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import httpx
import logging
import requests
from dotenv import load_dotenv
from typing import Any, Callable, Dict, Generator, List
from .base_api import BaseAPI, ProgressCallback, ProviderSampleResult
from .utils import RemoteModelCatalog
from ..core import APICallSample, ToolCall
_logger = logging.getLogger(f"sr_harness.{__name__}")
class _SiliconFlowRequestFailed(RuntimeError):
"""Carry one SiliconFlow request failure already prepared for the frontend."""
@BaseAPI.register("siliconflow")
class SiliconFlowAPI(BaseAPI):
"""LLM provider adapter for SiliconFlow chat and reasoning models."""
def __init__(self, model: str = "Qwen3-8B", **kwargs: Any) -> None:
"""Initialize the SiliconFlow API adapter.
Args:
model: SiliconFlow model identifier (defaults to ``"Qwen3-8B"``).
**kwargs: Additional arguments forwarded to :class:`BaseAPI`.
"""
super().__init__(model=model, **kwargs)
self._model_catalog = RemoteModelCatalog(
lambda url: httpx.Client(proxy=self.proxy_url(url), trust_env=False)
)
def cancel(self) -> str:
"""Request cancellation and note that synchronous SiliconFlow HTTP requests pause once the active call returns.
Returns:
str: Human-readable notice describing cancellation behavior.
"""
return (
"SiliconFlowAPI does not support force-interrupting an active non-streaming "
"request. The Agent will pause at its next safe boundary."
)
def format_response(
self, message: dict[str, Any], tool_calls: list[ToolCall], content: str | None = None
) -> APICallSample:
"""Convert a SiliconFlow response message into a normalized :class:`~sr_harness.core.APICallSample`.
Args:
message: Raw SiliconFlow assistant message dictionary.
tool_calls: Normalized tool calls extracted from the message.
content: Optional explicit text content override.
Returns:
APICallSample: Normalized assistant response sample.
"""
return self._build_response(message, tool_calls, content)
@property
def model_list(self) -> list[str]:
"""Fetch and return the available chat models from SiliconFlow's model catalog.
Returns:
list[str]: Supported SiliconFlow model identifiers.
"""
chat_endpoint = self.get_env(
"SILICONFLOW_ENDPOINT",
"https://api.siliconflow.cn/v1/chat/completions",
)
normalized_endpoint = chat_endpoint.rstrip("/")
default_models_endpoint = normalized_endpoint.removesuffix("/chat/completions") + "/models"
models_endpoint = self.get_env(
"SILICONFLOW_MODELS_ENDPOINT",
default_models_endpoint,
)
return self._model_catalog.get(
models_endpoint,
description="SiliconFlow chat-model catalog",
api_key=self.get_env("SILICONFLOW_API_KEY", required=True),
params={"sub_type": "chat"},
)
def _post_json(
self,
url: str,
headers: dict[str, str],
payload: dict[str, Any],
*,
description: str,
sample: int | None = None,
validate: Callable[[dict[str, Any]], None] | None = None,
callback: ProgressCallback | None = None,
) -> dict[str, Any]:
"""POST one SiliconFlow request with provider-local retry handling."""
for attempt in range(1, 4):
try:
proxy = self.proxy_url(url)
response = requests.request(
"POST",
url,
json=payload,
headers=headers,
proxies={"http": proxy, "https": proxy} if proxy else None,
)
if response.status_code != 200:
response.raise_for_status()
data = response.json()
if validate is not None:
validate(data)
return data
except InterruptedError:
raise
except Exception as error:
public_error = self.error_message(error, description=description)
if attempt == 3:
update = {"type": "failed", "error": public_error}
if sample is not None:
update["sample"] = sample
self.report_progress(callback, update)
raise _SiliconFlowRequestFailed(public_error) from error
update = {
"type": "retry",
"attempt": attempt,
"error": public_error,
}
if sample is not None:
update["sample"] = sample
self.report_progress(callback, update)
delay = self.retry_delay(error, attempt)
_logger.info(
f"Retrying {description} in {delay:g}s (attempt {attempt}/3)."
)
self.wait_before_retry(delay)
raise AssertionError("unreachable")
@staticmethod
def _validate_chat_response(response: dict[str, Any]) -> None:
choices = response.get("choices") or []
if not choices or any(
not (
((choice.get("message") or {}).get("content") or "").strip()
or (choice.get("message") or {}).get("tool_calls")
)
for choice in choices
):
raise ValueError("SiliconFlow returned an empty chat completion.")
