class CompletionRequest(OpenAIBaseModel):
# Ordered by official OpenAI API documentation
# https://platform.openai.com/docs/api-reference/completions/create
model: str | None = None
prompt: (
list[Annotated[int, Field(ge=0)]]
| list[list[Annotated[int, Field(ge=0)]]]
| str
| list[str]
| None
) = None
echo: bool | None = False
frequency_penalty: float | None = 0.0
logit_bias: dict[str, float] | None = None
logprobs: int | None = None
max_tokens: int | None = 16
n: int = 1
presence_penalty: float | None = 0.0
seed: int | None = Field(None, ge=_INT64_MIN, le=_INT64_MAX)
stop: str | list[str] | None = []
stream: bool | None = False
stream_options: StreamOptions | None = None
suffix: str | None = None
temperature: float | None = None
top_p: float | None = None
user: str | None = None
# --8<-- [start:completion-sampling-params]
use_beam_search: bool = False
top_k: int | None = None
min_p: float | None = None
repetition_penalty: float | None = None
length_penalty: float = 1.0
stop_token_ids: list[int] | None = []
include_stop_str_in_output: bool = False
ignore_eos: bool = False
min_tokens: int = 0
skip_special_tokens: bool = True
spaces_between_special_tokens: bool = True
truncate_prompt_tokens: Annotated[int, Field(ge=-1, le=_INT64_MAX)] | None = None
truncation_side: Literal["left", "right"] | None = Field(
default=None,
description=(
"Which side to truncate from when truncate_prompt_tokens is active. "
"'right' keeps the first N tokens. "
"'left' keeps the last N tokens."
),
)
allowed_token_ids: list[int] | None = None
prompt_logprobs: int | None = None
logprob_token_ids: list[int] | None = Field(
default=None,
description=(
"Specific vocab token IDs to return logprobs for at each generated "
"position, in addition to the sampled token. More efficient than "
"requesting the full vocab when only a small fixed label set is "
"needed (e.g. multilabel "
"scoring where each label corresponds to a known vocab id). When "
"set, this explicit token selection takes precedence over the "
"natural top-k selected by `logprobs`. Requires `logprobs` to be "
"set."
),
)
bad_words: list[str] = Field(default_factory=list)
# --8<-- [end:completion-sampling-params]
# --8<-- [start:completion-extra-params]
prompt_embeds: bytes | list[bytes] | None = None
add_special_tokens: bool = Field(
default=True,
description=(
"If true (the default), special tokens (e.g. BOS) will be added to "
"the prompt."
),
)
response_format: AnyResponseFormat | None = Field(
default=None,
description=(
"Similar to chat completion, this parameter specifies the format "
"of output. Only {'type': 'json_object'}, {'type': 'json_schema'}"
", {'type': 'structural_tag'}, or {'type': 'text' } is supported."
),
)
structured_outputs: StructuredOutputsParams | None = Field(
default=None,
description="Additional kwargs for structured outputs",
)
priority: int = Field(
default=0,
ge=_INT64_MIN,
le=_INT64_MAX,
description=(
"The priority of the request (lower means earlier handling; "
"default: 0). Any priority other than 0 will raise an error "
"if the served model does not use priority scheduling."
),
)
request_id: str = Field(
default_factory=random_uuid,
description=(
"The request_id related to this request. If the caller does "
"not set it, a random_uuid will be generated. This id is used "
"through out the inference process and return in response."
),
)
return_tokens_as_token_ids: bool | None = Field(
default=None,
description=(
"If specified with 'logprobs', tokens are represented "
" as strings of the form 'token_id:{token_id}' so that tokens "
"that are not JSON-encodable can be identified."
),
)
return_token_ids: bool | None = Field(
default=None,
description=(
"If specified, the result will include token IDs alongside the "
"generated text. In streaming mode, prompt_token_ids is included "
"only in the first chunk, and token_ids contains the delta tokens "
"for each chunk. This is useful for debugging or when you "
"need to map generated text back to input tokens."
