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Merge pull request #41934 from BerriAI/litellm_mistral_ocr_batches

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feat(batches): support Mistral files/batches and per-page OCR batch cost tracking (internal copy of #40484)
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Mateo Wang 2026-09-19 03:51:27 -07:00 • committed by GitHub
commit db04e7909e
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GPG key ID: B5690EEEBB952194
40 changed files with 2373 additions and 289 deletions
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41
litellm/batches/batch_utils.py
View file

@ -9,6 +9,7 @@ import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.get_litellm_params import AWS_CREDENTIAL_KWARGS_KEYS
from litellm.litellm_core_utils.llm_cost_calc.utils import parse_prompt_tokens_details
from litellm.llms.base_llm.ocr.transformation import OCRUsageInfo
from litellm.llms.bedrock.batches.transformation import titan_embedding_usage_from_batch_output
from litellm.llms.vertex_ai.batches.transformation import vertex_prompt_tokens_details
from litellm.types.llms.openai import Batch
@ -52,7 +53,7 @@ def batch_cost_is_final(batch: Batch) -> bool:
async def calculate_batch_cost_and_usage(
file_content_dictionary: list[dict],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "mistral"],
model_name: str | None = None,
model_info: ModelInfo | None = None,
) -> BatchCostUsageResult:
@ -82,7 +83,7 @@ async def calculate_batch_cost_and_usage(
async def _handle_completed_batch(
batch: Batch,
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "mistral"],
model_name: str | None = None,
litellm_params: dict | None = None,
model_info: ModelInfo | None = None,
@ -168,7 +169,7 @@ class _BatchOutputLineStats:
def _classify_output_line_stats(
entries: Iterable[dict],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock", "mistral"],
model_name: str | None,
model_info: ModelInfo | None,
) -> Iterator[_BatchOutputLineStats | _LineOutcome]:
@ -187,7 +188,7 @@ def _classify_output_line_stats(
def _safe_output_line_stats(
entry: Mapping[str, object],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock", "mistral"],
model_name: str | None,
model_info: ModelInfo | None,
) -> _BatchOutputLineStats | None:
@ -209,7 +210,7 @@ def _safe_output_line_stats(
def _compute_output_line_stats(
entry: Mapping[str, object],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock", "mistral"],
model_name: str | None,
model_info: ModelInfo | None,
) -> _BatchOutputLineStats:
@ -220,6 +221,7 @@ def _compute_output_line_stats(
response_model: Final = raw_model if isinstance(raw_model, str) and raw_model else None
completion_details: Final = usage.completion_tokens_details
line_prompt_cost, line_completion_cost = _output_line_cost(
response_body=response_body,
usage=usage,
custom_llm_provider=custom_llm_provider,
model_name=model_name,
@ -239,19 +241,36 @@ def _compute_output_line_stats(
)
def _ocr_usage_info_from_response_body(response_body: Mapping[str, object]) -> OCRUsageInfo | None:
"""OCR results report ``usage_info`` (pages) instead of ``usage`` (tokens); None for non-OCR lines."""
raw_usage_info: Final = response_body.get("usage_info")
if not isinstance(raw_usage_info, Mapping):
return None
return OCRUsageInfo.model_validate(raw_usage_info)
def _output_line_cost(
response_body: Mapping[str, object],
usage: Usage,
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock", "mistral"],
model_name: str | None,
response_model: str | None,
model_info: ModelInfo | None,
) -> tuple[float, float]:
"""(prompt_cost, completion_cost) for one output line, priced at batch rates."""
from litellm.cost_calculator import batch_cost_calculator
from litellm.cost_calculator import batch_cost_calculator, ocr_batch_cost
cost_model: Final = (
model_name if custom_llm_provider == "bedrock" and model_name else response_model or model_name or ""
)
ocr_usage: Final = _ocr_usage_info_from_response_body(response_body)
if ocr_usage is not None:
return ocr_batch_cost(
model=cost_model,
custom_llm_provider=custom_llm_provider,
usage_info=ocr_usage,
model_info=model_info,
)
return batch_cost_calculator(
usage=usage,
model=cost_model,
@ -262,7 +281,7 @@ def _output_line_cost(
def _aggregate_batch_cost_usage_models(
entries: Iterable[dict],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock", "mistral"],
model_name: str | None = None,
model_info: ModelInfo | None = None,
) -> BatchCostUsageResult:
@ -430,7 +449,7 @@ def _provider_output_file_id(output_file_id: str) -> str:
async def _fetch_batch_managed_file_content(
file_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai",
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "mistral"] = "openai",
litellm_params: dict | None = None,
) -> bytes:
"""
@ -460,7 +479,7 @@ async def _fetch_batch_managed_file_content(
async def _fetch_batch_output_file_content(
batch: Batch,
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai",
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "mistral"] = "openai",
litellm_params: dict | None = None,
) -> bytes:
"""
@ -482,7 +501,7 @@ async def _fetch_batch_output_file_content(
async def count_error_file_failed_requests(
batch: Batch,
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"],
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "mistral"],
litellm_params: dict | None,
) -> int:
"""Count failed requests reported only in the batch's separate error file.

20
litellm/batches/main.py
View file

@ -105,9 +105,11 @@ def _resolve_timeout(
@client
async def acreate_batch(
completion_window: Literal["24h"],
endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions", "/v1/responses"],
endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions", "/v1/responses", "/v1/ocr"],
input_file_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy"] = "openai",
custom_llm_provider: Literal[
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "mistral"
] = "openai",
metadata: dict[str, str] | None = None,
extra_headers: dict[str, str] | None = None,
extra_body: dict[str, str] | None = None,
@ -155,9 +157,11 @@ async def acreate_batch(
@client
def create_batch(
completion_window: Literal["24h"],
endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions", "/v1/responses"],
endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions", "/v1/responses", "/v1/ocr"],
input_file_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy"] = "openai",
custom_llm_provider: Literal[
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "mistral"
] = "openai",
metadata: dict[str, str] | None = None,
extra_headers: dict[str, str] | None = None,
extra_body: dict[str, str] | None = None,
@ -341,7 +345,7 @@ def create_batch(
async def aretrieve_batch(
batch_id: str,
custom_llm_provider: Literal[
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic"
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic", "mistral"
] = "openai",
metadata: dict[str, str] | None = None,
extra_headers: dict[str, str] | None = None,
@ -389,7 +393,7 @@ def _handle_retrieve_batch_providers_without_provider_config(
_retrieve_batch_request: RetrieveBatchRequest,
_is_async: bool,
custom_llm_provider: Literal[
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic"
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic", "mistral"
] = "openai",
logging_obj: LiteLLMLoggingObj | None = None,
):
@ -497,7 +501,7 @@ def _handle_retrieve_batch_providers_without_provider_config(
message=(
f"LiteLLM doesn't support custom_llm_provider={custom_llm_provider} for 'retrieve_batch' without a `model` kwarg. "
"Supported via this path: 'openai', 'azure', 'vertex_ai', 'anthropic'. "
"'bedrock' is supported but requires `model` to be passed so the provider config can be loaded."
"'bedrock' and 'mistral' are supported but require `model` to be passed so the provider config can be loaded."
),
model="n/a",
llm_provider=custom_llm_provider,
@ -514,7 +518,7 @@ def _handle_retrieve_batch_providers_without_provider_config(
def retrieve_batch(
batch_id: str,
custom_llm_provider: Literal[
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic"
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic", "mistral"
] = "openai",
metadata: dict[str, str] | None = None,
extra_headers: dict[str, str] | None = None,

82
litellm/cost_calculator.py
View file

@ -141,6 +141,7 @@ if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import (
Logging as LitellmLoggingObject,
)
from litellm.llms.base_llm.ocr.transformation import OCRUsageInfo
else:
LitellmLoggingObject = Any
@ -2114,6 +2115,87 @@ def ocr_cost(
return ocr_pages_cost + annotation_pages_cost, 0.0
_OCR_BATCH_PAGE_RATE_KEYS: Final = ("ocr_cost_per_page_batches", "ocr_cost_per_page")
_OCR_BATCH_ANNOTATION_RATE_KEYS: Final = ("annotation_cost_per_page_batches", "annotation_cost_per_page")
def ocr_batch_cost(
model: str,
custom_llm_provider: str | None,
usage_info: "OCRUsageInfo",
model_info: ModelInfo | None = None,
) -> tuple[float, float]:
"""Per-page cost of one OCR result inside a batch output file.
Batch OCR is billed per page at the ``*_batches`` rate, falling back to the
synchronous per-page rate when a model has no batch price recorded, the same
fallback ``batch_cost_calculator`` applies to per-token batch pricing. Each
per-page family (OCR pages, annotation pages) belongs to the deployment's
``model_info`` when it prices that family at either rate and to the published
cost map otherwise, so a deployment overriding one family keeps the model's
published rate for the other, and the cost map is only consulted for a family
the deployment leaves out. Returns ``(prompt_cost, completion_cost)`` with the
whole cost in the first slot, like ``ocr_cost``.
"""
pages_processed: Final = usage_info.pages_processed or 0
annotation_pages: Final = usage_info.pages_processed_annotation or 0
deployment_page_rate: Final = _first_price(model_info, *_OCR_BATCH_PAGE_RATE_KEYS)
deployment_annotation_rate: Final = _first_price(model_info, *_OCR_BATCH_ANNOTATION_RATE_KEYS)
needs_published_pricing: Final = (pages_processed > 0 and deployment_page_rate is None) or (
annotation_pages > 0 and deployment_annotation_rate is None
)
published: Final = (
_lookup_model_info_or_none(model=model, custom_llm_provider=custom_llm_provider)
if needs_published_pricing
else None
)
if needs_published_pricing and published is None:
verbose_logger.warning(
"OCR batch cost: model=%s custom_llm_provider=%s has no pricing entry; "
"billing only the per-page families the deployment prices.",
_single_log_line(model),
_single_log_line(custom_llm_provider),
)
page_rate: Final = (
deployment_page_rate
if deployment_page_rate is not None
else _first_price(published, *_OCR_BATCH_PAGE_RATE_KEYS)
)
annotation_rate: Final = (
deployment_annotation_rate
if deployment_annotation_rate is not None
else _first_price(published, *_OCR_BATCH_ANNOTATION_RATE_KEYS)
)
if page_rate is None and pages_processed > 0:
verbose_logger.warning(
"OCR batch cost: model=%s custom_llm_provider=%s reported pages_processed=%s but no "
"ocr_cost_per_page is configured; returning 0.0 cost for those pages.",
_single_log_line(model),
_single_log_line(custom_llm_provider),
pages_processed,
)
effective_annotation_rate: Final = annotation_rate if annotation_rate is not None else page_rate
return (page_rate or 0.0) * pages_processed + (effective_annotation_rate or 0.0) * annotation_pages, 0.0
def _single_log_line(value: str | None) -> str:
return str(value).replace("\n", "").replace("\r", "")
def _lookup_model_info_or_none(model: str, custom_llm_provider: str | None) -> ModelInfo | None:
try:
return litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)
except Exception: # noqa: BLE001 # get_model_info raises bare Exception for unmapped models; caller logs and bills 0.0
return None
def _first_price(model_info: ModelInfo | None, *keys: str) -> float | None:
if model_info is None:
return None
return next((price for price in (model_info.get(k) for k in keys) if isinstance(price, (int, float))), None)
def vector_store_search_cost(
model: str | None,
custom_llm_provider: str,

7
litellm/files/main.py
View file

@ -27,12 +27,13 @@ FileCreateProvider = Literal[
"litellm_proxy",
"manus",
"anthropic",
"mistral",
]
FileRetrieveProvider = Literal[
"openai", "azure", "gemini", "vertex_ai", "hosted_vllm", "litellm_proxy", "manus", "anthropic"
"openai", "azure", "gemini", "vertex_ai", "hosted_vllm", "litellm_proxy", "manus", "anthropic", "mistral"
]
FileDeleteProvider = Literal["openai", "azure", "gemini", "bedrock", "litellm_proxy", "manus", "anthropic"]
FileListProvider = Literal["openai", "azure", "litellm_proxy", "manus", "anthropic"]
FileDeleteProvider = Literal["openai", "azure", "gemini", "bedrock", "litellm_proxy", "manus", "anthropic", "mistral"]
FileListProvider = Literal["openai", "azure", "litellm_proxy", "manus", "anthropic", "mistral"]
import litellm
from litellm import get_secret_str
from litellm.files.streaming import FileContentStreamingResponse

2
litellm/files/types.py
View file

@ -2,7 +2,7 @@ from collections.abc import AsyncIterator, Iterator, Mapping
from typing import Literal, NamedTuple
FileContentProvider = Literal[
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic", "manus"
"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic", "manus", "mistral"
]

12
litellm/litellm_core_utils/litellm_logging.py
View file

@ -371,6 +371,10 @@ _DEPLOYMENT_PRICING_KEYS: Final = (
"output_cost_per_token",
"input_cost_per_token_batches",
"output_cost_per_token_batches",
"ocr_cost_per_page",
"ocr_cost_per_page_batches",
"annotation_cost_per_page",
"annotation_cost_per_page_batches",
)
@ -386,7 +390,9 @@ def deployment_pricing_model_info(model_id: str | None, deployment_model: str |
the model's published rates instead of billing as zero. Ownership is per
token direction: declaring either rate for a direction takes that whole
direction, so a published batch rate can never displace a standard rate
the deployment configured itself.
the deployment configured itself. OCR per-page rates count as declared
pricing too; they pass through as registered and ``ocr_batch_cost`` layers
the published rate under each per-page family the deployment leaves out.
"""
if model_id is None:
return None
@ -1239,8 +1245,8 @@ class Logging(LiteLLMLoggingBaseClass):
return {"error": f"Unable to parse raw request body. Got - {data}"}
return data
def _get_masked_api_base(self, api_base: str) -> str:
return str(mask_api_base_credentials(api_base))
def _get_masked_api_base(self, api_base: str | None) -> str:
return str(mask_api_base_credentials(api_base or ""))
def _pre_call(self, input, api_key, model=None, additional_args={}):
"""

26
litellm/llms/custom_httpx/llm_http_handler.py
View file

@ -293,6 +293,26 @@ def _has_pre_call_deployment_hook(logging_obj: LiteLLMLoggingObj) -> bool:
return False
def _mask_presigned_request_headers(transformed_request: bytes | str | dict) -> bytes | str | dict:
"""A pre-signed request carries its auth inside its own ``headers`` key, which
logging treats as request body (only the top-level headers channel gets masked),
so mask it here before the request is handed to ``pre_call``."""
if not isinstance(transformed_request, dict):
return transformed_request
request_headers: Final = transformed_request.get("headers")
if not isinstance(request_headers, dict):
return transformed_request
from litellm.litellm_core_utils.litellm_logging import (
_get_masked_values, # pyright: ignore[reportPrivateUsage] # the shared header-masking helper has no public name
)
return { # mutable-ok: logging's curl and raw-request builders take dict
**transformed_request,
"headers": _get_masked_values(request_headers),
}
def _aws_signing_overrides(optional_params: Mapping[str, Any], litellm_params: Mapping[str, Any]) -> Mapping[str, Any]:
return MappingProxyType(
{
@ -3734,7 +3754,7 @@ class BaseLLMHTTPHandler:
"complete_input_dict": (
"<streaming media upload>"
if isinstance(transformed_request, dict) and "streaming_media_upload" in transformed_request
else transformed_request
else _mask_presigned_request_headers(transformed_request)
),
"api_base": api_base,
"headers": headers,
@ -4157,7 +4177,7 @@ class BaseLLMHTTPHandler:
input="",
api_key="",
additional_args={
"complete_input_dict": transformed_request,
"complete_input_dict": _mask_presigned_request_headers(transformed_request),
"api_base": api_base,
"headers": headers,
},
@ -4236,7 +4256,7 @@ class BaseLLMHTTPHandler:
input="",
api_key="",
additional_args={
"complete_input_dict": transformed_request,
"complete_input_dict": _mask_presigned_request_headers(transformed_request),
"api_base": api_base,
"headers": headers,
"batch_id": batch_id,

