203 lines
8.3 KiB
Python
203 lines
8.3 KiB
Python
from __future__ import annotations
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from .shared import *
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class LLMChatTransportMixin:
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async def _chat_completion(
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self,
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*,
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provider: str,
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prompt: str,
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system_prompt: str,
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api_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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function_name: str = "",
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response_mode: str = "complete",
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routing_meta: dict[str, Any] | None = None,
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) -> str:
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result = await self._chat_completion_raw(
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provider=provider,
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prompt=prompt,
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system_prompt=system_prompt,
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api_key=api_key,
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base_url=base_url,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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function_name=function_name,
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response_mode=response_mode,
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routing_meta=routing_meta,
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)
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return result.text
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async def _chat_completion_raw(
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self,
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*,
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provider: str,
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prompt: str,
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system_prompt: str,
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api_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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function_name: str = "",
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response_mode: str = "complete",
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routing_meta: dict[str, Any] | None = None,
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) -> ChatCompletionResult:
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provider_name = _normalize_provider(provider)
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normalized_routing_meta = _normalize_routing_meta(routing_meta)
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structured_output = self._structured_output_required(function_name, routing_meta=normalized_routing_meta)
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request_timeout = self._request_timeout_for_function(
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function_name,
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response_mode=response_mode,
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routing_meta=normalized_routing_meta,
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)
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is_control_plane_gateway_request = self._is_control_plane_gateway_request(
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provider_name=provider_name,
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base_url=base_url,
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)
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control_plane_agents_timeout_seconds = self._control_plane_agents_timeout_seconds()
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if is_control_plane_gateway_request:
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# 网关内部 agent 超时需小于客户端超时，避免本地请求先被中止。
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request_timeout = max(float(request_timeout), float(control_plane_agents_timeout_seconds) + 5.0)
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use_provider_circuit_breaker = self._use_provider_circuit_breaker(
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function_name,
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routing_meta=normalized_routing_meta,
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)
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circuit_key = self._provider_circuit_key(
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provider=provider_name,
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base_url=base_url,
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model=model,
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)
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if use_provider_circuit_breaker and self._is_provider_circuit_open(circuit_key):
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raise ValueError(f"provider_circuit_open:{provider_name}")
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key = api_key.strip()
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messages: list[dict[str, str]] = []
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cleaned_system_prompt = self._inject_function_guardrails(
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system_prompt=system_prompt,
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function_name=function_name,
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)
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if cleaned_system_prompt:
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messages.append({"role": "system", "content": cleaned_system_prompt})
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messages.append({"role": "user", "content": prompt})
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request_trace_id = uuid4().hex[:12]
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request_started_at_perf = time.perf_counter()
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function_label = str(function_name or "llm_generate")
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control_plane_agent_timeout = int(control_plane_agents_timeout_seconds) if is_control_plane_gateway_request else None
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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"start ts={_cn_now_text_ms()} trace_id={request_trace_id} "
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f"function={function_label} "
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f"request_timeout={float(request_timeout):.2f}s "
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f"agent_timeout={control_plane_agent_timeout if control_plane_agent_timeout is not None else 'N/A'}"
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),
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)
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try:
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if provider_name == "ollama":
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result = await self._chat_completion_via_ollama(
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base_url=base_url,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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messages=messages,
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request_timeout=request_timeout,
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)
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else:
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if not key:
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raise ValueError("未配置 LLM API Key")
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result = await self._chat_completion_via_openai_compatible(
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key=key,
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base_url=base_url,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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messages=messages,
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request_timeout=request_timeout,
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structured_output=structured_output,
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function_label=function_label,
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provider_name=provider_name,
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is_control_plane_gateway_request=is_control_plane_gateway_request,
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control_plane_agents_timeout_seconds=control_plane_agents_timeout_seconds,
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request_started_at_perf=request_started_at_perf,
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request_trace_id=request_trace_id,
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control_plane_agent_timeout=control_plane_agent_timeout,
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routing_meta=normalized_routing_meta,
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)
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if use_provider_circuit_breaker:
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self._record_provider_success(circuit_key)
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return result
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except Exception as exc:
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if _is_control_plane_timeout_message(str(exc)):
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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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"LLM超时异常",
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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"error={type(exc).__name__}:{str(exc)[:260]}"
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),
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)
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logger.warning(
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"llm_timeout_exception_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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error_type=type(exc).__name__,
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error=str(exc)[: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"exception 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"error={type(exc).__name__}:{str(exc)[:260]}"
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),
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)
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if use_provider_circuit_breaker:
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self._record_provider_failure(circuit_key, exc)
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raise
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async def _chat_completion_via_ollama(
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self,
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*,
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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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) -> ChatCompletionResult:
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endpoint = f"{base_url.rstrip('/')}/api/chat"
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payload = {
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"model": model,
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"messages": messages,
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"stream": False,
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"options": {
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"temperature": temperature,
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"num_predict": max_tokens,
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},
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}
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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, json=payload)
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resp.raise_for_status()
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data = resp.json()
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message = data.get("message") if isinstance(data, dict) else {}
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content = message.get("content") if isinstance(message, dict) else None
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text = self._sanitize_visible_answer(self._extract_text_from_value(content))
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if not text:
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text = self._sanitize_visible_answer(self._extract_text_from_value(data.get("response")))
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if not text and not isinstance(data, dict):
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raise ValueError("Ollama 返回空内容")
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return ChatCompletionResult(text=text, raw_response=data)
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