litellm/litellm/exceptions.py
ishaan-berri 0afffe4366
feat: multiple concurrent budget windows per API key and team (#24883) (#25109)
* feat: multiple concurrent budget windows per API key and team (#24883)

* feat(proxy): add BudgetLimitEntry type and wire budget_limits into key/team models

* feat(schema): add budget_limits Json column to VerificationToken and TeamTable

* feat(migrations): add migration for budget_limits column on keys and teams

* feat(keys): initialize budget_limits windows with reset_at on key create/update

* feat(teams): initialize budget_limits windows with reset_at on team create/update

* feat(auth): add _virtual_key_multi_budget_check and _team_multi_budget_check

* feat(auth): call multi-budget checks from common_checks for keys and teams

* feat(proxy): increment per-window Redis spend counters after each request

* feat(budget): reset individual budget windows on schedule via reset_budget_job

* feat(ui): add hourly option to BudgetDurationDropdown

* feat(ui): add budget_limits field to KeyResponse type

* feat(ui): add Budget Windows editor to key edit view

* feat(ui): add Budget Windows editor to create key form

* fix(proxy): strip budget_limits=None before Prisma upsert to fix login 500

Prisma rejects nullable JSON fields (Json? without @default) when passed as
Python None — it needs the field omitted entirely so the DB stores NULL via
the column's nullable constraint. This was breaking /v2/login because the UI
session key creation path hit the upsert with budget_limits=None.

* ui(key-edit): use antd InputNumber+Button for budget windows, add reset hints

* ui(create-key): use antd InputNumber+Button for budget windows, add reset hints

* docs(users): add multiple budget windows section with API + dashboard walkthrough

* fix: BudgetExceededError returns HTTP 429 instead of 400

- Add status_code=429 to BudgetExceededError class
- auth_exception_handler hardcoded code=400 → code=429

* fix: no-op else branch in multi-budget auth checks causes KeyError

- BudgetLimitEntry objects must be coerced via model_dump() not left as-is
- Move _virtual_key_multi_budget_check into common_checks (was asymmetric
  with _team_multi_budget_check which already lived there)

* fix: len() on JSON string returns char count not window count

Guard with isinstance check + json.loads() before iterating per-window
Redis counters in increment_spend_counters

* fix: silent except:pass hides Redis reset failures in reset_budget_windows

Log Redis counter reset failures as warnings so they are observable

* test: add unit tests for multi-budget window enforcement

5 tests covering: no budget_limits passes, under budget passes,
over hourly window raises 429, over monthly window raises 429,
BudgetLimitEntry objects coerced without KeyError

* fix: key per-window counters stable across reorders (duration key, not index)

* fix: team+key per-window spend increments use duration key, not index

* fix: budget window reset uses duration key; log failures instead of swallowing

* refactor: extract BudgetWindowsEditor to shared component

* refactor: key_edit_view imports BudgetWindowsEditor from shared component

* refactor: create_key_button imports BudgetWindowsEditor from shared component

---------

Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>

* fix(reset_budget_job): extract _reset_expired_window helper to fix PLR0915 too many statements

* feat(skills): Skills Registry & Hub — register skills, browse in AI Hub, public skill hub (#25118)

* feat(skills): add domain and namespace fields to plugin types

* feat(skills): store and return domain/namespace inside manifest_json

* feat(skills): add /public/skill_hub endpoint for unauthenticated access

* feat(skills): whitelist /public/skill_hub from auth requirements

* feat(skills): add domain, namespace to Plugin and RegisterPluginRequest types

* feat(skills): smart URL parser — paste github URL, auto-detect source type and name

* feat(skills): replace enable toggle with Public badge, make rows clickable

* feat(skills): add skill detail view with Overview and How to Use tabs

* feat(skills): add MakeSkillPublicForm modal for publishing skills to the hub

* feat(skills): rename panel to Skills, wire in skill detail view on row click

* feat(skills): add skill hub table columns — name, description, domain, source, status

* feat(skills): add SkillHubDashboard with stats row, domain dropdown filter, and table

* feat(skills): add Skill Hub tab to AI Hub with Select Skills to Make Public button

* feat(skills): move Skills to top-level nav item directly under MCP Servers

* feat(skills): add skillHubPublicCall and NEXT_PUBLIC_BASE_URL support

* feat(skills): add Skill Hub tab to public AI Hub page

* feat(skills): add skills page routing in main app router

* feat(skills): add /skills page route

* chore: update package-lock after npm install

* docs(skills): add Skills Gateway doc page with mermaid architecture diagram

* docs(skills): add Skills Gateway to sidebar under Agent & MCP Gateway

* docs(skills): add loom walkthrough video to Skills Gateway doc

* chore: fixes

---------

Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
2026-04-06 14:02:04 -07:00

1031 lines
36 KiB
Python

# +-----------------------------------------------+
# | |
# | Give Feedback / Get Help |
# | https://github.com/BerriAI/litellm/issues/new |
# | |
# +-----------------------------------------------+
#
# Thank you users! We ❤️ you! - Krrish & Ishaan
## LiteLLM versions of the OpenAI Exception Types
from typing import Optional
import httpx
import openai
from litellm.types.utils import LiteLLMCommonStrings
_MINIMAL_ERROR_RESPONSE: Optional[httpx.Response] = 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): # 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 = 401
self.message = "litellm.AuthenticationError: {}".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
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): # type: ignore
def __init__(
self,
message,
model,
llm_provider,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
):
self.status_code = 404
self.message = "litellm.NotFoundError: {}".format(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): # type: ignore
def __init__(
self,
message,
model,
llm_provider,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
body: Optional[dict] = None,
):
self.status_code = 400
self.message = "litellm.BadRequestError: {}".format(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: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
body: Optional[dict] = 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): # type: ignore
def __init__(
self,
message,
model,
llm_provider,
response: httpx.Response,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
):
self.status_code = 422
self.message = "litellm.UnprocessableEntityError: {}".format(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): # type: ignore
def __init__(
self,
message,
model,
llm_provider,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
headers: Optional[dict] = None,
exception_status_code: Optional[int] = None,
):
request = 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 = "litellm.Timeout: {}".format(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
# 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): # type: ignore
def __init__(
self,
message,
llm_provider,
model,
response: httpx.Response,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
):
self.status_code = 403
self.message = "litellm.PermissionDeniedError: {}".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
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): # 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 = 429
self.message = "litellm.RateLimitError: {}".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=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
):
self.current_cost = current_cost
self.max_budget = max_budget
self.status_code = 429
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,
):
default_message = f"Guardrail raised an exception, Guardrail: {guardrail_name}, Message: {message}"
self.guardrail_name = guardrail_name
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,
):
"""
Raised when a blocked entity is detected by a guardrail.
"""
self.entity_type = entity_type
self.guardrail_name = guardrail_name
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 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__()