Add workflow support and enhance runtime configuration in models and services
- Introduce RuntimeModelResource and RuntimeKnowledgeBase classes to manage workflow resources. - Update AssistantConfig to include workflow_model_resources and workflow_knowledge_bases for better integration. - Refactor validation and processing logic in routes and services to accommodate workflow types. - Implement dynamic variable support for workflow assistants and enhance graph normalization. - Add ToolExecutor for reusable tool execution across different assistant types. - Update various services to ensure compatibility with new workflow features and improve error handling.
This commit is contained in:
Xin Wang
@@ -1,170 +1,140 @@
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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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- globalNode 不参与连线,按节点开关向会话节点注入统一提示词;
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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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"""Pure Workflow v3 graph queries and deterministic edge evaluation."""
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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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from services.node_specs import normalize_graph
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from services.runtime_variables import DynamicVariableStore
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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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self.global_id: str | None = next(
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(nid for nid, n in self.nodes.items() if n.get("type") == "globalNode"),
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self.graph = normalize_graph(graph)
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self.settings = self.graph.get("settings") or {}
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self.nodes: dict[str, dict] = {
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str(node["id"]): node
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for node in self.graph.get("nodes") or []
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if node.get("id")
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}
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self.edges: list[dict] = list(self.graph.get("edges") or [])
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self.start_id = next(
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(node_id for node_id, node in self.nodes.items() if node.get("type") == "start"),
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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 has_graph(self) -> bool:
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return bool(self.start_id)
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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 node_type(self, node_id: str | None) -> str | None:
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return self.nodes.get(node_id or "", {}).get("type")
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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 data(self, node_id: str | None) -> dict:
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return self.nodes.get(node_id or "", {}).get("data") 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 name(self, node_id: str | None) -> str:
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return str(self.data(node_id).get("name") or self.node_type(node_id) or "")
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def outgoing(self, node_id: str | None) -> list[dict]:
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result = [edge for edge in self.edges if edge.get("source") == node_id]
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return sorted(result, key=lambda edge: int((edge.get("data") or {}).get("priority", 10)))
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def edge_mode(self, edge: dict) -> str:
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return str((edge.get("data") or {}).get("mode") or "always")
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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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data = edge.get("data") or {}
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target = self.name(str(edge.get("target") or ""))
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condition = str(data.get("condition") or "").strip()
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if condition:
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return f"当满足以下条件时转到「{target}」：{condition}"
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return f"当当前阶段任务完成时转到「{target}」。"
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def edge_transition_speech(self, edge: dict | None) -> str:
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"""命中该边、切换节点瞬间播报的过渡语(可选,掩盖延迟,不写入上下文)。"""
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if not edge:
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return ""
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return (edge.get("data") or {}).get("transition_speech") or ""
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data = edge.get("data") or {}
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return str(data.get("transitionSpeech") or data.get("transition_speech") or "")
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def find_edge(self, source: str | None, target: str | None) -> dict | None:
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for edge in self.edges:
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if edge.get("source") == source and edge.get("target") == target:
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return edge
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return None
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def global_prompt(self) -> str:
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return str(self.settings.get("globalPrompt") or "").strip()
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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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"""组合当前节点提示与可选的全局提示(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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node_data = self.data(nid)
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prompt = str(node_data.get("prompt") or "").strip()
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node_type = self.node_type(nid)
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default_add_global = node_type in {"startCall", "agentNode"}
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add_global = bool(node_data.get("addGlobalPrompt", default_add_global))
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global_prompt = (
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str(self.data(self.global_id).get("prompt") or "").strip()
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if add_global and self.global_id
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else ""
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)
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sections = [header]
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if global_prompt:
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sections.append(f"[全局规则]\n{global_prompt}")
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def prompt_for(self, node_id: str, store: DynamicVariableStore) -> str:
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prompt = store.render(str(self.data(node_id).get("prompt") or "").strip())
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sections = [f"[当前阶段：{self.name(node_id)}]"]
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if self.global_prompt():
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sections.append(f"[全局规则]\n{store.render(self.global_prompt())}")
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if prompt:
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sections.append(f"[当前节点任务]\n{prompt}")
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sections.append(f"[当前阶段任务]\n{prompt}")
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return "\n\n".join(sections)
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# ---- 路由:决定下一节点 ----
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async def route(
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def greeting(self, store: DynamicVariableStore) -> str:
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return store.render(str(self.data(self.start_id).get("greeting") or ""))
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def expression_matches(self, expression: dict, values: dict[str, Any]) -> bool:
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results = []
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for rule in expression.get("rules") or []:
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name = str(rule.get("variable") or "")
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operator = str(rule.get("operator") or "")
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expected = rule.get("value")
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exists = name in values
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actual = values.get(name)
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try:
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if operator == "exists":
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matched = exists if expected is not False else not exists
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elif operator == "eq":
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matched = actual == expected
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elif operator == "neq":
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matched = actual != expected
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elif operator == "gt":
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matched = actual > expected
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elif operator == "gte":
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matched = actual >= expected
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elif operator == "lt":
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matched = actual < expected
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elif operator == "lte":
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matched = actual <= expected
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elif operator == "contains":
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matched = expected in actual
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elif operator == "in":
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matched = actual in expected
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else:
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matched = False
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except (TypeError, ValueError):
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matched = False
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results.append(matched)
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if not results:
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return False
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return all(results) if expression.get("combinator", "and") == "and" else any(results)
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def deterministic_edge(
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self,
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nid: str | None,
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history: list[dict],
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node_id: str,
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store: DynamicVariableStore,
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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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include_default: bool,
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) -> dict | None:
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default = None
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for edge in self.outgoing(node_id):
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data = edge.get("data") or {}
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mode = data.get("mode")
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if mode == "expression" and self.expression_matches(
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data.get("expression") or {}, store.values
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):
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return edge
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if mode == "always":
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default = edge
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return default if include_default else 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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def llm_edges(self, node_id: str) -> list[dict]:
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return [
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edge
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for edge in self.outgoing(node_id)
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if self.edge_mode(edge) in {"llm", "always"}
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]
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