77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
"""FastGPT 大脑:外部托管,context/KB/tools 全在 FastGPT 服务端。
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Pipeline-only（外部大脑不支持 Realtime）。每通电话持有一个稳定 chatId:
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greeting(get_chat_init)与后续每轮推理共用它,保证服务端上下文连续。
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"""
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from __future__ import annotations
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from typing import Any
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from uuid import uuid4
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from fastgpt_client import AsyncChatClient
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from loguru import logger
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from models import AssistantConfig
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.frame_processor import FrameProcessor
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from services.brains.base import BaseBrain, BrainSpec
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from services.brains.fastgpt_llm import FastGPTLLMService, normalize_base_url
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def _extract_welcome(payload: Any) -> str:
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"""从 get_chat_init 响应里取开场白(welcomeText),多层兜底。"""
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if not isinstance(payload, dict):
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return ""
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data = payload.get("data") if isinstance(payload.get("data"), dict) else payload
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app = data.get("app") if isinstance(data.get("app"), dict) else {}
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chat_config = app.get("chatConfig") if isinstance(app.get("chatConfig"), dict) else {}
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for value in (
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chat_config.get("welcomeText"),
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app.get("welcomeText"),
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data.get("welcomeText"),
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data.get("opener"),
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app.get("opener"),
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):
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if isinstance(value, str) and value.strip():
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return value.strip()
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return ""
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class FastGPTBrain(BaseBrain):
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def __init__(self):
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self._chat_id = uuid4().hex
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spec = BrainSpec(
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type="fastgpt",
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supported_runtime_modes=frozenset({"pipeline"}),
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owns_context=False,
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)
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async def greeting(self, cfg: AssistantConfig) -> str:
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"""优先用 FastGPT 后台配置的开场白;无 app_id 或取不到时回退 cfg.greeting。"""
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if not cfg.fastgpt_app_id:
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return cfg.greeting
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try:
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client = AsyncChatClient(
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api_key=cfg.fastgpt_api_key,
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base_url=normalize_base_url(cfg.fastgpt_api_url),
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)
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response = await client.get_chat_init(cfg.fastgpt_app_id, self._chat_id)
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welcome = _extract_welcome(response.json())
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return welcome or cfg.greeting
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except Exception as exc: # noqa: BLE001 - 拉取失败不应阻断通话
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logger.warning(f"FastGPT get_chat_init 失败,回退 cfg.greeting: {exc}")
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return cfg.greeting
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def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
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return FastGPTLLMService(cfg, chat_id=self._chat_id)
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def restore_text_state(self, snapshot: dict) -> None:
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if snapshot.get("type") != "fastgpt":
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raise ValueError("Responses runtime state does not belong to FastGPT")
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self._chat_id = str(snapshot.get("chat_id") or self._chat_id)
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def text_state_snapshot(self) -> dict:
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return {"type": "fastgpt", "chat_id": self._chat_id}
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