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"""The DashScope chat model class (OpenAI-compatible implementation)."""
import base64
import warnings
from collections import OrderedDict
from datetime import datetime
from typing import Any, AsyncGenerator, List, Literal, Type, TYPE_CHECKING
from pydantic import BaseModel, Field
from ..._utils._audio import _build_streaming_wav_header
from ..._utils._common import _generate_id
from .._base import ChatModelBase, _TOOL_CHOICE_LITERAL_MODES
from .._model_response import ChatResponse, StructuredResponse
from .._model_usage import ChatUsage
from ...credential import DashScopeCredential
from ...formatter import FormatterBase, DashScopeChatFormatter
from ...message import (
Msg,
TextBlock,
ThinkingBlock,
ToolCallBlock,
)
from ...tool import ToolChoice
if TYPE_CHECKING:
from openai.types.chat import ChatCompletion
from openai import AsyncStream
else:
ChatCompletion = Any
AsyncStream = Any
class DashScopeChatModel(ChatModelBase):
"""The DashScope chat model (OpenAI-compatible implementation).
This implementation uses the OpenAI Python SDK to call DashScope's
OpenAI-compatible endpoint (``compatible-mode/v1``), which supports
both text-only and multimodal (image/video) inputs through the same
unified API.
"""
class Parameters(BaseModel):
"""The parameters for DashScope LLM API."""
max_tokens: int | None = Field(
default=None,
title="Max Tokens",
description="The maximum number of tokens for the LLM output.",
gt=0,
)
thinking_enable: bool = Field(
default=False,
title="Thinking",
description="The thinking enable for the LLM output.",
)
thinking_budget: int | None = Field(
default=None,
title="Thinking Budget",
description="The thinking budget for the LLM output.",
gt=0,
)
temperature: float | None = Field(
default=None,
title="Temperature",
description="The temperature for the LLM output.",
ge=0,
lt=2,
)
top_p: float | None = Field(
default=None,
title="Top P",
description="The top P value for the LLM output.",
gt=0,
le=1,
)
top_k: int | None = Field(
default=None,
title="Top K",
description="The top K value for the LLM output.",
gt=0,
le=100,
)
parallel_tool_calls: bool = Field(
default=True,
title="Parallel Tool Calls",
description="If enable parallel tool calls for the LLM output.",
)
voice: str | None = Field(
default=None,
title="Voice",
description=(
"Voice for audio output on omni-style models (e.g. "
"``qwen3.5-omni-plus``). Setting this implicitly asks the "
"model to speak its response — ``modalities`` is filled in "
"automatically. Supported voices vary by model — see the "
"model card's ``voice.suggestions``. Any value the API "
"accepts works — the suggestions are convenience-only. "
"Leave unset for text-only "
"responses."
),
)
type: Literal["dashscope_chat"] = "dashscope_chat"
"""The type of the chat model."""
def __init__(
self,
credential: DashScopeCredential,
model: str,
parameters: "DashScopeChatModel.Parameters | None" = None,
stream: bool = True,
max_retries: int = 3,
retry_delay: float = 1.0,
context_size: int = 131072,
formatter: FormatterBase | None = None,
client_kwargs: dict[str, Any] | None = None,
) -> None:
"""Initialize the DashScope chat model.
Args:
credential (`DashScopeCredential`):
The DashScope credential used to authenticate API calls.
model (`str`):
The DashScope model name, e.g. ``qwen-plus``.
parameters (`DashScopeChatModel.Parameters | None`, defaults to \
`None`):
The DashScope API parameters. When ``None``, the default
parameters will be used.
stream (`bool`, defaults to `True`):
Whether to enable streaming output.
max_retries (`int`, defaults to `3`):
The maximum number of retries for the DashScope API.
retry_delay (`float`, defaults to `1.0`):
Seconds to sleep between retry attempts.
context_size (`int`, defaults to `131072`):
The model context size used for context compression.
formatter (`FormatterBase | None`, defaults to `None`):
The formatter that converts ``Msg`` objects to the format
required by the DashScope API. When ``None``, a
``DashScopeChatFormatter`` instance will be used.
client_kwargs (`dict[str, Any] | None`, defaults to `None`):
Extra keyword arguments forwarded to ``openai.AsyncClient``
(e.g. ``timeout``, ``default_headers``, ``http_client``).
