9bd7d1ec20
OpenAI's /v1/chat/completions spec defaults `stream` to false, so clients that omit the field (naive curl, minimal integrations) expect a single JSON response back. Studio was defaulting to true, silently switching those clients into SSE and breaking any parser that didn't also handle streaming. ResponsesRequest and AnthropicMessagesRequest already default to false correctly; only ChatCompletionRequest was wrong. Studio's own frontend always sets `stream` explicitly on every chat-adapter / chat-api / runtime-provider call site, so the flip has no UI impact. SDK users (OpenAI Python/JS SDK, opencode, Claude Code, Cursor, Continue) also always pass `stream` explicitly, so they're unaffected. The only clients feeling the change are raw-curl users who were relying on the wrong default -- those get the correct OpenAI behavior now. Added a regression test pinning the default so it can't silently flip back.
776 lines
27 KiB
Python
776 lines
27 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
|
|
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
|
|
|
|
"""
|
|
Pydantic schemas for Inference API
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import time
|
|
import uuid
|
|
from typing import Annotated, Any, Dict, Literal, Optional, List, Union
|
|
|
|
from pydantic import BaseModel, Discriminator, Field, Tag
|
|
|
|
|
|
class LoadRequest(BaseModel):
|
|
"""Request to load a model for inference"""
|
|
|
|
model_path: str = Field(..., description = "Model identifier or local path")
|
|
hf_token: Optional[str] = Field(
|
|
None, description = "HuggingFace token for gated models"
|
|
)
|
|
max_seq_length: int = Field(
|
|
0,
|
|
ge = 0,
|
|
le = 1048576,
|
|
description = "Maximum sequence length (0 = model default for GGUF)",
|
|
)
|
|
load_in_4bit: bool = Field(True, description = "Load model in 4-bit quantization")
|
|
is_lora: bool = Field(False, description = "Whether this is a LoRA adapter")
|
|
gguf_variant: Optional[str] = Field(
|
|
None, description = "GGUF quantization variant (e.g. 'Q4_K_M')"
|
|
)
|
|
trust_remote_code: bool = Field(
|
|
False,
|
|
description = "Allow loading models with custom code (e.g. NVIDIA Nemotron). Only enable for repos you trust.",
|
|
)
|
|
chat_template_override: Optional[str] = Field(
|
|
None,
|
|
description = "Custom Jinja2 chat template to use instead of the model's default",
|
|
)
|
|
cache_type_kv: Optional[str] = Field(
|
|
None,
|
|
description = "KV cache data type for both K and V (e.g. 'f16', 'bf16', 'q8_0', 'q4_1', 'q5_1')",
|
|
)
|
|
gpu_ids: Optional[List[int]] = Field(
|
|
None,
|
|
description = "Physical GPU indices to use, for example [0, 1]. Omit or pass [] to use automatic selection. Explicit gpu_ids are unsupported when the parent CUDA_VISIBLE_DEVICES uses UUID/MIG entries. Not supported for GGUF models.",
|
|
)
|
|
speculative_type: Optional[str] = Field(
|
|
None,
|
|
description = "Speculative decoding mode for GGUF models (e.g. 'ngram-simple', 'ngram-mod'). Ignored for non-GGUF and vision models.",
|
|
)
|
|
|
|
|
|
class UnloadRequest(BaseModel):
|
|
"""Request to unload a model"""
|
|
|
|
model_path: str = Field(..., description = "Model identifier to unload")
|
|
|
|
|
|
class ValidateModelRequest(BaseModel):
|
|
"""
|
|
Lightweight validation request to check whether a model identifier
|
|
*can be resolved* into a ModelConfig.
|
|
|
|
This does NOT actually load weights into GPU memory.
|
|
"""
|
|
|
|
model_path: str = Field(..., description = "Model identifier or local path")
|
|
hf_token: Optional[str] = Field(
|
|
None, description = "HuggingFace token for gated models"
|
|
)
|
|
gguf_variant: Optional[str] = Field(
|
|
None, description = "GGUF quantization variant (e.g. 'Q4_K_M')"
|
|
)
|
|
|
|
|
|
class ValidateModelResponse(BaseModel):
|
|
"""
|
|
Result of model validation.
