Merge branch 'refs/heads/develop' into fix/memory_mcp2_1

api/app/core/config.py

@@ -38,6 +38,7 @@ class Settings:
     REDIS_PORT: int = int(os.getenv("REDIS_PORT", "6379"))
     REDIS_DB: int = int(os.getenv("REDIS_DB", "1"))
     REDIS_PASSWORD: str = os.getenv("REDIS_PASSWORD", "")
+    
 
     # ElasticSearch configuration
     ELASTICSEARCH_HOST: str = os.getenv("ELASTICSEARCH_HOST", "https://127.0.0.1")

api/app/core/rag/llm/cv_model.py

@@ -243,6 +243,33 @@ class QWenCV(GptV4):
             tmp_path = tmp.name
 
             video_path = f"file://{tmp_path}"
+            prompt_ch = """
+                    你是一名专业的视频转录助手，能够将视频文件的内容转写为文本，并**精确标记每句话或每个段落对应的时间戳**（开始时间-结束时间）。\n
+                    **任务要求**：
+                    1.输入是MP4等视频文件,解析带时间戳的文本。
+                    2.时间戳格式为 `[HH:MM:SS.mmm]`（毫秒可选），例如 `[00:01:23.456]`。
+                    3.时间戳需尽可能贴近实际视频的起止时间，误差不超过1秒。
+                    4.如果无法确定具体时间，请根据上下文合理估算。
+                    5.最后总结:这段视频的内容是什么?,并用恰当的句子总结这个视频。
+
+                    **示例输出**：
+                    [00:00:00.000] 今天天气真好，
+                    [00:00:02.500] 我们一起去公园散步吧。
+                    [00:00:05.800] 公园里的花开得非常漂亮。
+                    这段视频的内容是关于如何在CREAMS系统中进行楼宇管理集合的编辑或删除操作。视频演示了 ..."""
+            prompt_en = """
+                    You are a professional video transcription assistant, capable of transcribing the content of video files into text and **precisely marking the timestamp (start time-end time) corresponding to each sentence or paragraph**. 
+                    **Task requirements**: 
+                    1. Input is MP4 or other video files, and parse the text with timestamps.
+                    2. The timestamp format is `[HH:MM:SS.mmm]` (milliseconds are optional), for example, `[00:01:23.456]`.
+                    3. The timestamp should be as close as possible to the actual start and end time of the video, with an error not exceeding 1 second.
+                    4. If the specific time cannot be determined, please make a reasonable estimation based on the context.
+                    5. Final summary: What is the content of this video? Summarize this video in an appropriate sentence.
+                    
+                    **Example output**: 
+                    [00:00:00.000] The weather is really nice today, [00:00:02.500] let's go for a walk in the park together.
+                    [00:00:05.800] The flowers in the park are blooming beautifully.
+                    The content of this video is about how to edit or delete building management collections in the CREAMS system. The video demonstrates .."""
             messages = [
                 {
                     "role": "user",
@@ -252,7 +279,7 @@ class QWenCV(GptV4):
                             "fps": 2,
                         },
                         {
-                            "text": "视频的内容是什么?,并且，请用恰当的句子总结这个视频。" if self.lang.lower() == "chinese" else "What is the content of the video? And please summarize this video in proper sentences.",
+                            "text": prompt_ch if self.lang.lower() == "chinese" else prompt_en,
                         },
                     ],
                 }

api/app/core/rag/llm/sequence2txt_model.py

@@ -60,6 +60,34 @@ class QWenSeq2txt(Base):
         from dashscope import MultiModalConversation
 
         audio_path = f"file://{audio_path}"
+        prompt_ch = """
+        你是一名专业的音频转录助手，能够将MP3音频文件的内容转写为文本，并**精确标记每句话或每个段落对应的时间戳**（开始时间-结束时间）。\n
+        **任务要求**：
+        1.输入是MP3,解析带时间戳的文本。
+        2.时间戳格式为 `[HH:MM:SS.mmm]`（毫秒可选），例如 `[00:01:23.456]`。
+        3.时间戳需尽可能贴近实际语音的起止时间，误差不超过1秒。
+        4.如果无法确定具体时间，请根据上下文合理估算。
+        5.最后总结:这段音频在说什么?
+        
+        **示例输出**：
+        [00:00:00.000] 今天天气真好，
+        [00:00:02.500] 我们一起去公园散步吧。
+        [00:00:05.800] 公园里的花开得非常漂亮。
+        这段音频讲述的是一个关于**“吃水不忘挖井人”**的感人故事，主 ..."""
+        prompt_en = """
+        You are a professional audio transcription assistant, capable of transcribing the content of MP3 audio files into text and **precisely marking the timestamps (start time - end time) corresponding to each sentence or paragraph**. 
+        **Task requirements**: 
+        1. Input is MP3, parse text with timestamps.
+        2. The timestamp format is `[HH:MM:SS.mmm]` (milliseconds are optional), for example, `[00:01:23.456]`.
+        3. The timestamp should be as close as possible to the actual start and end time of the voice, with an error not exceeding 1 second.
+        4. If a specific time cannot be determined, please make a reasonable estimation based on the context.
+        5. Final summary: What is this audio talking about?
+        
+        **Example Output**: 
+        [00:00:00.000] The weather is really nice today, 
+        [00:00:02.500] let's go for a walk in the park together.
+        [00:00:05.800] The flowers in the park are blooming beautifully.
+        This audio tells a touching story about **"Remembering the one who dug the well when drinking water"** .."""
         messages = [
             {
                 "role": "user",
@@ -68,7 +96,7 @@ class QWenSeq2txt(Base):
                         "audio": audio_path
                     },
                     {
-                        "text": "这段音频在说什么?" if self.lang.lower() == "chinese" else "What is this audio saying?",
+                        "text": prompt_ch if self.lang.lower() == "chinese" else prompt_en,
                     },
                 ],
             }

