Merge pull request #785 from wanxunyang/feat/app-log-wxy

feat(workflow): add opening statement and citation support

api/app/core/workflow/engine/result_builder.py

@@ -59,6 +59,9 @@ class WorkflowResultBuilder:
             conversation_vars = variable_pool.get_all_conversation_vars()
             sys_vars = variable_pool.get_all_system_vars()
 
+        # 汇总所有 knowledge 节点的 citations
+        citations = self.aggregate_citations(node_outputs)
+
         return {
             "status": "completed" if success else "failed",
             "output": final_output,
@@ -71,9 +74,25 @@ class WorkflowResultBuilder:
             "conversation_id": execution_context.conversation_id,
             "elapsed_time": elapsed_time,
             "token_usage": token_usage,
+            "citations": citations,
             "error": result.get("error"),
         }
 
+    @staticmethod
+    def aggregate_citations(node_outputs: dict) -> list:
+        """从所有 knowledge 节点的输出中汇总 citations，去重"""
+        seen = set()
+        citations = []
+        for node_output in node_outputs.values():
+            if not isinstance(node_output, dict):
+                continue
+            for c in node_output.get("citations", []):
+                key = c.get("document_id")
+                if key and key not in seen:
+                    seen.add(key)
+                    citations.append(c)
+        return citations
+
     @staticmethod
     def aggregate_token_usage(node_outputs: dict) -> dict[str, int] | None:
         """

api/app/core/workflow/nodes/base_node.py

@@ -395,7 +395,8 @@ class BaseNode(ABC):
             "output": output,
             "elapsed_time": elapsed_time,
             "token_usage": token_usage,
-            "error": None
+            "error": None,
+            **self._extract_extra_fields(business_result),
         }
         final_output = {
             "node_outputs": {self.node_id: node_output},
@@ -498,6 +499,13 @@ class BaseNode(ABC):
         # Default implementation returns the business result directly
         return business_result
 
+    def _extract_extra_fields(self, business_result: Any) -> dict:
+        """Extracts extra fields to merge into node_output (e.g. citations).
+
+        Subclasses may override to inject additional metadata.
+        """
+        return {}
+
     def _extract_token_usage(self, business_result: Any) -> dict[str, int] | None:
         """Extracts token usage information from the business result.
 

api/app/core/workflow/nodes/knowledge/node.py

@@ -34,6 +34,20 @@ class KnowledgeRetrievalNode(BaseNode):
             "output": VariableType.ARRAY_STRING
         }
 
+    def _extract_output(self, business_result: Any) -> Any:
+        """下游节点只拿 chunks 列表"""
+        if isinstance(business_result, dict) and "chunks" in business_result:
+            return business_result["chunks"]
+        return business_result
+
+    def _extract_citations(self, business_result: Any) -> list:
+        if isinstance(business_result, dict):
+            return business_result.get("citations", [])
+        return []
+
+    def _extract_extra_fields(self, business_result: Any) -> dict:
+        return {"citations": self._extract_citations(business_result)}
+
     def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
         return {
             "query": self._render_template(self.typed_config.query, variable_pool),
@@ -314,4 +328,20 @@ class KnowledgeRetrievalNode(BaseNode):
             logger.info(
                 f"Node {self.node_id}: knowledge base retrieval completed, results count: {len(final_rs)}"
             )
-            return [chunk.page_content for chunk in final_rs]
+            citations = []
+            seen_doc_ids = set()
+            for chunk in final_rs:
+                meta = chunk.metadata or {}
+                doc_id = meta.get("document_id") or meta.get("doc_id")
+                if doc_id and doc_id not in seen_doc_ids:
+                    seen_doc_ids.add(doc_id)
+                    citations.append({
+                        "document_id": str(doc_id),
+                        "file_name": meta.get("file_name", ""),
+                        "knowledge_id": str(meta.get("knowledge_id", kb_config.kb_id)),
+                        "score": meta.get("score", 0.0),
+                    })
+            return {
+                "chunks": [chunk.page_content for chunk in final_rs],
+                "citations": citations,
+            }