Merge pull request #688 from SuanmoSuanyangTechnology/feature/agent-tool_xjn

feat(agent)

api/app/schemas/app_schema.py

@@ -196,6 +196,13 @@ class CitationConfig(BaseModel):
     enabled: bool = Field(default=False)
 
 
+class Citation(BaseModel):
+    document_id: str
+    file_name: str
+    knowledge_id: str
+    score: float
+
+
 class WebSearchConfig(BaseModel):
     """联网搜索配置"""
     enabled: bool = Field(default=False)

api/app/services/app_chat_service.py

@@ -82,6 +82,12 @@ class AppChatService:
             )
             system_prompt = system_prompt_rendered.get_text_content() or system_prompt
 
+        # opening_statement：首轮对话注入开场白
+        is_new_conversation = not self.conversation_service.get_messages(conversation_id, limit=1)
+        system_prompt = self.agent_service._inject_opening_statement(
+            features_config, system_prompt, is_new_conversation
+        )
+
         # 准备工具列表
         tools = []
 
@@ -93,7 +99,8 @@ class AppChatService:
         tools.extend(skill_tools)
         if skill_prompts:
             system_prompt = f"{system_prompt}\n\n{skill_prompts}"
-        tools.extend(self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id))
+        kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id)
+        tools.extend(kb_tools)
         memory_flag = False
         if memory:
             memory_tools, memory_flag = self.agent_service.load_memory_config(
@@ -230,7 +237,7 @@ class AppChatService:
             }),
             "elapsed_time": elapsed_time,
             "suggested_questions": suggested_questions,
-            "citations": self.agent_service._filter_citations(features_config, result.get("citations", [])),
+            "citations": self.agent_service._filter_citations(features_config, citations_collector),
             "audio_url": audio_url,
             "audio_status": "pending"
         }
@@ -283,6 +290,12 @@ class AppChatService:
                 )
                 system_prompt = system_prompt_rendered.get_text_content() or system_prompt
 
+            # opening_statement：首轮对话注入开场白
+            is_new_conversation = not self.conversation_service.get_messages(conversation_id, limit=1)
+            system_prompt = self.agent_service._inject_opening_statement(
+                features_config, system_prompt, is_new_conversation
+            )
+
             # 准备工具列表
             tools = []
 
@@ -295,7 +308,8 @@ class AppChatService:
             tools.extend(skill_tools)
             if skill_prompts:
                 system_prompt = f"{system_prompt}\n\n{skill_prompts}"
-            tools.extend(self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id))
+            kb_tools, citations_collector = self.agent_service.load_knowledge_retrieval_config(config.knowledge_retrieval, user_id)
+            tools.extend(kb_tools)
             # 添加长期记忆工具
             memory_flag = False
             if memory:
@@ -409,7 +423,7 @@ class AppChatService:
                     logger.warning(f"TTS任务异常: {e}")
                     audio_status = "failed"
             end_data["audio_status"] = audio_status if stream_audio_url else None
-            end_data["citations"] = self.agent_service._filter_citations(features_config, [])
+            end_data["citations"] = self.agent_service._filter_citations(features_config, citations_collector)
 
             # 保存消息
             human_meta = {

api/app/services/draft_run_service.py

@@ -26,7 +26,7 @@ from app.core.rag.nlp.search import knowledge_retrieval
 from app.db import get_db_context
 from app.models import AgentConfig, ModelConfig, ModelType
 from app.repositories.tool_repository import ToolRepository
-from app.schemas.app_schema import FileInput
+from app.schemas.app_schema import FileInput, Citation
 from app.schemas.model_schema import ModelInfo
 from app.schemas.prompt_schema import PromptMessageRole, render_prompt_message
 from app.services import task_service
@@ -190,13 +190,19 @@ def create_web_search_tool(web_search_config: Dict[str, Any]):
     return web_search_tool
 
 
-def create_knowledge_retrieval_tool(kb_config, kb_ids, user_id):
+def create_knowledge_retrieval_tool(kb_config, kb_ids, user_id, citations_collector: Optional[List[Citation]] = None):
     """从知识库中检索相关信息。当用户的问题需要参考知识库、文档或历史记录时，使用此工具进行检索。
 
     Args:
         kb_config: 知识库配置
         kb_ids: 知识库ID列表
         user_id: 用户ID
+        citations_collector: 用于收集引用信息的列表（由外部传入，tool 执行时填充）
+            列表元素类型为 Citation，包含字段：
+            - document_id: 文档唯一标识
+            - file_name: 文件名
+            - knowledge_id: 知识库 ID
+            - score: 检索相关性得分
 