0
litellm/llms/mistral/batches/__init__.py Normal file
View file

220
litellm/llms/mistral/batches/transformation.py Normal file
View file

@ -0,0 +1,220 @@
"""
Mistral Batch API. Reference: https://docs.mistral.ai/api/#tag/batch
Mistral runs one model per job (set on the job, not per input line) and accepts
``/v1/ocr`` as a batch endpoint, which is how OCR gets its 50% batch discount.
Output and error files are OpenAI-shaped JSONL (``{custom_id, response: {status_code, body}}``),
so the shared batch cost accounting reads them without a provider branch.
"""
from collections.abc import Mapping, Sequence
from types import MappingProxyType
from typing import Final, Literal, TypeAlias
import httpx
from openai.types.batch import BatchRequestCounts
from openai.types.batch import Errors as BatchErrors
from openai.types.batch_error import BatchError
from pydantic import BaseModel, ConfigDict
from typing_extensions import NotRequired, ReadOnly, TypedDict
from litellm.litellm_core_utils.url_utils import encode_url_path_segment
from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.types.llms.openai import AllMessageValues, CreateBatchRequest
from litellm.types.utils import LiteLLMBatch, LlmProviders
from ..common_utils import get_mistral_api_base, get_mistral_auth_headers, mistral_error
MistralBatchStatus: TypeAlias = Literal[
"QUEUED", "RUNNING", "SUCCESS", "FAILED", "TIMEOUT_EXCEEDED", "CANCELLATION_REQUESTED", "CANCELLED"
]
OpenAIBatchStatus: TypeAlias = Literal[
"validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"
]
_NO_HEADERS: Final[Mapping[str, str]] = MappingProxyType({}) # mutable-ok: frozen at module scope
_STATUS_MAP: Final[MappingProxyType[MistralBatchStatus, OpenAIBatchStatus]] = MappingProxyType(
{
"QUEUED": "validating",
"RUNNING": "in_progress",
"SUCCESS": "completed",
"FAILED": "failed",
"TIMEOUT_EXCEEDED": "expired",
"CANCELLATION_REQUESTED": "cancelling",
"CANCELLED": "cancelled",
}
)
class MistralCreateBatchJobRequest(TypedDict):
"""Body of ``POST /v1/batch/jobs``."""
input_files: ReadOnly[tuple[str, ...]]
endpoint: ReadOnly[str]
model: ReadOnly[str]
metadata: NotRequired[ReadOnly[Mapping[str, str]]]
class MistralPresignedRequest(TypedDict):
"""A fully-formed request the shared HTTP handler sends as-is (its ``method`` branch)."""
method: ReadOnly[Literal["GET"]]
url: ReadOnly[str]
headers: ReadOnly[Mapping[str, str]]
class MistralBatchError(BaseModel):
model_config = ConfigDict(frozen=True, extra="ignore")
message: str
count: int = 1
class MistralBatchJob(BaseModel):
model_config = ConfigDict(frozen=True, extra="ignore")
id: str
input_files: tuple[str, ...] = ()
endpoint: str
model: str | None = None
status: MistralBatchStatus
created_at: int
started_at: int | None = None
completed_at: int | None = None
total_requests: int = 0
completed_requests: int = 0
succeeded_requests: int = 0
failed_requests: int = 0
output_file: str | None = None
error_file: str | None = None
errors: tuple[MistralBatchError, ...] = ()
metadata: dict[str, str] | None = None # mutable-ok: LiteLLMBatch.metadata is typed as dict
def _to_batch_errors(errors: Sequence[MistralBatchError]) -> BatchErrors | None:
if not errors:
return None
return BatchErrors(
object="list",
data=[ # mutable-ok: openai Batch.Errors.data is typed as list
BatchError(message=f"{e.message} (x{e.count})" if e.count > 1 else e.message) for e in errors
],
)
def _to_litellm_batch(job: MistralBatchJob) -> LiteLLMBatch:
status: Final = _STATUS_MAP[job.status]
terminal_at: Final = job.completed_at
return LiteLLMBatch(
id=job.id,
object="batch",
endpoint=job.endpoint,
input_file_id=job.input_files[0] if job.input_files else "",
completion_window="24h",
status=status,
created_at=job.created_at,
in_progress_at=job.started_at,
completed_at=terminal_at if status == "completed" else None,
failed_at=terminal_at if status == "failed" else None,
expired_at=terminal_at if status == "expired" else None,
cancelled_at=terminal_at if status == "cancelled" else None,
output_file_id=job.output_file,
error_file_id=job.error_file,
errors=_to_batch_errors(job.errors),
request_counts=BatchRequestCounts(
total=job.total_requests,
completed=job.succeeded_requests,
failed=job.failed_requests,
),
metadata=job.metadata,
)
class MistralBatchesConfig(BaseBatchesConfig):
@property
def custom_llm_provider(self) -> LlmProviders:
return LlmProviders.MISTRAL
def validate_environment(
self,
headers: Mapping[str, str],
model: str,
messages: Sequence[AllMessageValues],
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
api_key: str | None = None,
api_base: str | None = None,
) -> dict[str, str]: # mutable-ok: BaseBatchesConfig signature
return get_mistral_auth_headers(headers, api_key)
def get_complete_batch_url(
self,
api_base: str | None,
api_key: str | None,
model: str,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
data: CreateBatchRequest,
) -> str:
return f"{get_mistral_api_base(api_base)}/v1/batch/jobs"
def transform_create_batch_request(
self,
model: str,
create_batch_data: CreateBatchRequest,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
) -> dict[str, object]: # mutable-ok: BaseBatchesConfig signature
input_file_id: Final = create_batch_data.get("input_file_id")
endpoint: Final = create_batch_data.get("endpoint")
if input_file_id is None or endpoint is None:
raise ValueError("input_file_id and endpoint are required to create a Mistral batch job")
metadata: Final = create_batch_data.get("metadata")
body: Final = (
MistralCreateBatchJobRequest(
input_files=(input_file_id,), endpoint=endpoint, model=model, metadata=metadata
)
if metadata
else MistralCreateBatchJobRequest(input_files=(input_file_id,), endpoint=endpoint, model=model)
)
return dict(body) # mutable-ok: BaseBatchesConfig signature
def transform_create_batch_response(
self,
model: str | None,
raw_response: httpx.Response,
logging_obj: object,
litellm_params: Mapping[str, object],
) -> LiteLLMBatch:
return _to_litellm_batch(MistralBatchJob.model_validate(raw_response.json()))
def transform_retrieve_batch_request(
self,
batch_id: str,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
) -> dict[str, object]: # mutable-ok: BaseBatchesConfig signature
encoded_batch_id: Final = encode_url_path_segment(batch_id, field_name="batch_id")
api_base: Final = litellm_params.get("api_base")
api_key: Final = litellm_params.get("api_key")
request: Final = MistralPresignedRequest(
method="GET",
url=f"{get_mistral_api_base(api_base if isinstance(api_base, str) else None)}/v1/batch/jobs/{encoded_batch_id}",
headers=get_mistral_auth_headers(_NO_HEADERS, api_key if isinstance(api_key, str) else None),
)
return dict(request) # mutable-ok: BaseBatchesConfig signature
def transform_retrieve_batch_response(
self,
model: str | None,
raw_response: httpx.Response,
logging_obj: object,
litellm_params: Mapping[str, object],
) -> LiteLLMBatch:
return _to_litellm_batch(MistralBatchJob.model_validate(raw_response.json()))
def get_error_class(
self, error_message: str, status_code: int, headers: Mapping[str, str] | httpx.Headers
) -> BaseLLMException:
return mistral_error(error_message, status_code, headers)

41
litellm/llms/mistral/common_utils.py Normal file
View file

@ -0,0 +1,41 @@
from collections.abc import Mapping
from typing import Final
import httpx
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.secret_managers.main import get_secret_str
MISTRAL_API_BASE: Final = "https://api.mistral.ai"
MISTRAL_API_KEY_ENV_VAR: Final = "MISTRAL_API_KEY"
class MistralError(BaseLLMException):
pass
def get_mistral_api_base(api_base: str | None) -> str:
"""Return the Mistral origin without a trailing ``/v1``, so callers can append ``/v1/<route>``."""
resolved: Final = (api_base or get_secret_str("MISTRAL_API_BASE") or MISTRAL_API_BASE).rstrip("/")
return resolved.removesuffix("/v1")
def get_mistral_auth_headers(
headers: Mapping[str, str], api_key: str | None
) -> dict[str, str]: # mutable-ok: BaseConfig.validate_environment contract returns dict
resolved_key: Final = api_key or get_secret_str(MISTRAL_API_KEY_ENV_VAR)
if resolved_key is None:
raise ValueError(
"Missing Mistral API Key - A call is being made to Mistral but no key is set either in the environment variables or via params"
)
return dict(headers, Authorization=f"Bearer {resolved_key}") # mutable-ok: BaseConfig contract returns dict
def mistral_error(error_message: str, status_code: int, headers: Mapping[str, str] | httpx.Headers) -> MistralError:
return MistralError(
status_code=status_code,
message=error_message,
headers=headers
if isinstance(headers, httpx.Headers)
else httpx.Headers(dict(headers)), # mutable-ok: httpx.Headers takes a dict
)

0
litellm/llms/mistral/files/__init__.py Normal file
View file

267
litellm/llms/mistral/files/transformation.py Normal file
View file

@ -0,0 +1,267 @@
"""
Mistral Files API. Reference: https://docs.mistral.ai/api/#tag/files
Mistral's file objects already carry the OpenAI field names (id, bytes, created_at,
filename, purpose), so this config is URL routing, auth, and a purpose mapping:
Mistral only accepts ``fine-tune``, ``batch`` and ``ocr`` as upload purposes, while files
other Mistral products created read back with purposes outside that set and map onto ``user_data``.
"""
import time
from collections.abc import Mapping, Sequence
from types import MappingProxyType
from typing import Final, Literal, TypeAlias
import httpx
from openai.types.file_deleted import FileDeleted
from pydantic import BaseModel, ConfigDict
from typing_extensions import ReadOnly, TypedDict
from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
from litellm.litellm_core_utils.url_utils import encode_url_path_segment
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.base_llm.files.transformation import BaseFilesConfig, LiteLLMLoggingObj
from litellm.types.llms.openai import (
CreateFileRequest,
FileContentRequest,
HttpxBinaryResponseContent,
OpenAICreateFileRequestOptionalParams,
OpenAIFileObject,
OpenAIFilesPurpose,
)
from litellm.types.utils import LlmProviders
from ..common_utils import get_mistral_api_base, get_mistral_auth_headers, mistral_error
MistralFilePurpose: TypeAlias = Literal["fine-tune", "batch", "ocr"]
_OPENAI_PURPOSE_BY_MISTRAL: Final[Mapping[str, OpenAIFilesPurpose]] = MappingProxyType(
{"fine-tune": "fine-tune", "batch": "batch", "ocr": "user_data"}
)
_OPENAI_PURPOSE_FOR_UNMAPPED: Final[OpenAIFilesPurpose] = "user_data"
_MISTRAL_PURPOSE_BY_OPENAI: Final[Mapping[str, MistralFilePurpose]] = MappingProxyType(
{"fine-tune": "fine-tune", "batch": "batch", "ocr": "ocr", "user_data": "ocr"}
)
_SUPPORTED_PURPOSES: Final = ", ".join(_MISTRAL_PURPOSE_BY_OPENAI)
_NO_QUERY_PARAMS: Final[dict[str, str]] = {} # mutable-ok: BaseFilesConfig request transforms return tuple[str, dict]
class MistralMultipartUpload(TypedDict):
"""``files=`` payload for ``POST /v1/files``: each value is an httpx multipart tuple."""
file: ReadOnly[tuple[str, object, str]]
purpose: ReadOnly[tuple[None, MistralFilePurpose]]
class MistralFile(BaseModel):
model_config = ConfigDict(frozen=True, extra="ignore")
id: str
bytes: int = 0
created_at: int | None = None
filename: str = ""
purpose: str = "batch"
expires_at: int | None = None
class MistralFileList(BaseModel):
model_config = ConfigDict(frozen=True, extra="ignore")
data: tuple[MistralFile, ...] = ()
class MistralFileDeleted(BaseModel):
model_config = ConfigDict(frozen=True, extra="ignore")
id: str
deleted: bool = True
def _to_openai_file_object(file: MistralFile) -> OpenAIFileObject:
return OpenAIFileObject(
id=file.id,
bytes=file.bytes,
created_at=file.created_at if file.created_at is not None else int(time.time()),
filename=file.filename,
object="file",
purpose=_to_openai_purpose(file.purpose),
status="uploaded",
expires_at=file.expires_at,
)
def _to_openai_purpose(purpose: str) -> OpenAIFilesPurpose:
return _OPENAI_PURPOSE_BY_MISTRAL.get(purpose, _OPENAI_PURPOSE_FOR_UNMAPPED)
def _to_mistral_purpose(purpose: str) -> MistralFilePurpose:
"""``user_data`` is what an OCR file reads back as, since OpenAI's purpose literal has no ``ocr``,
so it maps back onto ``ocr``. Every other purpose Mistral lacks is rejected: silently rewriting
it to ``batch`` would let an upload skip the proxy's batch-file validation and guardrails, which
only run when the caller says ``purpose=batch``."""
mistral_purpose: Final = _MISTRAL_PURPOSE_BY_OPENAI.get(purpose)
if mistral_purpose is None:
raise mistral_error(
f"Mistral does not support purpose={purpose!r}. Use one of: {_SUPPORTED_PURPOSES}",
status_code=400,
headers=httpx.Headers(),
)
return mistral_purpose
def _api_base_from(litellm_params: Mapping[str, object]) -> str:
api_base: Final = litellm_params.get("api_base")
return get_mistral_api_base(api_base if isinstance(api_base, str) else None)
class MistralFilesConfig(BaseFilesConfig):
@property
def custom_llm_provider(self) -> LlmProviders:
return LlmProviders.MISTRAL
def get_complete_url(
self,
api_base: str | None,
api_key: str | None,
model: str,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
stream: bool | None = None,
) -> str:
return f"{get_mistral_api_base(api_base)}/v1/files"
def _file_url(self, file_id: str, litellm_params: Mapping[str, object], suffix: str = "") -> str:
encoded_file_id: Final = encode_url_path_segment(file_id, field_name="file_id")
return f"{_api_base_from(litellm_params)}/v1/files/{encoded_file_id}{suffix}"
def get_error_class(
self, error_message: str, status_code: int, headers: Mapping[str, str] | httpx.Headers
) -> BaseLLMException:
return mistral_error(error_message, status_code, headers)
def validate_environment(
self,
headers: Mapping[str, str],
model: str,
messages: Sequence[object],
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
api_key: str | None = None,
api_base: str | None = None,
) -> dict[str, str]: # mutable-ok: BaseFilesConfig signature
return get_mistral_auth_headers(headers, api_key)
def get_supported_openai_params(
self, model: str
) -> list[OpenAICreateFileRequestOptionalParams]: # mutable-ok: BaseFilesConfig signature
return ["purpose"] # mutable-ok: BaseFilesConfig signature
def map_openai_params(
self,
non_default_params: Mapping[str, object],
optional_params: dict[str, object], # mutable-ok: BaseConfig signature, returned as-is
model: str,
drop_params: bool,
) -> dict[str, object]: # mutable-ok: BaseConfig signature
return optional_params
def transform_create_file_request(
self,
model: str,
create_file_data: CreateFileRequest,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
) -> dict[str, object]: # mutable-ok: BaseFilesConfig signature
if "file" not in create_file_data:
raise ValueError("File data is required")
extracted: Final = extract_file_data(create_file_data["file"])
filename: Final = extracted["filename"] or f"file_{int(time.time())}.jsonl"
content_type: Final = extracted.get("content_type") or "application/octet-stream"
upload: Final = MistralMultipartUpload(
file=(filename, extracted["content"], content_type),
purpose=(None, _to_mistral_purpose(create_file_data.get("purpose") or "batch")),
)
return dict(upload) # mutable-ok: BaseFilesConfig signature
def transform_create_file_response(
self,
model: str | None,
raw_response: httpx.Response,
logging_obj: LiteLLMLoggingObj,
litellm_params: Mapping[str, object],
) -> OpenAIFileObject:
return _to_openai_file_object(MistralFile.model_validate(raw_response.json()))
def transform_retrieve_file_request(
self,
file_id: str,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
) -> tuple[str, dict[str, str]]: # mutable-ok: BaseFilesConfig signature
return self._file_url(file_id, litellm_params), _NO_QUERY_PARAMS
def transform_retrieve_file_response(
self,
raw_response: httpx.Response,
logging_obj: LiteLLMLoggingObj,
litellm_params: Mapping[str, object],
) -> OpenAIFileObject:
return _to_openai_file_object(MistralFile.model_validate(raw_response.json()))
def transform_delete_file_request(
self,
file_id: str,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
) -> tuple[str, dict[str, str]]: # mutable-ok: BaseFilesConfig signature
return self._file_url(file_id, litellm_params), _NO_QUERY_PARAMS
def transform_delete_file_response(
self,
raw_response: httpx.Response,
logging_obj: LiteLLMLoggingObj,
litellm_params: Mapping[str, object],
) -> FileDeleted:
deleted: Final = MistralFileDeleted.model_validate(raw_response.json())
return FileDeleted(id=deleted.id, deleted=deleted.deleted, object="file")
def transform_list_files_request(
self,
purpose: str | None,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
) -> tuple[str, dict[str, str]]: # mutable-ok: BaseFilesConfig signature
url: Final = f"{_api_base_from(litellm_params)}/v1/files"
if not purpose:
return url, _NO_QUERY_PARAMS
return url, {"purpose": _to_mistral_purpose(purpose)} # mutable-ok: BaseFilesConfig signature returns dict
def transform_list_files_response(
self,
raw_response: httpx.Response,
logging_obj: LiteLLMLoggingObj,
litellm_params: Mapping[str, object],
) -> list[OpenAIFileObject]: # mutable-ok: BaseFilesConfig signature
return [ # mutable-ok: BaseFilesConfig signature
_to_openai_file_object(f) for f in MistralFileList.model_validate(raw_response.json()).data
]
def transform_file_content_request(
self,
file_content_request: FileContentRequest,
optional_params: Mapping[str, object],
litellm_params: Mapping[str, object],
) -> tuple[str, dict[str, str]]: # mutable-ok: BaseFilesConfig signature
file_id: Final = file_content_request.get("file_id")
if file_id is None:
raise ValueError("file_id is required to download file content")
return self._file_url(file_id, litellm_params, suffix="/content"), _NO_QUERY_PARAMS
def transform_file_content_response(
self,
raw_response: httpx.Response,
logging_obj: LiteLLMLoggingObj,
litellm_params: Mapping[str, object],
) -> HttpxBinaryResponseContent:
return HttpxBinaryResponseContent(response=raw_response)

40
litellm/model_prices_and_context_window_backup.json
View file

@ -37476,51 +37476,66 @@
"mistral/mistral-ocr-latest": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-4-0": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-4-1": {
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"litellm_provider": "mistral",
"mode": "ocr",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"source": "https://docs.mistral.ai/models/model-cards/ocr-4-1",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
]
},
"mistral/mistral-ocr-2505-completion": {
"deprecation_date": "2026-05-31",
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.001,
"ocr_cost_per_page_batches": 0.0005,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-2512": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.002,
"ocr_cost_per_page_batches": 0.001,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
@ -63741,31 +63756,40 @@
"mistral/mistral-ocr-3": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.002,
"ocr_cost_per_page_batches": 0.001,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-3-0": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.002,
"ocr_cost_per_page_batches": 0.001,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-4": {
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"litellm_provider": "mistral",
"mode": "ocr",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"source": "https://docs.mistral.ai/models/model-cards/ocr-4-1",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
]
},
"mistral/voxtral-mini-latest": {

2
litellm/provider_endpoints_support_backup.json
View file

@ -1462,7 +1462,7 @@
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"batches": true,
"rerank": false,
"ocr": true,
"a2a": true,

