Enhance workflow engine and integration in backend and frontend
- Introduce a new WorkflowEngine class to manage workflow graphs, enabling dynamic node-based interactions. - Update AssistantConfig to include a graph field for workflow definitions, allowing for flexible configuration. - Modify pipeline execution to support workflow-driven dialogue, integrating node transitions and system prompts based on active nodes. - Enhance frontend components to visualize active nodes and provide debugging capabilities, including highlighting the current node during interactions. - Refactor existing components to accommodate new workflow functionalities and improve overall user experience.
This commit is contained in:
Xin Wang
@@ -66,6 +66,8 @@ async def resolve_runtime_config(
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prompt=assistant.prompt or "你是一个有帮助的助手。",
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runtimeMode=assistant.runtime_mode, # type: ignore[arg-type]
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enableInterrupt=assistant.enable_interrupt,
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# workflow 图:仅 workflow 类型非空,引擎据此启用图驱动对话
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graph=(assistant.graph or {}) if assistant.type == "workflow" else {},
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# 模型/音色:模型资源中的配置优先
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model=str(_value(llm_resource, "modelId", "")),
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asr=str(_value(stt_resource, "modelId", "")),
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@@ -23,7 +23,7 @@ NODE_SPECS: list[dict[str, Any]] = [
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"name": "startCall",
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"displayName": "开始",
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"category": "call_node",
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"description": "工作流入口,每个流程有且仅有一个。播放开场白并进入首个节点。",
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"description": "工作流入口,每个流程有且仅有一个。播放开场白,并用自己的提示词进行多轮对话,满足出边条件后流转。",
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"icon": "Play",
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"accent": "mint",
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"addable": False,
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@@ -31,7 +31,7 @@ NODE_SPECS: list[dict[str, Any]] = [
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"fields": [
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{"key": "name", "label": "节点名称", "type": "text"},
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{"key": "greeting", "label": "开场白", "type": "textarea"},
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{"key": "prompt", "label": "全局提示词", "type": "textarea"},
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{"key": "prompt", "label": "节点提示词", "type": "textarea"},
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],
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},
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{
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@@ -8,10 +8,14 @@
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from uuid import uuid4
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import config
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from loguru import logger
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from models import AssistantConfig
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from services.pipecat.service_factory import create_realtime_service, create_services
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from services.workflow_engine import WorkflowEngine
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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EndFrame,
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@@ -209,7 +213,32 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
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stt, llm, tts = create_services(cfg)
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context = LLMContext(messages=[{"role": "system", "content": cfg.prompt}])
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# ---- workflow 图引擎(可选)----
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# 有节点图时按图驱动:开场白/系统提示来自起始节点,每轮回复后按条件路由。
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engine = WorkflowEngine(cfg.graph or {})
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workflow_active = engine.has_graph()
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wf_state = {
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# 开始节点本身就是会话节点(有自己的 prompt,可多轮),从它开始
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"current": engine.start_id if workflow_active else None,
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"ended": False,
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"turns_in_node": 0,
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"end_frame_queued": False,
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}
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history: list[dict] = []
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# 当前节点没有可调用转移工具(全是空条件)时,才启用文本兜底路由
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FALLBACK_AFTER_TURNS = 2
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if workflow_active:
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greeting = engine.greeting() or cfg.greeting
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system_content = engine.system_prompt_for(wf_state["current"])
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logger.info(
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f"工作流模式启用: 起始节点={engine.name(wf_state['current'])}"
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)
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else:
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greeting = cfg.greeting
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system_content = cfg.prompt
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context = LLMContext(messages=[{"role": "system", "content": system_content}])
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user_aggregator = LLMUserAggregator(
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context,
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params=LLMUserAggregatorParams(
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@@ -267,6 +296,96 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
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greeting_transcript_sent = False
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pending_text_inputs: list[str] = []
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async def emit_node_active(node_id: str | None) -> None:
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"""通知前端当前激活的节点,画布据此高亮。"""
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if node_id:
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await worker.queue_frame(
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OutputTransportMessageUrgentFrame(