api/app/core/workflow/executor.py

@@ -8,6 +8,7 @@ import logging
 import uuid
 from typing import Any
 
+from langchain_core.runnables import RunnableConfig
 from langgraph.graph.state import CompiledStateGraph
 
 from app.core.workflow.graph_builder import GraphBuilder
@@ -53,11 +54,11 @@ class WorkflowExecutor:
         self.edges = workflow_config.get("edges", [])
         self.execution_config = workflow_config.get("execution_config", {})
 
-        self.checkpoint_config = {
-            "configurable": {
+        self.checkpoint_config = RunnableConfig(
+            configurable={
                 "thread_id": uuid.uuid4(),
             }
-        }
+        )
 
     def _prepare_initial_state(self, input_data: dict[str, Any]) -> WorkflowState:
         """准备初始状态（注入系统变量和会话变量）
@@ -214,13 +215,13 @@ class WorkflowExecutor:
         return {
             "status": "completed",
             "output": final_output,
+            "variables": result.get("variables", {}),
             "node_outputs": node_outputs,
             "messages": result.get("messages", []),
             "conversation_id": conversation_id,
             "elapsed_time": elapsed_time,
             "token_usage": token_usage,
             "error": result.get("error"),
-            "variables": result.get("variables", {}),
         }
 
     def build_graph(self, stream=False) -> CompiledStateGraph:
@@ -326,11 +327,10 @@ class WorkflowExecutor:
         }
 
         # 1. 构建图
-        graph = self.build_graph(True)
+        graph = self.build_graph(stream=True)
 
         # 2. 初始化状态（自动注入系统变量）
         initial_state = self._prepare_initial_state(input_data)
-
         # 3. Execute workflow
         try:
             chunk_count = 0
@@ -346,14 +346,16 @@ class WorkflowExecutor:
                     mode, data = event
                 else:
                     # Unexpected format, log and skip
-                    logger.warning(f"[STREAM] Unexpected event format: {type(event)}, value: {event}")
+                    logger.warning(f"[STREAM] Unexpected event format: {type(event)}, value: {event}"
+                                   f"- execution_id: {self.execution_id}")
                     continue
 
                 if mode == "custom":
                     # Handle custom streaming events (chunks from nodes via stream writer)
                     chunk_count += 1
                     event_type = data.get("type", "node_chunk")  # "message" or "node_chunk"
-                    logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}")
+                    logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}"
+                                f"- execution_id: {self.execution_id}")
                     yield {
                         "event": event_type,  # "message" or "node_chunk"
                         "data": {
@@ -380,7 +382,8 @@ class WorkflowExecutor:
                         variables_sys = variables.get("sys", {})
                         conversation_id = input_data.get("conversation_id")
                         execution_id = variables_sys.get("execution_id")
-                        logger.info(f"[DEBUG] Node starts execution: {node_name}")
+                        logger.info(f"[NODE-START] Node starts execution: {node_name} "
+                                    f"- execution_id: {self.execution_id}")
 
                         yield {
                             "event": "node_start",
@@ -399,7 +402,8 @@ class WorkflowExecutor:
                         variables_sys = variables.get("sys", {})
                         conversation_id = input_data.get("conversation_id")
                         execution_id = variables_sys.get("execution_id")
-                        logger.info(f"[DEBUG] Node execution completed: {node_name}")
+                        logger.info(f"[NODE-END] Node execution completed: {node_name} "
+                                    f"- execution_id: {self.execution_id}")
 