39
litellm/proxy/batches_endpoints/endpoints.py
View file

@ -32,16 +32,18 @@ from litellm.proxy.common_utils.openai_endpoint_utils import (
from litellm.proxy.openai_files_endpoints.common_utils import (
BATCH_CREATE_HIDDEN_PARAM,
_is_base64_encoded_unified_file_id,
add_deployment_model_info,
add_internal_model_credentials,
apply_team_provider_credentials,
authorize_model_for_key,
batch_cost_poller_is_active,
decode_model_from_file_id,
encode_batch_response_ids,
encode_file_id_with_model,
ensure_batch_response_managed_file_ids,
get_authorized_credentials_for_model,
get_batch_from_database,
get_batch_id_from_unified_batch_id,
get_credentials_for_model,
get_model_id_from_unified_batch_id,
get_models_from_unified_file_id,
get_original_file_id,
@ -223,9 +225,10 @@ async def create_batch(
# SCENARIO 1: File ID is encoded with model info
if model_from_file_id is not None and input_file_id:
credentials = get_credentials_for_model(
credentials = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model_from_file_id,
user_api_key_dict=user_api_key_dict,
operation_context="batch creation (file created with model)",
)
@ -290,6 +293,7 @@ async def create_batch(
detail={"error": f"Expected 1 model, got {len(target_model_names)}"},
)
model: Final = target_model_names[0]
await authorize_model_for_key(model_id=model, llm_router=llm_router, user_api_key_dict=user_api_key_dict)
_create_batch_data["model"] = model
resolved_storage_url: Final = await _resolve_managed_input_file_storage_url(input_file_id)
@ -315,9 +319,10 @@ async def create_batch(
# SCENARIO 2 & 3: Model from header/query OR custom_llm_provider fallback
if model_param:
# SCENARIO 2: Use model-based routing from header/query/body
credentials = get_credentials_for_model(
credentials = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model_param,
user_api_key_dict=user_api_key_dict,
operation_context="batch creation",
)
@ -466,6 +471,17 @@ async def retrieve_batch(
route_type="aretrieve_batch",
)
unified_model_id: Final = get_model_id_from_unified_batch_id(unified_batch_id) if unified_batch_id else None
if unified_model_id is not None:
resolved_unified_model: Final = (
llm_router.resolve_model_name_from_model_id(unified_model_id) if llm_router is not None else None
)
await authorize_model_for_key(
model_id=resolved_unified_model or unified_model_id,
llm_router=llm_router,
user_api_key_dict=user_api_key_dict,
)
# FIX: First, try to read from ManagedObjectTable for consistent state
managed_files_obj: Final = proxy_logging_obj.get_proxy_hook("managed_files")
from litellm.proxy.proxy_server import prisma_client
@ -546,9 +562,10 @@ async def retrieve_batch(
# Retrieve from provider (for non-terminal states or if DB lookup failed)
# SCENARIO 1: Batch ID is encoded with model info
if model_from_id is not None:
credentials: Final = get_credentials_for_model(
credentials: Final = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model_from_id,
user_api_key_dict=user_api_key_dict,
operation_context="batch retrieval (batch created with model)",
)
@ -564,6 +581,7 @@ async def retrieve_batch(
# so litellm.aretrieve_batch can load BedrockBatchesConfig. Without
# it the call falls into the legacy provider switch and 400s.
data["model"] = model_from_id
add_deployment_model_info(data=data, llm_router=llm_router, model_id=model_from_id)
# Retrieve batch using model credentials
response = await litellm.aretrieve_batch(
@ -588,7 +606,7 @@ async def retrieve_batch(
add_internal_model_credentials(
data=data,
llm_router=llm_router,
model_id=get_model_id_from_unified_batch_id(unified_batch_id),
model_id=unified_model_id,
)
response = await llm_router.aretrieve_batch(**data)
@ -772,9 +790,10 @@ async def list_batches(
data.get("model") or request.query_params.get("model") or request.headers.get("x-litellm-model")
):
# SCENARIO 2: Use model-based routing from header/query/body
credentials: Final = get_credentials_for_model(
credentials: Final = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model_param,
user_api_key_dict=user_api_key_dict,
operation_context="batch listing",
)
@ -961,9 +980,10 @@ async def cancel_batch(
# SCENARIO 1: Batch ID is encoded with model info
if model_from_id is not None:
credentials: Final = get_credentials_for_model(
credentials: Final = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model_from_id,
user_api_key_dict=user_api_key_dict,
operation_context="batch cancellation (batch created with model)",
)
@ -1002,6 +1022,11 @@ async def cancel_batch(
status_code=400,
detail={"error": "Invalid LiteLLM managed batch ID. Missing model_id."},
)
await authorize_model_for_key(
model_id=llm_router.resolve_model_name_from_model_id(model_id_from_batch) or model_id_from_batch,
llm_router=llm_router,
user_api_key_dict=user_api_key_dict,
)
data["model"] = model_id_from_batch
data["batch_id"] = get_batch_id_from_unified_batch_id(unified_batch_id)
response = await llm_router.acancel_batch(**data)

80
litellm/proxy/openai_files_endpoints/common_utils.py
View file

@ -350,6 +350,10 @@ def get_credentials_for_model(
"""
Retrieve API credentials for a model from the LLM Router.
Does not check whether the caller may use ``model_id``; use
``get_authorized_credentials_for_model`` for anything driven by a caller-supplied
model name (request body, header, query param, or a model-encoded resource id).
Args:
llm_router: LiteLLM Router instance
model_id: Model name or deployment ID
@ -380,6 +384,48 @@ def get_credentials_for_model(
return credentials
async def authorize_model_for_key(
model_id: str,
llm_router: Optional["Router"],
user_api_key_dict: "UserAPIKeyAuth",
) -> None:
"""
Enforce the caller's model grants on a model name the auth layer never saw.
The files and batches routes carry their model in a header, query param, or a
model-encoded resource id rather than the request body, so ``user_api_key_auth``
cannot check it. Run the same key, team (incl. team-member and access-group
fallbacks), org and project allowlist checks a chat request would get, so a
restricted key cannot borrow another deployment's server-side credentials.
Raises:
ProxyException (403): the caller is not allowed to use ``model_id``
"""
from litellm.proxy.auth.auth_checks import can_key_call_resolved_model
await can_key_call_resolved_model(
model=model_id,
llm_model_list=None,
valid_token=user_api_key_dict,
llm_router=llm_router,
)
async def get_authorized_credentials_for_model(
llm_router: Optional["Router"],
model_id: str,
user_api_key_dict: "UserAPIKeyAuth",
operation_context: str = "file operation",
) -> dict: # mutable-ok: same contract as get_credentials_for_model, callers merge it into request data
"""``get_credentials_for_model`` gated by ``authorize_model_for_key``."""
await authorize_model_for_key(model_id=model_id, llm_router=llm_router, user_api_key_dict=user_api_key_dict)
return get_credentials_for_model(
llm_router=llm_router,
model_id=model_id,
operation_context=operation_context,
)
def get_team_provider_credentials(
llm_router: Optional["Router"],
user_api_key_dict: "UserAPIKeyAuth",
@ -547,6 +593,25 @@ def add_internal_model_credentials(
data["_litellm_internal_model_credentials"] = MappingProxyType(dict(credentials))
def add_deployment_model_info(
data: dict,
llm_router: Optional["Router"],
model_id: str,
) -> None:
"""
Stamp the resolved deployment's `model_info` onto a direct (non-router) batch call
(in-place), the way the router does for routed calls, so the completed batch is
priced by its deployment id instead of the published model rate.
"""
deployment: Final = llm_router.get_credential_deployment(model_id=model_id) if llm_router is not None else None
if deployment is None:
return
data["litellm_metadata"] = {
**(data.get("litellm_metadata") or {}),
"model_info": deployment.model_info.model_dump(),
}
def prepare_data_with_credentials(
data: dict,
credentials: dict,
@ -572,21 +637,27 @@ def prepare_data_with_credentials(
data["file_id"] = file_id
def handle_model_based_routing(
async def handle_model_based_routing(
file_id: str,
request, # FastAPI Request object
llm_router, # Router instance
data: dict,
user_api_key_dict: "UserAPIKeyAuth",
check_file_id_encoding: bool = True,
) -> tuple[bool, str | None, str | None, dict | None]:
"""
Orchestrate model-based credential routing for file operations.
The model name comes from the caller (embedded in the file id, or a header, query
param or body field), so it is authorized against the caller's key, team, org and
project grants before any deployment credentials are resolved.
Args:
file_id: File ID (may contain embedded model info)
request: FastAPI request object
llm_router: LiteLLM Router instance
data: Request data dictionary
user_api_key_dict: The authenticated caller
check_file_id_encoding: Whether to check for embedded model in file_id
Returns:
@ -598,6 +669,7 @@ def handle_model_based_routing(
Raises:
HTTPException: If router unavailable or model not found
ProxyException: If the caller is not allowed to use the model
"""
model_from_id, model_from_param = extract_model_from_sources(
file_id=file_id,
@ -607,9 +679,10 @@ def handle_model_based_routing(
# Priority 1: Model embedded in file_id
if check_file_id_encoding and model_from_id is not None:
credentials = get_credentials_for_model(
credentials = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model_from_id,
user_api_key_dict=user_api_key_dict,
operation_context=f"file operation (file created with model '{model_from_id}')",
)
original_file_id: Final = get_original_file_id(file_id)
@ -617,9 +690,10 @@ def handle_model_based_routing(
# Priority 2: Model from header/query/body
elif model_from_param is not None:
credentials = get_credentials_for_model(
credentials = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model_from_param,
user_api_key_dict=user_api_key_dict,
operation_context="file operation",
)
return True, model_from_param, None, credentials

27
litellm/proxy/openai_files_endpoints/files_endpoints.py
View file

@ -69,7 +69,7 @@ from litellm.proxy.openai_files_endpoints.common_utils import (
apply_team_provider_credentials,
encode_file_id_with_model,
extract_file_creation_params,
get_credentials_for_model,
get_authorized_credentials_for_model,
handle_model_based_routing,
prepare_data_with_credentials,
validate_file_list_limit,
@ -271,9 +271,10 @@ async def route_create_file(
# NEW: Handle model-based routing (no DB required)
if model is not None:
# Get credentials from model_list via router
credentials: Final = get_credentials_for_model(
credentials: Final = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=model,
user_api_key_dict=user_api_key_dict,
operation_context="file upload",
)
@ -916,11 +917,12 @@ async def get_file_content(
model_used,
original_file_id,
credentials,
) = handle_model_based_routing(
) = await handle_model_based_routing(
file_id=file_id,
request=request,
llm_router=llm_router,
data=data,
user_api_key_dict=user_api_key_dict,
check_file_id_encoding=True,
)
@ -1131,15 +1133,16 @@ async def get_file(
model_used,
original_file_id,
credentials,
) = handle_model_based_routing(
) = await handle_model_based_routing(
file_id=file_id,
request=request,
llm_router=llm_router,
data=data,
user_api_key_dict=user_api_key_dict,
check_file_id_encoding=True,
)
if should_route:
if should_route and credentials is not None:
# Use model-based routing with credentials from config
prepare_data_with_credentials(
data=data,
@ -1148,7 +1151,10 @@ async def get_file(
include_internal_credentials=True,
)
response = await litellm.afile_retrieve(**data)
response = await litellm.afile_retrieve(
custom_llm_provider=credentials["custom_llm_provider"],
**data,
)
# Keep the encoded ID in response if it was originally encoded
if original_file_id and response and hasattr(response, "id") and response.id:
@ -1341,11 +1347,12 @@ async def delete_file(
model_used,
original_file_id,
credentials,
) = handle_model_based_routing(
) = await handle_model_based_routing(
file_id=file_id,
request=request,
llm_router=llm_router,
data=data,
user_api_key_dict=user_api_key_dict,
check_file_id_encoding=True,
)
@ -1534,11 +1541,12 @@ async def list_files(
response: Any | None = None
# Check for model-based credential routing (no file_id encoding check for list)
should_route, model_used, _, credentials = handle_model_based_routing(
should_route, model_used, _, credentials = await handle_model_based_routing(
file_id="", # No file_id for list endpoint
request=request,
llm_router=llm_router,
data=data,
user_api_key_dict=user_api_key_dict,
check_file_id_encoding=False,
)
@ -1565,9 +1573,10 @@ async def list_files(
status_code=500,
detail="LLM Router not initialized. Ensure models added to proxy.",
)
credentials = get_credentials_for_model(
credentials = await get_authorized_credentials_for_model(
llm_router=llm_router,
model_id=target_model_names_list[0],
user_api_key_dict=user_api_key_dict,
operation_context="file list",
)
prepare_data_with_credentials(data=data, credentials=credentials, include_internal_credentials=True)

45
litellm/proxy/vector_store_files_endpoints/endpoints.py
View file

@ -5,7 +5,6 @@ from fastapi.responses import ORJSONResponse
import litellm
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.auth.auth_checks import _can_object_call_model, can_key_call_model
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
from litellm.proxy.common_utils.openai_endpoint_utils import (
@ -14,6 +13,8 @@ from litellm.proxy.common_utils.openai_endpoint_utils import (
get_custom_llm_provider_from_request_query,
)
from litellm.proxy.openai_files_endpoints.common_utils import (
authorize_model_for_key,
get_credentials_for_model,
handle_model_based_routing,
prepare_data_with_credentials,
)
@ -144,11 +145,12 @@ async def _update_request_data_with_managed_file_id(
model_used,
original_file_id,
credentials,
) = handle_model_based_routing(
) = await handle_model_based_routing(
file_id=file_id,
request=request,
llm_router=llm_router,
data=data,
user_api_key_dict=user_api_key_dict,
check_file_id_encoding=True,
)
@ -210,26 +212,7 @@ async def _authorize_model_routing_hint(
) -> None:
if user_api_key_dict is None:
return
key_models: Final = getattr(user_api_key_dict, "models", None)
if not (isinstance(key_models, list) and "all-team-models" in key_models):
await can_key_call_model(
model=model,
llm_model_list=None,
valid_token=user_api_key_dict,
llm_router=llm_router,
)
team_models: Final = getattr(user_api_key_dict, "team_models", None)
if isinstance(team_models, list) and len(team_models) > 0:
_can_object_call_model(
model=model,
llm_router=llm_router,
models=team_models,
team_model_aliases=user_api_key_dict.team_model_aliases,
team_id=user_api_key_dict.team_id,
object_type="team",
)
await authorize_model_for_key(model_id=model, llm_router=llm_router, user_api_key_dict=user_api_key_dict)
async def _update_request_data_with_model_routing_hint(
@ -261,25 +244,15 @@ async def _update_request_data_with_model_routing_hint(
model_id=model_hint, team_id=caller_team_id
)
should_route = credentials is not None
else:
if isinstance(model_hint, str) and should_authorize_model_hint:
elif isinstance(model_hint, str):
if should_authorize_model_hint:
await _authorize_model_routing_hint(
model=model_hint,
llm_router=llm_router,
user_api_key_dict=user_api_key_dict,
)
(
should_route,
_model_used,
_original_file_id,
credentials,
) = handle_model_based_routing(
file_id="",
request=request,
llm_router=llm_router,
data=data,
check_file_id_encoding=False,
)
credentials = get_credentials_for_model(llm_router=llm_router, model_id=model_hint)
should_route = True
if should_route and credentials is not None:
prepare_data_with_credentials(

90
litellm/router.py
View file

@ -10313,6 +10313,55 @@ class Router:
return display_name
return None
def get_credential_deployment(self, model_id: str, team_id: str | None = None) -> Deployment | None:
"""
The deployment a passthrough endpoint (files, batches, etc.) resolves for a
model id or model name: by deployment id first, then by model_name, then by
the team's exact public model name, then by wildcard pattern (team wildcards
before global ones, so a global "openai/*" never shadows the team's own
entry). Name and wildcard lookups never resolve another team's deployment.
Returns None when nothing matches or the match is paused via
`LiteLLM_ProxyModelTable.blocked`, so callers cannot bypass an admin pause
by resolving the deployment directly.
"""
deployment: Final = (
self.get_deployment(model_id=model_id)
or self._get_model_group_deployment_usable_by_team(model_group_name=model_id, team_id=team_id)
or self._get_team_public_name_deployment(model_id=model_id, team_id=team_id)
or self._get_wildcard_deployment_usable_by_team(model_id=model_id, team_id=team_id)
)
if deployment is None or self._is_deployment_blocked(deployment):
return None
return deployment
def _get_team_public_name_deployment(self, model_id: str, team_id: str | None) -> Deployment | None:
if team_id is None:
return None
team_indices: Final = self.team_model_to_deployment_indices.get((team_id, model_id))
if not team_indices:
return None
team_model: Final = self.model_list[team_indices[0]]
return Deployment(**team_model) if isinstance(team_model, dict) else team_model
def _get_wildcard_deployment_usable_by_team(self, model_id: str, team_id: str | None) -> Deployment | None:
team_pattern_router: Final = self.team_pattern_routers.get(team_id) if team_id is not None else None
team_wildcard_models: Final = team_pattern_router.route(model_id) if team_pattern_router else None
global_wildcard_models: Final = tuple(
wildcard_model
for wildcard_model in (self.pattern_router.route(model_id) or ())
if self._deployment_usable_by_team(wildcard_model, team_id)
)
potential_wildcard_models: Final = team_wildcard_models or global_wildcard_models
if not potential_wildcard_models:
return None
wildcard_deployment: Final = potential_wildcard_models[0]
if isinstance(wildcard_deployment, dict):
return Deployment(**wildcard_deployment)
if isinstance(wildcard_deployment, Deployment):
return wildcard_deployment
return None
def get_deployment_credentials_with_provider(
self, model_id: str, team_id: str | None = None
) -> dict[str, Any] | None:
@ -10320,8 +10369,8 @@ class Router:
Get API credentials and provider info from a model name in model_list.
Useful for passthrough endpoints (files, batches, etc.) that need credentials.
This method tries to find a deployment by model_id first, and if not found,
it tries to find by model_group_name (model_name).
Resolves the deployment with `get_credential_deployment` (by deployment id,
then model_name, team public model name, and wildcard pattern).
Args:
model_id: Model ID or model name from model_list (e.g., "gpt-4o-litellm")
@ -10342,43 +10391,8 @@ class Router:
credentials = router.get_deployment_credentials_with_provider("gpt-4o-litellm")
# Returns: {"api_key": "sk-...", "custom_llm_provider": "openai", "model": "gpt-4o", ...}
"""
# Try to get deployment by model_id first
deployment = self.get_deployment(model_id=model_id)
# If not found, try by model_group_name
deployment: Final = self.get_credential_deployment(model_id=model_id, team_id=team_id)
if deployment is None:
deployment = self._get_model_group_deployment_usable_by_team(model_group_name=model_id, team_id=team_id)
# If not found, check team-scoped deployments whose team public model
# name exactly matches model_id (wildcard team names are matched via
# team_pattern_routers below).
if deployment is None and team_id is not None:
team_indices: Final = self.team_model_to_deployment_indices.get((team_id, model_id), [])
if team_indices:
team_model: Final = self.model_list[team_indices[0]]
deployment = Deployment(**team_model) if isinstance(team_model, dict) else team_model
# If still not found, check for wildcard pattern matches. Team wildcard
# matches take priority so a global pattern (e.g. "openai/*") doesn't
# shadow the team's own entry.
if deployment is None:
team_pattern_router: Final = self.team_pattern_routers.get(team_id) if team_id is not None else None
team_wildcard_models: Final = (team_pattern_router.route(model_id) or []) if team_pattern_router else []
global_wildcard_models: Final = [
wildcard_model
for wildcard_model in (self.pattern_router.route(model_id) or [])
if self._deployment_usable_by_team(wildcard_model, team_id)
]
potential_wildcard_models: Final = team_wildcard_models or global_wildcard_models
if potential_wildcard_models:
# Use the first matching wildcard deployment
deployment_dict: Final = potential_wildcard_models[0]
if isinstance(deployment_dict, dict):
deployment = Deployment(**deployment_dict)
elif isinstance(deployment_dict, Deployment):
deployment = deployment_dict
if deployment is None or self._is_deployment_blocked(deployment):
return None
# Get basic credentials

