269 lines
12 KiB
Python
269 lines
12 KiB
Python
from __future__ import annotations
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from .shared import *
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class LLMEmptyContentError(ValueError):
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"""Raised when the model still returns empty content after one forced retry."""
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class LLMChatOpenAIMixin:
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@staticmethod
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def _append_direct_answer_retry_instruction(messages: list[dict[str, str]]) -> None:
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retry_instruction = "请直接输出答案"
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for idx in range(len(messages) - 1, -1, -1):
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row = messages[idx]
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if not isinstance(row, dict):
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continue
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role = str(row.get("role") or "").strip().lower()
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if role != "user":
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continue
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original = str(row.get("content") or "").strip()
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if retry_instruction in original:
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return
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patched = dict(row)
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patched["content"] = f"{original}\n\n{retry_instruction}" if original else retry_instruction
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messages[idx] = patched
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return
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messages.append({"role": "user", "content": retry_instruction})
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async def _chat_completion_via_openai_compatible(
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self,
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*,
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key: str,
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base_url: str,
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model: str,
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temperature: float,
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max_tokens: int,
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messages: list[dict[str, str]],
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request_timeout: float,
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structured_output: bool,
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function_label: str,
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provider_name: str,
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is_control_plane_gateway_request: bool,
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control_plane_agents_timeout_seconds: int,
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request_started_at_perf: float,
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request_trace_id: str,
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control_plane_agent_timeout: Optional[int],
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) -> ChatCompletionResult:
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endpoint = f"{base_url.rstrip('/')}/chat/completions"
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headers = {
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"Authorization": f"Bearer {key}",
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"Content-Type": "application/json",
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}
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current_max_tokens = max(int(max_tokens or 0), 1)
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http_status_retry_budget = self._http_status_retry_budget_for_function(function_label)
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http_status_retry_attempt = 0
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network_retry_budget = 1
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network_retry_attempt = 0
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empty_chat_retry_budget = self._empty_chat_retry_budget(
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function_name=function_label,
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current_max_tokens=current_max_tokens,
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)
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use_json_response_format = bool(structured_output)
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direct_answer_retry_used = False
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while True:
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": current_max_tokens,
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}
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if use_json_response_format:
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payload["response_format"] = {"type": "json_object"}
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if is_control_plane_gateway_request:
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payload["agents"] = {
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"defaults": {
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"timeoutSeconds": int(control_plane_agents_timeout_seconds),
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}
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}
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try:
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async with httpx.AsyncClient(timeout=request_timeout, trust_env=False) as client:
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resp = await client.post(endpoint, headers=headers, json=payload)
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except httpx.RequestError as exc:
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if network_retry_budget > 0:
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retry_delay = min(0.8 * (2 ** network_retry_attempt), 3.0)
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_runtime_log(
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"LLM",
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(
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"网络异常重试："
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f"function={function_label}, "
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f"retry_in={retry_delay:.2f}s, left={network_retry_budget - 1}, "
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f"error={type(exc).__name__}"
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),
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)
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logger.warning(
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"llm_network_retry trace_id=%s function_name=%s retry_attempt=%s retry_delay_seconds=%.2f error_type=%s error=%s",
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request_trace_id,
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function_label,
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network_retry_attempt,
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retry_delay,
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type(exc).__name__,
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str(exc)[:260],
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)
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network_retry_budget -= 1
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network_retry_attempt += 1
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await asyncio.sleep(retry_delay)
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continue
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raise
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else:
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if is_control_plane_gateway_request:
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elapsed_ms = (time.perf_counter() - request_started_at_perf) * 1000.0
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_runtime_log(
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"PMFlow 本地路由层调用",
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(
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f"http ts={_cn_now_text_ms()} trace_id={request_trace_id} "
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f"function={function_label} status={resp.status_code} elapsed_ms={elapsed_ms:.0f}"
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),
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)
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if resp.status_code >= 400:
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body_preview = str(resp.text or "").replace("\n", " ").strip()[:260]
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if is_control_plane_gateway_request:
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_runtime_log(
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"PMFlow 本地路由层调用",
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(
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f"http_error ts={_cn_now_text_ms()} trace_id={request_trace_id} "
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f"function={function_label} status={resp.status_code} body={body_preview}"
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),
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)
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status_code = int(resp.status_code)
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can_retry_http_status = (
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http_status_retry_budget > 0
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and self._is_retryable_http_status(status_code)
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)
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if can_retry_http_status:
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retry_delay = self._http_retry_delay_seconds(
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status_code=status_code,
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attempt=http_status_retry_attempt,
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retry_after_header=resp.headers.get("Retry-After"),