),
)
return_token_offsets: bool | None = Field(
default=False,
description=(
"If true, return char-level (start, end) offsets for each "
"token relative to the tokenized source string in the "
"`token_offsets` field of the rendered response. Only "
"supported on the `/v1/completions/render` and "
"`/v1/chat/completions/render` endpoints; ignored on regular "
"generation endpoints. Honored only for Fast (Rust-backed) "
"tokenizers; otherwise `token_offsets` is null. For chat "
"requests, offsets are relative to the templated prompt "
"string (after applying the chat template). Multimodal "
"inputs and pre-tokenized inputs always yield null."
),
)
cache_salt: str | None = Field(
default=None,
description=(
"If specified, the prefix cache will be salted with the provided "
"string to prevent an attacker to guess prompts in multi-user "
"environments. The salt should be random, protected from "
"access by 3rd parties, and long enough to be "
"unpredictable (e.g., 43 characters base64-encoded, corresponding "
"to 256 bit)."
),
)
kv_transfer_params: dict[str, Any] | None = Field(
default=None,
description="KVTransfer parameters used for disaggregated serving.",
)
ec_transfer_params: dict[str, Any] | None = Field(
default=None,
description=(
"ECTransfer parameters used for encoder-cache disaggregated serving."
),
)
vllm_xargs: dict[str, str | int | float | list[str | int | float]] | None = Field(
default=None,
description=(
"Additional request parameters with (list of) string or "
"numeric values, used by custom extensions."
),
)
repetition_detection: RepetitionDetectionParams | None = Field(
default=None,
description="Parameters for detecting repetitive N-gram patterns "
"in output tokens. If such repetition is detected, generation will "
"be ended early. LLMs can sometimes generate repetitive, unhelpful "
"token patterns, stopping only when they hit the maximum output length "
"(e.g. 'abcdabcdabcd...' or '\\emoji \\emoji \\emoji ...'). This feature "
"can detect such behavior and terminate early, saving time and tokens.",
)
thinking_token_budget: ThinkingTokenBudget = Field(
default=None,
description=(
"Maximum number of tokens allowed for thinking operations "
"(reasoning models). Non-negative integer sets the limit; "
"-1 means unlimited (treated as unset)."
),
)
# --8<-- [end:completion-extra-params]
def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
return TokenizeParams(
max_total_tokens=model_config.max_model_len,
max_output_tokens=self.max_tokens or 0,
truncate_prompt_tokens=self.truncate_prompt_tokens,
truncation_side=self.truncation_side,
add_special_tokens=self.add_special_tokens,
needs_detokenization=bool(self.echo and not self.return_token_ids),
max_total_tokens_param="max_model_len",
max_output_tokens_param="max_tokens",
return_token_offsets=bool(self.return_token_offsets),
)
# Default sampling parameters for completion requests
_DEFAULT_SAMPLING_PARAMS: dict = {
"repetition_penalty": 1.0,
"temperature": 1.0,
"top_p": 1.0,
"top_k": 0,
"min_p": 0.0,
}
def to_beam_search_params(
self,
max_tokens: int,
default_sampling_params: dict | None = None,
) -> BeamSearchParams:
if default_sampling_params is None:
default_sampling_params = {}
n = self.n if self.n is not None else 1
if (temperature := self.temperature) is None:
temperature = default_sampling_params.get("temperature", 1.0)
return BeamSearchParams(
beam_width=n,
max_tokens=max_tokens,
ignore_eos=self.ignore_eos,
temperature=temperature,
length_penalty=self.length_penalty,
include_stop_str_in_output=self.include_stop_str_in_output,
)
def extract_structured_outputs(self) -> StructuredOutputsParams | None:
"""Normalize request constraints into ``StructuredOutputsParams``."""