2
litellm/types/llms/openai.py
View file

@ -501,7 +501,7 @@ class CreateBatchRequest(TypedDict, total=False):
"""
completion_window: Literal["24h"]
endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions", "/v1/responses"]
endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions", "/v1/responses", "/v1/ocr"]
input_file_id: str
metadata: dict[str, str] | None
output_expires_after: FileExpiresAfter

4
litellm/types/utils.py
View file

@ -329,8 +329,10 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
output_cost_per_second_720p: ReadOnly[float | None]
output_cost_per_second_4k: ReadOnly[float | None]
ocr_cost_per_page: float | None # for OCR models
ocr_cost_per_page_batches: ReadOnly[float | None]
ocr_cost_per_credit: float | None # for OCR models priced by credit
annotation_cost_per_page: float | None # for OCR models
annotation_cost_per_page_batches: ReadOnly[float | None]
search_context_cost_per_query: SearchContextCostPerQuery | None # Cost for using web search tool
web_search_billing_unit: (
Literal["per_query", "per_prompt"] | None
@ -3669,8 +3671,10 @@ class CustomPricingLiteLLMParams(MirroredPricingParams):
output_cost_per_token_above_512k_tokens: float | None = None
output_vector_size: int | None = None
ocr_cost_per_page: float | None = None
ocr_cost_per_page_batches: float | None = None
ocr_cost_per_credit: float | None = None
annotation_cost_per_page: float | None = None
annotation_cost_per_page_batches: float | None = None
regional_processing_uplift_multiplier_eu: float | None = None
regional_processing_uplift_multiplier_us: float | None = None
regional_endpoint_uplift_multiplier: float | None = None

10
litellm/utils.py
View file

@ -6118,8 +6118,10 @@ def _get_model_info_helper(
tpm=_model_info.get("tpm", None),
rpm=_model_info.get("rpm", None),
ocr_cost_per_page=_model_info.get("ocr_cost_per_page", None),
ocr_cost_per_page_batches=_model_info.get("ocr_cost_per_page_batches", None),
ocr_cost_per_credit=_model_info.get("ocr_cost_per_credit", None),
annotation_cost_per_page=_model_info.get("annotation_cost_per_page", None),
annotation_cost_per_page_batches=_model_info.get("annotation_cost_per_page_batches", None),
provider_specific_entry=_model_info.get("provider_specific_entry", None),
uses_embed_content=_model_info.get("uses_embed_content", None),
supports_image_size=_model_info.get("supports_image_size", None),
@ -9114,6 +9116,10 @@ class ProviderConfigManager:
from litellm.llms.anthropic.files.transformation import AnthropicFilesConfig
return AnthropicFilesConfig()
elif LlmProviders.MISTRAL == provider:
from litellm.llms.mistral.files.transformation import MistralFilesConfig
return MistralFilesConfig()
return None
@staticmethod
@ -9125,6 +9131,10 @@ class ProviderConfigManager:
from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig
return BedrockBatchesConfig()
elif LlmProviders.MISTRAL == provider:
from litellm.llms.mistral.batches.transformation import MistralBatchesConfig
return MistralBatchesConfig()
return None
@staticmethod

40
model_prices_and_context_window.json
View file

@ -37476,51 +37476,66 @@
"mistral/mistral-ocr-latest": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-4-0": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-4-1": {
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"litellm_provider": "mistral",
"mode": "ocr",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"source": "https://docs.mistral.ai/models/model-cards/ocr-4-1",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
]
},
"mistral/mistral-ocr-2505-completion": {
"deprecation_date": "2026-05-31",
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.001,
"ocr_cost_per_page_batches": 0.0005,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-2512": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.002,
"ocr_cost_per_page_batches": 0.001,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
@ -63741,31 +63756,40 @@
"mistral/mistral-ocr-3": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.002,
"ocr_cost_per_page_batches": 0.001,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-3-0": {
"litellm_provider": "mistral",
"ocr_cost_per_page": 0.002,
"ocr_cost_per_page_batches": 0.001,
"annotation_cost_per_page": 0.003,
"annotation_cost_per_page_batches": 0.0015,
"mode": "ocr",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
],
"source": "https://mistral.ai/pricing#api-pricing"
},
"mistral/mistral-ocr-4": {
"annotation_cost_per_page": 0.005,
"annotation_cost_per_page_batches": 0.0025,
"litellm_provider": "mistral",
"mode": "ocr",
"ocr_cost_per_page": 0.004,
"ocr_cost_per_page_batches": 0.002,
"source": "https://docs.mistral.ai/models/model-cards/ocr-4-1",
"supported_endpoints": [
"/v1/ocr"
"/v1/ocr",
"/v1/batch"
]
},
"mistral/voxtral-mini-latest": {

8
model_prices_and_context_window.schema.json
View file

@ -53,6 +53,10 @@
"type": "number",
"minimum": 0
},
"annotation_cost_per_page_batches": {
"type": "number",
"minimum": 0
},
"audio_transcription_config": {
"type": "string"
},
@ -449,6 +453,10 @@
"type": "number",
"minimum": 0
},
"ocr_cost_per_page_batches": {
"type": "number",
"minimum": 0
},
"output_cost_per_audio_token": {
"type": "number",
"minimum": 0

2
provider_endpoints_support.json
View file

@ -1577,7 +1577,7 @@
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"batches": true,
"rerank": false,
"ocr": true,
"a2a": true,

162
tests/test_litellm/batches/test_batch_utils.py
View file

@ -647,9 +647,7 @@ async def test_calculate_vertex_disable_transform_needs_model_name(monkeypatch):
lambda content, model: pytest.fail("raw vertex path should not run"),
)
result = await bu.calculate_batch_cost_and_usage(
file_content_dictionary=[], custom_llm_provider="vertex_ai"
)
result = await bu.calculate_batch_cost_and_usage(file_content_dictionary=[], custom_llm_provider="vertex_ai")
assert result.cost == 0.0
assert result.usage.total_tokens == 0
assert result.models == []
@ -1284,6 +1282,7 @@ async def test_handle_completed_batch_no_output_file_is_zero(monkeypatch):
result set - zero cost, zero usage, no models - instead of letting the file
fetch raise "Output file id is None" on every aretrieve_batch logging poll.
"""
# The output-file fetch must not even be attempted when there is no output file.
async def _must_not_fetch(*args, **kwargs):
pytest.fail("_fetch_batch_output_file_content should not be called")
@ -1410,7 +1409,10 @@ def test_anthropic_response_body_is_result_message():
def test_anthropic_usage_conversion_includes_cache_tokens():
body = {"model": "claude-sonnet-4-5-20250929", "usage": _anthropic_usage(1000, 200, cache_creation=2000, cache_read=8000)}
body = {
"model": "claude-sonnet-4-5-20250929",
"usage": _anthropic_usage(1000, 200, cache_creation=2000, cache_read=8000),
}
usage = bu._get_batch_job_usage_from_response_body(body, custom_llm_provider="anthropic")
assert usage.prompt_tokens == 11000
assert usage.completion_tokens == 200
@ -1425,7 +1427,9 @@ def test_bedrock_model_output_line_success_check():
"modelOutput": {"model": "claude-sonnet-4-6", "usage": {"input_tokens": 13, "output_tokens": 5}},
}
assert bu._batch_response_was_successful(row, custom_llm_provider="bedrock") is True
assert bu._get_response_from_batch_job_output_file(row, custom_llm_provider="bedrock")["model"] == "claude-sonnet-4-6"
assert (
bu._get_response_from_batch_job_output_file(row, custom_llm_provider="bedrock")["model"] == "claude-sonnet-4-6"
)
def test_bedrock_cost_uses_deployment_model_name():
@ -1479,7 +1483,13 @@ def test_total_usage_without_cache_tokens_has_no_prompt_details(monkeypatch):
rows = [
{
"custom_id": "req-1",
"response": {"status_code": 200, "body": {"model": "gpt-5.2", "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}}},
"response": {
"status_code": 200,
"body": {
"model": "gpt-5.2",
"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
},
},
}
]
result = bu._aggregate_batch_cost_usage_models(entries=rows, custom_llm_provider="openai")
@ -1521,7 +1531,9 @@ def test_anthropic_cost_without_model_info_uses_batch_cost_calculator(monkeypatc
lambda **kw: pytest.fail("anthropic rows must not go through completion_cost"),
)
result = bu._aggregate_batch_cost_usage_models(entries=[_anthropic_succeeded_row()], custom_llm_provider="anthropic")
result = bu._aggregate_batch_cost_usage_models(
entries=[_anthropic_succeeded_row()], custom_llm_provider="anthropic"
)
assert result.cost == pytest.approx(0.3)
assert seen[0]["model"] == "claude-sonnet-4-5-20250929"
@ -1556,7 +1568,11 @@ async def test_calculate_batch_cost_and_usage_anthropic_end_to_end():
)
assert result.cost == pytest.approx(1000 * 3e-6 / 2 + 8000 * 3e-7 / 2 + 2000 * 3.75e-6 / 2 + 200 * 15e-6 / 2)
assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (11000, 200, 11200)
assert (result.usage.prompt_tokens, result.usage.completion_tokens, result.usage.total_tokens) == (
11000,
200,
11200,
)
assert result.models == ["claude-sonnet-4-5"]
@ -1721,7 +1737,10 @@ async def test_handle_completed_batch_honors_deployment_pricing(monkeypatch) ->
def test_bedrock_converse_shaped_batch_usage_is_parsed():
body = {"model": "us.amazon.nova-lite-v1:0", "usage": {"inputTokens": 2202, "outputTokens": 540, "totalTokens": 2742}}
body = {
"model": "us.amazon.nova-lite-v1:0",
"usage": {"inputTokens": 2202, "outputTokens": 540, "totalTokens": 2742},
}
usage = bu._get_batch_job_usage_from_response_body(body, custom_llm_provider="bedrock")
assert (usage.prompt_tokens, usage.completion_tokens, usage.total_tokens) == (2202, 540, 2742)
@ -1809,6 +1828,7 @@ def test_unparsable_bedrock_batch_usage_warns(caplog):
# batch_cost_is_final
# --------------------------------------------------------------------------- #
def _retrieved_batch(
status: str, output_file_id: str | None = None, counts: BatchRequestCounts | None = None
) -> LiteLLMBatch:
@ -1857,3 +1877,127 @@ class TestBatchCostIsFinal:
@pytest.mark.parametrize("status", ["failed", "expired", "cancelled"])
def test_other_terminal_statuses_are_final(self, status):
assert bu.batch_cost_is_final(_retrieved_batch(status)) is True
def _ocr_row(pages_processed, annotation_pages=None, model="mistral-ocr-latest"):
usage_info = {"pages_processed": pages_processed, "doc_size_bytes": 4096}
if annotation_pages is not None:
usage_info["pages_processed_annotation"] = annotation_pages
return _success_row(
model=model, pages=[{"index": i, "markdown": "x"} for i in range(pages_processed)], usage_info=usage_info
)
def test_ocr_rows_are_priced_per_page_at_batch_rate(monkeypatch):
monkeypatch.setattr(
litellm,
"get_model_info",
lambda model, custom_llm_provider=None: {"ocr_cost_per_page": 0.004, "ocr_cost_per_page_batches": 0.002},
)
result = bu._aggregate_batch_cost_usage_models(
entries=[_ocr_row(3), _ocr_row(5), _failed_row(model="mistral-ocr-latest")],
custom_llm_provider="mistral",
model_name="mistral/mistral-ocr-latest",
)
assert result.cost == pytest.approx(8 * 0.002)
assert result.prompt_cost == pytest.approx(8 * 0.002)
assert result.completion_cost == 0.0
assert (result.successful_requests, result.failed_requests) == (2, 1)
assert result.usage.total_tokens == 0
assert result.models == ["mistral/mistral-ocr-latest"]
def test_ocr_rows_fall_back_to_sync_page_rate_without_batch_price(monkeypatch):
monkeypatch.setattr(litellm, "get_model_info", lambda model, custom_llm_provider=None: {"ocr_cost_per_page": 0.004})
result = bu._aggregate_batch_cost_usage_models(entries=[_ocr_row(2)], custom_llm_provider="mistral")
assert result.cost == pytest.approx(2 * 0.004)
def test_ocr_rows_bill_annotation_pages_separately(monkeypatch):
monkeypatch.setattr(
litellm,
"get_model_info",
lambda model, custom_llm_provider=None: {
"ocr_cost_per_page_batches": 0.002,
"annotation_cost_per_page_batches": 0.0025,
},
)
result = bu._aggregate_batch_cost_usage_models(
entries=[_ocr_row(4, annotation_pages=4)], custom_llm_provider="mistral"
)
assert result.cost == pytest.approx(4 * 0.002 + 4 * 0.0025)
def test_ocr_rows_use_deployment_model_info_pricing_over_cost_map(monkeypatch):
monkeypatch.setattr(
litellm, "get_model_info", lambda model, custom_llm_provider=None: pytest.fail("cost map must not be consulted")
)
result = bu._aggregate_batch_cost_usage_models(
entries=[_ocr_row(10)],
custom_llm_provider="mistral",
model_info={"ocr_cost_per_page_batches": 0.001},
)
assert result.cost == pytest.approx(0.01)
def test_ocr_rows_keep_the_published_page_rate_when_the_deployment_prices_only_annotations(monkeypatch):
monkeypatch.setattr(
litellm,
"get_model_info",
lambda model, custom_llm_provider=None: {
"ocr_cost_per_page_batches": 0.002,
"annotation_cost_per_page_batches": 0.0025,
},
)
result = bu._aggregate_batch_cost_usage_models(
entries=[_ocr_row(4, annotation_pages=4)],
custom_llm_provider="mistral",
model_info={"annotation_cost_per_page_batches": 0.01},
)
assert result.cost == pytest.approx(4 * 0.002 + 4 * 0.01)
def test_ocr_rows_keep_the_deployment_page_rate_when_the_unmapped_model_has_no_annotation_price(monkeypatch):
def _unmapped(model, custom_llm_provider=None):
raise Exception(f"This model isn't mapped yet: {model}")
monkeypatch.setattr(litellm, "get_model_info", _unmapped)
result = bu._aggregate_batch_cost_usage_models(
entries=[_ocr_row(4, annotation_pages=4, model="my-private-ocr-model")],
custom_llm_provider="mistral",
model_info={"ocr_cost_per_page_batches": 0.001},
)
assert result.cost == pytest.approx(4 * 0.001 + 4 * 0.001)
def test_ocr_rows_bill_the_deployment_sync_page_rate_over_the_published_batch_rate(monkeypatch):
monkeypatch.setattr(
litellm, "get_model_info", lambda model, custom_llm_provider=None: pytest.fail("cost map must not be consulted")
)
result = bu._aggregate_batch_cost_usage_models(
entries=[_ocr_row(3)],
custom_llm_provider="mistral",
model_info={"ocr_cost_per_page": 0.0912},
)
assert result.cost == pytest.approx(3 * 0.0912)
def test_ocr_rows_without_pricing_bill_zero_but_count_as_successful(monkeypatch):
monkeypatch.setattr(litellm, "get_model_info", lambda model, custom_llm_provider=None: {"mode": "ocr"})
result = bu._aggregate_batch_cost_usage_models(entries=[_ocr_row(3)], custom_llm_provider="mistral")
assert result.cost == 0.0
assert (result.successful_requests, result.failed_requests) == (1, 0)
def test_chat_rows_from_mistral_still_use_token_pricing(monkeypatch):
monkeypatch.setattr(
litellm,
"get_model_info",
lambda model, custom_llm_provider=None: {"input_cost_per_token": 0.001, "output_cost_per_token": 0.002},
)
result = bu._aggregate_batch_cost_usage_models(
entries=[_success_row(model="mistral-small-latest", usage=_usage(10, 5))],
custom_llm_provider="mistral",
)
assert result.cost == pytest.approx((10 * 0.001 + 5 * 0.002) / 2)
assert result.usage.total_tokens == 15