|
|
|
|
valid == True means ModelConfig.from_identifier() succeeded and basic
|
|
introspection (GGUF / LoRA / vision flags) is available.
|
|
"""
|
|
|
|
valid: bool = Field(..., description = "Whether the model identifier looks valid")
|
|
message: str = Field(..., description = "Human-readable validation message")
|
|
identifier: Optional[str] = Field(None, description = "Resolved model identifier")
|
|
display_name: Optional[str] = Field(
|
|
None, description = "Display name derived from identifier"
|
|
)
|
|
is_gguf: bool = Field(False, description = "Whether this is a GGUF model (llama.cpp)")
|
|
is_lora: bool = Field(False, description = "Whether this is a LoRA adapter")
|
|
is_vision: bool = Field(False, description = "Whether this is a vision-capable model")
|
|
requires_trust_remote_code: bool = Field(
|
|
False,
|
|
description = "Whether the model defaults require trust_remote_code to be enabled for loading.",
|
|
)
|
|
|
|
|
|
class GenerateRequest(BaseModel):
|
|
"""Request for text generation (legacy /generate/stream endpoint)"""
|
|
|
|
messages: List[dict] = Field(..., description = "Chat messages in OpenAI format")
|
|
system_prompt: str = Field("", description = "System prompt")
|
|
temperature: float = Field(0.6, ge = 0.0, le = 2.0, description = "Sampling temperature")
|
|
top_p: float = Field(0.95, ge = 0.0, le = 1.0, description = "Top-p sampling")
|
|
top_k: int = Field(20, ge = -1, le = 100, description = "Top-k sampling")
|
|
max_new_tokens: int = Field(
|
|
2048, ge = 1, le = 4096, description = "Maximum tokens to generate"
|
|
)
|
|
repetition_penalty: float = Field(
|
|
1.0, ge = 1.0, le = 2.0, description = "Repetition penalty"
|
|
)
|
|
presence_penalty: float = Field(0.0, ge = 0.0, le = 2.0, description = "Presence penalty")
|
|
image_base64: Optional[str] = Field(
|
|
None, description = "Base64 encoded image for vision models"
|
|
)
|
|
|
|
|
|
class LoadResponse(BaseModel):
|
|
"""Response after loading a model"""
|
|
|
|
status: str = Field(..., description = "Load status")
|
|
model: str = Field(..., description = "Model identifier")
|
|
display_name: str = Field(..., description = "Display name of the model")
|
|
is_vision: bool = Field(False, description = "Whether model is a vision model")
|
|
is_lora: bool = Field(False, description = "Whether model is a LoRA adapter")
|
|
is_gguf: bool = Field(
|
|
False, description = "Whether model is a GGUF model (llama.cpp)"
|
|
)
|
|
is_audio: bool = Field(False, description = "Whether model is a TTS audio model")
|
|
audio_type: Optional[str] = Field(
|
|
None, description = "Audio codec type: snac, csm, bicodec, dac"
|
|
)
|
|
has_audio_input: bool = Field(
|
|
False, description = "Whether model accepts audio input (ASR)"
|
|
)
|
|
inference: dict = Field(
|
|
..., description = "Inference parameters (temperature, top_p, top_k, min_p)"
|
|
)
|
|
requires_trust_remote_code: bool = Field(
|
|
False,
|
|
description = "Whether the model defaults require trust_remote_code to be enabled for loading.",
|
|
)
|
|
context_length: Optional[int] = Field(
|
|
None, description = "Model's native context length (from GGUF metadata)"
|
|
)
|
|
max_context_length: Optional[int] = Field(
|
|
None, description = "Maximum context length currently available on this hardware"
|
|
)
|
|
native_context_length: Optional[int] = Field(
|
|
None,
|
|
description = "Model's native context length from GGUF metadata (not capped by VRAM)",
|
|
)
|
|
supports_reasoning: bool = Field(
|
|
False,
|
|
description = "Whether model supports thinking/reasoning mode (enable_thinking)",
|
|
)
|
|
reasoning_always_on: bool = Field(
|
|
False,
|
|
description = "Whether reasoning is always on (hardcoded <think> tags, not toggleable)",
|
|
)
|
|
supports_tools: bool = Field(
|
|
False,
|
|
description = "Whether model supports tool calling (web search, etc.)",
|
|
)
|
|
cache_type_kv: Optional[str] = Field(
|
|
None,
|
|
description = "KV cache data type for K and V (e.g. 'f16', 'bf16', 'q8_0')",
|
|
)
|
|
chat_template: Optional[str] = Field(
|
|
None,
|
|
description = "Jinja2 chat template string (from GGUF metadata or tokenizer)",
