aae0342a57
- 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.
139 lines
5.3 KiB
Python
139 lines
5.3 KiB
Python
"""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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)
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text = (resp.choices[0].message.content or "").strip()
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except Exception as exc: # noqa: BLE001 - 路由失败不应中断通话
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logger.warning(f"工作流路由调用失败,停留当前节点: {exc}")
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return None
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match = re.search(r"\d+", text)
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if not match:
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return None
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idx = int(match.group())
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if idx < 1 or idx > len(outs):
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return None
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target = outs[idx - 1].get("target")
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logger.info(f"工作流路由: {self.name(nid)} → {self.name(target)} (edge {idx})")
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return target
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