100
tests/test_litellm/batches/test_main.py
View file

@ -66,9 +66,7 @@ def seams():
stack.enter_context(patch.object(bm, "openai_batches_instance", openai_i))
stack.enter_context(patch.object(bm, "azure_batches_instance", azure_i))
stack.enter_context(patch.object(bm, "vertex_ai_batches_instance", vertex_i))
stack.enter_context(
patch.object(bm, "anthropic_batches_instance", anthropic_i)
)
stack.enter_context(patch.object(bm, "anthropic_batches_instance", anthropic_i))
stack.enter_context(patch.object(bm, "base_llm_http_handler", base_http))
stack.enter_context(patch.object(bm, "BedrockBatchesHandler", bedrock_arn))
yield Seams(
@ -174,9 +172,7 @@ def test_create__provider_config_routes_to_base_http_handler(seams):
"get_provider_batches_config",
return_value=MagicMock(name="provider_config"),
):
result = bm.create_batch(
**CREATE_KW, custom_llm_provider="bedrock", model="bedrock/my-batch-model"
)
result = bm.create_batch(**CREATE_KW, custom_llm_provider="bedrock", model="bedrock/my-batch-model")
assert result is seams.base_http.create_batch.return_value
_assert_only(seams.base_http.create_batch, seams, "create_batch")
@ -281,9 +277,7 @@ def test_retrieve__bedrock_model_invocation_job_arn(seams):
result = bm.retrieve_batch(batch_id=arn, custom_llm_provider="bedrock")
seams.bedrock_arn._handle_model_invocation_job_status.assert_called_once()
assert (
result is seams.bedrock_arn._handle_model_invocation_job_status.return_value
)
assert result is seams.bedrock_arn._handle_model_invocation_job_status.return_value
seams.bedrock_arn._handle_async_invoke_status.assert_not_called()
@ -385,9 +379,7 @@ def test_cancel__unsupported_provider_raises_badrequest(seams):
def test_cancel__async_flag_propagates_is_async(seams):
bm.cancel_batch(
batch_id="batch-1", custom_llm_provider="openai", acancel_batch=True
)
bm.cancel_batch(batch_id="batch-1", custom_llm_provider="openai", acancel_batch=True)
assert seams.openai.cancel_batch.call_args.kwargs["_is_async"] is True
@ -415,9 +407,7 @@ async def test_acreate_batch_delegates_to_create_batch():
@pytest.mark.asyncio
async def test_aretrieve_batch_delegates_to_retrieve_batch():
with patch.object(bm, "retrieve_batch", MagicMock(return_value="SENTINEL")) as m:
result = await bm.aretrieve_batch(
batch_id="batch-1", custom_llm_provider="azure"
)
result = await bm.aretrieve_batch(batch_id="batch-1", custom_llm_provider="azure")
assert result == "SENTINEL"
assert m.call_count == 1
@ -429,9 +419,7 @@ async def test_aretrieve_batch_delegates_to_retrieve_batch():
@pytest.mark.asyncio
async def test_alist_batches_delegates_to_list_batches():
with patch.object(bm, "list_batches", MagicMock(return_value="SENTINEL")) as m:
result = await bm.alist_batches(
after="cur", limit=3, custom_llm_provider="vertex_ai"
)
result = await bm.alist_batches(after="cur", limit=3, custom_llm_provider="vertex_ai")
assert result == "SENTINEL"
assert m.call_count == 1
@ -444,9 +432,7 @@ async def test_alist_batches_delegates_to_list_batches():
@pytest.mark.asyncio
async def test_acancel_batch_delegates_to_cancel_batch():
with patch.object(bm, "cancel_batch", MagicMock(return_value="SENTINEL")) as m:
result = await bm.acancel_batch(
batch_id="batch-1", custom_llm_provider="openai"
)
result = await bm.acancel_batch(batch_id="batch-1", custom_llm_provider="openai")
assert result == "SENTINEL"
assert m.call_count == 1
@ -499,9 +485,7 @@ def _sent(mock_method, *keys):
def test_create__openai_credentials_passthrough(seams):
bm.create_batch(**CREATE_KW, custom_llm_provider="openai", **OPENAI_CREDS)
assert _sent(
seams.openai.create_batch, "api_key", "api_base", "organization", "max_retries"
) == {
assert _sent(seams.openai.create_batch, "api_key", "api_base", "organization", "max_retries") == {
"api_key": "sk-user-openai",
"api_base": "https://openai.user.test",
"organization": "org-user-123",
@ -512,9 +496,7 @@ def test_create__openai_credentials_passthrough(seams):
def test_create__azure_credentials_passthrough(seams):
bm.create_batch(**CREATE_KW, custom_llm_provider="azure", **AZURE_CREDS)
assert _sent(
seams.azure.create_batch, "api_key", "api_base", "api_version"
) == {
assert _sent(seams.azure.create_batch, "api_key", "api_base", "api_version") == {
"api_key": "sk-user-azure",
"api_base": "https://azure.user.test",
"api_version": "2024-12-99",
@ -564,9 +546,7 @@ def test_create__provider_config_credentials_passthrough(seams):
def test_retrieve__openai_credentials_passthrough(seams):
bm.retrieve_batch(batch_id="b1", custom_llm_provider="openai", **OPENAI_CREDS)
assert _sent(
seams.openai.retrieve_batch, "api_key", "api_base", "organization"
) == {
assert _sent(seams.openai.retrieve_batch, "api_key", "api_base", "organization") == {
"api_key": "sk-user-openai",
"api_base": "https://openai.user.test",
"organization": "org-user-123",
@ -576,9 +556,7 @@ def test_retrieve__openai_credentials_passthrough(seams):
def test_retrieve__azure_credentials_passthrough(seams):
bm.retrieve_batch(batch_id="b1", custom_llm_provider="azure", **AZURE_CREDS)
assert _sent(
seams.azure.retrieve_batch, "api_key", "api_base", "api_version"
) == {
assert _sent(seams.azure.retrieve_batch, "api_key", "api_base", "api_version") == {
"api_key": "sk-user-azure",
"api_base": "https://azure.user.test",
"api_version": "2024-12-99",
@ -640,9 +618,7 @@ def test_retrieve__provider_config_credentials_passthrough(seams):
def test_list__openai_credentials_passthrough(seams):
bm.list_batches(custom_llm_provider="openai", **OPENAI_CREDS)
assert _sent(
seams.openai.list_batches, "api_key", "api_base", "organization"
) == {
assert _sent(seams.openai.list_batches, "api_key", "api_base", "organization") == {
"api_key": "sk-user-openai",
"api_base": "https://openai.user.test",
"organization": "org-user-123",
@ -652,9 +628,7 @@ def test_list__openai_credentials_passthrough(seams):
def test_list__azure_credentials_passthrough(seams):
bm.list_batches(custom_llm_provider="azure", **AZURE_CREDS)
assert _sent(
seams.azure.list_batches, "api_key", "api_base", "api_version"
) == {
assert _sent(seams.azure.list_batches, "api_key", "api_base", "api_version") == {
"api_key": "sk-user-azure",
"api_base": "https://azure.user.test",
"api_version": "2024-12-99",
@ -682,9 +656,7 @@ def test_list__vertex_credentials_passthrough(seams):
def test_cancel__openai_credentials_passthrough(seams):
bm.cancel_batch(batch_id="b1", custom_llm_provider="openai", **OPENAI_CREDS)
assert _sent(
seams.openai.cancel_batch, "api_key", "api_base", "organization"
) == {
assert _sent(seams.openai.cancel_batch, "api_key", "api_base", "organization") == {
"api_key": "sk-user-openai",
"api_base": "https://openai.user.test",
"organization": "org-user-123",
@ -694,9 +666,7 @@ def test_cancel__openai_credentials_passthrough(seams):
def test_cancel__azure_credentials_passthrough(seams):
bm.cancel_batch(batch_id="b1", custom_llm_provider="azure", **AZURE_CREDS)
assert _sent(
seams.azure.cancel_batch, "api_key", "api_base", "api_version"
) == {
assert _sent(seams.azure.cancel_batch, "api_key", "api_base", "api_version") == {
"api_key": "sk-user-azure",
"api_base": "https://azure.user.test",
"api_version": "2024-12-99",
@ -778,3 +748,43 @@ def test_retrieve__omits_trusted_model_credentials_when_not_supplied(seams):
litellm_params = logging_obj.update_from_kwargs.call_args.kwargs["litellm_params"]
assert "_litellm_internal_model_credentials" not in litellm_params
# =========================================================================== #
# mistral - a provider-config provider, like bedrock, so it requires `model`
# =========================================================================== #
def test_create__mistral_ocr_routes_to_base_http_handler_with_mistral_config(seams):
result = bm.create_batch(
completion_window="24h",
endpoint="/v1/ocr",
input_file_id="file-abc",
custom_llm_provider="mistral",
model="mistral/mistral-ocr-latest",
)
assert result is seams.base_http.create_batch.return_value
_assert_only(seams.base_http.create_batch, seams, "create_batch")
forwarded = seams.base_http.create_batch.call_args.kwargs
assert type(forwarded["provider_config"]).__name__ == "MistralBatchesConfig"
assert forwarded["model"] == "mistral-ocr-latest"
assert forwarded["create_batch_data"]["endpoint"] == "/v1/ocr"
def test_create__mistral_without_model_raises_badrequest(seams):
with pytest.raises(litellm.exceptions.BadRequestError):
bm.create_batch(**CREATE_KW, custom_llm_provider="mistral")
for m in _all_seam_methods(seams, "create_batch"):
m.assert_not_called()
def test_retrieve__mistral_routes_to_base_http_handler_with_mistral_config(seams):
result = bm.retrieve_batch(batch_id="job-1", custom_llm_provider="mistral", model="mistral/mistral-ocr-latest")
assert result is seams.base_http.retrieve_batch.return_value
_assert_only(seams.base_http.retrieve_batch, seams, "retrieve_batch")
forwarded = seams.base_http.retrieve_batch.call_args.kwargs
assert type(forwarded["provider_config"]).__name__ == "MistralBatchesConfig"
assert forwarded["batch_id"] == "job-1"

95
tests/test_litellm/litellm_core_utils/test_litellm_logging.py
View file

@ -17,12 +17,14 @@ from openai._legacy_response import HttpxBinaryResponseContent
import litellm
from litellm._logging import session_id_var, trace_id_var
from litellm.constants import SENTRY_DENYLIST, SENTRY_PII_DENYLIST
from litellm.cost_calculator import ocr_batch_cost
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging
from litellm.litellm_core_utils.litellm_logging import (
_get_status_fields,
set_callbacks,
)
from litellm.llms.base_llm.ocr.transformation import OCRUsageInfo
from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse
from litellm.types.utils import (
CallTypes,
@ -59,6 +61,16 @@ def test_get_masked_api_base(logging_obj):
assert type(masked_api_base) == str
def test_pre_call_tolerates_missing_api_base(logging_obj):
"""Presigned batch retrieves (Mistral, Bedrock) build their own URL and pass api_base=None
to pre_call; masking must not raise or the request's pre-call logging is silently lost."""
logging_obj.update_environment_variables(litellm_params={}, optional_params={})
logging_obj.pre_call(input="", api_key="", additional_args={"api_base": None, "headers": {}})
assert logging_obj.model_call_details["litellm_params"]["api_base"] == ""
def test_post_call_serializes_dict_with_datetime(logging_obj):
import datetime
@ -519,6 +531,36 @@ class TestGetRouterDeploymentModelInfo:
finally:
litellm.model_cost.pop(deployment_id, None)
def test_ocr_only_deployment_pricing_reaches_batch_ocr_cost(self, logging_obj) -> None:
"""Regression: a deployment priced only per page was treated as unpriced, so a retrieved OCR batch
billed at the published rate while the same deployment's synchronous OCR calls billed at its own."""
deployment_id = "deploy-ocr-only-pricing-1"
litellm.model_cost[deployment_id] = {
"id": deployment_id,
"litellm_provider": "mistral",
"mode": "ocr",
"ocr_cost_per_page": 0.0456,
"ocr_cost_per_page_batches": 0.0123,
}
logging_obj.litellm_params = {
"litellm_metadata": {"model_info": {"id": deployment_id}},
"model": "mistral/mistral-ocr-latest",
}
logging_obj.model_call_details["model"] = "mistral/mistral-ocr-latest"
published_annotation_rate = litellm.model_cost["mistral/mistral-ocr-latest"]["annotation_cost_per_page_batches"]
try:
info = logging_obj.get_router_deployment_model_info()
assert info is not None
assert info["ocr_cost_per_page_batches"] == 0.0123
pages_only = OCRUsageInfo(pages_processed=3)
assert ocr_batch_cost("mistral-ocr-latest", "mistral", pages_only, info)[0] == pytest.approx(3 * 0.0123)
with_annotations = OCRUsageInfo(pages_processed=3, pages_processed_annotation=2)
assert ocr_batch_cost("mistral-ocr-latest", "mistral", with_annotations, info)[0] == pytest.approx(
3 * 0.0123 + 2 * published_annotation_rate
)
finally:
litellm.model_cost.pop(deployment_id, None)
class TestRetrieveBatchCostPassesModelIdentity:
"""Regression: retrieving a batch priced it with no model identity at all.
@ -3958,9 +4000,7 @@ def test_get_standard_logging_object_payload_carries_matched_access_groups(loggi
"model": "gpt-4o",
"messages": [],
"litellm_params": {
"metadata": {
"user_api_key_matched_model_access_groups": ["premium-pool", "shared-pool"]
},
"metadata": {"user_api_key_matched_model_access_groups": ["premium-pool", "shared-pool"]},
"proxy_server_request": {"body": {}},
},
},
@ -4044,9 +4084,7 @@ def _model_router_response(selected_model: str, stamp: bool):
from litellm.types.utils import ModelResponse
response = ModelResponse(model=selected_model)
response._hidden_params = (
{AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY: selected_model} if stamp else {}
)
response._hidden_params = {AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY: selected_model} if stamp else {}
return response
@ -4070,9 +4108,7 @@ def test_standard_logging_payload_uses_stamped_model_router_model(logging_obj):
"messages": [],
"litellm_params": {"metadata": {}},
},
init_response_obj=_model_router_response(
"azure_ai/grok-4-1-fast-reasoning", stamp=True
),
init_response_obj=_model_router_response("azure_ai/grok-4-1-fast-reasoning", stamp=True),
start_time=now,
end_time=now,
logging_obj=logging_obj,
@ -4104,9 +4140,7 @@ def test_standard_logging_payload_keeps_requested_model_without_router_stamp(
"messages": [],
"litellm_params": {"metadata": {}},
},
init_response_obj=_model_router_response(
"azure_ai/grok-4-1-fast-reasoning", stamp=False
),
init_response_obj=_model_router_response("azure_ai/grok-4-1-fast-reasoning", stamp=False),
start_time=now,
end_time=now,
logging_obj=logging_obj,
@ -5594,9 +5628,7 @@ class TestNonInferenceCallTypesAreNotBilled:
init_response_obj=self._retrieved_response(),
start_time=now,
end_time=now,
logging_obj=self._logging_obj(
"aget_responses", litellm_metadata=self.BACKGROUND_POLL_METADATA
),
logging_obj=self._logging_obj("aget_responses", litellm_metadata=self.BACKGROUND_POLL_METADATA),
status="success",
)
@ -5842,9 +5874,7 @@ async def test_streaming_success_callbacks_survive_cost_calculation_failure():
releasing.async_log_success_event = AsyncMock()
patcher, logging_obj = _streaming_logging_obj_with_callbacks([releasing])
with patcher, patch.object(
logging_obj, "_response_cost_calculator", side_effect=ValueError("bad usage block")
):
with patcher, patch.object(logging_obj, "_response_cost_calculator", side_effect=ValueError("bad usage block")):
await logging_obj.async_success_handler(result=_assembled_stream_result())
assert logging_obj.model_call_details["response_cost"] is None
@ -5857,8 +5887,9 @@ async def test_streaming_success_callbacks_survive_standard_logging_payload_fail
releasing.async_log_success_event = AsyncMock()
patcher, logging_obj = _streaming_logging_obj_with_callbacks([releasing])
with patcher, patch.object(
logging_obj, "_build_standard_logging_payload", side_effect=ValueError("incomplete stream")
with (
patcher,
patch.object(logging_obj, "_build_standard_logging_payload", side_effect=ValueError("incomplete stream")),
):
await logging_obj.async_success_handler(result=_assembled_stream_result())
@ -6206,6 +6237,8 @@ def test_prompt_hooks_skip_prompt_managers_when_no_prompt_id(logging_obj, tmp_pa
)
for hook in [cb for cb in litellm.callbacks if isinstance(cb, VectorStorePreCallHook)]:
litellm.logging_callback_manager.remove_callback_from_list_by_object(litellm.callbacks, hook)
def test_newrelic_dispatch_prefers_otel_v2_when_flag_on(monkeypatch):
"""With LITELLM_OTEL_V2 on and operator credentials present, the "newrelic"
callback builds the OTel v2 logger (per-team credential routing); with the
@ -6361,7 +6394,9 @@ def test_get_error_information_skips_traceback_for_budget_rejection_with_provide
from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup
assert litellm.log_client_error_tracebacks is False
over_budget = _raise_and_catch(litellm.BudgetExceededError(current_cost=0.01, max_budget=0.0, llm_provider="anthropic"))
over_budget = _raise_and_catch(
litellm.BudgetExceededError(current_cost=0.01, max_budget=0.0, llm_provider="anthropic")
)
result = StandardLoggingPayloadSetup.get_error_information(over_budget)
assert result["error_code"] == "429"
assert result["llm_provider"] == "anthropic"
@ -6934,9 +6969,7 @@ def test_passthrough_embeddings_result_swapped_for_callbacks():
],
"model": "EmbeddingsGigaR",
},
request=httpx.Request(
"POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings"
),
request=httpx.Request("POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings"),
)
_, _, swapped_result = logging_obj._success_handler_helper_fn(
@ -6955,12 +6988,14 @@ def test_get_status_fields_ranks_guardrail_flagged_between_success_and_intervene
request-level guardrail_status but never mask an intervention."""
flagged = {"guardrail_status": "guardrail_flagged"}
assert _get_status_fields(
"success", [{"guardrail_status": "success"}, flagged], None
)["guardrail_status"] == "guardrail_flagged"
assert _get_status_fields(
"success", [flagged, {"guardrail_status": "guardrail_intervened"}], None
)["guardrail_status"] == "guardrail_intervened"
assert (
_get_status_fields("success", [{"guardrail_status": "success"}, flagged], None)["guardrail_status"]
== "guardrail_flagged"
)
assert (
_get_status_fields("success", [flagged, {"guardrail_status": "guardrail_intervened"}], None)["guardrail_status"]
== "guardrail_intervened"
)
def test_get_error_information_redacts_provider_key_from_upstream_url():

