import json import time from typing import Dict, List from pydantic import BaseModel, Field class GenerationOptions(BaseModel): preset: str | None = Field(default=None, description="The name of a file under text-generation-webui/presets (without the .yaml extension). The sampling parameters that get overwritten by this option are the keys in the default_preset() function in modules/presets.py.") min_p: float = 0 dynamic_temperature: bool = False dynatemp_low: float = 1 dynatemp_high: float = 1 dynatemp_exponent: float = 1 smoothing_factor: float = 0 smoothing_curve: float = 1 top_k: int = 0 repetition_penalty: float = 1 repetition_penalty_range: int = 1024 typical_p: float = 1 tfs: float = 1 top_a: float = 0 epsilon_cutoff: float = 0 eta_cutoff: float = 0 guidance_scale: float = 1 negative_prompt: str = '' penalty_alpha: float = 0 mirostat_mode: int = 0 mirostat_tau: float = 5 mirostat_eta: float = 0.1 temperature_last: bool = False do_sample: bool = True seed: int = -1 encoder_repetition_penalty: float = 1 no_repeat_ngram_size: int = 0 min_length: int = 0 num_beams: int = 1 length_penalty: float = 1 early_stopping: bool = False truncation_length: int = 0 max_tokens_second: int = 0 prompt_lookup_num_tokens: int = 0 custom_token_bans: str = "" sampler_priority: List[str] | str | None = Field(default=None, description="List of samplers where the first items will appear first in the stack. Example: [\"top_k\", \"temperature\", \"top_p\"].") auto_max_new_tokens: bool = False ban_eos_token: bool = False add_bos_token: bool = True skip_special_tokens: bool = True grammar_string: str = "" class CompletionRequestParams(BaseModel): model: str | None = Field(default=None, description="Unused parameter. To change the model, use the /v1/internal/model/load endpoint.") prompt: str | List[str] best_of: int | None = Field(default=1, description="Unused parameter.") echo: bool | None = False frequency_penalty: float | None = 0 logit_bias: dict | None = None logprobs: int | None = None max_tokens: int | None = 16 n: int | None = Field(default=1, description="Unused parameter.") presence_penalty: float | None = 0 stop: str | List[str] | None = None stream: bool | None = False suffix: str | None = None temperature: float | None = 1 top_p: float | None = 1 user: str | None = Field(default=None, description="Unused parameter.") class CompletionRequest(GenerationOptions, CompletionRequestParams): pass class CompletionResponse(BaseModel): id: str choices: List[dict] created: int = int(time.time()) model: str object: str = "text_completion" usage: dict class ChatCompletionRequestParams(BaseModel): messages: List[dict] model: str | None = Field(default=None, description="Unused parameter. To change the model, use the /v1/internal/model/load endpoint.") frequency_penalty: float | None = 0 function_call: str | dict | None = Field(default=None, description="Unused parameter.") functions: List[dict] | None = Field(default=None, description="Unused parameter.") logit_bias: dict | None = None max_tokens: int | None = None n: int | None = Field(default=1, description="Unused parameter.") presence_penalty: float | None = 0 stop: str | List[str] | None = None stream: bool | None = False temperature: float | None = 1 top_p: float | None = 1 user: str | None = Field(default=None, description="Unused parameter.") mode: str = Field(default='instruct', description="Valid options: instruct, chat, chat-instruct.") instruction_template: str | None = Field(default=None, description="An instruction template defined under text-generation-webui/instruction-templates. If not set, the correct template will be automatically obtained from the model metadata.") instruction_template_str: str | None = Field(default=None, description="A Jinja2 instruction template. If set, will take precedence over everything else.") character: str | None = Field(default=None, description="A character defined under text-generation-webui/characters. If not set, the default \"Assistant\" character will be used.") bot_name: str | None = Field(default=None, description="Overwrites the value set by character field.", alias="name2") context: str | None = Field(default=None, description="Overwrites the value set by character field.") greeting: str | None = Field(default=None, description="Overwrites the value set by character field.") user_name: str | None = Field(default=None, description="Your name (the user). By default, it's \"You\".", alias="name1") user_bio: str | None = Field(default=None, description="The user description/personality.") chat_template_str: str | None = Field(default=None, description="Jinja2 template for chat.") chat_instruct_command: str | None = None continue_: bool = Field(default=False, description="Makes the last bot message in the history be continued instead of starting a new message.") class ChatCompletionRequest(GenerationOptions, ChatCompletionRequestParams): pass class ChatCompletionResponse(BaseModel): id: str choices: List[dict] created: int = int(time.time()) model: str object: str = "chat.completion" usage: dict class EmbeddingsRequest(BaseModel): input: str | List[str] | List[int] | List[List[int]] model: str | None = Field(default=None, description="Unused parameter. To change the model, set the OPENEDAI_EMBEDDING_MODEL and OPENEDAI_EMBEDDING_DEVICE environment variables before starting the server.") encoding_format: str = Field(default="float", description="Can be float or base64.") user: str | None = Field(default=None, description="Unused parameter.") class EmbeddingsResponse(BaseModel): index: int embedding: List[float] object: str = "embedding" class EncodeRequest(BaseModel): text: str class EncodeResponse(BaseModel): tokens: List[int] length: int class DecodeRequest(BaseModel): tokens: List[int] class DecodeResponse(BaseModel): text: str class TokenCountResponse(BaseModel): length: int class LogitsRequestParams(BaseModel): prompt: str use_samplers: bool = False top_logits: int | None = 50 frequency_penalty: float | None = 0 max_tokens: int | None = 16 presence_penalty: float | None = 0 temperature: float | None = 1 top_p: float | None = 1 class LogitsRequest(GenerationOptions, LogitsRequestParams): pass class LogitsResponse(BaseModel): logits: Dict[str, float] class ModelInfoResponse(BaseModel): model_name: str lora_names: List[str] class ModelListResponse(BaseModel): model_names: List[str] class LoadModelRequest(BaseModel): model_name: str args: dict | None = None settings: dict | None = None class LoraListResponse(BaseModel): lora_names: List[str] class LoadLorasRequest(BaseModel): lora_names: List[str] def to_json(obj): return json.dumps(obj.__dict__, indent=4) def to_dict(obj): return obj.__dict__