     Returns:
         检索到的相关知识内容
@@ -229,6 +235,21 @@ def create_knowledge_retrieval_tool(kb_config, kb_ids, user_id):
                     }
                 )
 
+                # 收集引用信息
+                if citations_collector is not None:
+                    seen_doc_ids = {c.get("document_id") for c in citations_collector}
+                    for chunk in retrieve_chunks_result:
+                        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_collector.append(Citation(
+                                document_id=doc_id,
+                                file_name=meta.get("file_name", ""),
+                                knowledge_id=str(meta.get("knowledge_id", "")),
+                                score=meta.get("score", 0)
+                            ))
+
                 return f"检索到以下相关信息：\n\n{context}"
             else:
                 logger.warning("知识库检索未找到结果")
@@ -320,26 +341,26 @@ class AgentRunService:
             self,
             knowledge_retrieval_config: dict | None,
             user_id
-    ) -> list:
+    ) -> tuple[list, list]:
+        """返回 (tools, citations_collector)"""
         if not knowledge_retrieval_config:
-            return []
+            return [], []
 
+        citations_collector = []
         tools = []
         knowledge_bases = knowledge_retrieval_config.get("knowledge_bases", [])
-        kb_ids = bool(knowledge_bases and knowledge_bases[0].get("kb_id"))
+        kb_ids = [kb["kb_id"] for kb in knowledge_bases if kb.get("kb_id")]
         if kb_ids:
-            # 创建知识库检索工具
+            kb_tool = create_knowledge_retrieval_tool(
-            kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval_config, kb_ids, user_id)
+                knowledge_retrieval_config, kb_ids, user_id,
+                citations_collector=citations_collector
+            )
             tools.append(kb_tool)
-
             logger.debug(
                 "已添加知识库检索工具",
-                extra={
+                extra={"kb_ids": kb_ids, "tool_count": len(tools)}
-                    "kb_ids": kb_ids,
-                    "tool_count": len(tools)
-                }
             )
-        return tools
+        return tools, citations_collector
 
     def load_memory_config(
             self,
@@ -441,12 +462,12 @@ class AgentRunService:
     @staticmethod
     def _filter_citations(
             features_config: Dict[str, Any],
-            citations: List[Any]
+            citations: List[Citation]
     ) -> List[Any]:
         """根据 citation 开关决定是否返回引用来源"""
         citation_cfg = features_config.get("citation", {})
         if isinstance(citation_cfg, dict) and citation_cfg.get("enabled"):
-            return citations
+            return [cit.model_dump() for cit in citations]
         return []
 
     async def run(
@@ -549,7 +570,8 @@ class AgentRunService:
             tools.extend(skill_tools)
             if skill_prompts:
                 system_prompt = f"{system_prompt}\n\n{skill_prompts}"
-            tools.extend(self.load_knowledge_retrieval_config(knowledge_retrieval_config, user_id))
+            kb_tools, citations_collector = self.load_knowledge_retrieval_config(knowledge_retrieval_config, user_id)
+            tools.extend(kb_tools)
             # 添加长期记忆工具
             memory_flag = False
             if memory:
@@ -680,7 +702,7 @@ class AgentRunService:
                 "suggested_questions": await self._generate_suggested_questions(
                     features_config, result["content"], api_key_config, effective_params
                 ) if not sub_agent else [],
-                "citations": self._filter_citations(features_config, result.get("citations", [])),
+                "citations": self._filter_citations(features_config, citations_collector),
                 "audio_url": audio_url,
                 "audio_status": "pending"
             }
@@ -790,7 +812,8 @@ class AgentRunService:
             tools.extend(skill_tools)
             if skill_prompts:
                 system_prompt = f"{system_prompt}\n\n{skill_prompts}"
-            tools.extend(self.load_knowledge_retrieval_config(knowledge_retrieval_config, user_id))
+            kb_tools, citations_collector = self.load_knowledge_retrieval_config(knowledge_retrieval_config, user_id)
+            tools.extend(kb_tools)
 
             # 添加长期记忆工具
             memory_flag = False
@@ -943,7 +966,7 @@ class AgentRunService:
                         logger.warning(f"TTS任务异常: {e}")
                         audio_status = "failed"
                 end_data["audio_status"] = audio_status if stream_audio_url else None
-                end_data["citations"] = self._filter_citations(features_config, [])
+                end_data["citations"] = self._filter_citations(features_config, citations_collector)
             yield self._format_sse_event("end", end_data)
 
             logger.info(