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message={"type": "node-active", "nodeId": node_id}
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)
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)
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def set_system_prompt(text: str) -> None:
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"""替换上下文里的系统提示(节点切换时整体替换,而非追加)。"""
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messages = context.get_messages()
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if messages and messages[0].get("role") == "system":
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messages[0] = {"role": "system", "content": text}
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else:
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messages.insert(0, {"role": "system", "content": text})
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def apply_node(node_id: str | None) -> None:
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"""进入节点:设置系统提示 + 把出边注册为可调用的转移工具。"""
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set_system_prompt(engine.system_prompt_for(node_id))
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if engine.is_end(node_id):
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context.set_tools() # 终止节点无工具
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return
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schemas = [
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FunctionSchema(
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name=engine.edge_fn_name(edge),
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description=engine.edge_description(edge),
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properties={},
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required=[],
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)
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for edge in engine.outgoing(node_id)
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]
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if schemas:
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context.set_tools(ToolsSchema(standard_tools=schemas))
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else:
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context.set_tools() # 无出边:清空工具
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async def go_to_node(target: str) -> None:
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"""执行转移:切当前节点、重置计数、点亮画布、设置提示/工具。
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结束节点:设 ended 标记,apply_node 会清空工具,模型据结束语提示说完后,
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on_assistant_text_end 里排入 EndFrame 挂断,不再多轮。
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"""
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wf_state["current"] = target
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wf_state["turns_in_node"] = 0
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if engine.is_end(target):
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wf_state["ended"] = True
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await emit_node_active(target)
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apply_node(target)
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def make_transition_handler(target: str):
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async def handler(params):
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logger.info(f"LLM 触发转移 → {engine.name(target)}")
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await go_to_node(target)
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# 返回工具结果,pipecat 随即在新节点的提示/工具下继续生成
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await params.result_callback({"status": "ok"})
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return handler
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async def fallback_route() -> None:
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"""文本兜底:模型迟迟不调用转移工具时,用一次轻量分类器判断是否转移。"""
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if not workflow_active or wf_state["ended"]:
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return
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if wf_state["turns_in_node"] < FALLBACK_AFTER_TURNS:
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return
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if not engine.outgoing(wf_state["current"]):
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return
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target = await engine.route(
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wf_state["current"],
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history,
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api_key=cfg.llm_api_key or config.LLM_API_KEY,
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base_url=cfg.llm_base_url or config.LLM_BASE_URL,
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model=cfg.model or config.LLM_MODEL,
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)
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if target and target != wf_state["current"]:
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logger.info(f"文本兜底触发转移 → {engine.name(target)}")
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# 仅切换节点提示/工具,下一轮用户输入即在新节点处理
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await go_to_node(target)
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# 把每条边注册成 LLM 可调用的转移函数(按边唯一命名,处理器全局注册一次,
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# 由各节点的 context.tools 控制当前可见哪些)。
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if workflow_active:
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for edge in engine.edges:
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target = edge.get("target")
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if target:
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llm.register_function(
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engine.edge_fn_name(edge), make_transition_handler(target)
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)
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apply_node(wf_state["current"]) # 设初始节点的提示与工具
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async def append_user_text_to_context(text: str, *, run_llm: bool) -> None:
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await worker.queue_frame(
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LLMMessagesAppendFrame(
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@@ -277,6 +396,8 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
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@user_aggregator.event_handler("on_user_turn_stopped")
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async def on_user_turn_stopped(_aggregator, _strategy, message):
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if message.content:
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history.append({"role": "user", "content": message.content})