62
tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py
View file

@ -2853,6 +2853,68 @@ def test_direct_vector_store_search_debug_log_omits_stored_credentials(caplog, i
assert "sk-embedding-s3cret" not in logged
@pytest.mark.asyncio
async def test_async_retrieve_batch_masks_presigned_auth_header_in_raw_request_log():
"""Regression: a pre-signed retrieve-batch request (Mistral, Bedrock) embeds its auth
header inside the transformed request, which pre_call logs verbatim as the raw request
body, so the provider key landed unmasked in raw_request_typed_dict and every
raw-request callback."""
from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging
from litellm.llms.mistral.batches.transformation import MistralBatchesConfig
provider_key = "mistral-s3cret-provider-key-123456"
job_payload = {
"id": "batch-1",
"input_files": ["file-1"],
"endpoint": "/v1/ocr",
"model": "mistral-ocr-latest",
"status": "SUCCESS",
"created_at": 1_757_400_000,
}
sent_requests = []
def _capture(request: httpx.Request) -> httpx.Response:
sent_requests.append(request)
return httpx.Response(200, json=job_payload)
client = AsyncHTTPHandler()
client.client = httpx.AsyncClient(transport=httpx.MockTransport(_capture))
logging_obj = LitellmLogging(
model="mistral/mistral-ocr-latest",
messages=[],
stream=False,
call_type="batch_retrieve",
start_time=time.time(),
litellm_call_id="batch-retrieve-call-id",
function_id="batch-retrieve-function-id",
log_raw_request_response=True,
)
logging_obj.update_environment_variables(
model="mistral/mistral-ocr-latest",
optional_params={},
litellm_params={"litellm_call_id": "batch-retrieve-call-id", "metadata": {}},
)
result = await BaseLLMHTTPHandler().retrieve_batch(
batch_id="batch-1",
litellm_params={"api_key": provider_key},
provider_config=MistralBatchesConfig(),
headers={},
api_base=None,
api_key=provider_key,
logging_obj=logging_obj,
_is_async=True,
client=client,
model="mistral/mistral-ocr-latest",
)
assert result.id == "batch-1"
assert sent_requests[0].headers["Authorization"] == f"Bearer {provider_key}"
raw_request_body = logging_obj.model_call_details["raw_request_typed_dict"]["raw_request_body"]
assert provider_key not in json.dumps(raw_request_body)
@pytest.mark.asyncio
async def test_async_anthropic_messages_handler_carries_deployment_vertex_location_for_pricing(monkeypatch):
"""

0
tests/test_litellm/llms/mistral/batches/__init__.py Normal file
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258
tests/test_litellm/llms/mistral/batches/test_mistral_batches_transformation.py Normal file
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@ -0,0 +1,258 @@
"""
Regression tests for ``MistralBatchesConfig``, the BaseBatchesConfig implementation
behind ``custom_llm_provider="mistral"`` on /v1/batches.
Locks the request shape Mistral's ``POST /v1/batch/jobs`` accepts (input_files list,
model set on the job, endpoint passed through untouched so ``/v1/ocr`` batches work),
the Mistral -> OpenAI status mapping, request-count and file-id mapping, and auth.
Everything runs for real against canned httpx responses; only the API key env var is
set.
"""
import json
import httpx
import pytest
from litellm.llms.mistral.batches.transformation import MistralBatchesConfig
from litellm.llms.mistral.common_utils import MistralError
from litellm.types.llms.openai import CreateBatchRequest
from litellm.types.utils import LiteLLMBatch, LlmProviders
STATUS_MAP = {
"QUEUED": "validating",
"RUNNING": "in_progress",
"SUCCESS": "completed",
"FAILED": "failed",
"TIMEOUT_EXCEEDED": "expired",
"CANCELLATION_REQUESTED": "cancelling",
"CANCELLED": "cancelled",
}
def _job(**overrides):
base = {
"id": "8ff5e0d1-6bc2-4c3a-9f7d-0d1c2e3f4a5b",
"object": "batch",
"input_files": ["c1a2b3d4-0000-4000-8000-000000000001"],
"endpoint": "/v1/ocr",
"model": "mistral-ocr-latest",
"status": "SUCCESS",
"created_at": 1_757_400_000,
"started_at": 1_757_400_010,
"completed_at": 1_757_400_500,
"total_requests": 3,
"completed_requests": 3,
"succeeded_requests": 2,
"failed_requests": 1,
"output_file": "out-0000-4000-8000-000000000002",
"error_file": "err-0000-4000-8000-000000000003",
"errors": [],
"metadata": {"job_type": "testing"},
}
return {**base, **overrides}
def _response(payload: dict, status_code: int = 200) -> httpx.Response:
return httpx.Response(
status_code=status_code,
content=json.dumps(payload).encode(),
request=httpx.Request("GET", "https://api.mistral.ai/v1/batch/jobs/x"),
)
@pytest.fixture
def config() -> MistralBatchesConfig:
return MistralBatchesConfig()
@pytest.fixture
def api_key(monkeypatch) -> str:
monkeypatch.setenv("MISTRAL_API_KEY", "sk-mistral-test")
return "sk-mistral-test"
def test_custom_llm_provider(config):
assert config.custom_llm_provider == LlmProviders.MISTRAL
def test_create_request_maps_openai_fields_onto_mistral_job(config):
data = CreateBatchRequest(
completion_window="24h",
endpoint="/v1/ocr",
input_file_id="file-123",
metadata={"team": "docs"},
)
body = config.transform_create_batch_request(
model="mistral-ocr-latest", create_batch_data=data, optional_params={}, litellm_params={}
)
assert body == {
"input_files": ("file-123",),
"endpoint": "/v1/ocr",
"model": "mistral-ocr-latest",
"metadata": {"team": "docs"},
}
def test_create_request_omits_empty_metadata(config):
data = CreateBatchRequest(
completion_window="24h",
endpoint="/v1/chat/completions",
input_file_id="file-123",
metadata=None,
)
body = config.transform_create_batch_request(
model="mistral-small-latest", create_batch_data=data, optional_params={}, litellm_params={}
)
assert "metadata" not in body
def test_create_request_requires_input_file_and_endpoint(config):
with pytest.raises(ValueError, match="input_file_id and endpoint are required"):
config.transform_create_batch_request(
model="m",
create_batch_data=CreateBatchRequest(completion_window="24h"),
optional_params={},
litellm_params={},
)
@pytest.mark.parametrize(
"api_base,expected",
[
(None, "https://api.mistral.ai/v1/batch/jobs"),
("https://api.mistral.ai/v1", "https://api.mistral.ai/v1/batch/jobs"),
("https://proxy.example.com/", "https://proxy.example.com/v1/batch/jobs"),
],
)
def test_create_url(config, api_base, expected):
url = config.get_complete_batch_url(
api_base=api_base, api_key="k", model="m", optional_params={}, litellm_params={}, data={}
)
assert url == expected
def test_validate_environment_uses_bearer_auth(config, api_key):
headers = config.validate_environment(
headers={"x-extra": "1"}, model="m", messages=[], optional_params={}, litellm_params={}
)
assert headers == {"x-extra": "1", "Authorization": f"Bearer {api_key}"}
def test_validate_environment_explicit_key_wins(config, api_key):
headers = config.validate_environment(
headers={}, model="m", messages=[], optional_params={}, litellm_params={}, api_key="sk-explicit"
)
assert headers["Authorization"] == "Bearer sk-explicit"
def test_validate_environment_without_key_raises(config, monkeypatch):
monkeypatch.delenv("MISTRAL_API_KEY", raising=False)
with pytest.raises(ValueError, match="Missing Mistral API Key"):
config.validate_environment(headers={}, model="m", messages=[], optional_params={}, litellm_params={})
def test_create_response_maps_job_onto_openai_batch(config):
batch = config.transform_create_batch_response(
model="mistral-ocr-latest",
raw_response=_response(_job(status="QUEUED", started_at=None, completed_at=None)),
logging_obj=None,
litellm_params={},
)
assert isinstance(batch, LiteLLMBatch)
assert batch.id == "8ff5e0d1-6bc2-4c3a-9f7d-0d1c2e3f4a5b"
assert batch.endpoint == "/v1/ocr"
assert batch.input_file_id == "c1a2b3d4-0000-4000-8000-000000000001"
assert batch.status == "validating"
assert batch.created_at == 1_757_400_000
assert batch.in_progress_at is None
assert batch.completed_at is None
assert batch.metadata == {"job_type": "testing"}
def test_retrieve_request_is_presigned_get_with_auth(config, api_key):
req = config.transform_retrieve_batch_request(
batch_id="job/with slash", optional_params={}, litellm_params={"api_base": "https://api.mistral.ai"}
)
assert req["method"] == "GET"
assert req["url"] == "https://api.mistral.ai/v1/batch/jobs/job%2Fwith%20slash"
assert req["headers"] == {"Authorization": f"Bearer {api_key}"}
def test_retrieve_request_prefers_litellm_params_api_key(config, api_key):
req = config.transform_retrieve_batch_request(
batch_id="job-1", optional_params={}, litellm_params={"api_key": "sk-from-deployment"}
)
assert req["headers"]["Authorization"] == "Bearer sk-from-deployment"
@pytest.mark.parametrize("mistral_status,openai_status", sorted(STATUS_MAP.items()))
def test_retrieve_response_status_mapping(config, mistral_status, openai_status):
batch = config.transform_retrieve_batch_response(
model=None, raw_response=_response(_job(status=mistral_status)), logging_obj=None, litellm_params={}
)
assert batch.status == openai_status
@pytest.mark.parametrize(
"mistral_status,populated_field",
[
("SUCCESS", "completed_at"),
("FAILED", "failed_at"),
("TIMEOUT_EXCEEDED", "expired_at"),
("CANCELLED", "cancelled_at"),
],
)
def test_retrieve_response_terminal_timestamp_lands_on_matching_field(config, mistral_status, populated_field):
batch = config.transform_retrieve_batch_response(
model=None, raw_response=_response(_job(status=mistral_status)), logging_obj=None, litellm_params={}
)
terminal_fields = {"completed_at", "failed_at", "expired_at", "cancelled_at"}
assert getattr(batch, populated_field) == 1_757_400_500
for other in terminal_fields - {populated_field}:
assert getattr(batch, other) is None
assert batch.in_progress_at == 1_757_400_010
def test_retrieve_response_maps_counts_and_files(config):
batch = config.transform_retrieve_batch_response(
model=None, raw_response=_response(_job()), logging_obj=None, litellm_params={}
)
assert batch.request_counts.total == 3
assert batch.request_counts.completed == 2
assert batch.request_counts.failed == 1
assert batch.output_file_id == "out-0000-4000-8000-000000000002"
assert batch.error_file_id == "err-0000-4000-8000-000000000003"
assert batch.errors is None
def test_retrieve_response_surfaces_job_errors(config):
batch = config.transform_retrieve_batch_response(
model=None,
raw_response=_response(
_job(status="FAILED", errors=[{"message": "invalid document", "count": 2}, {"message": "timeout"}])
),
logging_obj=None,
litellm_params={},
)
assert [e.message for e in batch.errors.data] == ["invalid document (x2)", "timeout"]
def test_retrieve_response_without_files_or_input(config):
batch = config.transform_retrieve_batch_response(
model=None,
raw_response=_response(_job(input_files=[], output_file=None, error_file=None, metadata=None)),
logging_obj=None,
litellm_params={},
)
assert batch.input_file_id == ""
assert batch.output_file_id is None
assert batch.error_file_id is None
assert batch.metadata is None
def test_get_error_class(config):
err = config.get_error_class("nope", 401, {"x-request-id": "r1"})
assert isinstance(err, MistralError)
assert err.status_code == 401
assert err.message == "nope"

0
tests/test_litellm/llms/mistral/files/__init__.py Normal file
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249
tests/test_litellm/llms/mistral/files/test_mistral_files_transformation.py Normal file
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@ -0,0 +1,249 @@
"""
Regression tests for ``MistralFilesConfig``, the BaseFilesConfig implementation behind
``custom_llm_provider="mistral"`` on /v1/files.
Locks the URL routing for each file operation, the multipart upload shape Mistral's
``POST /v1/files`` accepts (purpose restricted to fine-tune/batch/ocr), and the
Mistral -> OpenAI file object mapping. Runs against canned httpx responses.
"""
import json
import httpx
import pytest
from openai.types.file_deleted import FileDeleted
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.mistral.files.transformation import MistralFilesConfig
from litellm.types.llms.openai import CreateFileRequest, FileContentRequest, OpenAIFileObject
from litellm.types.utils import LlmProviders
FILE_ID = "497f6eca-6276-4993-bfeb-53cbbbba6f09"
def _file(**overrides):
base = {
"id": FILE_ID,
"object": "file",
"bytes": 13000,
"created_at": 1_716_963_433,
"filename": "batch_input.jsonl",
"purpose": "batch",
"sample_type": "batch_request",
"num_lines": 3,
"source": "upload",
}
return {**base, **overrides}
def _response(payload) -> httpx.Response:
return httpx.Response(
status_code=200,
content=json.dumps(payload).encode(),
request=httpx.Request("GET", "https://api.mistral.ai/v1/files"),
)
@pytest.fixture
def config() -> MistralFilesConfig:
return MistralFilesConfig()
@pytest.fixture
def api_key(monkeypatch) -> str:
monkeypatch.setenv("MISTRAL_API_KEY", "sk-mistral-test")
return "sk-mistral-test"
def test_custom_llm_provider(config):
assert config.custom_llm_provider == LlmProviders.MISTRAL
@pytest.mark.parametrize(
"api_base,expected",
[
(None, "https://api.mistral.ai/v1/files"),
("https://api.mistral.ai/v1/", "https://api.mistral.ai/v1/files"),
("https://proxy.example.com", "https://proxy.example.com/v1/files"),
],
)
def test_upload_url(config, api_base, expected):
url = config.get_complete_url(api_base=api_base, api_key="k", model="", optional_params={}, litellm_params={})
assert url == expected
def test_validate_environment_uses_bearer_auth(config, api_key):
headers = config.validate_environment(headers={}, model="", messages=[], optional_params={}, litellm_params={})
assert headers == {"Authorization": f"Bearer {api_key}"}
def test_upload_request_is_multipart_with_batch_purpose(config):
body = config.transform_create_file_request(
model="",
create_file_data=CreateFileRequest(
file=("in.jsonl", b'{"custom_id":"0"}\n', "application/jsonl"), purpose="batch"
),
optional_params={},
litellm_params={},
)
assert body == {
"file": ("in.jsonl", b'{"custom_id":"0"}\n', "application/jsonl"),
"purpose": (None, "batch"),
}
@pytest.mark.parametrize("purpose", ["batch", "fine-tune", "ocr"])
def test_upload_request_passes_mistral_purposes_through(config, purpose):
body = config.transform_create_file_request(
model="",
create_file_data=CreateFileRequest(file=("f.bin", b"x"), purpose=purpose),
optional_params={},
litellm_params={},
)
assert body["purpose"] == (None, purpose)
def test_upload_request_maps_user_data_onto_ocr(config):
body = config.transform_create_file_request(
model="",
create_file_data=CreateFileRequest(file=("scan.pdf", b"%PDF"), purpose="user_data"),
optional_params={},
litellm_params={},
)
assert body["purpose"] == (None, "ocr")
@pytest.mark.parametrize("purpose", ["assistants", "vision", "evals"])
def test_upload_request_rejects_purposes_mistral_lacks(config, purpose):
"""Regression: these used to be silently rewritten to ``batch``, so an upload that skipped the
proxy's batch-only validation and guardrails still landed on Mistral as a batch input file. The
rejection is a 400 provider error, so the proxy answers invalid_request_error instead of a 500."""
with pytest.raises(BaseLLMException, match=f"purpose={purpose!r}") as exc_info:
config.transform_create_file_request(
model="",
create_file_data=CreateFileRequest(file=("f.bin", b"x"), purpose=purpose),
optional_params={},
litellm_params={},
)
assert exc_info.value.status_code == 400
def test_upload_request_requires_file(config):
with pytest.raises(ValueError, match="File data is required"):
config.transform_create_file_request(
model="", create_file_data=CreateFileRequest(purpose="batch"), optional_params={}, litellm_params={}
)
def test_upload_response_maps_onto_openai_file_object(config):
obj = config.transform_create_file_response(
model=None, raw_response=_response(_file()), logging_obj=None, litellm_params={}
)
assert obj == OpenAIFileObject(
id=FILE_ID,
bytes=13000,
created_at=1_716_963_433,
filename="batch_input.jsonl",
object="file",
purpose="batch",
status="uploaded",
)
def test_file_response_with_ocr_purpose_maps_onto_user_data(config):
obj = config.transform_retrieve_file_response(
raw_response=_response(_file(purpose="ocr", expires_at=1_800_000_000)), logging_obj=None, litellm_params={}
)
assert obj.purpose == "user_data"
assert obj.expires_at == 1_800_000_000
@pytest.mark.parametrize("purpose", ["playground", "audio", "code_interpreter"])
def test_files_with_purposes_mistral_never_lets_us_upload_still_read_back(config, purpose):
"""Regression: Mistral's live API returns purposes its upload endpoint rejects for files
other Mistral products created, and both the unfiltered list and a retrieve of such a file
used to fail validation, so one playground file 500'd ``GET /v1/files`` for the whole key."""
retrieved = config.transform_retrieve_file_response(
raw_response=_response(_file(purpose=purpose)), logging_obj=None, litellm_params={}
)
assert retrieved.purpose == "user_data"
listed = config.transform_list_files_response(
raw_response=_response({"data": [_file(purpose=purpose), _file(id="second")], "object": "list", "total": 2}),
logging_obj=None,
litellm_params={},
)
assert [(f.id, f.purpose) for f in listed] == [(FILE_ID, "user_data"), ("second", "batch")]
@pytest.mark.parametrize(
"method,suffix",
[
("transform_retrieve_file_request", ""),
("transform_delete_file_request", ""),
],
)
def test_single_file_urls_encode_id_and_honor_api_base(config, method, suffix):
url, params = getattr(config, method)(
file_id="id/with slash", optional_params={}, litellm_params={"api_base": "https://mistral.internal/v1"}
)
assert url == f"https://mistral.internal/v1/files/id%2Fwith%20slash{suffix}"
assert params == {}
def test_file_content_url(config):
url, params = config.transform_file_content_request(
file_content_request=FileContentRequest(file_id=FILE_ID), optional_params={}, litellm_params={}
)
assert url == f"https://api.mistral.ai/v1/files/{FILE_ID}/content"
assert params == {}
def test_file_content_response_is_binary_passthrough(config):
raw = httpx.Response(
200, content=b'{"custom_id":"0","response":{"status_code":200}}\n', request=httpx.Request("GET", "https://x")
)
out = config.transform_file_content_response(raw_response=raw, logging_obj=None, litellm_params={})
assert out.content == b'{"custom_id":"0","response":{"status_code":200}}\n'
def test_delete_response(config):
out = config.transform_delete_file_response(
raw_response=_response({"id": FILE_ID, "object": "file", "deleted": True}), logging_obj=None, litellm_params={}
)
assert out == FileDeleted(id=FILE_ID, deleted=True, object="file")
def test_list_request_filters_by_mapped_purpose(config):
url, params = config.transform_list_files_request(purpose="batch", optional_params={}, litellm_params={})
assert url == "https://api.mistral.ai/v1/files"
assert params == {"purpose": "batch"}
_, no_params = config.transform_list_files_request(purpose=None, optional_params={}, litellm_params={})
assert no_params == {}
def test_list_request_accepts_the_purpose_an_ocr_file_reads_back_as(config):
"""Regression: an OCR file reads back as ``purpose=user_data``, and listing with that purpose
used to raise, so ``files.list(purpose=file.purpose)`` could never find OCR files."""
ocr_file = config.transform_retrieve_file_response(
raw_response=_response(_file(purpose="ocr")), logging_obj=None, litellm_params={}
)
_, params = config.transform_list_files_request(purpose=ocr_file.purpose, optional_params={}, litellm_params={})
assert params == {"purpose": "ocr"}
def test_list_request_rejects_purposes_mistral_lacks(config):
with pytest.raises(BaseLLMException, match="purpose='assistants'") as exc_info:
config.transform_list_files_request(purpose="assistants", optional_params={}, litellm_params={})
assert exc_info.value.status_code == 400
def test_list_response(config):
out = config.transform_list_files_response(
raw_response=_response(
{"data": [_file(), _file(id="second", filename="b.jsonl")], "object": "list", "total": 2}
),
logging_obj=None,
litellm_params={},
)
assert [f.id for f in out] == [FILE_ID, "second"]
assert out[1].filename == "b.jsonl"

