diff --git a/backend-fastapi/ai_platform/nodes/builtin/intent_node.py b/backend-fastapi/ai_platform/nodes/builtin/intent_node.py index 8ec3cd2..ca7a86c 100644 --- a/backend-fastapi/ai_platform/nodes/builtin/intent_node.py +++ b/backend-fastapi/ai_platform/nodes/builtin/intent_node.py @@ -116,17 +116,12 @@ class IntentNode(BaseNode): error='请至少配置一个意图', ) - if not model_id: - return NodeResult( - success=False, - error='请选择用于意图识别的模型', - ) - + llm_service = LLMService(context.db_session) + model_id = await llm_service.resolve_chat_model_id(model_id) + # 检查模型是否支持 Function Calling supports_fc = self._check_model_supports_function_call(model_id) - llm_service = LLMService(context.db_session) - if supports_fc: # 使用 Function Calling 方式（更准确） intent_name, confidence, total_tokens = await self._execute_with_function_calling_async( @@ -169,6 +164,7 @@ class IntentNode(BaseNode): 'intent': intent_name, 'confidence': confidence, 'matched_branch': next_node_id, + 'model_id': model_id, }, ) diff --git a/backend-fastapi/ai_platform/nodes/builtin/llm_node.py b/backend-fastapi/ai_platform/nodes/builtin/llm_node.py index 00330e8..95e8d2c 100644 --- a/backend-fastapi/ai_platform/nodes/builtin/llm_node.py +++ b/backend-fastapi/ai_platform/nodes/builtin/llm_node.py @@ -163,6 +163,7 @@ class LLMNode(BaseNode): # 调用 LLM（异步） llm_service = LLMService(context.db_session) + model_id = await llm_service.resolve_chat_model_id(model_id) output_var = self.config.get('output_variable', 'llm_response') # 根据输出模式选择执行方式 @@ -172,6 +173,11 @@ class LLMNode(BaseNode): llm_service, model_id, messages, temperature, max_tokens, output_schema, output_var, start_time ) + result.metadata = { + **(result.metadata or {}), + 'model_id': str(model_id), + 'output_variable': output_var, + } return result else: # 普通文本输出模式（异步） @@ -194,9 +200,11 @@ class LLMNode(BaseNode): tokens_used=response.total_tokens, elapsed_time=elapsed_time, metadata={ + 'model_id': str(model_id), 'model': response.model, 'prompt_tokens': response.prompt_tokens, 'completion_tokens': response.completion_tokens, + 'output_variable': output_var, }, ) @@ -314,6 +322,11 @@ class LLMNode(BaseNode): llm_service, model_id, messages, temperature, max_tokens, output_schema, output_var, start_time ) + result.metadata = { + **(result.metadata or {}), + 'model_id': str(model_id), + 'output_variable': output_var, + } return result else: # 普通文本输出模式 @@ -350,6 +363,10 @@ class LLMNode(BaseNode): }, tokens_used=total_tokens, elapsed_time=elapsed_time, + metadata={ + 'model_id': str(model_id), + 'output_variable': output_var, + }, ) except Exception as e: diff --git a/backend-fastapi/ai_platform/schemas/agent_schema.py b/backend-fastapi/ai_platform/schemas/agent_schema.py index 559c6d3..6957377 100644 --- a/backend-fastapi/ai_platform/schemas/agent_schema.py +++ b/backend-fastapi/ai_platform/schemas/agent_schema.py @@ -251,7 +251,7 @@ class AgentMessageResponse(BaseModel): content: str = "" attachments: List[AttachmentResponse] = Field(default_factory=list) status: str = "completed" - reasoning_steps: List[ReasoningStep] = Field(default_factory=list) + reasoning_steps: List[Dict[str, Any]] = Field(default_factory=list) tool_calls: List[ToolCallRecord] = Field(default_factory=list) prompt_tokens: int = 0 completion_tokens: int = 0 diff --git a/backend-fastapi/ai_platform/services/agent_service.py b/backend-fastapi/ai_platform/services/agent_service.py index 47249b4..76bb58e 100644 --- a/backend-fastapi/ai_platform/services/agent_service.py +++ b/backend-fastapi/ai_platform/services/agent_service.py @@ -3,6 +3,7 @@ """ import logging import time +from datetime import datetime from typing import Any, Dict, List, Optional from sqlalchemy import select @@ -45,7 +46,93 @@ class AgentService: for key, value in self._agent_collaboration_metadata(agent).items(): collaboration.setdefault(key, value) enriched["collaboration"] = collaboration + communication = dict(enriched.get("communication") or {}) + actor = dict(communication.get("actor") or {}) + for key in ( + "agent_id", + "agent_code", + "agent_name", + "collaboration_role", + "collaboration_mode", + "model_id", + ): + if