Add Dify integration and enhance workflow node specifications
- Introduce new fields `dify_api_url` and `dify_api_key` in `AssistantConfig` for Dify API integration. - Update `requirements.txt` to include `dify-client-python` for Dify SDK support. - Modify `config_resolver` to handle Dify connection information. - Add a new `globalNode` type in workflow specifications to provide unified settings across workflows. - Enhance node specifications with additional constraints and default values for better configuration management. - Update frontend components to support the new `globalNode` type and its properties, improving workflow editor functionality.
This commit is contained in:
Xin Wang
188
backend/services/brains/workflow_brain.py
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188
backend/services/brains/workflow_brain.py
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"""Local graph-driven workflow assistant and its per-call state."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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from loguru import logger
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from models import AssistantConfig
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.frame_processor import FrameProcessor
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from services.brains.base import BaseBrain, BrainRuntime, BrainSpec
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from services.workflow_engine import WorkflowEngine
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@dataclass
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class WorkflowState:
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current: str
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ended: bool = False
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turns_in_node: int = 0
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end_turn_id: str | None = None
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class WorkflowBrain(BaseBrain):
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spec = BrainSpec(
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type="workflow",
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supported_runtime_modes=frozenset({"pipeline"}),
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owns_context=True,
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)
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_FALLBACK_AFTER_TURNS = 2
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def __init__(self, graph: dict[str, Any]):
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self._engine = WorkflowEngine(graph or {})
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if not self._engine.has_graph() or not self._engine.start_id:
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raise ValueError("WorkflowBrain 缺少有效的 startCall 节点")
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self._state = WorkflowState(current=self._engine.start_id)
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self._history: list[dict[str, str]] = []
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self._cfg: AssistantConfig | None = None
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self._runtime: BrainRuntime | None = None
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async def greeting(self, cfg: AssistantConfig) -> str:
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return self._engine.greeting() or cfg.greeting
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def system_prompt(self, cfg: AssistantConfig) -> str:
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return self._engine.system_prompt_for(self._state.current)
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def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
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from services.pipecat.service_factory import create_llm
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return create_llm(cfg)
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async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None:
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self._cfg = cfg
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self._runtime = runtime
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for edge in self._engine.edges:
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if edge.get("target"):
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runtime.llm.register_function(
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self._engine.edge_fn_name(edge),
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self._make_transition_handler(edge),
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)
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self._apply_node(self._state.current)
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logger.info(
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f"工作流模式启用: 起始节点={self._engine.name(self._state.current)}"
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)
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async def on_connected(self) -> None:
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await self._emit_node_active(self._state.current)
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def record_user_message(self, content: str) -> None:
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if content:
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self._history.append({"role": "user", "content": content})
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async def on_assistant_text_start(self, turn_id: str) -> None:
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if self._state.ended and self._state.end_turn_id is None:
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self._state.end_turn_id = turn_id
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async def on_assistant_text_end(
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self,
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turn_id: str,
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content: str,
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interrupted: bool,
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) -> None:
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if not content or interrupted:
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return
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self._history.append({"role": "assistant", "content": content})
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if turn_id == self._state.end_turn_id:
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runtime = self._require_runtime()
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runtime.call_end.begin("completed")
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runtime.call_end.arm_after_speech()
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elif not self._state.ended:
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self._state.turns_in_node += 1
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await self._fallback_route()
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def _apply_node(self, node_id: str) -> None:
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runtime = self._require_runtime()
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runtime.set_system_prompt(self._engine.system_prompt_for(node_id))
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if self._engine.is_end(node_id):
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runtime.set_tools([])
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return
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runtime.set_tools(
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[
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FunctionSchema(
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name=self._engine.edge_fn_name(edge),
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description=self._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 self._engine.outgoing(node_id)
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]
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)
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async def _go_to_node(self, target: str) -> None:
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self._state.current = target
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self._state.turns_in_node = 0
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if self._engine.is_end(target):
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self._state.ended = True
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await self._emit_node_active(target)
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self._apply_node(target)
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async def _emit_node_active(self, node_id: str | None) -> None:
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if node_id:
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await self._require_runtime().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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async def _speak_transition(self, edge: dict | None) -> None:
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speech = self._engine.edge_transition_speech(edge)
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if speech:
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await self._require_runtime().queue_frame(
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TTSSpeakFrame(speech, append_to_context=False)
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)
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def _make_transition_handler(self, edge: dict):
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target = str(edge.get("target"))
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async def handler(params) -> None:
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logger.info(f"LLM 触发转移 → {self._engine.name(target)}")
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if not self._engine.is_end(target):
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await self._speak_transition(edge)
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await self._go_to_node(target)
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await params.result_callback({"status": "ok"})
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return handler
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async def _fallback_route(self) -> None:
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if self._state.ended:
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return
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if self._state.turns_in_node < self._FALLBACK_AFTER_TURNS:
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return
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if not self._engine.outgoing(self._state.current):
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return
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cfg = self._require_config()
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target = await self._engine.route(
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self._state.current,
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self._history,
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api_key=self._require(cfg.llm_api_key, "LLM apiKey"),
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base_url=self._require(cfg.llm_base_url, "LLM apiUrl"),
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model=self._require(cfg.model, "LLM modelId"),
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)
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if target and target != self._state.current:
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logger.info(f"文本兜底触发转移 → {self._engine.name(target)}")
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if not self._engine.is_end(target):
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await self._speak_transition(
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self._engine.find_edge(self._state.current, target)
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)
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await self._go_to_node(target)
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def _require_runtime(self) -> BrainRuntime:
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if self._runtime is None:
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raise RuntimeError("WorkflowBrain 尚未绑定 pipeline runtime")
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return self._runtime
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def _require_config(self) -> AssistantConfig:
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if self._cfg is None:
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raise RuntimeError("WorkflowBrain 尚未初始化配置")
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return self._cfg
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@staticmethod
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def _require(value: str, label: str) -> str:
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if value:
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return value
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raise ValueError(f"缺少模型资源配置: {label}")
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