                         yield {
                             "event": "node_end",
@@ -407,13 +411,15 @@ class WorkflowExecutor:
                                 "node_id": node_name,
                                 "conversation_id": conversation_id,
                                 "execution_id": execution_id,
-                                "timestamp": data.get("timestamp")
+                                "timestamp": data.get("timestamp"),
+                                "state": result.get("node_outputs", {}).get(node_name),
                             }
                         }
 
                 elif mode == "updates":
                     # Handle state updates - store final state
-                    logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())}")
+                    logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())} "
+                                 f"- execution_id: {self.execution_id}")
 
             # 计算耗时
             end_time = datetime.datetime.now()
@@ -421,7 +427,7 @@ class WorkflowExecutor:
             result = graph.get_state(self.checkpoint_config).values
             logger.info(
                 f"Workflow execution completed (streaming), "
-                f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s"
+                f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s, execution_id: {self.execution_id}"
             )
 
             # 发送 workflow_end 事件
@@ -449,7 +455,8 @@ class WorkflowExecutor:
                 }
             }
 
-    def _extract_final_output(self, node_outputs: dict[str, Any]) -> str | None:
+    @staticmethod
+    def _extract_final_output(node_outputs: dict[str, Any]) -> str | None:
         """从节点输出中提取最终输出
 
         优先级：
@@ -473,7 +480,8 @@ class WorkflowExecutor:
 
         return None
 
-    def _aggregate_token_usage(self, node_outputs: dict[str, Any]) -> dict[str, int] | None:
+    @staticmethod
+    def _aggregate_token_usage(node_outputs: dict[str, Any]) -> dict[str, int] | None:
         """聚合所有节点的 token 使用情况
 
         Args:

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

@@ -25,7 +25,7 @@ class WorkflowState(TypedDict):
     The state object passed between nodes in a workflow, containing messages, variables, node outputs, etc.
     """
     # List of messages (append mode)
-    messages: list[dict[str, str]]
+    messages: Annotated[list[dict[str, str]], lambda x, y: y]
 
     # Set of loop node IDs, used for assigning values in loop nodes
     cycle_nodes: list

api/app/core/workflow/nodes/cycle_graph/iteration.py

@@ -21,6 +21,7 @@ class IterationRuntime:
     optional parallel execution, flattening of output, and loop control via
     the workflow state.
     """
+
     def __init__(
             self,
             graph: CompiledStateGraph,
@@ -87,6 +88,7 @@ class IterationRuntime:
             self.result.append(output)
         if not result["looping"]:
             self.looping = False
+        return result
 
     def _create_iteration_tasks(self, array_obj, idx):
         """
@@ -124,7 +126,7 @@ class IterationRuntime:
         array_obj = VariablePool(self.state).get(input_expression)
         if not isinstance(array_obj, list):
             raise RuntimeError("Cannot iterate over a non-list variable")
-
+        child_state = []
         idx = 0
         if self.typed_config.parallel:
             # Execute iterations in parallel batches
@@ -132,15 +134,14 @@ class IterationRuntime:
                 tasks = self._create_iteration_tasks(array_obj, idx)
                 logger.info(f"Iteration node {self.node_id}: running, concurrency {len(tasks)}")
                 idx += self.typed_config.parallel_count
-                await asyncio.gather(*tasks)
-            logger.info(f"Iteration node {self.node_id}: execution completed")
-            return self.result
+                child_state.extend(await asyncio.gather(*tasks))
         else:
             # Execute iterations sequentially
             while idx < len(array_obj) and self.looping:
                 logger.info(f"Iteration node {self.node_id}: running")
                 item = array_obj[idx]
                 result = await self.graph.ainvoke(self._init_iteration_state(item, idx))
+                child_state.append(result)
                 output = VariablePool(result).get(self.output_value)
                 if isinstance(output, list) and self.typed_config.flatten:
                     self.result.extend(output)
@@ -150,5 +151,8 @@ class IterationRuntime:
                     self.looping = False
                 idx += 1
 
-            logger.info(f"Iteration node {self.node_id}: execution completed")
-            return self.result
+        logger.info(f"Iteration node {self.node_id}: execution completed")
+        return {
+            "output": self.result,
+            "__child_state": child_state
+        }