def _request_samples(
self,
messages: List[Dict[str, str]],
n=1,
max_tokens=None,
temperature=1.0,
top_p=1.0,
callback: ProgressCallback | None = None,
) -> Generator[APICallSample, None, dict[str, Any]]:
yield from []
load_dotenv()
url = self.get_env(
"SILICONFLOW_ENDPOINT",
"https://api.siliconflow.cn/v1/chat/completions",
)
headers = {
"Authorization": f"Bearer {self.get_env('SILICONFLOW_API_KEY', required=True)}",
"Content-Type": "application/json",
}
payload = {
"messages": [dict(message) for message in messages],
"n": n,
"stop": [],
"top_k": 50,
"top_p": top_p,
"min_p": 0.05,
"stream": False,
"thinking_budget": 1024,
**({"max_tokens": max_tokens} if max_tokens is not None else {}),
"temperature": temperature,
"frequency_penalty": 0.5,
}
if self.tool_box is None:
pass
elif not self.tool_box.native_tool_calls:
description = (
"Use the following tools when a tool call is needed. "
"Return tool calls in the specified format.\n\n"
f"{self.tool_box.format_tools()}"
)
if payload["messages"][0]["role"] in {"system", "developer"}:
first = payload["messages"][0]
payload["messages"][0] = {
**first,
"content": f"{first['content']}\n\n{description}",
}
else:
payload["messages"].insert(0, {"role": "system", "content": description})
else:
tools = self.tool_box.format_tools()
if not isinstance(tools, list):
raise TypeError("Native tool parser must return JSON tool descriptions")
payload["tools"] = tools
payload["tool_choice"] = "auto"
model = self.model
try:
if model == "Qwen3-8B":
results = yield from self._request_qwen3_8b(url, headers, payload, callback)
elif model == "Deepseek-V3":
results = yield from self._request_deepseek_v3(url, headers, payload, callback)
else:
raise ValueError(f"Model {model} not supported in SiliconFlowAPI.")
except _SiliconFlowRequestFailed as error:
return {
"ok": False,
"error": str(error),
"token_usage": {},
"money_usage": {},
"extra": {},
}
return results
def _request_qwen3_8b(
self,
url: str,
headers: dict[str, str],
payload: dict[str, Any],
callback: ProgressCallback | None,
) -> Generator[APICallSample, None, dict[str, Any]]:
"""Run the ``qwen3 8b`` operation.
Args:
url: The url value.
headers: The headers value.
payload: Serializable event payload.
Returns:
Generator yielding call samples and returning aggregate metadata.
"""
yield from []
payload = {
"model": "Qwen/Qwen3-8B",
"enable_thinking": True,
**payload,
}
self.report_progress(callback, {"type": "start", "sample": 1})
responses = self._post_json(
url,
headers,
payload,
description=f"SiliconFlowAPI({self.model})",
sample=1,
validate=self._validate_chat_response,
callback=callback,
)
for sample, choice in enumerate(responses["choices"], 1):
content = choice["message"].get("content") or ""
message = dict(choice["message"])
truncated = choice.get("finish_reason") == "length"
if truncated:
message.pop("tool_calls", None)
tool_call = [] if truncated else self.parse_tool_calls(message, content)
sample_response = self.format_response(message, tool_call, content)
sample_response.finish_reason = choice.get("finish_reason")
if callback is not None:
self.report_progress(callback, {
"type": "complete",
"sample": sample,
"content": sample_response.content,
"reasoning": sample_response.reasoning,
"tool_calls": [call.raw for call in sample_response.tool_calls],
})
if sample < len(responses["choices"]):
self.report_progress(callback, {
"type": "start",
"sample": sample + 1,
})
yield sample_response
prompt_tokens = responses["usage"]["prompt_tokens"]
reason_tokens = (
responses["usage"].get("completion_tokens_details", {}).get("reasoning_tokens", 0)
)
answer_tokens = responses["usage"]["completion_tokens"] - reason_tokens
token_usage = {
"prompt": prompt_tokens,
"answer": answer_tokens,
"reason": reason_tokens,
}
if (
other := responses["usage"]["total_tokens"]
- prompt_tokens
- answer_tokens
- reason_tokens
) != 0:
token_usage["other"] = other
return {
"ok": True,
"error": None,
"token_usage": token_usage,
"money_usage": {"prompt": 0.0, "answer": 0.0, "reason": 0.0},
"extra": {"responses": [responses]},
}
def _request_deepseek_v3(
self,
url: str,
headers: dict[str, str],
payload: dict[str, Any],
callback: ProgressCallback | None,
) -> Generator[APICallSample, None, dict[str, Any]]:
"""Run the ``deepseek v3`` operation.
Args:
url: The url value.
headers: The headers value.
payload: Serializable event payload.
Returns:
Generator yielding call samples and returning aggregate metadata.
"""
yield from []
payload = {
"model": "deepseek-ai/DeepSeek-V3",
**payload,
}
n, payload["n"] = payload["n"], 1
model = payload["model"]
details = []
for idx in range(1, n + 1):
self.report_progress(callback, {"type": "start", "sample": idx})
response = self._post_json(
url,
headers,
payload,
description=f"SiliconFlowAPI({model})",
sample=idx,
validate=self._validate_chat_response,
callback=callback,
)
prompt_tokens = response["usage"]["prompt_tokens"]
answer_tokens = response["usage"]["completion_tokens"]
token_usage = {"prompt": prompt_tokens, "answer": answer_tokens}
if (other := response["usage"]["total_tokens"] - prompt_tokens - answer_tokens) > 0:
token_usage["other"] = other
price_usage = {
"prompt": 2 / 7.0 * prompt_tokens / 1e6,
"answer": 8 / 7.0 * answer_tokens / 1e6,
}
message = response["choices"][0]["message"]
content = message["content"] or ""
tool_call = self.parse_tool_calls(message, content)
sample_response = self.format_response(message, tool_call, content)
details.append(
ProviderSampleResult(
response=sample_response,
token_usage=token_usage,
money_usage=price_usage,
raw_response=response,
)
)
if callback is not None:
self.report_progress(callback, {
"type": "complete",
"sample": idx,
"content": sample_response.content,
"reasoning": sample_response.reasoning,
"tool_calls": [call.raw for call in sample_response.tool_calls],
})
yield sample_response
return self.aggregate_sample_results(details)
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