"""
super().__init__(
credential=credential,
model=model,
parameters=parameters or self.Parameters(),
stream=stream,
max_retries=max_retries,
retry_delay=retry_delay,
context_size=context_size,
)
self.formatter = formatter or DashScopeChatFormatter()
self.client_kwargs = client_kwargs or {}
@classmethod
def _get_retryable_exceptions(cls) -> tuple[Type[Exception], ...]:
import openai
return (
openai.APIConnectionError,
openai.APITimeoutError,
openai.RateLimitError,
openai.InternalServerError,
)
async def _call_api(
self,
model_name: str,
messages: list[Msg],
tools: list[dict] | None = None,
tool_choice: ToolChoice | None = None,
**kwargs: Any,
) -> ChatResponse | AsyncGenerator[ChatResponse, None]:
"""Call the DashScope chat completions API via OpenAI-compatible
endpoint.
Args:
model_name (`str`):
The model name to use for this call.
messages (`list`):
The Msg objects that will be formatted and sent to the API.
tools (`list[dict] | None`, default `None`):
The tools JSON schemas that the model can use.
tool_choice (`ToolChoice | None`, default `None`):
Controls which (if any) tool is called by the model.
**kwargs (`Any`):
The keyword arguments for DashScope chat completions API,
e.g. ``temperature``, ``max_tokens``, ``top_p``, etc.
"""
import openai
client = openai.AsyncClient(
**{
"api_key": self.credential.api_key.get_secret_value(),
"base_url": self.credential.base_url,
**self.client_kwargs,
},
)
formatted_messages = await self.formatter.format(messages)
request_kwargs: dict[str, Any] = {
"model": model_name,
"messages": formatted_messages,
"stream": self.stream,
}
if self.parameters.max_tokens is not None:
request_kwargs["max_tokens"] = self.parameters.max_tokens
if self.parameters.temperature is not None:
request_kwargs["temperature"] = self.parameters.temperature
if self.parameters.top_p is not None:
request_kwargs["top_p"] = self.parameters.top_p
if self.parameters.voice is not None:
# Requesting audio output implies ``modalities`` must include
# ``"audio"``; set it automatically so callers don't have to.
# ``format`` is forced to ``pcm16``: omni streaming delivers raw
# PCM upstream regardless of the requested format, and we wrap
# it as WAV in ``_parse_stream_response`` before yielding.
request_kwargs["audio"] = {
"voice": self.parameters.voice,
"format": "pcm16",
}
request_kwargs["modalities"] = ["text", "audio"]
request_kwargs.update(kwargs)
fmt_tools, fmt_tool_choice = self._format_tools(tools, tool_choice)
if fmt_tools is not None:
request_kwargs["tools"] = fmt_tools
if not self.parameters.parallel_tool_calls:
request_kwargs["parallel_tool_calls"] = False
if fmt_tool_choice is not None:
request_kwargs["tool_choice"] = fmt_tool_choice
extra_body: dict[str, Any] = {}
if self.parameters.thinking_enable is not None:
extra_body["enable_thinking"] = self.parameters.thinking_enable
if self.parameters.thinking_budget is not None:
extra_body["thinking_budget"] = self.parameters.thinking_budget
if self.parameters.top_k is not None:
extra_body["top_k"] = self.parameters.top_k
if extra_body:
request_kwargs.setdefault("extra_body", {})
request_kwargs["extra_body"].update(extra_body)
if self.stream:
request_kwargs["stream_options"] = {"include_usage": True}
start_datetime = datetime.now()
response = await client.chat.completions.create(**request_kwargs)
if self.stream:
return self._parse_stream_response(start_datetime, response)
return self._parse_completion_response(start_datetime, response)
async def _parse_stream_response(
self,
start_datetime: datetime,
response: AsyncStream,
) -> AsyncGenerator[ChatResponse, None]:
"""Parse the DashScope streaming response (OpenAI-compatible format).
Args:
start_datetime (`datetime`):
The start datetime of the response generation.
response (`AsyncStream`):
The OpenAI-compatible async stream object.