|
|
)
|
|
speculative_type: Optional[str] = Field(
|
|
None,
|
|
description = "Active speculative decoding mode (e.g. 'ngram-simple', 'ngram-mod'), or None if disabled",
|
|
)
|
|
|
|
|
|
class UnloadResponse(BaseModel):
|
|
"""Response after unloading a model"""
|
|
|
|
status: str = Field(..., description = "Unload status")
|
|
model: str = Field(..., description = "Model identifier that was unloaded")
|
|
|
|
|
|
class LoadProgressResponse(BaseModel):
|
|
"""Progress of the active GGUF load, sampled on demand.
|
|
|
|
Used by the UI to show a real progress bar during the
|
|
post-download warmup window (mmap + CUDA upload), rather than a
|
|
generic "Starting model..." spinner that freezes for minutes on
|
|
large MoE models.
|
|
"""
|
|
|
|
phase: Optional[str] = Field(
|
|
None,
|
|
description = (
|
|
"Load phase: 'mmap' (weights paging into RAM via mmap), "
|
|
"'ready' (llama-server reported healthy), or null when no "
|
|
"load is in flight."
|
|
),
|
|
)
|
|
bytes_loaded: int = Field(
|
|
0,
|
|
description = (
|
|
"Bytes of the model already resident in the llama-server "
|
|
"process (VmRSS on Linux)."
|
|
),
|
|
)
|
|
bytes_total: int = Field(
|
|
0,
|
|
description = "Total bytes across all GGUF shards for the active model.",
|
|
)
|
|
fraction: float = Field(
|
|
0.0, description = "bytes_loaded / bytes_total, clamped to 0..1."
|
|
)
|
|
|
|
|
|
class InferenceStatusResponse(BaseModel):
|
|
"""Current inference backend status"""
|
|
|
|
active_model: Optional[str] = Field(
|
|
None, description = "Currently active model identifier"
|
|
)
|
|
is_vision: bool = Field(
|
|
False, description = "Whether the active model is a vision model"
|
|
)
|
|
is_gguf: bool = Field(
|
|
False, description = "Whether the active model is a GGUF model (llama.cpp)"
|
|
)
|
|
gguf_variant: Optional[str] = Field(
|
|
None, description = "GGUF quantization variant (e.g. Q4_K_M)"
|
|
)
|
|
is_audio: bool = Field(
|
|
False, description = "Whether the active model is a TTS audio model"
|
|
)
|
|
audio_type: Optional[str] = Field(
|
|
None, description = "Audio codec type: snac, csm, bicodec, dac"
|
|
)
|
|
has_audio_input: bool = Field(
|
|
False, description = "Whether model accepts audio input (ASR)"
|
|
)
|
|
loading: List[str] = Field(
|
|
default_factory = list, description = "Models currently being loaded"
|
|
)
|
|
loaded: List[str] = Field(
|
|
default_factory = list, description = "Models currently loaded"
|
|
)
|
|
inference: Optional[Dict[str, Any]] = Field(
|
|
None, description = "Recommended inference parameters for the active model"
|
|
)
|
|
requires_trust_remote_code: bool = Field(
|
|
False,
|
|
description = "Whether the active model requires trust_remote_code to be enabled for loading.",
|
|
)
|
|
supports_reasoning: bool = Field(
|
|
False, description = "Whether the active model supports reasoning/thinking mode"
|
|
)
|
|
reasoning_always_on: bool = Field(
|
|
False, description = "Whether reasoning is always on (not toggleable)"
|
|
)
|
|
supports_tools: bool = Field(
|
|
False, description = "Whether the active model supports tool calling"
|
|
)
|
|
context_length: Optional[int] = Field(
|
|
None, description = "Context length of the active model"
|
|
)
|
|
max_context_length: Optional[int] = Field(
|
|
None,
|
|
description = "Maximum context length currently available for the active model",
|
|
)
|
|
native_context_length: Optional[int] = Field(
|
|
None,
|
|
description = "Model's native context length from GGUF metadata (not capped by VRAM)",
|
|
)
|
|
speculative_type: Optional[str] = Field(
|
|
None,
|
|
description = "Active speculative decoding mode (e.g. 'ngram-simple', 'ngram-mod'), or None if disabled",
|
|
)
|
|
|
|
|
|
# =====================================================================
|
|
# OpenAI-Compatible Chat Completions Models
|
|
# =====================================================================
|
|
|
|
|
|
# ── Multimodal content parts (OpenAI vision format) ──────────────
|
|
|
|
|
|
class TextContentPart(BaseModel):
|
|
"""Text content part in a multimodal message."""
