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