return structured_outputs_from_response_format(
self.structured_outputs,
self.response_format,
)
def to_sampling_params(
self,
max_tokens: int,
default_sampling_params: dict | None = None,
) -> SamplingParams:
if default_sampling_params is None:
default_sampling_params = {}
# Default parameters
if (repetition_penalty := self.repetition_penalty) is None:
repetition_penalty = default_sampling_params.get(
"repetition_penalty",
self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"],
)
if (temperature := self.temperature) is None:
temperature = default_sampling_params.get(
"temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"]
)
if (top_p := self.top_p) is None:
top_p = default_sampling_params.get(
"top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"]
)
if (top_k := self.top_k) is None:
top_k = default_sampling_params.get(
"top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"]
)
if (min_p := self.min_p) is None:
min_p = default_sampling_params.get(
"min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"]
)
# Merge server-default stop_token_ids (e.g., model-specific tokens
# like </call> for gpt-oss) with any request-specified ones
stop_token_ids = self.stop_token_ids
default_stop_ids = default_sampling_params.get("stop_token_ids")
if default_stop_ids:
if not stop_token_ids:
stop_token_ids = list(default_stop_ids)
else:
stop_token_ids = list(
dict.fromkeys([*stop_token_ids, *default_stop_ids])
)
prompt_logprobs = self.prompt_logprobs
if prompt_logprobs is None and self.echo:
prompt_logprobs = self.logprobs
echo_without_generation = self.echo and self.max_tokens == 0
extra_args: dict[str, Any] = self.vllm_xargs if self.vllm_xargs else {}
if self.kv_transfer_params:
# Pass in kv_transfer_params via extra_args
extra_args["kv_transfer_params"] = self.kv_transfer_params
if self.ec_transfer_params:
# Pass in ec_transfer_params via extra_args
extra_args["ec_transfer_params"] = self.ec_transfer_params
return SamplingParams.from_optional(
n=self.n,
presence_penalty=self.presence_penalty,
frequency_penalty=self.frequency_penalty,
repetition_penalty=repetition_penalty,
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
seed=self.seed,
stop=self.stop,
stop_token_ids=stop_token_ids,
logprobs=None if self.logprob_token_ids else self.logprobs,
ignore_eos=self.ignore_eos,
max_tokens=max_tokens if not echo_without_generation else 1,
min_tokens=self.min_tokens,
prompt_logprobs=prompt_logprobs,
logprob_token_ids=self.logprob_token_ids or None,
skip_special_tokens=self.skip_special_tokens,
spaces_between_special_tokens=self.spaces_between_special_tokens,
include_stop_str_in_output=self.include_stop_str_in_output,
output_kind=RequestOutputKind.DELTA
if self.stream
else RequestOutputKind.FINAL_ONLY,
structured_outputs=self.extract_structured_outputs(),
logit_bias=self.logit_bias,
allowed_token_ids=self.allowed_token_ids,
bad_words=self.bad_words,
extra_args=extra_args or None,
skip_clone=True, # Created fresh per request, safe to skip clone
repetition_detection=self.repetition_detection,
thinking_token_budget=self.thinking_token_budget,
)
@model_validator(mode="before")
@classmethod
def normalize_null_max_tokens(cls, data):
if isinstance(data, dict) and data.get("max_tokens") is None:
data = data.copy()
data["max_tokens"] = cls.model_fields["max_tokens"].default
return data
@model_validator(mode="before")
@classmethod
def validate_response_format(cls, data):
response_format = data.get("response_format")
if response_format is None:
return data
rf_type = (
response_format.get("type")
if isinstance(response_format, dict)
else getattr(response_format, "type", None)
)
if rf_type == "json_schema":
json_schema = (
response_format.get("json_schema")
if isinstance(response_format, dict)
else getattr(response_format, "json_schema", None)
)
if json_schema is None:
raise VLLMValidationError(
"When response_format type is 'json_schema', the "
"'json_schema' field must be provided.",
parameter="response_format",
)
if rf_type == "structural_tag":
validate_structural_tag_response_format(response_format)
return data
@model_validator(mode="before")
@classmethod
def check_structured_outputs_count(cls, data):
if data.get("structured_outputs", None) is None:
return data
structured_outputs_kwargs = data["structured_outputs"]
