diff --git a/api/app/controllers/public_share_controller.py b/api/app/controllers/public_share_controller.py index 02c73718..354a58ef 100644 --- a/api/app/controllers/public_share_controller.py +++ b/api/app/controllers/public_share_controller.py @@ -583,7 +583,7 @@ async def chat( event_data = event.get("data", {}) # 转换为标准 SSE 格式（字符串） - sse_message = f"event: {event_type}\ndata: {json.dumps(event_data)}\n\n" + sse_message = f"event: {event_type}\ndata: {json.dumps(event_data, default=str, ensure_ascii=False)}\n\n" yield sse_message return StreamingResponse( diff --git a/api/app/core/memory/agent/mcp_server/tools/summary_tools.py b/api/app/core/memory/agent/mcp_server/tools/summary_tools.py index 6d5012f1..0f306572 100644 --- a/api/app/core/memory/agent/mcp_server/tools/summary_tools.py +++ b/api/app/core/memory/agent/mcp_server/tools/summary_tools.py @@ -425,15 +425,9 @@ async def Input_Summary( try: # Extract services from context - template_service = get_context_resource(ctx, "template_service") session_service = get_context_resource(ctx, "session_service") search_service = get_context_resource(ctx, "search_service") - # Get LLM client from memory_config - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client_from_config(memory_config) - # Resolve session ID sessionid = Resolve_username(usermessages) or "" sessionid = sessionid.replace('call_id_', '') @@ -539,31 +533,11 @@ async def Input_Summary( ) retrieve_info, question, raw_results = "", query, [] + # Return retrieved information directly without LLM processing + # Use the raw retrieved info as the answer + aimessages = retrieve_info if retrieve_info else "信息不足，无法回答" - # Render template - system_prompt = await template_service.render_template( - template_name='Retrieve_Summary_prompt.jinja2', - operation_name='input_summary', - query=query, - history=history, - retrieve_info=retrieve_info - ) - - # Call LLM with structured response - try: - structured = await llm_client.response_structured( - messages=[{"role": "system", "content": system_prompt}], - response_model=RetrieveSummaryResponse - ) - aimessages = structured.data.query_answer or "信息不足，无法回答" - except Exception as e: - logger.error( - f"Input_Summary: response_structured failed, using default answer: {e}", - exc_info=True - ) - aimessages = "信息不足，无法回答" - - logger.info(f"Quick answer summary: {storage_type}--{user_rag_memory_id}--{aimessages}") + logger.info(f"Quick answer (no LLM): {storage_type}--{user_rag_memory_id}--{aimessages[:500]}...") # Emit intermediate output for frontend return { diff --git a/api/app/core/tools/custom/schema_parser.py b/api/app/core/tools/custom/schema_parser.py index a22e2cfa..ea261dba 100644 --- a/api/app/core/tools/custom/schema_parser.py +++ b/api/app/core/tools/custom/schema_parser.py @@ -10,9 +10,6 @@ from app.core.logging_config import get_business_logger logger = get_business_logger() -# 为了兼容性，创建别名 -# SchemaParser = OpenAPISchemaParser = None - class OpenAPISchemaParser: """OpenAPI Schema解析器 - 解析OpenAPI 3.0规范""" @@ -213,7 +210,9 @@ class OpenAPISchemaParser: if not isinstance(operation, dict): continue - + + summary = operation.get("summary", "") + # 生成操作ID operation_id = operation.get("operationId") if not operation_id: @@ -223,7 +222,7 @@ class OpenAPISchemaParser: operations[operation_id] = { "method": method.upper(), "path": path, - "summary": operation.get("summary", ""), + "summary": summary if summary else operation_id, "description": operation.get("description", ""), "parameters": self._extract_parameters(operation), "request_body": self._extract_request_body(operation), diff --git a/api/app/core/workflow/nodes/llm/node.py b/api/app/core/workflow/nodes/llm/node.py index 6395d3b8..bfa1b99f 100644 --- a/api/app/core/workflow/nodes/llm/node.py +++ b/api/app/core/workflow/nodes/llm/node.py @@ -226,6 +226,7 @@ class LLMNode(BaseNode): Yields: 文本片段（chunk）或完成标记 """ + self.typed_config = LLMNodeConfig(**self.config) from langgraph.config import get_stream_writer llm, prompt_or_messages = self._prepare_llm(state, True) diff --git a/api/app/schemas/memory_episodic_schema.py b/api/app/schemas/memory_episodic_schema.py index 7b3f3d2d..832bf34b 100644 --- a/api/app/schemas/memory_episodic_schema.py +++ b/api/app/schemas/memory_episodic_schema.py @@ -1,9 +1,51 @@ """ 情景记忆的请求和响应模型 """ +from abc import ABC from pydantic import BaseModel, Field from typing import Optional +type_mapping = { + "Person": "人物实体节点", + "Organization": "组织实体节点", + "ORG": "组织实体节点", + "Location": "地点实体节点", + "LOC": "地点实体节点", + "Event": "事件实体节点", + "Concept": "概念实体节点", + "Time": "时间实体节点", + "Position": "职位实体节点", + "WorkRole": "职业实体节点", + "System": "系统实体节点", + "Policy": "政策实体节点", + "HistoricalPeriod": "历史时期实体节点", + "HistoricalState": "历史国家实体节点", + "HistoricalEvent": "历史事件实体节点", + "EconomicFactor": "经济因素实体节点", + "Condition": "条件实体节点", + "Numeric": "数值实体节点" + } +class EmotionType(ABC): + JOY_TYPE = "joy" + SURPRISE_TYPE = "surprise" + SANDROWNESS_TYPE = "sadness" + FEAR_TYPE = "fear" + ANGET_TYPE="anger" + NEUTRAL_TYPE="neutral" + EMOTION_MAPPING={ + "joy":"愉快", + "surprise":"惊喜", + "sadness":"悲伤", + "fear":"恐惧", + "anger":"生气", + "neutral":"中性" + } +class EmotionSubject(ABC): + SUBJECT_MAPPING={ + "self":"自己", + "other":"别人", + "object":"事物对象" + } class EpisodicMemoryOverviewRequest(BaseModel): """情景记忆总览查询请求""" diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index f650217d..ca97fb39 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -15,6 +15,8 @@ from neo4j.time import DateTime as Neo4jDateTime import json from datetime import datetime +from app.schemas.memory_episodic_schema import EmotionType + logger = logging.getLogger(__name__) class MemoryEntityService: @@ -123,7 +125,7 @@ class MemoryEntityService: extracted_entity_list = self._deduplicate_dict_list(extracted_entity_list) # 合并所有数据并处理相同text的合并 - all_timeline_data = memory_summary_list + statement_list + extracted_entity_list + all_timeline_data = memory_summary_list + statement_list all_timeline_data = self._merge_same_text_items(all_timeline_data) result = { @@ -496,11 +498,11 @@ class MemoryEmotion: length_data.append(emotion_intensity) if emotion_type is not None and emotion_intensity is not None and formatted_created_at is not None: # 使用(emotion_type, created_at)作为分组键 - if emotion_type in {"joy", "surprise"}: + if emotion_type in {EmotionType.JOY_TYPE, EmotionType.SURPRISE_TYPE}: emotion_type='positive' - elif emotion_type in {"sadness", "fear", "anger"}: + elif emotion_type in {EmotionType.SANDROWNESS_TYPE, EmotionType.FEAR_TYPE, EmotionType.ANGET_TYPE}: emotion_type='negative' - elif emotion_type=='neutral': + elif emotion_type==EmotionType.NEUTRAL_TYPE: emotion_type='neutral' group_key = (emotion_type, formatted_created_at) # 累加emotion_intensity diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 67a6ab2c..8f25f477 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -13,10 +13,14 @@ from typing import Any, Dict, List, Optional, Tuple from app.core.logging_config import get_logger from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context +from app.repositories.conversation_repository import ConversationRepository from app.repositories.end_user_repository import EndUserRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.services.implicit_memory_service import ImplicitMemoryService from app.services.memory_base_service import MemoryBaseService from app.services.memory_config_service import MemoryConfigService +from app.services.memory_perceptual_service import MemoryPerceptualService +from app.services.memory_short_service import ShortService from pydantic import BaseModel, Field from sqlalchemy.orm import Session @@ -1196,18 +1200,17 @@ async def analytics_memory_types( end_user_id: Optional[str] = None ) -> List[Dict[str, Any]]: """ - 统计9种记忆类型的数量和百分比 + 统计8种记忆类型的数量和百分比 计算规则： - 1. 感知记忆 (PERCEPTUAL_MEMORY) = statement + entity - 2. 工作记忆 (WORKING_MEMORY) = chunk + entity - 3. 短期记忆 (SHORT_TERM_MEMORY) = chunk - 4. 长期记忆 (LONG_TERM_MEMORY) = entity - 5. 显性记忆 (EXPLICIT_MEMORY) = 情景记忆 + 语义记忆（通过 MemoryBaseService.get_explicit_memory_count 获取） - 6. 隐性记忆 (IMPLICIT_MEMORY) = 1/3 * entity - 7. 情绪记忆 (EMOTIONAL_MEMORY) = 情绪标签统计总数（通过 MemoryBaseService.get_emotional_memory_count 获取） - 8. 情景记忆 (EPISODIC_MEMORY) = memory_summary（通过 MemoryBaseService.get_episodic_memory_count 获取） - 9. 遗忘记忆 (FORGET_MEMORY) = 激活值低于阈值的节点数（通过 MemoryBaseService.get_forget_memory_count 获取） + 1. 感知记忆 (PERCEPTUAL_MEMORY) = 通过 MemoryPerceptualService.get_memory_count 获取的 total_count + 2. 工作记忆 (WORKING_MEMORY) = 会话数量（通过 ConversationRepository.get_conversation_by_user_id 获取） + 3. 短期记忆 (SHORT_TERM_MEMORY) = /short_term 接口返回的问答对数量 + 4. 显性记忆 (EXPLICIT_MEMORY) = 情景记忆 + 语义记忆（通过 MemoryBaseService.get_explicit_memory_count 获取） + 5. 隐性记忆 (IMPLICIT_MEMORY) = Statement 节点数量的三分之一 + 6. 情绪记忆 (EMOTIONAL_MEMORY) = 情绪标签统计总数（通过 MemoryBaseService.get_emotional_memory_count 获取） + 7. 情景记忆 (EPISODIC_MEMORY) = memory_summary（通过 MemoryBaseService.get_episodic_memory_count 获取） + 8. 遗忘记忆 (FORGET_MEMORY) = 激活值低于阈值的节点数（通过 MemoryBaseService.get_forget_memory_count 获取） Args: db: 数据库会话 @@ -1227,7 +1230,6 @@ async def analytics_memory_types( - PERCEPTUAL_MEMORY: 感知记忆 - WORKING_MEMORY: 工作记忆 - SHORT_TERM_MEMORY: 短期记忆 - - LONG_TERM_MEMORY: 长期记忆 - EXPLICIT_MEMORY: 显性记忆 - IMPLICIT_MEMORY: 隐性记忆 - EMOTIONAL_MEMORY: 情绪记忆 @@ -1237,40 +1239,78 @@ async def analytics_memory_types( # 初始化基础服务 base_service = MemoryBaseService() - # 定义需要查询的基础节点类型 - node_types = { - "Statement": "Statement", - "Entity": "ExtractedEntity", - "Chunk": "Chunk" - } + # 初始化感知记忆服务 + perceptual_service = MemoryPerceptualService(db) - # 存储每种节点类型的计数 - node_counts = {} + # 获取感知记忆数量 + if end_user_id: + perceptual_stats = perceptual_service.get_memory_count(uuid.UUID(end_user_id)) + perceptual_count = perceptual_stats.get("total", 0) + else: + perceptual_count = 0 - # 查询每种节点类型的数量 - for key, node_type in node_types.items(): - if end_user_id: - query = f""" - MATCH (n:{node_type}) + # 获取工作记忆数量（基于会话数量） + work_count = 0 + if end_user_id: + try: + conversation_repo = ConversationRepository(db) + conversations = conversation_repo.get_conversation_by_user_id( + user_id=uuid.UUID(end_user_id), + limit=100, # 获取更多会话以准确统计 + is_activate=True + ) + work_count = len(conversations) + logger.debug(f"工作记忆数量（会话数）: {work_count} (end_user_id={end_user_id})") + except Exception as e: + logger.warning(f"获取会话数量失败，工作记忆数量设为0: {str(e)}") + work_count = 0 + + # 获取隐性记忆数量（基于 Statement 节点数量的三分之一） + implicit_count = 0 + if end_user_id: + try: + # 查询 Statement 节点数量 + query = """ + MATCH (n:Statement) WHERE n.group_id = $group_id RETURN count(n) as count """ result = await _neo4j_connector.execute_query(query, group_id=end_user_id) - else: - query = f""" - MATCH (n:{node_type}) - RETURN count(n) as count - """ - result = await _neo4j_connector.execute_query(query) - - # 提取计数结果 - count = result[0]["count"] if result and len(result) > 0 else 0 - node_counts[key] = count + statement_count = result[0]["count"] if result and len(result) > 0 else 0 + # 取三分之一作为隐性记忆数量 + implicit_count = round(statement_count / 3) + logger.debug(f"隐性记忆数量（Statement数量的1/3）: {implicit_count} (Statement总数={statement_count}, end_user_id={end_user_id})") + except Exception as e: + logger.warning(f"获取Statement数量失败，隐性记忆数量设为0: {str(e)}") + implicit_count = 0 - # 获取各节点类型的数量 - statement_count = node_counts.get("Statement", 0) - entity_count = node_counts.get("Entity", 0) - chunk_count = node_counts.get("Chunk", 0) + # 原有的基于行为习惯的统计方式（已注释） + # implicit_count = 0 + # if end_user_id: + # try: + # implicit_service = ImplicitMemoryService(db, end_user_id) + # behavior_habits = await implicit_service.get_behavior_habits( + # user_id=end_user_id + # ) + # implicit_count = len(behavior_habits) + # logger.debug(f"隐性记忆数量（行为习惯数）: {implicit_count} (end_user_id={end_user_id})") + # except Exception as e: + # logger.warning(f"获取行为习惯数量失败，隐性记忆数量设为0: {str(e)}") + # implicit_count = 0 + + # 获取短期记忆数量（基于 /short_term 接口返回的问答对数量） + short_term_count = 0 + if end_user_id: + try: + short_term_service = ShortService(end_user_id) + short_term_data = short_term_service.get_short_databasets() + # 统计 short_term 数组的长度 + if short_term_data: + short_term_count = len(short_term_data) + logger.debug(f"短期记忆数量（问答对数）: {short_term_count} (end_user_id={end_user_id})") + except Exception as e: + logger.warning(f"获取短期记忆数量失败，短期记忆数量设为0: {str(e)}") + short_term_count = 0 # 获取用户的遗忘阈值配置 forgetting_threshold = 0.3 # 默认值 @@ -1296,17 +1336,16 @@ async def analytics_memory_types( # 使用 MemoryBaseService 的共享方法获取特殊记忆类型的数量 episodic_count = await base_service.get_episodic_memory_count(end_user_id) explicit_count = await base_service.get_explicit_memory_count(end_user_id) - emotion_count = await base_service.get_emotional_memory_count(end_user_id, statement_count) + emotion_count = await base_service.get_emotional_memory_count(end_user_id, perceptual_count) forget_count = await base_service.get_forget_memory_count(end_user_id, forgetting_threshold) - # 按规则计算9种记忆类型的数量（使用英文枚举作为key） + # 按规则计算8种记忆类型的数量（使用英文枚举作为key） memory_counts = { - "PERCEPTUAL_MEMORY": statement_count + entity_count, # 感知记忆 - "WORKING_MEMORY": chunk_count + entity_count, # 工作记忆 - "SHORT_TERM_MEMORY": chunk_count, # 短期记忆 - "LONG_TERM_MEMORY": entity_count, # 长期记忆 + "PERCEPTUAL_MEMORY": perceptual_count, # 感知记忆 + "WORKING_MEMORY": work_count, # 工作记忆（基于会话数量） + "SHORT_TERM_MEMORY": short_term_count, # 