|
|
|
|
type: Literal["text"]
|
|
text: str
|
|
|
|
|
|
class ImageUrl(BaseModel):
|
|
"""Image URL object — supports data URIs and remote URLs."""
|
|
|
|
url: str = Field(..., description = "data:image/png;base64,... or https://...")
|
|
detail: Optional[Literal["auto", "low", "high"]] = "auto"
|
|
|
|
|
|
class ImageContentPart(BaseModel):
|
|
"""Image content part in a multimodal message."""
|
|
|
|
type: Literal["image_url"]
|
|
image_url: ImageUrl
|
|
|
|
|
|
def _content_part_discriminator(v):
|
|
if isinstance(v, dict):
|
|
return v.get("type")
|
|
return getattr(v, "type", None)
|
|
|
|
|
|
ContentPart = Annotated[
|
|
Union[
|
|
Annotated[TextContentPart, Tag("text")],
|
|
Annotated[ImageContentPart, Tag("image_url")],
|
|
],
|
|
Discriminator(_content_part_discriminator),
|
|
]
|
|
"""Union type for multimodal content parts, discriminated by the 'type' field."""
|
|
|
|
|
|
# ── Messages ─────────────────────────────────────────────────────
|
|
|
|
|
|
class ChatMessage(BaseModel):
|
|
"""
|
|
A single message in the conversation.
|
|
|
|
``content`` may be a plain string (text-only) or a list of
|
|
content parts for multimodal messages (OpenAI vision format).
|
|
Assistant messages that only contain tool calls may set ``content``
|
|
to ``None`` with ``tool_calls`` populated. ``role="tool"`` messages
|
|
carry the result of a client-executed tool call and require
|
|
``tool_call_id`` per the OpenAI spec.
|
|
"""
|
|
|
|
role: Literal["system", "user", "assistant", "tool"] = Field(
|
|
..., description = "Message role"
|
|
)
|
|
content: Optional[Union[str, list[ContentPart]]] = Field(
|
|
None, description = "Message content (string or multimodal parts)"
|
|
)
|
|
tool_call_id: Optional[str] = Field(
|
|
None,
|
|
description = "OpenAI tool-result messages: id of the tool call this result belongs to.",
|
|
)
|
|
tool_calls: Optional[list[dict]] = Field(
|
|
None,
|
|
description = "OpenAI assistant messages: structured tool calls the model decided to make.",
|
|
)
|
|
name: Optional[str] = Field(
|
|
None,
|
|
description = "OpenAI tool-result messages: name of the tool whose result this is.",
|
|
)
|
|
|
|
|
|
class ChatCompletionRequest(BaseModel):
|
|
"""
|
|
OpenAI-compatible chat completion request.
|
|
|
|
Extensions (non-OpenAI fields) are marked with 'x-unsloth'.