157
tests/test_litellm/proxy/batches_endpoints/test_endpoints.py
View file

@ -51,6 +51,7 @@ from litellm.proxy.openai_files_endpoints.common_utils import (
from litellm.proxy.utils import ProxyLogging
from litellm.router import Router
from litellm.types.llms.openai import BatchJobStatus
from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo
from litellm.types.utils import CredentialItem, LiteLLMBatch
from fastapi import Request, Response
@ -178,6 +179,10 @@ def harness():
logging.get_proxy_hook = MagicMock(return_value=None)
router = MagicMock(spec=Router)
router.model_group_alias = {}
router.get_model_access_groups = MagicMock(return_value={})
router.resolve_model_name_from_model_id = MagicMock(side_effect=lambda model_id: model_id)
router.model_list = []
router.acreate_batch = AsyncMock(return_value=make_batch())
router.get_deployment_credentials_with_provider = MagicMock(side_effect=_creds_lookup)
@ -1184,8 +1189,13 @@ def retrieve_harness():
logging.get_proxy_hook = MagicMock(return_value=None)
router = MagicMock(spec=Router)
router.model_group_alias = {}
router.get_model_access_groups = MagicMock(return_value={})
router.resolve_model_name_from_model_id = MagicMock(side_effect=lambda model_id: model_id)
router.model_list = []
router.aretrieve_batch = AsyncMock(return_value=make_batch())
router.get_deployment_credentials_with_provider = MagicMock(side_effect=_creds_lookup)
router.get_credential_deployment = MagicMock(return_value=None)
pre_call = AsyncMock(side_effect=lambda **kw: (data_holder["data"], MagicMock()))
get_headers = MagicMock(return_value={})
@ -1304,6 +1314,30 @@ async def test_retrieve__model_encoded_id(retrieve_harness):
assert retrieve_harness.update_batch_in_db.call_args.kwargs["operation"] == "retrieve"
@pytest.mark.asyncio
async def test_retrieve__model_encoded_id__stamps_deployment_model_info_for_cost(retrieve_harness):
"""Regression: this path calls litellm.aretrieve_batch directly, so nothing stamped the
deployment's model_info the way the router does for routed calls. Cost tracking then never
saw the deployment id, and a completed batch on a deployment with its own per-page pricing
was billed at the published rate with an empty model_id on the spend row."""
retrieve_harness.router.get_credential_deployment.return_value = Deployment(
model_name="azure-gpt",
litellm_params=LiteLLM_Params(model="azure/gpt-4o"),
model_info=ModelInfo(id="dep-123"),
)
retrieve_harness.pre_call.side_effect = lambda **kw: (
{**retrieve_harness.data["data"], "litellm_metadata": {"user_api_key_alias": "qa-key"}},
MagicMock(),
)
await call_retrieve(retrieve_harness, AZURE_BATCH_ID)
retrieve_harness.router.get_credential_deployment.assert_called_once_with(model_id="azure/gpt-4o")
litellm_metadata = retrieve_harness.aretrieve_kwargs()["litellm_metadata"]
assert litellm_metadata["model_info"]["id"] == "dep-123"
assert litellm_metadata["user_api_key_alias"] == "qa-key"
@pytest.mark.asyncio
async def test_retrieve__model_encoded_id__forwards_decoded_model_not_deployment(
retrieve_harness,
@ -1639,6 +1673,10 @@ def list_harness():
logging.get_proxy_hook = MagicMock(return_value=None)
router = MagicMock(spec=Router)
router.model_group_alias = {}
router.get_model_access_groups = MagicMock(return_value={})
router.resolve_model_name_from_model_id = MagicMock(side_effect=lambda model_id: model_id)
router.model_list = []
router.alist_batches = AsyncMock(return_value=FakeListPage([]))
router.get_deployment_credentials_with_provider = MagicMock(side_effect=_creds_lookup)
@ -2053,6 +2091,10 @@ def cancel_harness():
logging.get_proxy_hook = MagicMock(return_value=None)
router = MagicMock(spec=Router)
router.model_group_alias = {}
router.get_model_access_groups = MagicMock(return_value={})
router.resolve_model_name_from_model_id = MagicMock(side_effect=lambda model_id: model_id)
router.model_list = []
router.acancel_batch = AsyncMock(return_value=make_batch())
router.get_deployment_credentials_with_provider = MagicMock(side_effect=_creds_lookup)
@ -2774,8 +2816,6 @@ async def test_cancel__unified_batch_id_allowed_when_managed_files_required(canc
assert cancel_harness.router_acancel.call_count == 1
@pytest.mark.asyncio
async def test_retrieve__managed_batch_defers_cost_to_the_poller_when_it_is_running(retrieve_harness):
with patch.object(endpoints, "batch_cost_poller_is_active", MagicMock(return_value=True)):
@ -2803,3 +2843,116 @@ async def test_retrieve__raw_batch_id_is_untouched_by_the_poller_handoff(retriev
metadata = retrieve_harness.litellm_aretrieve.await_args.kwargs.get("litellm_metadata") or {}
assert metadata.get("batch_ignore_default_logging") is None
def _key_restricted_to(*models: str) -> UserAPIKeyAuth:
return UserAPIKeyAuth(api_key="sk-restricted", team_id="team-a", team_models=list(models), models=list(models))
@pytest.mark.asyncio
async def test_create__header_model_rejects_key_without_model_grant(harness):
"""A key not granted the model named in x-litellm-model must not receive that deployment's credentials."""
set_body(harness, {"input_file_id": "file-plain", "endpoint": "/v1/chat/completions", "completion_window": "24h"})
with pytest.raises(ProxyException) as exc_info:
await call_create(harness, user=_key_restricted_to("azure/gpt-4o"), headers={"x-litellm-model": "vertex-model"})
assert exc_info.value.code == "403"
harness.creds_resolver.assert_not_called()
harness.litellm_acreate.assert_not_called()
@pytest.mark.asyncio
async def test_create__header_model_allows_key_with_model_grant(harness):
set_body(harness, {"input_file_id": "file-plain", "endpoint": "/v1/chat/completions", "completion_window": "24h"})
await call_create(harness, user=_key_restricted_to("vertex-model"), headers={"x-litellm-model": "vertex-model"})
harness.creds_resolver.assert_called_once_with(model_id="vertex-model")
assert harness.acreate_kwargs()["custom_llm_provider"] == "vertex_ai"
@pytest.mark.asyncio
async def test_retrieve__model_encoded_id_rejects_key_without_model_grant(retrieve_harness):
"""The model embedded in a batch id is caller-controlled, so it is checked against the key's grants too."""
with pytest.raises(ProxyException) as exc_info:
await call_retrieve(retrieve_harness, AZURE_BATCH_ID, user=_key_restricted_to("vertex-model"))
assert exc_info.value.code == "403"
retrieve_harness.creds_resolver.assert_not_called()
retrieve_harness.litellm_aretrieve.assert_not_called()
@pytest.mark.asyncio
async def test_cancel__model_encoded_id_rejects_key_without_model_grant(cancel_harness):
with pytest.raises(ProxyException) as exc_info:
await call_cancel(cancel_harness, AZURE_BATCH_ID, user=_key_restricted_to("vertex-model"))
assert exc_info.value.code == "403"
cancel_harness.creds_resolver.assert_not_called()
cancel_harness.litellm_acancel.assert_not_called()
def _b64_unified_id(decoded: str) -> str:
return base64.urlsafe_b64encode(decoded.encode()).decode().rstrip("=")
UNIFIED_FILE_ID_FOR_GPT4O_MINI = _b64_unified_id(
"litellm_proxy:application/octet-stream;unified_id,c4843482-b176-4901-8292-7523fd0f2c6e;"
"target_model_names,gpt-4o-mini;llm_output_file_id,file-provider;llm_output_file_model_id,dep-1"
)
UNIFIED_BATCH_ID_FOR_GPT4O_MINI = _b64_unified_id(UNIFIED_BATCH_ID)
@pytest.mark.asyncio
async def test_create__unified_file_id_rejects_key_without_model_grant(harness):
"""The model carried inside a unified file id is caller-controlled too, so it is checked against the key's grants."""
set_body(
harness,
{
"input_file_id": UNIFIED_FILE_ID_FOR_GPT4O_MINI,
"endpoint": "/v1/chat/completions",
"completion_window": "24h",
},
)
with pytest.raises(ProxyException) as exc_info:
await call_create(harness, user=_key_restricted_to("vertex-model"))
assert exc_info.value.code == "403"
harness.router_acreate.assert_not_called()
harness.litellm_acreate.assert_not_called()
@pytest.mark.asyncio
async def test_retrieve__unified_batch_id_rejects_key_without_model_grant(retrieve_harness):
with pytest.raises(ProxyException) as exc_info:
await call_retrieve(retrieve_harness, UNIFIED_BATCH_ID_FOR_GPT4O_MINI, user=_key_restricted_to("vertex-model"))
assert exc_info.value.code == "403"
retrieve_harness.router_aretrieve.assert_not_called()
retrieve_harness.creds_resolver.assert_not_called()
@pytest.mark.asyncio
async def test_retrieve__unified_batch_id_rejects_key_without_model_grant_before_db_terminal_shortcut(
retrieve_harness,
):
retrieve_harness.get_batch_from_db.return_value = (MagicMock(), make_batch(id="batch-from-db", status="completed"))
with pytest.raises(ProxyException) as exc_info:
await call_retrieve(retrieve_harness, UNIFIED_BATCH_ID_FOR_GPT4O_MINI, user=_key_restricted_to("vertex-model"))
assert exc_info.value.code == "403"
retrieve_harness.logging.post_call_success_hook.assert_not_called()
retrieve_harness.ensure_managed_files.assert_not_called()
retrieve_harness.router_aretrieve.assert_not_called()
@pytest.mark.asyncio
async def test_cancel__unified_batch_id_rejects_key_without_model_grant(cancel_harness):
with pytest.raises(ProxyException) as exc_info:
await call_cancel(cancel_harness, UNIFIED_BATCH_ID_FOR_GPT4O_MINI, user=_key_restricted_to("vertex-model"))
assert exc_info.value.code == "403"
cancel_harness.router_acancel.assert_not_called()

243
tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py
View file

@ -1877,7 +1877,7 @@ def test_get_file_content_streams_openai_direct_path(
monkeypatch.setattr(litellm, "afile_content", _mock_afile_content)
monkeypatch.setattr(
"litellm.proxy.openai_files_endpoints.files_endpoints.handle_model_based_routing",
lambda **kwargs: (False, None, None, None),
AsyncMock(return_value=(False, None, None, None)),
)
app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth(
@ -1942,15 +1942,17 @@ def test_get_file_content_routed_provider_skips_streaming_when_resolved_provider
)
monkeypatch.setattr(
"litellm.proxy.openai_files_endpoints.files_endpoints.handle_model_based_routing",
lambda **kwargs: (
True,
"azure-gpt-3-5-turbo",
"file-original-123",
{
"custom_llm_provider": "azure",
"api_key": "azure-key",
"api_base": "https://azure.example.com",
},
AsyncMock(
return_value=(
True,
"azure-gpt-3-5-turbo",
"file-original-123",
{
"custom_llm_provider": "azure",
"api_key": "azure-key",
"api_base": "https://azure.example.com",
},
)
),
)
@ -2015,7 +2017,7 @@ def test_get_file_content_non_openai_provider_skips_streaming_handler(
)
monkeypatch.setattr(
"litellm.proxy.openai_files_endpoints.files_endpoints.handle_model_based_routing",
lambda **kwargs: (False, None, None, None),
AsyncMock(return_value=(False, None, None, None)),
)
app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth(
@ -2520,14 +2522,16 @@ def test_list_files_model_routing_does_not_forward_custom_llm_provider_twice(
monkeypatch.setattr(litellm, "afile_list", _mock_afile_list)
monkeypatch.setattr(
"litellm.proxy.openai_files_endpoints.files_endpoints.handle_model_based_routing",
lambda **kwargs: (
True,
"azure-gpt-4o",
None,
{
"custom_llm_provider": "azure",
"api_key": "azure-key",
},
AsyncMock(
return_value=(
True,
"azure-gpt-4o",
None,
{
"custom_llm_provider": "azure",
"api_key": "azure-key",
},
)
),
)
@ -5224,3 +5228,204 @@ def test_get_file_content_keeps_the_status_of_a_rejection_raised_inside_the_rout
error = response.json()["error"]
assert error["message"].startswith("Storage backend error")
assert (error["type"], error["param"], error["code"]) == ("invalid_request_error", "file_id", "400")
def test_get_file_model_routed_id_forwards_deployment_provider(mocker: MockerFixture, monkeypatch):
"""
Regression: a file id encoded with a non-OpenAI deployment (here Mistral) must be
retrieved from that deployment's provider. Before the fix the retrieve path only
forwarded the credentials and let ``custom_llm_provider`` default to openai, so a
Mistral file id was sent to api.openai.com with the Mistral key and 401'd.
"""
import litellm.proxy.proxy_server as ps
from litellm.proxy._types import LitellmUserRoles
from litellm.proxy.openai_files_endpoints.common_utils import encode_file_id_with_model
router = Router(
model_list=[
{
"model_name": "mistral-ocr",
"litellm_params": {"model": "mistral/mistral-ocr-latest", "api_key": "mistral-key"},
"model_info": {"id": "mistral-ocr-id"},
}
]
)
proxy_logging_obj = setup_proxy_logging_object(monkeypatch, router)
monkeypatch.setattr("litellm.proxy.proxy_server.master_key", None)
monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", None)
monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", router)
proxy_logging_obj.update_request_status = mocker.AsyncMock()
proxy_logging_obj.post_call_failure_hook = mocker.AsyncMock()
captured_kwargs: dict = {}
async def _mock_afile_retrieve(**kwargs):
captured_kwargs.update(kwargs)
return OpenAIFileObject(
id="7a13fa8e-fcf8-42c5-aa61-c93c10e2c7df",
object="file",
bytes=2,
created_at=1234567890,
filename="batch.jsonl",
purpose="batch",
status="uploaded",
)
monkeypatch.setattr(litellm, "afile_retrieve", _mock_afile_retrieve)
app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth(
api_key="test-key", user_role=LitellmUserRoles.PROXY_ADMIN, user_id="test-user"
)
encoded_id = encode_file_id_with_model("7a13fa8e-fcf8-42c5-aa61-c93c10e2c7df", "mistral-ocr")
try:
response = client.get(f"/v1/files/{encoded_id}", headers={"Authorization": "Bearer test-key"})
finally:
app.dependency_overrides.pop(ps.user_api_key_auth, None)
assert response.status_code == 200, response.text
assert captured_kwargs["custom_llm_provider"] == "mistral"
assert captured_kwargs["api_key"] == "mistral-key"
assert captured_kwargs["file_id"] == "7a13fa8e-fcf8-42c5-aa61-c93c10e2c7df"
assert response.json()["id"] == encoded_id
def _mistral_plus_anthropic_router() -> Router:
return Router(
model_list=[
{
"model_name": "mistral-ocr",
"litellm_params": {"model": "mistral/mistral-ocr-latest", "api_key": "mistral-key"},
"model_info": {"id": "mistral-ocr-id"},
},
{
"model_name": "claude-opus-4-6",
"litellm_params": {"model": "anthropic/claude-opus-4-6", "api_key": "anthropic-key"},
"model_info": {"id": "claude-id"},
},
]
)
def _restricted_key(key_models: list[str]) -> UserAPIKeyAuth:
from litellm.proxy._types import LitellmUserRoles
return UserAPIKeyAuth(
api_key="test-key",
user_role=LitellmUserRoles.INTERNAL_USER,
user_id="test-user",
team_id="team-a",
team_models=["claude-opus-4-6", "mistral-ocr"],
models=key_models,
)
@pytest.mark.parametrize(
"http_method, path_suffix, litellm_fn",
[
("get", "", "afile_retrieve"),
("get", "/content", "afile_content"),
("delete", "", "afile_delete"),
],
)
def test_model_routed_file_ops_reject_key_without_model_grant(
mocker: MockerFixture, monkeypatch, http_method: str, path_suffix: str, litellm_fn: str
):
"""
Regression: a key whose allowlist does not include the deployment named in a
model-encoded file id must be refused before that deployment's server-side
credentials are resolved. Previously any key could name any deployment via the
id (or the x-litellm-model header) and act on that provider account's files.
"""
import litellm.proxy.proxy_server as ps
from litellm.proxy.openai_files_endpoints.common_utils import encode_file_id_with_model
router = _mistral_plus_anthropic_router()
proxy_logging_obj = setup_proxy_logging_object(monkeypatch, router)
monkeypatch.setattr("litellm.proxy.proxy_server.master_key", None)
monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", None)
monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", router)
proxy_logging_obj.update_request_status = mocker.AsyncMock()
proxy_logging_obj.post_call_failure_hook = mocker.AsyncMock()
upstream = mocker.AsyncMock(side_effect=AssertionError("provider must not be called"))
monkeypatch.setattr(litellm, litellm_fn, upstream)
app.dependency_overrides[ps.user_api_key_auth] = lambda: _restricted_key(["claude-opus-4-6"])
encoded_id = encode_file_id_with_model("7a13fa8e-fcf8-42c5-aa61-c93c10e2c7df", "mistral-ocr")
try:
response = getattr(client, http_method)(
f"/v1/files/{encoded_id}{path_suffix}", headers={"Authorization": "Bearer test-key"}
)
finally:
app.dependency_overrides.pop(ps.user_api_key_auth, None)
assert response.status_code == 403, response.text
assert response.json()["error"]["type"] == "key_model_access_denied"
upstream.assert_not_called()
def test_list_files_header_model_rejects_key_without_model_grant(mocker: MockerFixture, monkeypatch):
import litellm.proxy.proxy_server as ps
router = _mistral_plus_anthropic_router()
proxy_logging_obj = setup_proxy_logging_object(monkeypatch, router)
monkeypatch.setattr("litellm.proxy.proxy_server.master_key", None)
monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", None)
monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", router)
proxy_logging_obj.update_request_status = mocker.AsyncMock()
proxy_logging_obj.post_call_failure_hook = mocker.AsyncMock()
upstream = mocker.AsyncMock(side_effect=AssertionError("provider must not be called"))
monkeypatch.setattr(litellm, "afile_list", upstream)
app.dependency_overrides[ps.user_api_key_auth] = lambda: _restricted_key(["claude-opus-4-6"])
try:
response = client.get(
"/v1/files", headers={"Authorization": "Bearer test-key", "x-litellm-model": "mistral-ocr"}
)
finally:
app.dependency_overrides.pop(ps.user_api_key_auth, None)
assert response.status_code == 403, response.text
upstream.assert_not_called()
def test_model_routed_file_retrieve_allows_key_with_model_grant(mocker: MockerFixture, monkeypatch):
"""The grant check must not break the happy path: a key allowed the deployment still resolves its credentials."""
import litellm.proxy.proxy_server as ps
from litellm.proxy.openai_files_endpoints.common_utils import encode_file_id_with_model
router = _mistral_plus_anthropic_router()
proxy_logging_obj = setup_proxy_logging_object(monkeypatch, router)
monkeypatch.setattr("litellm.proxy.proxy_server.master_key", None)
monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", None)
monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", router)
proxy_logging_obj.update_request_status = mocker.AsyncMock()
proxy_logging_obj.post_call_failure_hook = mocker.AsyncMock()
captured_kwargs: dict = {}
async def _mock_afile_retrieve(**kwargs):
captured_kwargs.update(kwargs)
return OpenAIFileObject(
id="7a13fa8e-fcf8-42c5-aa61-c93c10e2c7df",
object="file",
bytes=2,
created_at=1234567890,
filename="batch.jsonl",
purpose="batch",
status="uploaded",
)
monkeypatch.setattr(litellm, "afile_retrieve", _mock_afile_retrieve)
app.dependency_overrides[ps.user_api_key_auth] = lambda: _restricted_key(["mistral-ocr"])
encoded_id = encode_file_id_with_model("7a13fa8e-fcf8-42c5-aa61-c93c10e2c7df", "mistral-ocr")
try:
response = client.get(f"/v1/files/{encoded_id}", headers={"Authorization": "Bearer test-key"})
finally:
app.dependency_overrides.pop(ps.user_api_key_auth, None)
assert response.status_code == 200, response.text
assert captured_kwargs["api_key"] == "mistral-key"
assert captured_kwargs["custom_llm_provider"] == "mistral"