短期记忆（基于问答对数量） "EXPLICIT_MEMORY": explicit_count, # 显性记忆（情景记忆 + 语义记忆） - "IMPLICIT_MEMORY": entity_count // 3, # 隐性记忆 (1/3 entity) + "IMPLICIT_MEMORY": implicit_count, # 隐性记忆（Statement数量的1/3） "EMOTIONAL_MEMORY": emotion_count, # 情绪记忆（使用情绪标签统计） "EPISODIC_MEMORY": episodic_count, # 情景记忆 "FORGET_MEMORY": forget_count # 遗忘记忆（激活值低于阈值） @@ -1332,7 +1371,7 @@ async def analytics_graph_data( db: Session, end_user_id: str, node_types: Optional[List[str]] = None, - limit: int = 100, + limit: int = 130, depth: int = 1, center_node_id: Optional[str] = None ) -> Dict[str, Any]: @@ -1423,6 +1462,7 @@ async def analytics_graph_data( "limit": limit } + # 执行节点查询 node_results = await _neo4j_connector.execute_query(node_query, **node_params) @@ -1567,9 +1607,9 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ allowed_fields = field_whitelist.get(label, []) # 如果没有定义白名单，返回空字典（或者可以返回所有字段） - if not allowed_fields: - # 对于未定义的节点类型，只返回基本字段 - allowed_fields = ["name", "created_at", "caption"] + # if not allowed_fields: + # # 对于未定义的节点类型，只返回基本字段 + # allowed_fields = ["name", "created_at", "caption"] count_neo4j=f"""MATCH (n)-[r]-(m) WHERE elementId(n) ="{node_id}" RETURN count(r) AS rel_count;""" node_results = await (_neo4j_connector.execute_query(count_neo4j)) # 提取白名单中的字段 @@ -1577,10 +1617,15 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ for field in allowed_fields: if field in properties: value = properties[field] + if str(field) == 'entity_type': + value=type_mapping.get(value,'') + if str(field)=="emotion_type": + value=EmotionType.EMOTION_MAPPING.get(value) + if str(field)=="emotion_subject": + value=EmotionSubject.SUBJECT_MAPPING.get(value) # 清理 Neo4j 特殊类型 filtered_props[field] = _clean_neo4j_value(value) filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0] - print(filtered_props) return filtered_props @@ -1621,6 +1666,5 @@ def _clean_neo4j_value(value: Any) -> Any: return str(value) except Exception: return None - # 返回原始值 - return value + return value \ No newline at end of file diff --git a/web/src/views/Conversation/index.tsx b/web/src/views/Conversation/index.tsx index 7fa1ada1..6ccb35ec 100644 --- a/web/src/views/Conversation/index.tsx +++ b/web/src/views/Conversation/index.tsx @@ -181,10 +181,15 @@ const Conversation: FC = () => { currentConversationId = newId break case 'message': - const { content } = item.data as { content: string } - updateAssistantMessage(content) + const { content, chunk, conversation_id: curId } = item.data as { content: string; chunk: string; conversation_id: string; } + updateAssistantMessage(content ?? chunk) + + if (curId) { + currentConversationId = curId; + } break case 'end': + case 'workflow_end': setLoading(false) if (currentConversationId && currentConversationId !== conversation_id) { setConversationId(currentConversationId) diff --git a/web/src/views/Workflow/components/AddChatVariable/ChatVariableModal.tsx b/web/src/views/Workflow/components/AddChatVariable/ChatVariableModal.tsx index aaaa2ab5..52394ea1 100644 --- a/web/src/views/Workflow/components/AddChatVariable/ChatVariableModal.tsx +++ b/web/src/views/Workflow/components/AddChatVariable/ChatVariableModal.tsx @@ -103,7 +103,11 @@ const ChatVariableModal = forwardRef {type === 'number' - ? + ? form.setFieldValue('defaultValue', value)} + /> : type === 'boolean' ?