: {}".format(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 = (
|
|
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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message,
|
|
llm_provider,
|
|
model,
|
|
response: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.status_code = 502
|
|
self.message = "litellm.BadGatewayError: {}".format(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 = (
|
|
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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message,
|
|
llm_provider,
|
|
model,
|
|
response: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.status_code = 500
|
|
self.message = "litellm.InternalServerError: {}".format(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 = (
|
|
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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
status_code: int,
|
|
message,
|
|
llm_provider,
|
|
model,
|
|
request: Optional[httpx.Request] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.status_code = status_code
|
|
self.message = "litellm.APIError: {}".format(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) # type: ignore
|
|
|
|
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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message,
|
|
llm_provider,
|
|
model,
|
|
request: Optional[httpx.Request] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.message = "litellm.APIConnectionError: {}".format(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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message,
|
|
llm_provider,
|
|
model,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.message = "litellm.APIResponseValidationError: {}".format(message)
|
|
self.llm_provider = llm_provider
|
|
self.model = model
|
|
request = httpx.Request(method="POST", url="https://api.openai.com/v1")
|
|
response = 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 = "litellm.JSONSchemaValidationError: model={}, returned an invalid response={}, for schema={}.\nAccess raw response with `e.raw_response`".format(
|
|
model, raw_response, schema
|
|
)
|
|
self.message = message
|
|
super().__init__(model=model, message=message, llm_provider=llm_provider)
|
|
|
|
|
|
class OpenAIError(openai.OpenAIError): # type: ignore
|
|
def __init__(self, original_exception=None):
|
|
super().__init__()
|
|
self.llm_provider = "openai"
|
|
|
|
|
|
class UnsupportedParamsError(BadRequestError):
|
|
def __init__(
|
|
self,
|
|
message,
|
|
llm_provider: Optional[str] = None,
|
|
model: Optional[str] = None,
|
|
status_code: int = 400,
|
|
response: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.status_code = 400
|
|
self.message = "litellm.UnsupportedParamsError: {}".format(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 = [
|
|
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: Optional[str] = None,
|
|
llm_provider: Optional[str] = None,
|
|
):
|
|
self.current_cost = current_cost
|
|
self.max_budget = max_budget
|
|
self.status_code = 429
|
|
self.llm_provider = llm_provider or ""
|
|
# 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): # type: ignore
|
|
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: Optional[httpx.Request] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
):
|
|
self.status_code = status_code
|
|
self.message = "litellm.MockException: {}".format(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) # type: ignore
|
|
|
|
|
|
class LiteLLMUnknownProvider(BadRequestError):
|
|
def __init__(self, model: str, custom_llm_provider: Optional[str] = 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):
|
|
def __init__(
|
|
self,
|
|
guardrail_name: Optional[str] = None,
|
|
message: str = "",
|
|
should_wrap_with_default_message: bool = True,
|
|
status_code: int = 400,
|
|
):
|
|
default_message = f"Guardrail raised an exception, Guardrail: {guardrail_name}, Message: {message}"
|
|
self.guardrail_name = guardrail_name
|
|
self.status_code = status_code
|
|
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: Optional[str] = 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): # type: ignore
|
|
def __init__(
|
|
self,
|
|
message: str,
|
|
model: str,
|
|
llm_provider: str,
|
|
original_exception: Optional[Exception] = None,
|
|
response: Optional[httpx.Response] = None,
|
|
litellm_debug_info: Optional[str] = None,
|
|
max_retries: Optional[int] = None,
|
|
num_retries: Optional[int] = None,
|
|
generated_content: str = "",
|
|
is_pre_first_chunk: bool = False,
|
|
):
|
|
original_status = 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 = self.response
|
|
_saved_request = getattr(self.response, "request", None) or httpx.Request(
|
|
method="POST", url=f"https://{llm_provider}.com/v1/"
|
|
)
|
|
_saved_message = 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__}: {str(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: Optional[str] = None,
|
|
detection_info: Optional[Dict[str, Any]] = None,
|
|
):
|
|
self.message = message
|
|
self.model = model
|
|
self.request_data = request_data
|
|
self.guardrail_name = guardrail_name
|
|
self.detection_info = detection_info or {}
|
|
super().__init__(message)
|
|
|
|
|
|
class GuardrailInterventionNormalStringError(
|
|
Exception
|
|
): # custom exception to raise when a guardrail intervenes, but we want to return a normal string to the user
|
|
def __init__(self, message: str):
|
|
self.message = message
|
|
super().__init__(self.message)
|
|
|
|
def __str__(self):
|
|
return self.message
|
|
|
|
def __repr__(self):
|
|
return self.__str__()
|
|
|
|
|
|
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: Optional[str] = None,
|
|
detection_info: Optional[Dict[str, Any]] = None,
|
|
message: Optional[str] = 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)
|