api/app/core/workflow/nodes/cycle_graph/loop.py

@@ -67,7 +67,9 @@ class LoopRuntime:
                 variables=pool.get_all_conversation_vars(),
                 node_outputs=pool.get_all_node_outputs(),
                 system_vars=pool.get_all_system_vars(),
-            ) if variable.input_type == ValueInputType.VARIABLE else TypeTransformer.transform(variable.value, variable.type)
+            )
+            if variable.input_type == ValueInputType.VARIABLE
+            else TypeTransformer.transform(variable.value, variable.type)
             for variable in self.typed_config.cycle_vars
         }
         self.state["node_outputs"][self.node_id] = {
@@ -76,7 +78,9 @@ class LoopRuntime:
                 variables=pool.get_all_conversation_vars(),
                 node_outputs=pool.get_all_node_outputs(),
                 system_vars=pool.get_all_system_vars(),
-            ) if variable.input_type == ValueInputType.VARIABLE else TypeTransformer.transform(variable.value, variable.type)
+            )
+            if variable.input_type == ValueInputType.VARIABLE
+            else TypeTransformer.transform(variable.value, variable.type)
             for variable in self.typed_config.cycle_vars
         }
         loopstate = WorkflowState(
@@ -171,10 +175,11 @@ class LoopRuntime:
         """
         loopstate = self._init_loop_state()
         loop_time = self.typed_config.max_loop
+        child_state = []
         while self.evaluate_conditional(loopstate) and loopstate["looping"] and loop_time > 0:
             logger.info(f"loop node {self.node_id}: running")
-            await self.graph.ainvoke(loopstate)
+            child_state.append(await self.graph.ainvoke(loopstate))
             loop_time -= 1
 
         logger.info(f"loop node {self.node_id}: execution completed")
-        return loopstate["runtime_vars"][self.node_id]
+        return loopstate["runtime_vars"][self.node_id] | {"__child_state": child_state}

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

@@ -10,9 +10,8 @@ from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
 from app.core.workflow.nodes.knowledge import KnowledgeRetrievalNodeConfig
 from app.db import get_db_read
 from app.models import knowledge_model, knowledgeshare_model, ModelType
-from app.repositories import knowledge_repository
+from app.repositories import knowledge_repository, knowledgeshare_repository
 from app.schemas.chunk_schema import RetrieveType
-from app.services import knowledge_service, knowledgeshare_service
 from app.services.model_service import ModelConfigService
 
 logger = logging.getLogger(__name__)
@@ -96,7 +95,7 @@ class KnowledgeRetrievalNode(BaseNode):
 
         filters = self._build_kb_filter(kb_ids, knowledge_model.PermissionType.Share)
 
-        share_ids = knowledge_service.knowledge_repository.get_chunked_knowledgeids(
+        share_ids = knowledge_repository.get_chunked_knowledgeids(
             db=db,
             filters=filters
         )
@@ -105,7 +104,7 @@ class KnowledgeRetrievalNode(BaseNode):
             filters = [
                 knowledgeshare_model.KnowledgeShare.target_kb_id.in_(kb_ids)
             ]
-            items = knowledgeshare_service.knowledgeshare_repository.get_source_kb_ids_by_target_kb_id(
+            items = knowledgeshare_repository.get_source_kb_ids_by_target_kb_id(
                 db=db,
                 filters=filters
             )

api/app/core/workflow/nodes/llm/config.py

@@ -66,7 +66,7 @@ class LLMNodeConfig(BaseNodeConfig):
     )
 
     memory: MemoryWindowSetting = Field(
-        ...,
+        default_factory=MemoryWindowSetting,
         description="对话上下文窗口"
     )
 

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

@@ -85,6 +85,7 @@ class LLMNode(BaseNode):
         """
 
         # 1. 处理消息格式（优先使用 messages）
+        self.typed_config = LLMNodeConfig(**self.config)
         messages_config = self.typed_config.messages
 
         if messages_config:
@@ -167,7 +168,7 @@ class LLMNode(BaseNode):
         Returns:
             LLM 响应消息
         """
-        self.typed_config = LLMNodeConfig(**self.config)
+        # self.typed_config = LLMNodeConfig(**self.config)
         llm, prompt_or_messages = self._prepare_llm(state, True)
 
         logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（非流式）")
@@ -269,12 +270,16 @@ class LLMNode(BaseNode):
         chunk_count = 0
 
         # 调用 LLM（流式，支持字符串或消息列表）
-        async for chunk in llm.astream(prompt_or_messages):
+        last_meta_data = {}
+        async for chunk in llm.astream(prompt_or_messages, stream_usage=True):
             # 提取内容
             if hasattr(chunk, 'content'):
                 content = chunk.content
             else:
                 content = str(chunk)
+            if hasattr(chunk, 'response_metadata'):
+                if chunk.response_metadata:
+                    last_meta_data = chunk.response_metadata
 
             # 只有当内容不为空时才处理
             if content:
@@ -288,13 +293,10 @@ class LLMNode(BaseNode):
         logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(full_response)}, 总 chunks: {chunk_count}")
 
         # 构建完整的 AIMessage（包含元数据）
-        if isinstance(last_chunk, AIMessage):
-            final_message = AIMessage(
-                content=full_response,
-                response_metadata=last_chunk.response_metadata if hasattr(last_chunk, 'response_metadata') else {}
-            )
-        else:
-            final_message = AIMessage(content=full_response)
+        final_message = AIMessage(
+            content=full_response,
+            response_metadata=last_meta_data
+        )
 
         # yield 完成标记
         yield {"__final__": True, "result": final_message}