|
|
"""
|
|
|
|
# Accept unknown fields defensively so future OpenAI fields (seed,
|
|
# response_format, logprobs, frequency_penalty, etc.) don't get
|
|
# silently dropped by Pydantic before route code runs. Mirrors
|
|
# AnthropicMessagesRequest and ResponsesRequest.
|
|
model_config = {"extra": "allow"}
|
|
|
|
model: str = Field(
|
|
"default",
|
|
description = "Model identifier (informational; the active model is used)",
|
|
)
|
|
messages: list[ChatMessage] = Field(..., description = "Conversation messages")
|
|
stream: bool = Field(
|
|
False,
|
|
description = (
|
|
"Whether to stream the response via SSE. Default matches OpenAI's "
|
|
"spec (`false`); opt into streaming by sending `stream: true`."
|
|
),
|
|
)
|
|
temperature: float = Field(0.6, ge = 0.0, le = 2.0)
|
|
top_p: float = Field(0.95, ge = 0.0, le = 1.0)
|
|
max_tokens: Optional[int] = Field(
|
|
None, ge = 1, description = "Maximum tokens to generate (None = until EOS)"
|
|
)
|
|
presence_penalty: float = Field(0.0, ge = 0.0, le = 2.0, description = "Presence penalty")
|
|
stop: Optional[Union[str, list[str]]] = Field(
|
|
None,
|
|
description = "OpenAI stop sequences: a single string or list of strings at which generation halts.",
|
|
)
|
|
tools: Optional[list[dict]] = Field(
|
|
None,
|
|
description = (
|
|
"OpenAI function-tool definitions. When provided without `enable_tools=true`, "
|
|
"Studio forwards the tools to the backend so the model returns structured "
|
|
"tool_calls for the client to execute (standard OpenAI function calling)."
|
|
),
|
|
)
|
|
tool_choice: Optional[Union[str, dict]] = Field(
|
|
None,
|
|
description = (
|
|
"OpenAI tool choice: 'auto' | 'required' | 'none' | "
|
|
"{'type': 'function', 'function': {'name': ...}}"
|
|
),
|
|
)
|
|
|
|
# ── Unsloth extensions (ignored by standard OpenAI clients) ──
|
|
top_k: int = Field(20, ge = -1, le = 100, description = "[x-unsloth] Top-k sampling")
|
|
min_p: float = Field(
|
|
0.01, ge = 0.0, le = 1.0, description = "[x-unsloth] Min-p sampling threshold"
|
|
)
|
|
repetition_penalty: float = Field(
|
|
1.0, ge = 1.0, le = 2.0, description = "[x-unsloth] Repetition penalty"
|
|
)
|
|
image_base64: Optional[str] = Field(
|
|
None, description = "[x-unsloth] Base64-encoded image for vision models"
|
|
)
|
|
audio_base64: Optional[str] = Field(
|
|
None, description = "[x-unsloth] Base64-encoded WAV for audio-input models (ASR)"
|
|
)
|
|
use_adapter: Optional[Union[bool, str]] = Field(
|
|
None,
|
|
description = (
|
|
"[x-unsloth] Adapter control for compare mode. "
|
|
"null = no change (default), "
|
|
"false = disable adapters (base model), "
|
|
"true = enable the current adapter, "
|
|
"string = enable a specific adapter by name."
|
|
),
|
|
)
|
|
enable_thinking: Optional[bool] = Field(
|
|
None,
|
|
description = "[x-unsloth] Enable/disable thinking/reasoning mode for supported models",
|
|
)
|
|
enable_tools: Optional[bool] = Field(
|
|
None,
|
|
description = "[x-unsloth] Enable tool calling for supported models",
|
|
)
|
|
enabled_tools: Optional[list[str]] = Field(
|
|
None,
|
|
description = "[x-unsloth] List of enabled tool names (e.g. ['web_search', 'python', 'terminal']). If None, all tools are enabled.",
|
|
)
|
|
auto_heal_tool_calls: Optional[bool] = Field(
|
|
True,
|
|
description = "[x-unsloth] Auto-detect and fix malformed tool calls from model output.",
|
|
)
|
|
max_tool_calls_per_message: Optional[int] = Field(
|
|
25,
|
|
ge = 0,
|
|
description = "[x-unsloth] Maximum number of tool call iterations per message (0 = disabled, 9999 = unlimited).",
|
|
)
|
|
tool_call_timeout: Optional[int] = Field(
|
|
300,
|
|
ge = 1,
|
|
description = "[x-unsloth] Timeout in seconds for each tool call execution (9999 = no limit).",
|
|
)
|
|
session_id: Optional[str] = Field(
|
|
None,
|
|
description = "[x-unsloth] Session/thread ID for scoping tool execution sandbox.",
|
|
)
|
|
|
|
|
|
# ── Streaming response chunks ────────────────────────────────────
|
|
|
|
|
|
class ChoiceDelta(BaseModel):
|
|
"""Delta content for a streaming chunk."""