Yields:
`ChatResponse`:
Incremental ``ChatResponse`` objects with ``is_last=False``
followed by a final one with ``is_last=True``.
"""
# ``True`` once the first audio chunk has been prefixed with a
# streaming WAV header and yielded.
audio_header_sent: bool = False
usage = None
response_id: str = _generate_id()
text_id: str = _generate_id()
thinking_id: str = _generate_id()
audio_id = _generate_id()
# The mapping from index to tool call id
tool_call_mapping: dict = OrderedDict()
async with response as stream:
async for chunk in stream:
delta_res = ChatResponse(
content=[],
is_last=False,
id=response_id,
)
# Update the response ID if exists
response_id = getattr(chunk, "id", None) or response_id
delta_res.id = response_id
if chunk.usage:
u = chunk.usage
ptd = getattr(u, "prompt_tokens_details", None)
if ptd and hasattr(ptd, "cached_tokens"):
cache_read = ptd.cached_tokens or 0
else:
cache_read = 0
usage = ChatUsage(
input_tokens=u.prompt_tokens or 0,
output_tokens=u.completion_tokens or 0,
time=(datetime.now() - start_datetime).total_seconds(),
cache_input_tokens=cache_read,
)
if not chunk.choices:
if usage is not None:
delta_res.usage = usage
yield delta_res
continue
choice = chunk.choices[0]
delta = choice.delta
# Thinking
if getattr(delta, "reasoning_content", None):
delta_res.append_thinking(
block_id=thinking_id,
thinking=delta.reasoning_content,
)
# Text
if getattr(delta, "content", None):
delta_res.append_text(
block_id=text_id,
text=delta.content,
)
# Tool call
for tool_call in getattr(delta, "tool_calls", None) or []:
index = tool_call.index
fn = getattr(tool_call, "function", None)
delta_name = getattr(fn, "name", None) if fn else None
delta_args = getattr(fn, "arguments", None) if fn else None
# Record the id and name in case following deltas
# don't provide them
if index not in tool_call_mapping:
tool_call_mapping[index] = (
tool_call.id,
delta_name or "unknown",
)
stored_id, stored_name = tool_call_mapping[index]
delta_res.append_tool_call(
block_id=tool_call.id or stored_id,
name=delta_name or stored_name,
input=delta_args or "",
)
# Data block
if getattr(delta, "audio", None):
delta_audio = getattr(delta, "audio", None)
if isinstance(delta_audio, dict):
audio_chunk = delta_audio.get("data", "")
else:
audio_chunk = getattr(delta_audio, "data", "")
if audio_chunk:
pcm_bytes = base64.b64decode(audio_chunk)
if not audio_header_sent:
payload = _build_streaming_wav_header() + pcm_bytes
audio_header_sent = True
else:
payload = pcm_bytes
# ``append_data_block`` expects the raw incremental
# media bytes and handles base64 encoding internally
# (see ``ChatResponse.append_data_block``); passing an
# already base64-encoded string here would result in
# double-encoding.
delta_res.append_data_block(
block_id=audio_id,
data=payload,
media_type="audio/wav",
)
if delta_res.content or usage:
delta_res.usage = usage
yield delta_res
def _parse_completion_response(
self,
start_datetime: datetime,
response: ChatCompletion,
) -> ChatResponse:
"""Parse the DashScope non-streaming response (OpenAI-compatible
format).
Args:
start_datetime (`datetime`):
The start datetime of the response generation.
response (`ChatCompletion`):
The OpenAI-compatible chat completion object.
Returns:
`ChatResponse`:
A single ``ChatResponse`` with ``is_last=True``.
