110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
import logging
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import os
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import time
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from datetime import UTC, datetime
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import httpx
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from .base import AIEngineAdapter, AIQueryResult, EngineType
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logger = logging.getLogger(__name__)
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_DEFAULT_MODEL = "moonshot-v1-8k"
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_DEFAULT_BASE_URL = "https://api.moonshot.cn/v1"
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class KimiAdapter(AIEngineAdapter):
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def __init__(
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self,
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api_key: str | None = None,
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model: str | None = None,
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base_url: str | None = None,
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rate_limiter=None,
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proxy: str | None = None,
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key_manager=None,
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user_id: str | None = None,
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):
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super().__init__(
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api_key=api_key,
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rate_limiter=rate_limiter,
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proxy=proxy,
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key_manager=key_manager,
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user_id=user_id,
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)
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self._model = model or _DEFAULT_MODEL
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self._base_url = (
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base_url or _DEFAULT_BASE_URL
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).rstrip("/")
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self._endpoint = f"{self._base_url}/chat/completions"
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self._client = httpx.AsyncClient(
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timeout=httpx.Timeout(connect=10.0, read=60.0, write=10.0, pool=10.0),
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headers={
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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},
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)
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def get_engine_type(self) -> EngineType:
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return EngineType.KIMI
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def _get_env_key(self) -> str | None:
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return os.getenv("MOONSHOT_API_KEY", "")
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def _load_proxy(self) -> str | None:
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return os.getenv("MOONSHOT_PROXY") or os.getenv("HTTPS_PROXY") or os.getenv("https_proxy")
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async def query(
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self,
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query: str,
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brand_name: str,
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competitor_names: list[str] | None = None,
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) -> AIQueryResult:
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start_time = time.perf_counter()
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messages = [
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{
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"role": "system",
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"content": "你是一个专业的AI搜索助手。请基于你的知识，详细回答用户的问题。如果引用了外部来源，请在回答中标注来源URL或出处名称。",
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},
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{"role": "user", "content": query},
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]
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payload = {
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"model": self._model,
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"messages": messages,
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"temperature": 0.7,
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"max_tokens": 2000,
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}
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data = await self._request_with_retry(payload)
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content = data["choices"][0]["message"]["content"]
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elapsed_ms = int((time.perf_counter() - start_time) * 1000)
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has_brand, has_comp, brand_ctx, comp_ctx = self._detect_citations(
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content, brand_name, competitor_names
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)
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usage = data.get("usage", {})
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input_tokens = usage.get("prompt_tokens", 0)
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output_tokens = usage.get("completion_tokens", 0)
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logger.info(
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f"[kimi] query='{query[:50]}...' brand={has_brand} "
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f"competitor={has_comp} time={elapsed_ms}ms"
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)
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return AIQueryResult(
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engine_type=self.get_engine_type(),
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query=query,
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raw_response=content,
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citations=[],
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has_brand_citation=has_brand,
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has_competitor_citation=has_comp,
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brand_context=brand_ctx,
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competitor_contexts=comp_ctx,
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response_time_ms=elapsed_ms,
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timestamp=datetime.now(UTC),
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metadata={"model": data.get("model", self._model), "usage": usage},
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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)
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