60
tests/test_litellm/proxy/test_batch_x_litellm_model_encoding.py
View file

@ -11,6 +11,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.openai_files_endpoints.common_utils import (
decode_model_from_file_id,
get_batch_id_from_unified_batch_id,
@ -58,10 +59,7 @@ def _make_batch_response(
def test_get_batch_id_from_unified_batch_id_handles_appended_fields():
decoded_id = (
"litellm_proxy;model_id:deployment-123;"
"llm_batch_id:batch_openai_123;llm_output_file_id:file-output"
)
decoded_id = "litellm_proxy;model_id:deployment-123;llm_batch_id:batch_openai_123;llm_output_file_id:file-output"
assert get_batch_id_from_unified_batch_id(decoded_id) == "batch_openai_123"
@ -107,12 +105,10 @@ async def test_create_batch_with_x_litellm_model_encodes_batch_id():
}
),
),
patch("litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing") as mock_processor_cls,
patch(
"litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing"
) as mock_processor_cls,
patch(
"litellm.proxy.batches_endpoints.endpoints.get_credentials_for_model",
return_value=mock_credentials,
"litellm.proxy.batches_endpoints.endpoints.get_authorized_credentials_for_model",
new=AsyncMock(return_value=mock_credentials),
),
patch(
"litellm.proxy.batches_endpoints.endpoints.prepare_data_with_credentials",
@ -165,23 +161,15 @@ async def test_create_batch_with_x_litellm_model_encodes_batch_id():
)
# The batch_id should be encoded with model info
assert (
response.id != raw_batch_id
), f"Expected batch_id to be encoded, but got raw ID: {response.id}"
assert response.id.startswith(
"batch_"
), f"Encoded batch_id should keep batch_ prefix, got: {response.id}"
assert response.id != raw_batch_id, f"Expected batch_id to be encoded, but got raw ID: {response.id}"
assert response.id.startswith("batch_"), f"Encoded batch_id should keep batch_ prefix, got: {response.id}"
# Should be decodable back to the original
decoded_model = decode_model_from_file_id(response.id)
assert (
decoded_model == model_name
), f"Expected model '{model_name}' from decoded batch_id, got: {decoded_model}"
assert decoded_model == model_name, f"Expected model '{model_name}' from decoded batch_id, got: {decoded_model}"
original_id = get_original_file_id(response.id)
assert (
original_id == raw_batch_id
), f"Expected original ID '{raw_batch_id}', got: {original_id}"
assert original_id == raw_batch_id, f"Expected original ID '{raw_batch_id}', got: {original_id}"
assert mock_create_batch.call_args.kwargs["metadata"] == {"customer_id": "cust-123"}
@ -227,12 +215,10 @@ async def test_create_batch_with_x_litellm_model_encodes_output_and_error_file_i
}
),
),
patch("litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing") as mock_processor_cls,
patch(
"litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing"
) as mock_processor_cls,
patch(
"litellm.proxy.batches_endpoints.endpoints.get_credentials_for_model",
return_value=mock_credentials,
"litellm.proxy.batches_endpoints.endpoints.get_authorized_credentials_for_model",
new=AsyncMock(return_value=mock_credentials),
),
patch(
"litellm.proxy.batches_endpoints.endpoints.prepare_data_with_credentials",
@ -316,9 +302,7 @@ async def test_create_batch_without_x_litellm_model_returns_raw_ids(monkeypatch)
}
),
),
patch(
"litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing"
) as mock_processor_cls,
patch("litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing") as mock_processor_cls,
patch(
"litellm.acreate_batch",
new=AsyncMock(return_value=mock_response),
@ -383,9 +367,7 @@ class TestBatchIdRoundTripWithRetrieve:
raw_batch_id = "batch_vllm_12345"
# What create_batch does:
encoded_id = encode_file_id_with_model(
file_id=raw_batch_id, model=model_name, id_type="batch"
)
encoded_id = encode_file_id_with_model(file_id=raw_batch_id, model=model_name, id_type="batch")
# What retrieve_batch does:
decoded_model = decode_model_from_file_id(encoded_id)
@ -410,9 +392,7 @@ class TestBatchIdRoundTripWithRetrieve:
]
for raw_id, model in test_cases:
encoded = encode_file_id_with_model(
file_id=raw_id, model=model, id_type="batch"
)
encoded = encode_file_id_with_model(file_id=raw_id, model=model, id_type="batch")
assert encoded.startswith("batch_")
assert decode_model_from_file_id(encoded) == model
assert get_original_file_id(encoded) == raw_id
@ -433,16 +413,10 @@ async def test_cancel_batch_with_unified_id_routes_with_decoded_model_and_batch_
mock_request.url.path = f"/v1/batches/{unified_batch_id}/cancel"
mock_fastapi_response = MagicMock()
mock_fastapi_response.headers = {}
mock_user_api_key_dict = MagicMock()
mock_user_api_key_dict.parent_otel_span = None
mock_user_api_key_dict.user_id = "test_user"
mock_user_api_key_dict.allowed_model_region = None
mock_user_api_key_dict.team_metadata = {}
mock_user_api_key_dict = UserAPIKeyAuth(api_key="sk-test", user_id="test_user", team_metadata={})
with (
patch(
"litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing"
) as mock_processor_cls,
patch("litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing") as mock_processor_cls,
patch(
"litellm.proxy.batches_endpoints.endpoints.update_batch_in_database",
new=AsyncMock(),

72
tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py
View file

@ -306,6 +306,40 @@ async def test_vector_store_file_list_resolves_credentials_from_model_query_para
)
@pytest.mark.asyncio
async def test_vector_store_file_list_registry_routed_model_skips_key_model_grant():
request = MagicMock(spec=Request)
request.query_params = {}
request.headers = {}
llm_router = MagicMock()
llm_router.get_deployment_credentials_with_provider.return_value = {
"api_key": "sk-team-openai",
"api_base": "https://api.openai.com/v1",
"custom_llm_provider": "openai",
"model": "openai/gpt-4o-mini",
}
data = {"vector_store_id": "vs_123", "model": "team-openai"}
user_api_key_dict = UserAPIKeyAuth(
models=["restricted-deployment"],
team_models=["restricted-deployment"],
)
result = await _update_request_data_with_model_routing_hint(
data=data,
request=request,
llm_router=llm_router,
user_api_key_dict=user_api_key_dict,
)
assert result["api_key"] == "sk-team-openai"
assert result["model"] == "openai/gpt-4o-mini"
llm_router.get_deployment_credentials_with_provider.assert_called_once_with(
model_id="team-openai"
)
@pytest.mark.asyncio
async def test_vector_store_file_list_resolves_single_openai_team_deployment():
request = MagicMock(spec=Request)
@ -575,6 +609,44 @@ async def test_vector_store_file_list_authorizes_model_query_param_before_creden
llm_router.get_deployment_credentials_with_provider.assert_not_called()
@pytest.mark.asyncio
async def test_vector_store_file_list_model_query_param_enforces_project_model_grant():
from litellm.proxy._types import LiteLLM_ProjectTableCachedObj, LiteLLM_TeamTableCachedObj
from litellm.proxy.auth.auth_checks import ProxyException
from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache, project_cache_key
request = MagicMock(spec=Request)
request.query_params = {"model": "team-openai"}
request.headers = {}
llm_router = MagicMock()
llm_router.model_group_alias = {}
cache = UserApiKeyCache()
await cache.async_set_cache(
key="team_id:team-123",
value=LiteLLM_TeamTableCachedObj(team_id="team-123", models=["team-openai"]),
)
await cache.async_set_cache(
key=project_cache_key("proj-1"),
value=LiteLLM_ProjectTableCachedObj(project_id="proj-1", models=["other-deployment"]),
)
user_api_key_dict = UserAPIKeyAuth(team_id="team-123", team_models=["team-openai"], project_id="proj-1")
with (
patch("litellm.proxy.proxy_server.prisma_client", MagicMock()), # test-quality-ok: proxy_server global, no seam
patch("litellm.proxy.proxy_server.user_api_key_cache", cache), # test-quality-ok: proxy_server global, no seam
):
with pytest.raises(ProxyException):
await _update_request_data_with_model_routing_hint(
data={"vector_store_id": "vs_123"},
request=request,
llm_router=llm_router,
user_api_key_dict=user_api_key_dict,
)
llm_router.get_deployment_credentials_with_provider.assert_not_called()
@pytest.mark.asyncio
async def test_update_request_data_with_litellm_managed_vector_store_registry():
"""

87
tests/test_litellm/test_router.py
View file

@ -5745,6 +5745,93 @@ async def test_router_unknown_model_error_message_renders_model_name_literally()
assert " " not in message # no padding run from an expanded format field
def test_get_credential_deployment_is_the_deployment_credentials_resolve_to():
"""Regression: a batch retrieved with credentials resolved by model name was priced
without its deployment id, so per-deployment pricing never applied. The deployment
behind the credentials must be reachable by name and by id, carrying its model_info."""
router = litellm.Router(
model_list=[
{
"model_name": "mistral-ocr",
"litellm_params": {"model": "mistral/mistral-ocr-latest", "api_key": "sk-ocr"},
"model_info": {"id": "ocr-dep", "ocr_cost_per_page_batches": 0.0123},
}
]
)
by_name = router.get_credential_deployment(model_id="mistral-ocr")
by_id = router.get_credential_deployment(model_id="ocr-dep")
assert by_name is not None and by_id is not None
assert by_name.model_info.id == by_id.model_info.id == "ocr-dep"
assert by_name.model_info.model_dump()["ocr_cost_per_page_batches"] == 0.0123
assert router.get_deployment_credentials_with_provider(model_id="mistral-ocr")["api_key"] == "sk-ocr"
assert router.get_credential_deployment(model_id="no-such-model") is None
def test_get_credential_deployment_skips_a_paused_deployment():
router = litellm.Router(
model_list=[
{
"model_name": "paused-ocr",
"litellm_params": {"model": "mistral/mistral-ocr-latest", "api_key": "sk-ocr"},
"model_info": {"id": "paused-dep", "blocked": True},
}
]
)
assert router.get_credential_deployment(model_id="paused-ocr") is None
assert router.get_credential_deployment(model_id="paused-dep") is None
def test_get_team_public_name_deployment_only_resolves_the_owning_team():
router = litellm.Router(
model_list=[
{
"model_name": "mistral/mistral-ocr-latest",
"litellm_params": {"model": "mistral/mistral-ocr-latest", "api_key": "sk-team-a"},
"model_info": {"id": "team-a-ocr", "team_id": "team-a", "team_public_model_name": "ocr"},
}
]
)
owning_team = router._get_team_public_name_deployment(model_id="ocr", team_id="team-a")
assert owning_team is not None and owning_team.model_info.id == "team-a-ocr"
assert router._get_team_public_name_deployment(model_id="ocr", team_id="team-b") is None
assert router._get_team_public_name_deployment(model_id="ocr", team_id=None) is None
assert router.get_credential_deployment(model_id="ocr", team_id="team-a").model_info.id == "team-a-ocr"
assert router.get_credential_deployment(model_id="ocr", team_id="team-b") is None
def test_get_wildcard_deployment_usable_by_team_prefers_the_team_pattern():
router = litellm.Router(
model_list=[
{
"model_name": "mistral/*",
"litellm_params": {"model": "mistral/*", "api_key": "sk-shared"},
"model_info": {"id": "shared-wildcard"},
},
{
"model_name": "mistral/*",
"litellm_params": {"model": "mistral/*", "api_key": "sk-team-a"},
"model_info": {"id": "team-a-wildcard", "team_id": "team-a", "team_public_model_name": "mistral/*"},
},
]
)
ocr = "mistral/mistral-ocr-latest"
team_match = router._get_wildcard_deployment_usable_by_team(model_id=ocr, team_id="team-a")
other_team_match = router._get_wildcard_deployment_usable_by_team(model_id=ocr, team_id="team-b")
anonymous_match = router._get_wildcard_deployment_usable_by_team(model_id=ocr, team_id=None)
assert team_match is not None and team_match.model_info.id == "team-a-wildcard"
assert other_team_match is not None and other_team_match.model_info.id == "shared-wildcard"
assert anonymous_match is not None and anonymous_match.model_info.id == "shared-wildcard"
assert router._get_wildcard_deployment_usable_by_team(model_id="openai/gpt-5.6", team_id="team-a") is None
assert router.get_credential_deployment(model_id=ocr, team_id="team-b").model_info.id == "shared-wildcard"
def test_get_deployment_credentials_with_provider_aws_bedrock_runtime_endpoint():
"""
Test that get_deployment_credentials_with_provider correctly copies

2
tests/test_litellm/test_utils.py
View file

@ -755,7 +755,9 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
"input_cost_per_video_per_second_above_128k_tokens": {"type": "number"},
"input_dbu_cost_per_token": {"type": "number"},
"annotation_cost_per_page": {"type": "number"},
"annotation_cost_per_page_batches": {"type": "number"},
"ocr_cost_per_page": {"type": "number"},
"ocr_cost_per_page_batches": {"type": "number"},
"ocr_cost_per_credit": {"type": "number"},
"code_interpreter_cost_per_session": {"type": "number"},
"inference_geo": {"type": "string"},

8
ui/litellm-dashboard/src/lib/http/schema.d.ts generated vendored
View file

@ -30665,6 +30665,8 @@ export interface components {
allow_client_keepalive_override: boolean | null;
/** Annotation Cost Per Page */
annotation_cost_per_page?: number | null;
/** Annotation Cost Per Page Batches */
annotation_cost_per_page_batches?: number | null;
/** Api Base */
api_base?: string | null;
/** Api Key */
@ -30894,6 +30896,8 @@ export interface components {
ocr_cost_per_credit?: number | null;
/** Ocr Cost Per Page */
ocr_cost_per_page?: number | null;
/** Ocr Cost Per Page Batches */
ocr_cost_per_page_batches?: number | null;
/** Organization */
organization?: string | null;
/** Otpm */
@ -41315,6 +41319,8 @@ export interface components {
allow_client_keepalive_override: boolean | null;
/** Annotation Cost Per Page */
annotation_cost_per_page?: number | null;
/** Annotation Cost Per Page Batches */
annotation_cost_per_page_batches?: number | null;
/** Api Base */
api_base?: string | null;
/** Api Key */
@ -41544,6 +41550,8 @@ export interface components {
ocr_cost_per_credit?: number | null;
/** Ocr Cost Per Page */
ocr_cost_per_page?: number | null;
/** Ocr Cost Per Page Batches */
ocr_cost_per_page_batches?: number | null;
/** Organization */
organization?: string | null;
/** Otpm */