99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
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import json
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import logging
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from collections.abc import AsyncGenerator
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from pathlib import Path
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from app.engine.providers.base import AIProvider
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from app.engine.providers.glm import GLMProvider
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from app.engine.config import settings
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logger = logging.getLogger(__name__)
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PROMPTS_DIR = Path(__file__).resolve().parent.parent / "prompts"
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def _load_prompt(filename: str) -> str:
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return (PROMPTS_DIR / filename).read_text(encoding="utf-8")
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def create_provider() -> AIProvider:
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if settings.AI_PROVIDER == "glm":
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return GLMProvider(
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api_key=settings.ZHIPU_API_KEY,
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model=settings.AI_MODEL,
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base_url=settings.ZHIPU_BASE_URL,
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)
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raise ValueError(f"Unknown AI provider: {settings.AI_PROVIDER}")
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SYSTEM_PROMPT = _load_prompt("system.md")
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COMPANY_FACTS = _load_prompt("company_facts.md") # 公司事实卡(知识与行为分离)
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EXTRACTOR_PROMPT = _load_prompt("extractor.md")
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EVALUATOR_PROMPT = _load_prompt("evaluator.md")
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# 对话基座: 行为 prompt 在前(红线显著性),事实卡居中(参考资料),
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# 当轮 context 提醒最后拼接(在 chat/stream_chat 内,保持最新鲜)
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_BASE_CHAT_PROMPT = SYSTEM_PROMPT + "\n\n" + COMPANY_FACTS
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class AIService:
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def __init__(self, provider: AIProvider | None = None):
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self.provider = provider or create_provider()
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async def chat(self, messages: list[dict], context: str = "") -> str:
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"""Main conversation: get AI reply to user message."""
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prompt = _BASE_CHAT_PROMPT + ("\n\n" + context if context else "")
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return await self.provider.chat(messages, prompt)
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async def stream_chat(self, messages: list[dict], context: str = "") -> AsyncGenerator[str, None]:
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"""Stream AI reply token by token."""
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prompt = _BASE_CHAT_PROMPT + ("\n\n" + context if context else "")
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async for token in self.provider.stream_chat(messages, prompt):
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yield token
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async def extract_info(
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self, messages: list[dict], existing_info: dict
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) -> dict:
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"""Extract structured info from conversation history."""
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prompt = EXTRACTOR_PROMPT.replace(
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"$existing_info", json.dumps(existing_info or {}, ensure_ascii=False)
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).replace("$messages", self._format_messages(messages))
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_, extracted = await self.provider.chat_with_extraction(
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messages=[], system_prompt=prompt
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)
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return extracted
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async def evaluate(
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self, profile: dict, recent_messages: list[dict]
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) -> dict:
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"""Evaluate customer intent level."""
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prompt = EVALUATOR_PROMPT.replace(
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"$profile", json.dumps(profile, ensure_ascii=False)
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).replace("$recent_messages", self._format_messages(recent_messages[-6:]))
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_, result = await self.provider.chat_with_extraction(
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messages=[], system_prompt=prompt
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)
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return self._normalize_evaluation(result)
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@staticmethod
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def _format_messages(messages: list[dict]) -> str:
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lines = []
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for m in messages:
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role = m.get("role", "unknown")
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content = m.get("content", "")
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label = {"user": "客户", "assistant": "顾问"}.get(role, role)
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lines.append(f"{label}: {content}")
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return "\n".join(lines)
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@staticmethod
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def _normalize_evaluation(result: dict) -> dict:
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intent = result.get("intent_level", "pending")
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if intent not in ("interested", "not_interested", "pending"):
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intent = "pending"
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return {
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"intent_level": intent,
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"reasoning": result.get("reasoning", ""),
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"contact_info": result.get("contact_info", {}),
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"info_sufficient": result.get("info_sufficient", False),
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}
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