|
|
|
|
role: Optional[str] = None
|
|
content: Optional[str] = None
|
|
|
|
|
|
class ChunkChoice(BaseModel):
|
|
"""A single choice in a streaming chunk."""
|
|
|
|
index: int = 0
|
|
delta: ChoiceDelta
|
|
finish_reason: Optional[Literal["stop", "length"]] = None
|
|
|
|
|
|
class ChatCompletionChunk(BaseModel):
|
|
"""A single SSE chunk in OpenAI streaming format."""
|
|
|
|
id: str = Field(default_factory = lambda: f"chatcmpl-{uuid.uuid4().hex[:12]}")
|
|
object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
|
|
created: int = Field(default_factory = lambda: int(time.time()))
|
|
model: str = "default"
|
|
choices: list[ChunkChoice]
|
|
usage: Optional[CompletionUsage] = None
|
|
timings: Optional[dict] = None
|
|
|
|
|
|
# ── Non-streaming response ───────────────────────────────────────
|
|
|
|
|
|
class CompletionMessage(BaseModel):
|
|
"""The assistant's complete response message."""
|
|
|
|
role: Literal["assistant"] = "assistant"
|
|
content: str
|
|
|
|
|
|
class CompletionChoice(BaseModel):
|
|
"""A single choice in a non-streaming response."""
|
|
|
|
index: int = 0
|
|
message: CompletionMessage
|
|
finish_reason: Literal["stop", "length"] = "stop"
|
|
|
|
|
|
class CompletionUsage(BaseModel):
|
|
"""Token usage statistics (approximate)."""
|
|
|
|
prompt_tokens: int = 0
|
|
completion_tokens: int = 0
|
|
total_tokens: int = 0
|
|
|
|
|
|
class ChatCompletion(BaseModel):
|
|
"""Non-streaming chat completion response."""
|
|
|
|
id: str = Field(default_factory = lambda: f"chatcmpl-{uuid.uuid4().hex[:12]}")
|
|
object: Literal["chat.completion"] = "chat.completion"
|
|
created: int = Field(default_factory = lambda: int(time.time()))
|
|
model: str = "default"
|
|
choices: list[CompletionChoice]
|
|
usage: CompletionUsage = Field(default_factory = CompletionUsage)
|
|
|
|
|
|
# =====================================================================
|
|
# OpenAI Responses API Models (/v1/responses)
|
|
# =====================================================================
|
|
|
|
|
|
# ── Request models ──────────────────────────────────────────────
|
|
|
|
|
|
class ResponsesInputTextPart(BaseModel):
|
|
"""Text content part in a Responses API message (type=input_text)."""
|
|
|
|
type: Literal["input_text"]
|
|
text: str
|
|
|
|
|
|
class ResponsesInputImagePart(BaseModel):
|
|
"""Image content part in a Responses API message (type=input_image)."""
|
|
|
|
type: Literal["input_image"]
|
|
image_url: str = Field(..., description = "data:image/png;base64,... or https://...")
|
|
detail: Optional[Literal["auto", "low", "high"]] = "auto"
|
|
|
|
|
|
ResponsesContentPart = Union[ResponsesInputTextPart, ResponsesInputImagePart]
|
|
|
|
|
|
class ResponsesInputMessage(BaseModel):
|
|
"""A single message in the Responses API input array."""
|
|
|
|
role: Literal["system", "user", "assistant", "developer"]
|
|
content: Union[str, list[ResponsesContentPart]]
|
|
|
|
|
|
class ResponsesRequest(BaseModel):
|
|
"""OpenAI Responses API request."""