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await queue_transcript("user", message.content, message.timestamp)
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@assistant_aggregator.event_handler("on_assistant_text_start")
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@@ -315,10 +436,26 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
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}
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)
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)
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# 助手把话说完(未被打断)后:累加本节点轮次,必要时走文本兜底路由。
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# 正常情况下转移由 LLM 直接调用转移工具完成(go_to_node),无需这里处理。
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if content and not interrupted and workflow_active:
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history.append({"role": "assistant", "content": content})
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if wf_state["ended"]:
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# 结束节点:说完结束语后挂断,不再继续多轮对话
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if not wf_state["end_frame_queued"]:
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wf_state["end_frame_queued"] = True
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logger.info("结束节点结束语已播报,挂断通话")
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await worker.queue_frame(EndFrame())
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else:
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wf_state["turns_in_node"] += 1
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await fallback_route()
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elif content and not interrupted:
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history.append({"role": "assistant", "content": content})
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@text_input.event_handler("on_text_input")
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async def on_text_input(_processor, text):
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pending_text_inputs.append(text)
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history.append({"role": "user", "content": text})
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# 前端显示不依赖 interruption 后续事件,必须在打断前先排入发送队列。
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await queue_transcript("user", text, time_now_iso8601())
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@@ -333,21 +470,25 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
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@text_input.event_handler("on_text_append")
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async def on_text_append(_processor, text):
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# 静默追加:写进上下文但不打断、不触发推理;transcript 照常上报
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history.append({"role": "user", "content": text})
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await queue_transcript("user", text, time_now_iso8601())
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await append_user_text_to_context(text, run_llm=False)
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@text_input.event_handler("on_client_ready")
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async def on_client_ready(_processor):
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nonlocal greeting_transcript_sent
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if cfg.greeting and not greeting_transcript_sent:
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if greeting and not greeting_transcript_sent:
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greeting_transcript_sent = True
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await queue_transcript("assistant", cfg.greeting, time_now_iso8601())
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await queue_transcript("assistant", greeting, time_now_iso8601())
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@transport.event_handler("on_client_connected")
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async def on_client_connected(_transport, _client):
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if cfg.greeting:
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context.add_message({"role": "assistant", "content": cfg.greeting})
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await worker.queue_frame(TTSSpeakFrame(cfg.greeting, append_to_context=False))
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if greeting:
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context.add_message({"role": "assistant", "content": greeting})
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await worker.queue_frame(TTSSpeakFrame(greeting, append_to_context=False))
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# 工作流:点亮当前(开始)节点。开始节点即首个会话节点。
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if workflow_active:
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await emit_node_active(wf_state["current"])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(_transport, _client):
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138
backend/services/workflow_engine.py
Normal file
138
backend/services/workflow_engine.py
Normal file
@@ -0,0 +1,138 @@
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"""工作流图引擎(第一版)。
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对应 dograh 的 pipecat_engine.py,极简实现:
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- 单个 startCall 入口,开场白来自该节点;
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- agentNode 用各自的 prompt 驱动多轮对话;
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- 每轮助手回复后,用一次轻量 LLM「路由」判断是否满足某条出边的 condition,
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满足则切换当前节点(linear = 单边;branching = 多边按条件分流);
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- 到达 endCall 播放结束语并停止路由。
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只读图结构,不持有对话状态(当前节点由 pipeline 维护),便于单测。
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"""
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from __future__ import annotations
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import re
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from typing import Any
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from loguru import logger
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class WorkflowEngine:
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def __init__(self, graph: dict[str, Any]):
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nodes = graph.get("nodes") or []
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self.nodes: dict[str, dict] = {n["id"]: n for n in nodes if n.get("id")}
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self.edges: list[dict] = graph.get("edges") or []
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self.start_id: str | None = next(
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(nid for nid, n in self.nodes.items() if n.get("type") == "startCall"),
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None,
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)