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)
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_runtime_log(
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"LLM",
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(
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"HTTP状态重试："
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f"function={function_label}, "
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f"status={status_code}, retry_in={retry_delay:.2f}s, "
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f"left={http_status_retry_budget - 1}"
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),
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)
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http_status_retry_budget -= 1
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http_status_retry_attempt += 1
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await asyncio.sleep(retry_delay)
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continue
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if use_json_response_format and status_code in {400, 422}:
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lower_preview = body_preview.lower()
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if "response_format" in lower_preview or "json_object" in lower_preview or "json schema" in lower_preview:
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_runtime_log(
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"LLM",
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(
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"结构化参数降级重试："
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f"function={function_label}, status={status_code}"
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),
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)
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use_json_response_format = False
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continue
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resp.raise_for_status()
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data = resp.json()
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choices = data.get("choices") or []
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if not choices:
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raise ValueError("LLM 响应无 choices")
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first_choice = choices[0] if isinstance(choices[0], dict) else {}
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message = first_choice.get("message") if isinstance(first_choice, dict) else {}
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text = self._extract_text_from_chat_response(
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message=message,
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choice=first_choice,
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response=data,
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structured_output=structured_output,
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)
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if not text and structured_output:
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reasoning_text = self._extract_reasoning_fallback_text(
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message=message,
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choice=first_choice,
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response=data,
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)
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normalized_reasoning = self._strip_code_fence(reasoning_text)
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if self._contains_json_container(normalized_reasoning):
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candidate = self._extract_json_candidate(normalized_reasoning)
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if candidate:
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_runtime_log(
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"LLM",
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(
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"结构化reasoning_content回退："
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f"function={function_label}, provider={provider_name}"
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),
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)
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text = candidate
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if text:
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break
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msg_keys = list(message.keys()) if isinstance(message, dict) else []
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choice_keys = list(first_choice.keys()) if isinstance(first_choice, dict) else []
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choice_preview = ""
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try:
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choice_preview = json.dumps(first_choice, ensure_ascii=False)[:300]
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except Exception:
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choice_preview = str(first_choice)[:300]
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empty_error = LLMEmptyContentError(
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"LLM 返回空内容: "
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f"message_keys={msg_keys}, choice_keys={choice_keys}, choice_preview={choice_preview}"
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)
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if not direct_answer_retry_used:
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direct_answer_retry_used = True
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self._append_direct_answer_retry_instruction(messages)
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_runtime_log(
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"LLM",
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(
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"空content定向重试："
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f"function={function_label}, retry_with=请直接输出答案"
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),
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)
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continue
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if empty_chat_retry_budget > 0 and self._should_retry_empty_chat_response(
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structured_output=structured_output,
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message=message,
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choice=first_choice,
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current_max_tokens=current_max_tokens,
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):
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next_max_tokens = self._next_retry_max_tokens_for_empty_chat(current_max_tokens)
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if next_max_tokens > current_max_tokens:
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empty_chat_retry_budget -= 1
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current_max_tokens = next_max_tokens
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_runtime_log(
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"LLM",
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(
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"空content扩容重试："
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f"function={function_label}, next_max_tokens={current_max_tokens}, "
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f"retries_left={empty_chat_retry_budget}"
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),
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)
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continue
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raise empty_error
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if _is_control_plane_timeout_message(text):
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elapsed_ms = (time.perf_counter() - request_started_at_perf) * 1000.0
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_runtime_log(
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"PMFlow 本地路由层超时" if is_control_plane_gateway_request else "LLM超时文本",
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(
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f"detected ts={_cn_now_text_ms()} trace_id={request_trace_id} "
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f"function={function_label} elapsed_ms={elapsed_ms:.0f} "
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f"text={text.replace(chr(10), ' ')[:260]}"
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),
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)
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logger.warning(
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"llm_timeout_payload_detected",
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trace_id=request_trace_id,
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function_name=function_label,
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request_timeout_seconds=float(request_timeout),
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agent_timeout_seconds=control_plane_agent_timeout,
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text_preview=text.replace("\n", " ")[:260],
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)
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if is_control_plane_gateway_request:
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elapsed_ms = (time.perf_counter() - request_started_at_perf) * 1000.0
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_runtime_log(
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"PMFlow 本地路由层调用",
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(
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f"end ts={_cn_now_text_ms()} trace_id={request_trace_id} "
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f"function={function_label} elapsed_ms={elapsed_ms:.0f} text_len={len(text)}"
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),
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)
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return ChatCompletionResult(text=text, raw_response=data)
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