138 lines
4.9 KiB
Python
138 lines
4.9 KiB
Python
"""Resolve and apply one Workflow Agent's complete stage configuration."""
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from __future__ import annotations
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from models import AssistantConfig
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from pipecat.flows import ContextStrategy, ContextStrategyConfig, NodeConfig
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from pipecat.frames.frames import LLMUpdateSettingsFrame
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from pipecat.services.settings import LLMSettings
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from services.brains.base import BrainRuntime
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from services.runtime_variables import DynamicVariableStore
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from services.vision import VISION_SYSTEM_HINT
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from services.workflow_engine import WorkflowEngine
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from services.workflow_router import WorkflowLLMRouter
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AGENT_STAGE_INSTRUCTION = (
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"工作流路由已在用户一轮输入结束时完成。只执行当前阶段任务，"
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"不要自行解释、模拟或宣布节点切换。"
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)
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class WorkflowAgentStage:
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"""Translate graph-level Agent data into Pipecat Flows configuration."""
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def __init__(
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self,
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*,
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cfg: AssistantConfig,
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engine: WorkflowEngine,
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store: DynamicVariableStore,
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runtime: BrainRuntime,
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) -> None:
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self._cfg = cfg
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self._engine = engine
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self._store = store
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self._runtime = runtime
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def role_message(self, node_id: str) -> str:
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stage_prompt = self._engine.prompt_for(node_id, self._store)
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stage = self._engine.agent_stage_config(node_id)
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if stage.vision_enabled:
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stage_prompt = f"{stage_prompt}\n\n[视觉能力]\n{VISION_SYSTEM_HINT}"
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return (
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f"{stage_prompt}\n\n[工作流执行规则]\n"
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f"{AGENT_STAGE_INSTRUCTION}"
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)
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async def refresh_prompt(self, node_id: str) -> None:
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await self._runtime.queue_frame(
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LLMUpdateSettingsFrame(
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delta=LLMSettings(
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system_instruction=self.role_message(node_id)
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)
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)
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)
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def router_for_node(
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self,
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node_id: str,
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default_router: WorkflowLLMRouter,
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) -> WorkflowLLMRouter:
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resource_id = self._engine.agent_stage_config(node_id).llm_resource_id
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resource = self._cfg.workflow_model_resources.get(resource_id)
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if not resource:
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return default_router
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from services.pipecat.service_factory import config_with_resource
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return WorkflowLLMRouter(config_with_resource(self._cfg, resource))
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async def apply(self, node_id: str) -> None:
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stage = self._engine.agent_stage_config(node_id)
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if self._runtime.set_input_enabled:
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self._runtime.set_input_enabled(True)
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if self._runtime.apply_turn_config:
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await self._runtime.apply_turn_config(
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stage.enable_interrupt,
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stage.turn_config,
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)
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if self._runtime.switch_services:
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await self._runtime.switch_services(
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stage.llm_resource_id or None,
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stage.asr_resource_id or None,
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stage.tts_resource_id or None,
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)
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if self._runtime.set_knowledge_scope:
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self._runtime.set_knowledge_scope(
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{
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"knowledge_base_id": stage.knowledge_base_id,
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"mode": stage.knowledge_mode,
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"top_n": stage.knowledge_top_n,
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"score_threshold": stage.knowledge_score_threshold,
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}
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)
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if self._runtime.set_vision_scope:
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self._runtime.set_vision_scope(
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{
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"enabled": stage.vision_enabled,
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"vision_model_resource_id": stage.vision_model_resource_id,
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"llm_resource_id": stage.llm_resource_id,
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}
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)
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def node_config(
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self,
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node_id: str,
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*,
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functions: list,
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leading_messages: list[dict[str, str]] | None = None,
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) -> NodeConfig:
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data = self._engine.data(node_id)
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entry_mode = str(data.get("entryMode") or "wait_user")
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entry_speech = self._store.render(str(data.get("entrySpeech") or ""))
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strategy = (
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ContextStrategy.RESET
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if data.get("contextPolicy") == "fresh"
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else ContextStrategy.APPEND
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)
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fixed_reply_messages = (
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[{"role": "assistant", "content": entry_speech}]
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if entry_mode == "fixed_speech" and entry_speech
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else []
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)
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return {
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"name": node_id,
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"role_message": self.role_message(node_id),
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"task_messages": [
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*(leading_messages or []),
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*fixed_reply_messages,
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],
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"functions": functions,
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"context_strategy": ContextStrategyConfig(strategy=strategy),
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# Direct node activations let WorkflowRuntime decide whether to run
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# the LLM. A Pipecat function transition may explicitly override
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# this because FlowManager must coordinate the tool result first.
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"respond_immediately": False,
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}
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