|
|
|
|
model: str = Field("default", description = "Model identifier")
|
|
input: Union[str, list[ResponsesInputMessage]] = Field(
|
|
default = [],
|
|
description = "Input text or message list",
|
|
)
|
|
instructions: Optional[str] = Field(
|
|
None, description = "System / developer instructions"
|
|
)
|
|
temperature: Optional[float] = Field(None, ge = 0.0, le = 2.0)
|
|
top_p: Optional[float] = Field(None, ge = 0.0, le = 1.0)
|
|
max_output_tokens: Optional[int] = Field(None, ge = 1)
|
|
stream: bool = Field(False, description = "Whether to stream the response via SSE")
|
|
|
|
# Accepted but ignored -- keeps SDK clients from failing on unsupported fields
|
|
tools: Optional[list] = None
|
|
tool_choice: Optional[Any] = None
|
|
previous_response_id: Optional[str] = None
|
|
store: Optional[bool] = None
|
|
metadata: Optional[dict] = None
|
|
truncation: Optional[Any] = None
|
|
user: Optional[str] = None
|
|
text: Optional[Any] = None
|
|
reasoning: Optional[Any] = None
|
|
|
|
model_config = {"extra": "allow"}
|
|
|
|
|
|
# ── Response models ─────────────────────────────────────────────
|
|
|
|
|
|
class ResponsesOutputTextContent(BaseModel):
|
|
"""A text content block inside an output message."""
|
|
|
|
type: Literal["output_text"] = "output_text"
|
|
text: str
|
|
annotations: list = Field(default_factory = list)
|
|
|
|
|
|
class ResponsesOutputMessage(BaseModel):
|
|
"""An output message in the Responses API response."""
|
|
|
|
type: Literal["message"] = "message"
|
|
id: str = Field(default_factory = lambda: f"msg_{uuid.uuid4().hex[:12]}")
|
|
status: Literal["completed", "in_progress"] = "completed"
|
|
role: Literal["assistant"] = "assistant"
|
|
content: list[ResponsesOutputTextContent] = Field(default_factory = list)
|
|
|
|
|
|
class ResponsesUsage(BaseModel):
|
|
"""Token usage for a Responses API response (input_tokens, not prompt_tokens)."""
|
|
|
|
input_tokens: int = 0
|
|
output_tokens: int = 0
|
|
total_tokens: int = 0
|
|
|
|
|
|
class ResponsesResponse(BaseModel):
|
|
"""Top-level Responses API response object."""
|
|
|
|
id: str = Field(default_factory = lambda: f"resp_{uuid.uuid4().hex[:12]}")
|
|
object: Literal["response"] = "response"
|
|
created_at: int = Field(default_factory = lambda: int(time.time()))
|
|
status: Literal["completed", "in_progress", "failed"] = "completed"
|
|
model: str = "default"
|
|
output: list[ResponsesOutputMessage] = Field(default_factory = list)
|
|
usage: ResponsesUsage = Field(default_factory = ResponsesUsage)
|
|
error: Optional[Any] = None
|
|
incomplete_details: Optional[Any] = None
|
|
instructions: Optional[str] = None
|
|
metadata: dict = Field(default_factory = dict)
|
|
temperature: Optional[float] = None
|
|
top_p: Optional[float] = None
|
|
max_output_tokens: Optional[int] = None
|
|
previous_response_id: Optional[str] = None
|
|
text: Optional[Any] = None
|
|
tool_choice: Optional[Any] = None
|
|
tools: list = Field(default_factory = list)
|
|
truncation: Optional[Any] = None
|
|
|
|
|
|
# =====================================================================
|
|
# Anthropic Messages API Models (/v1/messages)
|
|
# =====================================================================
|
|
|
|
|
|
