refactor: remove legacy assistant compatibility
This commit is contained in:
Xin Wang
+47
-241
@@ -10,7 +10,6 @@ from models import VISION_PROMPT_MODES
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from services.message_policy import (
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MESSAGE_COMPLETION_POLICIES,
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MESSAGE_CONFIRMATION,
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MESSAGE_PLAYBACK,
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)
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@@ -170,236 +169,9 @@ def node_types_response() -> dict[str, Any]:
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return {"specVersion": SPEC_VERSION, "nodeTypes": NODE_SPECS}
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def _edge_data_v3(edge: dict) -> dict:
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data = deepcopy(edge.get("data") or {})
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data.pop("transitionSpeech", None)
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data.pop("transition_speech", None)
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if data.get("mode") in EDGE_MODES:
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data.setdefault("priority", 10)
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return data
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condition = str(data.pop("condition", "") or "").strip()
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data.update(
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{
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"mode": "llm" if condition else "always",
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"priority": 10,
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"condition": condition,
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}
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)
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return data
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def _normalize_agent_data(data: dict[str, Any]) -> None:
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"""Keep Agent entry focused on conversation, not fixed interaction."""
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data.setdefault("contextPolicy", "inherit")
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if data.get("entryMode") not in AGENT_ENTRY_MODES:
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data["entryMode"] = "wait_user"
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data.pop("entrySpeech", None)
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data.setdefault("toolBindings", [])
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data.setdefault("builtInTools", {})
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legacy_prompt = str(data.pop("visionFocusPrompt", "") or "").strip()
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if "visionPrompt" not in data:
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data["visionPrompt"] = legacy_prompt
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if "visionPromptMode" not in data:
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data["visionPromptMode"] = "guided" if legacy_prompt else "runtime"
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if "inheritGlobalConfig" not in data:
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has_node_overrides = any(
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(
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data.get("llmResourceId"),
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data.get("asrResourceId"),
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data.get("ttsResourceId"),
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data.get("visionEnabled"),
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data.get("visionModelResourceId"),
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data.get("visionPrompt"),
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data.get("knowledgeBaseId"),
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data.get("toolBindings"),
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)
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)
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data["inheritGlobalConfig"] = not has_node_overrides
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def _normalize_action_data(data: dict[str, Any]) -> None:
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"""Add deterministic Action defaults."""
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data.setdefault(
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"toolBinding",
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{
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"toolId": "",
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"argumentBindings": {},
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"templateBindings": {},
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"resultAssignments": {},
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},
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)
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data.setdefault("userInputPolicy", "queue")
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data.pop("speech", None)
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def _normalize_update_state_data(data: dict[str, Any]) -> None:
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"""Keep deterministic state updates as typed expression rules."""
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data.setdefault("updates", [])
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def _normalize_message_data(data: dict[str, Any]) -> None:
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"""Fill the small built-in Message contract used by runtime and editor."""
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data.setdefault("speech", "")
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data.setdefault("title", "重要提示")
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data.setdefault("message", "")
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data.setdefault("confirmLabel", "确认")
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if "completionPolicy" not in data:
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data["completionPolicy"] = (
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MESSAGE_CONFIRMATION
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if data.get("requireConfirmation") is True
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else MESSAGE_PLAYBACK
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)
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data.pop("requireConfirmation", None)
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data.pop("showMessage", None)
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def _normalize_settings(settings: dict[str, Any], *, global_prompt: str = "") -> None:
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# Missing values belong to graphs saved before workflow runtime/routing
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# became explicit. Preserve their dedicated-router behavior.