"""
content_blocks: List[TextBlock | ToolCallBlock | ThinkingBlock] = []
if response.choices:
choice = response.choices[0]
reasoning = getattr(choice.message, "reasoning_content", None)
if isinstance(reasoning, str) and reasoning:
content_blocks.append(ThinkingBlock(thinking=reasoning))
if choice.message.content:
content_blocks.append(TextBlock(text=choice.message.content))
for tool_call in choice.message.tool_calls or []:
content_blocks.append(
ToolCallBlock(
id=tool_call.id,
name=tool_call.function.name,
input=tool_call.function.arguments,
),
)
usage = None
if response.usage:
u = response.usage
ptd = getattr(u, "prompt_tokens_details", None)
if ptd and hasattr(ptd, "cached_tokens"):
cache_read = ptd.cached_tokens or 0
else:
cache_read = 0
usage = ChatUsage(
input_tokens=u.prompt_tokens,
output_tokens=u.completion_tokens,
time=(datetime.now() - start_datetime).total_seconds(),
cache_input_tokens=cache_read,
)
resp_kwargs: dict[str, Any] = {
"content": content_blocks,
"is_last": True,
"usage": usage,
}
response_id = getattr(response, "id", None)
if response_id:
resp_kwargs["id"] = response_id
return ChatResponse(**resp_kwargs)
def _format_tools(
self,
tools: list[dict] | None,
tool_choice: ToolChoice | None,
) -> tuple[list[dict] | None, str | dict | None]:
"""Validate and format tools and tool_choice for DashScope.
DashScope supports "auto", "none", and "required" modes in
OpenAI-compatible format. When ``tool_choice.tools`` is specified
the schemas list is filtered to only those tools. When
``tool_choice.mode`` is a specific tool name (str) the model is
forced to call exactly that tool.
Args:
tools (`list[dict] | None`, optional):
The raw tool schemas.
tool_choice (`ToolChoice | None`, optional):
The tool choice configuration.
Returns:
`tuple[list[dict] | None, str | dict | None]`:
A tuple of (formatted_tools, formatted_tool_choice).
"""
if tool_choice and tools:
self._validate_tool_choice(tool_choice, tools)
if tool_choice.tools:
allowed = set(tool_choice.tools)
tools = [t for t in tools if t["function"]["name"] in allowed]
fmt_tools = None
if tools:
for value in tools:
if (
not isinstance(value, dict)
or "type" not in value
or value["type"] != "function"
or "function" not in value
):
raise ValueError(
f"Each schema must be a dict with 'type' as "
f"'function' and 'function' key, got {value}",
)
fmt_tools = tools
if not tool_choice:
return fmt_tools, None
mode = tool_choice.mode
if mode not in _TOOL_CHOICE_LITERAL_MODES:
return fmt_tools, {
"type": "function",
"function": {"name": mode},
}
if mode == "required":
warnings.warn(
f"'{mode}' is not fully supported by DashScope API. "
"It will be converted to 'auto'.",
DeprecationWarning,
stacklevel=2,
)
return fmt_tools, "auto"
return fmt_tools, mode
async def _call_api_with_structured_output(
self,
model_name: str,
messages: list[Msg],
structured_model: Type[BaseModel] | dict,
tool_choice: ToolChoice | None = None,
**kwargs: Any,
) -> StructuredResponse:
"""DashScope-specific override for structured output.
DashScope rejects ``tool_choice="required"`` or an object-form
``tool_choice`` when thinking mode is enabled. In that case we
default ``tool_choice`` to ``"auto"`` and rely on the base class's
injected system-reminder prompt to guide the model. When thinking
is disabled, this falls through to the base implementation.
See: https://help.aliyun.com/en/model-studio/qwen-function-calling
Args:
model_name (`str`):
The model name to use for this call.
messages (`list[Msg]`):
The context for the LLM to generate the structured output.
structured_model (`Type[BaseModel] | dict`):
A Pydantic model class or a JSON schema dict describing the
required output structure.
tool_choice (`ToolChoice | None`, defaults to `None`):
The tool_choice forwarded to ``_call_api``. When ``None``
and thinking mode is enabled, it is downgraded to
``ToolChoice(mode="auto")``; otherwise the base default
(force the structured-output tool) is used.
**kwargs (`Any`):
Additional keyword arguments forwarded to ``_call_api``.
Returns:
`StructuredResponse`:
The structured response whose ``content`` is the validated
output dict matching ``structured_model``.
"""
if tool_choice is None and self.parameters.thinking_enable:
tool_choice = ToolChoice(mode="auto")
return await super()._call_api_with_structured_output(
model_name=model_name,
messages=messages,
structured_model=structured_model,
tool_choice=tool_choice,
**kwargs,
)
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