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# ---- 结构查询 ----
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def node_type(self, nid: str | None) -> str | None:
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return self.nodes.get(nid or "", {}).get("type")
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def data(self, nid: str | None) -> dict:
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return self.nodes.get(nid or "", {}).get("data") or {}
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def name(self, nid: str | None) -> str:
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return self.data(nid).get("name") or (self.node_type(nid) or "")
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def outgoing(self, nid: str | None) -> list[dict]:
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return [e for e in self.edges if e.get("source") == nid]
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def edge_fn_name(self, edge: dict) -> str:
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"""每条边对应一个 LLM 函数名(稳定、合法标识符)。"""
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raw = edge.get("id") or f"{edge.get('source')}_{edge.get('target')}"
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slug = re.sub(r"[^a-z0-9]+", "_", str(raw).lower()).strip("_")
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return f"goto_{slug or 'next'}"
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def edge_condition(self, edge: dict) -> str:
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return (edge.get("data") or {}).get("condition") or ""
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def edge_description(self, edge: dict) -> str:
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"""作为转移函数的 description 交给 LLM:满足该条件时模型应调用此函数。"""
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cond = self.edge_condition(edge)
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target = self.name(edge.get("target"))
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if cond:
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return f"当满足以下条件时调用以转到节点「{target}」:{cond}"
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return f"当当前节点任务完成、应继续推进对话时调用以转到节点「{target}」。"
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def is_end(self, nid: str | None) -> bool:
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return self.node_type(nid) == "endCall"
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def has_graph(self) -> bool:
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return self.start_id is not None
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def greeting(self) -> str:
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return self.data(self.start_id).get("greeting") or ""
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def system_prompt_for(self, nid: str | None) -> str:
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"""节点系统提示:仅用该节点自己的 prompt(开始节点也是会话节点)。"""
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header = f"[当前节点:{self.name(nid)}]"
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prompt = str(self.data(nid).get("prompt") or "").strip()
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return f"{header}\n{prompt}" if prompt else header
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# ---- 路由:决定下一节点 ----
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async def route(
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self,
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nid: str | None,
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history: list[dict],
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*,
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api_key: str,
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base_url: str,
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model: str,
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) -> str | None:
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"""根据对话历史判断当前节点是否应转移。返回目标节点 id,或 None 表示停留。"""
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outs = self.outgoing(nid)
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if not outs:
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return None
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options = []
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for i, edge in enumerate(outs, 1):
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edata = edge.get("data") or {}
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cond = edata.get("condition") or "(无明确条件,作为默认后继)"
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tgt_name = self.name(edge.get("target"))
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options.append(f"{i}. 条件:{cond} → 目标节点:{tgt_name}")
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convo = "\n".join(
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f"{m['role']}: {m['content']}" for m in history[-8:] if m.get("content")
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)
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system = (
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"你是语音对话的流程路由器。根据最近的对话,判断是否已满足某条转移条件。\n"
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"规则:仅当某条件被明确满足时,返回其编号;若都不满足或不确定,返回 0 "
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"(停留在当前节点继续对话)。只输出一个数字,不要任何解释。"
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)
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user = (
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"可选转移:\n"
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+ "\n".join(options)
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+ f"\n\n对话记录:\n{convo}\n\n请只返回编号(0 表示停留):"
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)
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try:
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from openai import AsyncOpenAI
|
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|
||||
client = AsyncOpenAI(api_key=api_key, base_url=base_url or None)
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resp = await client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": system},
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||||
{"role": "user", "content": user},
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||||
],
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temperature=0,
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||||
max_tokens=5,
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||||
)
|
||||
text = (resp.choices[0].message.content or "").strip()
|
||||
except Exception as exc: # noqa: BLE001 - 路由失败不应中断通话
|
||||
logger.warning(f"工作流路由调用失败,停留当前节点: {exc}")
|
||||
return None
|
||||
|
||||
match = re.search(r"\d+", text)
|
||||
if not match:
|
||||
return None
|
||||
idx = int(match.group())
|
||||
if idx < 1 or idx > len(outs):
|
||||
return None
|
||||
target = outs[idx - 1].get("target")
|
||||
logger.info(f"工作流路由: {self.name(nid)} → {self.name(target)} (edge {idx})")
|
||||
return target
|
||||
Reference in New Issue
Block a user