809b634420
- Add new fields in AssistantConfig for FastGPT connection details, including `fastgpt_api_url`, `fastgpt_api_key`, and `fastgpt_app_id`. - Update the pipeline to utilize the new FastGPT configuration, ensuring proper integration with external services. - Introduce type handling for different assistant types, including support for realtime modes and external brain management. - Refactor frontend components to include hints for FastGPT configuration inputs, improving user guidance during setup.
38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
"""内部 LLM 大脑:prompt 与 workflow。
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二者都用本地维护的 LLMContext + OpenAI 兼容 LLM,支持 cascade 与 realtime。
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workflow 的图编排(切提示/转移工具/node-active)阶段 1 仍内联在 pipeline.py,
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这里只负责提供 LLM 槽位与元数据,行为与改造前完全一致。
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"""
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from __future__ import annotations
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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 BrainSpec
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_CASCADE_AND_REALTIME = frozenset({"pipeline", "realtime"})
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class InternalBrain:
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"""prompt / workflow 共用。"""
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def __init__(self, brain_type: str):
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self.spec = BrainSpec(
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type=brain_type,
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supported_runtime_modes=_CASCADE_AND_REALTIME,
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owns_context=True,
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)
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async def greeting(self, cfg: AssistantConfig) -> str:
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# 内部类型的开场白由 pipeline.py 现有逻辑(workflow 起始节点 / cfg.greeting)决定,
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# 该方法仅为满足 Brain 协议,实际不在内部路径上被调用。
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return cfg.greeting
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def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
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from services.pipecat.service_factory import create_llm
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return create_llm(cfg)
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