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settings.setdefault("runtimeMode", "pipeline")
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settings.setdefault("llmRoutingMode", "llm_router")
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settings.setdefault("globalPrompt", global_prompt)
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settings.setdefault("defaultRealtimeResourceId", "")
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settings.setdefault("defaultLlmResourceId", "")
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settings.setdefault("defaultAsrResourceId", "")
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settings.setdefault("defaultTtsResourceId", "")
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settings.setdefault("visionEnabled", False)
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settings.setdefault("visionModelResourceId", "")
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legacy_prompt = str(settings.pop("visionFocusPrompt", "") or "").strip()
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settings.setdefault("visionPrompt", legacy_prompt)
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if "visionPromptMode" not in settings:
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settings["visionPromptMode"] = "guided" if legacy_prompt else "runtime"
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settings.setdefault("toolBindings", [])
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settings.setdefault("builtInTools", {})
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settings.setdefault("knowledgeBaseId", "")
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settings.setdefault("knowledgeMode", "automatic")
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settings.setdefault("knowledgeTopN", 5)
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settings.setdefault("knowledgeScoreThreshold", 0.0)
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settings.setdefault("enableInterrupt", True)
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settings.setdefault("turnConfig", {})
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def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
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"""Return a deep-copied v4 graph."""
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source = deepcopy(graph or {})
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if str(source.get("specVersion") or "") == SPEC_VERSION:
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settings = source.setdefault("settings", {})
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_normalize_settings(settings)
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source.setdefault("nodes", [])
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source.setdefault("edges", [])
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for edge in source["edges"]:
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edge["data"] = _edge_data_v3(edge)
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for node in source["nodes"]:
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data = node.setdefault("data", {})
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if node.get("type") == "start":
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data.pop("greeting", None)
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elif node.get("type") == "agent":
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_normalize_agent_data(data)
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elif node.get("type") == "message":
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_normalize_message_data(data)
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elif node.get("type") == "action":
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_normalize_action_data(data)
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elif node.get("type") == "update_state":
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_normalize_update_state_data(data)
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return source
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nodes = source.get("nodes") or []
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edges = source.get("edges") or []
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global_prompt = ""
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mapped_nodes: list[dict] = []
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start_prompt_nodes: dict[str, str] = {}
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type_map = {
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"startCall": "start",
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"agentNode": "agent",
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"endCall": "end",
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"start": "start",
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"agent": "agent",
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"message": "message",
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"action": "action",
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"update_state": "update_state",
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"handoff": "handoff",
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"end": "end",
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}
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for node in nodes:
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old_type = str(node.get("type") or "")
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data = deepcopy(node.get("data") or {})
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if old_type == "globalNode":
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global_prompt = str(data.get("prompt") or "")
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continue
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new_type = type_map.get(old_type, old_type)
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migrated = deepcopy(node)
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migrated["type"] = new_type
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if new_type == "end":
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data["message"] = data.pop("message", data.pop("prompt", ""))
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data.setdefault("scope", "session")
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elif new_type == "agent":
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_normalize_agent_data(data)
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elif new_type == "message":
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_normalize_message_data(data)
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elif new_type == "action":
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_normalize_action_data(data)
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elif new_type == "update_state":
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_normalize_update_state_data(data)
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elif new_type == "start":
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prompt = str(data.pop("prompt", "") or "").strip()
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data.pop("greeting", None)
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if prompt:
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start_prompt_nodes[str(node.get("id"))] = prompt
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for key in ("allowInterrupt", "addGlobalPrompt"):
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data.pop(key, None)
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migrated["data"] = data
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mapped_nodes.append(migrated)
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mapped_edges: list[dict] = []
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for edge in edges:
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migrated = deepcopy(edge)
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migrated["data"] = _edge_data_v3(migrated)
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mapped_edges.append(migrated)
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# A v2 Start was conversational. Insert a synthetic Agent so its prompt remains active.
