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.

backend/services/brains/internal_brain.py

@@ -1,37 +0,0 @@
-"""内部 LLM 大脑:prompt 与 workflow。
-
-二者都用本地维护的 LLMContext + OpenAI 兼容 LLM,支持 cascade 与 realtime。
-workflow 的图编排(切提示/转移工具/node-active)阶段 1 仍内联在 pipeline.py,
-这里只负责提供 LLM 槽位与元数据,行为与改造前完全一致。
-"""
-
-from __future__ import annotations
-
-from models import AssistantConfig
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.frame_processor import FrameProcessor
-
-from services.brains.base import BrainSpec
-
-_CASCADE_AND_REALTIME = frozenset({"pipeline", "realtime"})
-
-
-class InternalBrain:
-    """prompt / workflow 共用。"""
-
-    def __init__(self, brain_type: str):
-        self.spec = BrainSpec(
-            type=brain_type,
-            supported_runtime_modes=_CASCADE_AND_REALTIME,
-            owns_context=True,
-        )
-
-    async def greeting(self, cfg: AssistantConfig) -> str:
-        # 内部类型的开场白由 pipeline.py 现有逻辑(workflow 起始节点 / cfg.greeting)决定,
-        # 该方法仅为满足 Brain 协议,实际不在内部路径上被调用。
-        return cfg.greeting
-
-    def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
-        from services.pipecat.service_factory import create_llm
-
-        return create_llm(cfg)