# ── Request models ─────────────────────────────────────────────
|
|
|
|
|
|
class AnthropicTextBlock(BaseModel):
|
|
type: Literal["text"]
|
|
text: str
|
|
|
|
|
|
class AnthropicImageSource(BaseModel):
|
|
type: Literal["base64", "url"]
|
|
media_type: Optional[str] = None
|
|
data: Optional[str] = None
|
|
url: Optional[str] = None
|
|
|
|
|
|
class AnthropicImageBlock(BaseModel):
|
|
type: Literal["image"]
|
|
source: AnthropicImageSource
|
|
|
|
|
|
class AnthropicToolUseBlock(BaseModel):
|
|
type: Literal["tool_use"]
|
|
id: str
|
|
name: str
|
|
input: dict
|
|
|
|
|
|
class AnthropicToolResultBlock(BaseModel):
|
|
type: Literal["tool_result"]
|
|
tool_use_id: str
|
|
content: Union[str, list] = ""
|
|
|
|
|
|
AnthropicContentBlock = Union[
|
|
AnthropicTextBlock,
|
|
AnthropicImageBlock,
|
|
AnthropicToolUseBlock,
|
|
AnthropicToolResultBlock,
|
|
]
|
|
|
|
|
|
class AnthropicMessage(BaseModel):
|
|
role: Literal["user", "assistant"]
|
|
content: Union[str, list[AnthropicContentBlock]]
|
|
|
|
|
|
class AnthropicTool(BaseModel):
|
|
name: str
|
|
description: Optional[str] = None
|
|
input_schema: dict
|
|
|
|
|
|
class AnthropicMessagesRequest(BaseModel):
|
|
model: str = "default"
|
|
max_tokens: Optional[int] = None
|
|
messages: list[AnthropicMessage]
|
|
system: Optional[Union[str, list]] = None
|
|
tools: Optional[list[AnthropicTool]] = None
|
|
tool_choice: Optional[Any] = None
|
|
stream: bool = False
|
|
temperature: Optional[float] = None
|
|
top_p: Optional[float] = None
|
|
top_k: Optional[int] = None
|
|
stop_sequences: Optional[list[str]] = None
|
|
metadata: Optional[dict] = None
|
|
# [x-unsloth] extensions — mirror the OpenAI endpoint convenience fields
|
|
min_p: Optional[float] = Field(
|
|
None, ge = 0.0, le = 1.0, description = "[x-unsloth] Min-p sampling threshold"
|
|
)
|
|
repetition_penalty: Optional[float] = Field(
|
|
None, ge = 1.0, le = 2.0, description = "[x-unsloth] Repetition penalty"
|
|
)
|
|
presence_penalty: Optional[float] = Field(
|
|
None, ge = 0.0, le = 2.0, description = "[x-unsloth] Presence penalty"
|
|
)
|
|
enable_tools: Optional[bool] = None
|
|
enabled_tools: Optional[list[str]] = None
|
|
session_id: Optional[str] = None
|
|
model_config = {"extra": "allow"}
|
|
|
|
|
|
# ── Response models ────────────────────────────────────────────
|
|
|
|
|
|
class AnthropicUsage(BaseModel):
|
|
input_tokens: int = 0
|
|
output_tokens: int = 0
|
|
|
|
|
|
class AnthropicResponseTextBlock(BaseModel):
|
|
type: Literal["text"] = "text"
|
|
text: str
|
|
|
|
|
|
class AnthropicResponseToolUseBlock(BaseModel):
|
|
type: Literal["tool_use"] = "tool_use"
|
|
id: str
|
|
name: str
|
|
input: dict
|
|
|
|
|
|
AnthropicResponseBlock = Union[
|
|
AnthropicResponseTextBlock, AnthropicResponseToolUseBlock
|
|
]
|
|
|
|
|
|
class AnthropicMessagesResponse(BaseModel):
|
|
id: str = Field(default_factory = lambda: f"msg_{uuid.uuid4().hex[:24]}")
|
|
type: Literal["message"] = "message"
|
|
role: Literal["assistant"] = "assistant"
|
|
content: list[AnthropicResponseBlock] = Field(default_factory = list)
|
|
model: str = "default"
|
|
stop_reason: Optional[str] = None
|
|
stop_sequence: Optional[str] = None
|
|
usage: AnthropicUsage = Field(default_factory = AnthropicUsage)
|