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for start_id, prompt in start_prompt_nodes.items():
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synthetic_id = f"{start_id}-migrated-agent"
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start_node = next((n for n in mapped_nodes if n.get("id") == start_id), None)
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position = (start_node or {}).get("position") or {"x": 100, "y": 120}
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mapped_nodes.append(
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{
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"id": synthetic_id,
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"type": "agent",
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"position": {"x": position.get("x", 100) + 300, "y": position.get("y", 120)},
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"data": {
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"name": "迁移的开场 Agent",
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"prompt": prompt,
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"contextPolicy": "inherit",
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"inheritGlobalConfig": True,
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"entryMode": "wait_user",
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},
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}
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)
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for edge in mapped_edges:
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if edge.get("source") == start_id:
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edge["source"] = synthetic_id
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edge["data"] = _edge_data_v3(edge)
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if str(edge["data"].get("condition") or "").strip():
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edge["data"]["mode"] = "llm"
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mapped_edges.append(
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{
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"id": f"e-{start_id}-{synthetic_id}",
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"source": start_id,
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"target": synthetic_id,
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"data": {"mode": "always", "priority": 0},
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}
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)
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settings = deepcopy(source.get("settings") or {})
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_normalize_settings(settings, global_prompt=global_prompt)
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return {
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"specVersion": 4,
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"settings": settings,
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"nodes": mapped_nodes,
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"edges": mapped_edges,
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**({"viewport": deepcopy(source["viewport"])} if source.get("viewport") else {}),
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}
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def copy_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
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"""Return an isolated copy of a current Workflow graph."""
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return deepcopy(graph or {})
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def _validate_expression(expression: Any) -> list[str]:
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@@ -421,16 +193,50 @@ def _validate_expression(expression: Any) -> list[str]:
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def validate_graph(graph: dict[str, Any]) -> list[str]:
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graph = normalize_graph(graph)
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nodes = graph.get("nodes") or []
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edges = graph.get("edges") or []
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if not nodes:
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return []
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graph = copy_graph(graph)
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errors: list[str] = []
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settings = graph.get("settings") or {}
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runtime_mode = str(settings.get("runtimeMode") or "pipeline")
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llm_routing_mode = str(settings.get("llmRoutingMode") or "llm_router")
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if str(graph.get("specVersion") or "") != SPEC_VERSION:
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errors.append(f"工作流只支持 specVersion {SPEC_VERSION}")
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nodes = graph.get("nodes")
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edges = graph.get("edges")
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settings = graph.get("settings")
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if not isinstance(nodes, list) or not nodes:
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errors.append("工作流必须包含节点")
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nodes = []
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if not isinstance(edges, list):
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errors.append("工作流 edges 必须是列表")
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edges = []
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if not isinstance(settings, dict):
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errors.append("工作流 settings 必须是对象")
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settings = {}
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required_settings = {
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"runtimeMode",
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"llmRoutingMode",
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"globalPrompt",
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"defaultRealtimeResourceId",
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"defaultLlmResourceId",
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"defaultAsrResourceId",
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"defaultTtsResourceId",
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"visionEnabled",
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"visionModelResourceId",
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"visionPromptMode",
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"visionPrompt",
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"toolBindings",
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"builtInTools",
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"knowledgeBaseId",
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"knowledgeMode",
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"knowledgeTopN",
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"knowledgeScoreThreshold",
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"enableInterrupt",
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"turnConfig",
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}
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missing_settings = sorted(required_settings - settings.keys())
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if missing_settings:
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errors.append("工作流 settings 缺少字段:" + ", ".join(missing_settings))
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runtime_mode = str(settings.get("runtimeMode") or "")
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llm_routing_mode = str(settings.get("llmRoutingMode") or "")
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if runtime_mode not in WORKFLOW_RUNTIME_MODES:
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errors.append(f"工作流运行模式无效:{runtime_mode}")
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if llm_routing_mode not in WORKFLOW_LLM_ROUTING_MODES:
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@@ -699,7 +505,7 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
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def graph_references(graph: dict[str, Any]) -> dict[str, set[str]]:
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"""Collect externally referenced IDs for save/runtime validation."""
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normalized = normalize_graph(graph)
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normalized = copy_graph(graph)
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settings = normalized.get("settings") or {}
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resources = {
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str(value)
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