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feat(workflow): incorporate model references and streamline parsing logic

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- Incorporate model reference metadata (name, provider, type) into workflow nodes and refactor parsing logic to support the new format.
- Streamline code structure by removing redundant model_id fields to enhance maintainability.
This commit is contained in:
wxy
2026-04-28 11:18:06 +08:00
parent a5670bfff6
commit c59e179cc2
1 changed files with 36 additions and 8 deletions
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44
api/app/services/app_dsl_service.py
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@@ -102,6 +102,11 @@ class AppDslService:
{**r, "_ref": self._agent_ref(r.get("target_agent_id"))} for r in (cfg["routing_rules"] or [])
]
return enriched
if app_type == AppType.WORKFLOW:
enriched = {**cfg}
if "nodes" in cfg:
enriched["nodes"] = self._enrich_workflow_nodes(cfg["nodes"])
return enriched
return cfg
def _export_draft(self, app: App, meta: dict, app_meta: dict) -> tuple[str, str]:
@@ -110,7 +115,7 @@ class AppDslService:
config_data = {
"variables": config.variables if config else [],
"edges": config.edges if config else [],
"nodes": config.nodes if config else [],
"nodes": self._enrich_workflow_nodes(config.nodes) if config else [],
"features": config.features if config else {},
"execution_config": config.execution_config if config else {},
"triggers": config.triggers if config else [],
@@ -190,6 +195,23 @@ class AppDslService:
def _enrich_tools(self, tools: list) -> list:
return [{**t, "_ref": self._tool_ref(t.get("tool_id"))} for t in (tools or [])]
def _enrich_workflow_nodes(self, nodes: list) -> list:
"""enrich 工作流节点中的模型引用，添加 name、provider、type 信息"""
from app.core.workflow.nodes.enums import NodeType
enriched_nodes = []
for node in (nodes or []):
node_type = node.get("type")
config = dict(node.get("config") or {})
if node_type in (NodeType.LLM.value, NodeType.QUESTION_CLASSIFIER.value, NodeType.PARAMETER_EXTRACTOR.value):
model_id = config.get("model_id")
if model_id:
config["model_ref"] = self._model_ref(model_id)
del config["model_id"]
enriched_nodes.append({**node, "config": config})
return enriched_nodes
def _skill_ref(self, skill_id) -> Optional[dict]:
if not skill_id:
return None
@@ -620,16 +642,16 @@ class AppDslService:
warnings.append(f"[{node_label}] 知识库 '{kb_id}' 未匹配，已移除，请导入后手动配置")
config["knowledge_bases"] = resolved_kbs
elif node_type in (NodeType.LLM.value, NodeType.QUESTION_CLASSIFIER.value, NodeType.PARAMETER_EXTRACTOR.value):
model_ref = config.get("model_id")
model_ref = config.get("model_ref") or config.get("model_id")
if model_ref:
ref_dict = None
if isinstance(model_ref, dict):
ref_id = model_ref.get("id")
ref_name = model_ref.get("name")
if ref_id:
ref_dict = {"id": ref_id}
elif ref_name is not None:
ref_dict = {"name": ref_name, "provider": model_ref.get("provider"), "type": model_ref.get("type")}
ref_dict = {
"id": model_ref.get("id"),
"name": model_ref.get("name"),
"provider": model_ref.get("provider"),
"type": model_ref.get("type")
}
elif isinstance(model_ref, str):
try:
uuid.UUID(model_ref)
@@ -640,12 +662,18 @@ class AppDslService:
resolved_model_id = self._resolve_model(ref_dict, tenant_id, warnings)
if resolved_model_id:
config["model_id"] = resolved_model_id
if "model_ref" in config:
del config["model_ref"]
else:
warnings.append(f"[{node_label}] 模型未匹配，已置空，请导入后手动配置")
config["model_id"] = None
if "model_ref" in config:
del config["model_ref"]
else:
warnings.append(f"[{node_label}] 模型未匹配，已置空，请导入后手动配置")
config["model_id"] = None
if "model_ref" in config:
del config["model_ref"]
resolved_nodes.append({**node, "config": config})
return resolved_nodes