# structured_outputs may arrive as a dict (from JSON/raw kwargs) or
# as a StructuredOutputsParams dataclass instance.
is_dataclass = isinstance(structured_outputs_kwargs, StructuredOutputsParams)
count = sum(
(
getattr(structured_outputs_kwargs, k, None)
if is_dataclass
else structured_outputs_kwargs.get(k)
)
is not None
for k in ("json", "regex", "choice")
)
if count > 1:
raise VLLMValidationError(
"You can only use one kind of constraints for structured "
"outputs ('json', 'regex' or 'choice').",
parameter="structured_outputs",
)
validate_structured_outputs_structural_tag(structured_outputs_kwargs)
return data
@model_validator(mode="before")
@classmethod
def check_logprobs(cls, data):
if data.get("logprob_token_ids") and data.get("use_beam_search"):
raise VLLMValidationError(
"`logprob_token_ids` is not supported with beam search.",
parameter="logprob_token_ids",
)
if (
data.get("logprob_token_ids")
and data.get("echo")
and data.get("max_tokens") == 0
):
raise VLLMValidationError(
"`logprob_token_ids` is not supported when `echo=True` and "
"`max_tokens=0` because no output tokens are generated.",
parameter="logprob_token_ids",
)
if data.get("logprob_token_ids") and data.get("logprobs") is None:
raise VLLMValidationError(
"when using `logprob_token_ids`, `logprobs` must be set.",
parameter="logprob_token_ids",
)
# These fields are integers, but `mode="before"` runs on the raw
# request data, so a non-numeric value (e.g. a JSON string) would
# reach the comparisons below and raise TypeError -> HTTP 500. Reject
# it here so the client gets a clean 400 instead.
for field_name in ("prompt_logprobs", "logprobs"):
field_value = data.get(field_name)
if field_value is not None and not isinstance(field_value, (int, float)):
raise VLLMValidationError(
f"`{field_name}` must be an integer.",
parameter=field_name,
value=field_value,
)
if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
if data.get("stream") and (prompt_logprobs > 0 or prompt_logprobs == -1):
raise VLLMValidationError(
"`prompt_logprobs` are not available when `stream=True`.",
parameter="prompt_logprobs",
)
if prompt_logprobs < 0 and prompt_logprobs != -1:
raise VLLMValidationError(
"`prompt_logprobs` must be a positive value or -1.",
parameter="prompt_logprobs",
value=prompt_logprobs,
)
if (logprobs := data.get("logprobs")) is not None and logprobs < 0:
raise VLLMValidationError(
"`logprobs` must be a positive value.",
parameter="logprobs",
value=logprobs,
)
return data
@model_validator(mode="before")
@classmethod
def validate_stream_options(cls, data):
if data.get("stream_options") and not data.get("stream"):
raise VLLMValidationError(
"Stream options can only be defined when `stream=True`.",
parameter="stream_options",
)
return data
@model_validator(mode="before")
@classmethod
def validate_prompt_and_prompt_embeds(cls, data):
prompt = data.get("prompt")
prompt_embeds = data.get("prompt_embeds")
prompt_is_empty = prompt is None or (isinstance(prompt, str) and prompt == "")
embeds_is_empty = prompt_embeds is None or (
isinstance(prompt_embeds, list) and len(prompt_embeds) == 0
)
if prompt_is_empty and embeds_is_empty:
raise VLLMValidationError(
"Either prompt or prompt_embeds must be provided and non-empty.",
parameter="prompt",
)
return data
@model_validator(mode="before")
@classmethod
def validate_prompt_list_length(cls, data):
max_prompts = envs.VLLM_MAX_COMPLETION_PROMPTS
prompt = data.get("prompt")
if (
isinstance(prompt, list)
and len(prompt) > 0
and not is_list_of(prompt, int)
and len(prompt) > max_prompts
):
raise VLLMValidationError(
f"prompt list length {len(prompt)} exceeds the maximum "
f"allowed count of {max_prompts}. To increase this "
"limit, set the VLLM_MAX_COMPLETION_PROMPTS "
"environment variable.",
parameter="prompt",
)
prompt_embeds = data.get("prompt_embeds")
if isinstance(prompt_embeds, list) and len(prompt_embeds) > max_prompts:
raise VLLMValidationError(
f"prompt_embeds list length {len(prompt_embeds)} exceeds "
f"the maximum allowed count of {max_prompts}. To increase "
"this limit, set the VLLM_MAX_COMPLETION_PROMPTS "
"environment variable.",
parameter="prompt_embeds",
)
return data
@model_validator(mode="before")
@classmethod
def check_cache_salt_support(cls, data):
if data.get("cache_salt") is not None and (
not isinstance(data["cache_salt"], str) or not data["cache_salt"]
):
raise VLLMValidationError(
"Parameter 'cache_salt' must be a non-empty string if provided.",
parameter="cache_salt",
)
return data