collaboration.get(key): + actor.setdefault(key, collaboration[key]) + communication.setdefault("event", enriched.get("type") or "") + communication.setdefault("status", "failed" if enriched.get("type") == "error" else "completed") + communication.setdefault("channel", "agent_chat") + communication.setdefault("actor", actor) + communication.setdefault( + "message", + enriched.get("content") + or enriched.get("message") + or enriched.get("accumulated_content") + or "", + ) + enriched["communication"] = communication return enriched + + @staticmethod + def _event_to_reasoning_step(event: Dict[str, Any]) -> Optional[Dict[str, Any]]: + event_type = event.get('type') + if event_type not in { + 'action', + 'annotation_reply', + 'error', + 'knowledge_retrieval', + 'loop_complete', + 'loop_iteration_complete', + 'loop_iteration_error', + 'loop_iteration_start', + 'node_complete', + 'node_start', + 'observation', + 'parallel_complete', + 'parallel_start', + 'thought', + 'tool_call', + 'tool_result', + 'waiting_input', + }: + return None + + collaboration = event.get('collaboration') or {} + communication = event.get('communication') or {} + config = event.get('waiting_config') or event.get('config') or {} + + content = ( + event.get('node_label') + or event.get('content') + or communication.get('message') + or config.get('title') + or config.get('question') + or event.get('node_type') + or event_type + ) + + step = { + 'type': event_type, + 'content': content, + 'tool': event.get('tool') or '', + 'params': event.get('params') or config, + 'node_id': event.get('node_id') or '', + 'node_type': event.get('node_type') or '', + 'branch_id': event.get('branch_id') or collaboration.get('branch_id') or '', + 'branch_label': event.get('branch_label') or collaboration.get('branch_label') or '', + 'agent_code': collaboration.get('agent_code') or event.get('agent_code') or '', + 'agent_name': collaboration.get('agent_name') or event.get('agent_name') or '', + 'model': collaboration.get('model') or event.get('model') or '', + 'model_id': collaboration.get('model_id') or event.get('model_id') or '', + 'subflow_name': collaboration.get('subflow_name') or event.get('subflow_name') or '', + 'from_subflow': bool(collaboration.get('from_subflow') or event.get('from_subflow')), + 'collaboration_role': collaboration.get('collaboration_role') or '', + 'collaboration_mode': collaboration.get('collaboration_mode') or event.get('collaboration_mode') or '', + 'communication': communication, + 'output': event.get('outputs') or event.get('output'), + 'status': 'running' if event_type in {'node_start', 'parallel_start', 'waiting_input', 'loop_iteration_start'} else 'completed', + 'timestamp': datetime.now().isoformat(), + } + return {key: value for key, value in step.items() if value not in (None, '', {}, [])} async def chat( self, @@ -209,16 +296,31 @@ class AgentService: ) self._db.add(assistant_msg) await self._db.flush() + reasoning_steps: List[Dict[str, Any]] = [] - yield { + yield self._attach_agent_collaboration({ 'type': 'start', 'conversation_id': str(conversation.id), 'message_id': str(assistant_msg.id), - } + }, agent) try: # 构建系统提示词 system_prompt = self._build_system_prompt_simple(agent) + model_id = await self._resolve_agent_model_id(agent) + if not model_id: + raise ValueError('未找到可用的 chat 模型，请先在模型配置中启用一个模型') + + event = self._attach_agent_collaboration({ + 'type': 'thought', + 'content': '智能体开始分析用户需求并调用默认 chat 模型', + 'model_id': model_id, + }, agent) + step = self._event_to_reasoning_step(event) + if step: + reasoning_steps.append(step) + assistant_msg.reasoning_steps = reasoning_steps.copy() + yield event # 标注直接回复检查（高相似度时跳过 LLM，直接返回标注答案） annotation_reply = await self._check_annotation_direct_reply(agent, user_message) @@ -257,11 +359,16 @@ class AgentService: knowledge_context = await self._retrieve_knowledge_context(agent, user_message) if knowledge_context: system_prompt = self._inject_knowledge_context(system_prompt, knowledge_context) - yield { + event = { 'type': 'knowledge_retrieval', 'content': knowledge_context['summary'], 'result_count': knowledge_context['result_count'], } + step = self._event_to_reasoning_step(event) + if step: + reasoning_steps.append(step) + assistant_msg.reasoning_steps = reasoning_steps.copy() + yield event # 获取对话历史 history = await self._get_conversation_history(conversation, limit=10) @@ -297,6 +404,7 @@ class AgentService: 'type': 'llm_chunk', 'content': chunk.content, 'accumulated_content': accumulated_content, + 'model_id': model_id, } if chunk.is_finished: @@ -317,6 +425,7 @@ class AgentService: yield { 'type': 'answer', 'content': final_answer, + 'model_id': model_id, } # 更新助手消息 @@ -325,6 +434,7 @@ class AgentService: assistant_msg.status = 'completed' assistant_msg.total_tokens = total_tokens assistant_msg.elapsed_time = elapsed_time + assistant_msg.reasoning_steps = reasoning_steps.copy() # 更新对话统计 conversation.message_count = (conversation.message_count or 0) + 2 @@ -352,6 +462,10 @@ class AgentService: logger.exception(f'Agent chat error: {e}') assistant_msg.status = 'failed' assistant_msg.error_message = str(e) + error_step = self._event_to_reasoning_step({'type': 'error', 'content': str(e)}) + if error_step: + reasoning_steps.append(error_step) + assistant_msg.reasoning_steps = reasoning_steps.copy() await self._db.commit() yield { 'type': 'error', @@ -438,6 +552,7 @@ class AgentService: ) self._db.add(assistant_msg) await self._db.flush() + reasoning_steps: List[Dict[str, Any]] = [] yield { 'type': 'start', @@ -507,6 +622,25 @@ class AgentService: output = (event.get('outputs') or {}).get('output', '') if output: final_content = output + + step = self._event_to_reasoning_step(event) + if step: + existing_index = next( + ( + index + for index, item in enumerate(reasoning_steps) + if item.get('node_id') + and item.get('node_id') == step.get('node_id') + and item.get('type') == 'node_start' + and step.get('type') == 'node_complete' + ), + -1, + ) + if existing_index >= 0: + reasoning_steps[existing_index] = step + else: + reasoning_steps.append(step) + assistant_msg.reasoning_steps = reasoning_steps.copy() yield event else: @@ -573,6 +707,25 @@ class AgentService: output = (event.get('outputs') or {}).get('output', '') if output: final_content = output + + step = self._event_to_reasoning_step(event) + if step: + existing_index = next( + ( + index + for index, item in enumerate(reasoning_steps) + if item.get('node_id') + and item.get('node_id') == step.get('node_id') + and item.get('type') == 'node_start' + and step.get('type') == 'node_complete' + ), + -1, + ) + if existing_index >= 0: + reasoning_steps[existing_index] = step + else: + reasoning_steps.append(step) + assistant_msg.reasoning_steps = reasoning_steps.copy() yield event @@ -585,6 +738,7 @@ class AgentService: assistant_msg.status = 'completed' assistant_msg.elapsed_time = elapsed_time assistant_msg.total_tokens = total_tokens + assistant_msg.reasoning_steps = reasoning_steps.copy() # 更新对话统计 conversation.message_count = (conversation.message_count or 0) + 2 @@ -614,6 +768,10 @@ class AgentService: logger.exception(f'Dialog flow error: {e}') assistant_msg.status = 'failed' assistant_msg.error_message = str(e) + error_step = self._event_to_reasoning_step({'type': 'error', 'content': str(e)}) + if error_step: + reasoning_steps.append(error_step) + assistant_msg.reasoning_steps = reasoning_steps.copy() await self._db.commit() yield { 'type': 'error', diff --git a/backend-fastapi/ai_platform/services/llm_service.py b/backend-fastapi/ai_platform/services/llm_service.py index 6288c21..12e034a 100644 --- a/backend-fastapi/ai_platform/services/llm_service.py +++ b/backend-fastapi/ai_platform/services/llm_service.py @@ -29,6 +29,38 @@ class LLMService: def __init__(self, db: Optional[AsyncSession] = None): self._provider_cache: Dict[str, BaseLLMProvider] = {} self._db = db + + @staticmethod + def _is_missing_model_id(model_id: Optional[str]) -> bool: + if model_id is None: + return True + return str(model_id).strip().lower() in {"", "none", "null", "undefined"} + + async def resolve_chat_model_id(self, model_id: Optional[str]) -> str: + """解析 chat 模型。未指定时使用当前启用的默认 chat 模型。""" + if not self._is_missing_model_id(model_id): + return str(model_id) + + if not self._db: + raise ValueError("未找到可用的 chat 模型，请先在模型配置中启用一个模型") + + from ai_platform.models import LLMModel + + result = await self._db.execute( + select(LLMModel) + .where( + LLMModel.is_deleted == False, + LLMModel.is_active == True, + LLMModel.model_type == "chat", + ) + .order_by(LLMModel.sort.desc(), LLMModel.sys_create_datetime.desc()) + ) + model = result.scalars().first() + if not model: + raise ValueError("未找到可用的 chat 模型，请先在模型配置中启用一个模型") + + logger.info("No model_id supplied, fallback to default chat model %s", model.id) + return str(model.id) async def _get_provider_async(self, model_id: str) -> tuple: """ @@ -45,6 +77,8 @@ class LLMService: if not self._db: raise ValueError("数据库会话未初始化") + model_id = await self.resolve_chat_model_id(model_id) + # 查询模型 result = await self._db.execute( select(LLMModel).where( @@ -319,14 +353,18 @@ class LLMService: async def get_provider(): return await self._get_provider_async(model_id) - loop = asyncio.get_event_loop() - if loop.is_running(): - # 如果已有事件循环在运行，使用线程池 - with concurrent.futures.ThreadPoolExecutor() as executor: - future = executor.submit(asyncio.run, get_provider()) - provider, model_name = future.result() + try: + loop = asyncio.get_event_loop() + except RuntimeError: + provider, model_name = asyncio.run(get_provider()) else: - provider, model_name = loop.run_until_complete(get_provider()) + if loop.is_running(): + # 如果已有事件循环在运行，使用线程池 + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit(asyncio.run, get_provider()) + provider, model_name = future.result() + else: + provider, model_name = loop.run_until_complete(get_provider()) # 使用获取到的 provider 进行流式调用 yield from self.chat_stream_sync_with_provider( diff --git a/backend-fastapi/ai_platform/services/workflow_service.py b/backend-fastapi/ai_platform/services/workflow_service.py index c6ee7f3..061b5bc 100644 --- a/backend-fastapi/ai_platform/services/workflow_service.py +++ b/backend-fastapi/ai_platform/services/workflow_service.py @@ -50,10 +50,48 @@ def _node_label_from_map(node_map: Dict[str, Any], node_id: str) -> str: return data.get('label') or node.get('label') or node_id +def _safe_preview(value: Any, max_length: int = 220) -> str: + if value is None or value == '': + return '' + if isinstance(value, str): + text = value + else: + import json + + try: + text = json.dumps(value, ensure_ascii=False) + except TypeError: + text = str(value) + text = text.strip() + return text if len(text) <= max_length else f'{text[:max_length]}...' + + +def _node_collaboration_mode(node_type: str, data: Dict[str, Any]) -> str: + if data.get('agent_code'): + return 'agent' + if node_type == 'parallel': + return 'parallel' + if node_type == 'subflow': + return 'subflow' + if node_type == 'intent': + return 'router' + if node_type in ('confirm', 'question', 'choice'): + return 'human_input' + if node_type == 'llm': + return 'llm' + return 'workflow' + + def _node_collaboration_metadata(node_map: Dict[str, Any], node_id: str, node_type: str) -> dict: node = node_map.get(node_id) or {} data = node.get('data') or {} - metadata: Dict[str, Any] = {} + metadata: Dict[str, Any] = { + 'node_id': node_id, + 'node_type': node_type, + 'node_label': _node_label_from_map(node_map, node_id), + 'collaboration_role': data.get('label') or node_id, + 'collaboration_mode': _node_collaboration_mode(node_type, data), + } agent_code = (data.get('agent_code') or '').strip() if agent_code: @@ -83,15 +121,96 @@ def _node_collaboration_metadata(node_map: Dict[str, Any], node_id: str, node_ty if branch.get('id') ] - if node_type in ('template', 'llm') and agent_code: - metadata['collaboration_role'] = data.get('label') or node_id - - if node_type == 'subflow': - metadata['collaboration_role'] = data.get('label') or node_id - return metadata +def _build_communication_metadata( + node_map: Dict[str, Any], + node_id: str, + node_type: str, + *, + event_type: str, + status: Optional[str] = None, + branch_id: Optional[str] = None, + branch_label: Optional[str] = None, + target_node_id: Optional[str] = None, + output: Any = None, + error: Any = None, + waiting_config: Optional[Dict[str, Any]] = None, +) -> dict: + actor = _node_collaboration_metadata(node_map, node_id, node_type) + message = _safe_preview(error or output) + if waiting_config: + message = ( + waiting_config.get('title') + or waiting_config.get('question') + or waiting_config.get('content') + or message + ) + + communication: Dict[str, Any] = { + 'event': event_type, + 'status': status or '', + 'actor': { + key: actor.get(key) + for key in ( + 'node_id', + 'node_type', + 'node_label', + 'agent_code', + 'agent_name', + 'collaboration_role', + 'collaboration_mode', + ) + if actor.get(key) + }, + 'message': _safe_preview(message), + } + if branch_id: + communication['channel'] = 'parallel_branch' + communication['branch_id'] = branch_id + communication['branch_label'] = branch_label or branch_id + elif node_type == 'subflow': + communication['channel'] = 'subflow' + elif node_type in ('confirm', 'question', 'choice') or event_type == 'waiting_input': + communication['channel'] = 'human_input' + else: + communication['channel'] = 'workflow_edge' + if target_node_id: + target_type = (node_map.get(target_node_id) or {}).get('type', '') + target = _node_collaboration_metadata(node_map, target_node_id, target_type) + communication['target'] = { + key: target.get(key) + for key in ( + 'node_id', + 'node_type', + 'node_label', + 'agent_code', + 'agent_name', + 'collaboration_role', + 'collaboration_mode', + ) + if target.get(key) + } + return communication + + +def _resolve_handoff_target( + current_node_id: str, + result: Optional[NodeResult], + edge_map: Dict[str, List[str]], + parallel_edge_map: Dict[str, Dict[str, str]], + node_map: Dict[str, Any], +) -> Optional[str]: + if result and result.next_node_id: + if result.next_node_id in parallel_edge_map.get(current_node_id, {}): + return parallel_edge_map[current_node_id][result.next_node_id] + if result.next_node_id in node_map: + return result.next_node_id + next_nodes = edge_map.get(current_node_id, []) + return next_nodes[0] if next_nodes else None + + def _make_execution_log_entry( node_map: Dict[str, Any], node_id: str, @@ -100,8 +219,25 @@ def _make_execution_log_entry( ) -> dict: metadata = dict(extra.pop('metadata', {}) or {}) collaboration = _node_collaboration_metadata(node_map, node_id, node_type) + branch_id = extra.get('branch') or extra.get('branch_id') + branch_label = extra.get('branch_label') + if branch_id: + collaboration['branch_id'] = branch_id + collaboration['branch_label'] = branch_label or branch_id if collaboration: metadata['collaboration'] = collaboration + metadata['communication'] = _build_communication_metadata( + node_map, + node_id, + node_type, + event_type='node_complete', + status=extra.get('status'), + branch_id=branch_id, + branch_label=branch_label, + target_node_id=extra.get('target_node_id'), + output=extra.get('output'), + error=extra.get('error'), + ) return { 'node_id': node_id, 'node_type': node_type, @@ -133,6 +269,10 @@ def _event_collaboration_metadata( 'model_id', 'output_variable', 'subflow_name', + 'prompt_tokens', + 'completion_tokens', + 'confidence', + 'matched_branch', ): value = result_metadata.get(key) if value is not None and value != '': @@ -159,8 +299,27 @@ def _merge_event_collaboration( result: Optional[NodeResult] = None, ) -> Dict[str, Any]: collaboration = _event_collaboration_metadata(node_map, node_id, node_type, result) + if event.get('branch_id'): + collaboration['branch_id'] = event.get('branch_id') + collaboration['branch_label'] = event.get('branch_label') or event.get('branch_id') if collaboration: event['collaboration'] = collaboration + event_error = None + if event.get('type') == 'error': + event_error = event.get('error_message') or event.get('message') or event.get('content') + event['communication'] = _build_communication_metadata( + node_map, + node_id, + node_type, + event_type=event.get('type') or '', + status=event.get('status'), + branch_id=event.get('branch_id'), + branch_label=event.get('branch_label'), + target_node_id=event.get('target_node_id'), + output=(event.get('outputs') or {}).get('output') if isinstance(event.get('outputs'), dict) else event.get('outputs'), + error=event_error, + waiting_config=event.get('waiting_config') or event.get('config'), + ) return event @@ -653,6 +812,13 @@ class AIWorkflowService: start_time = time.time() result = await node_instance.execute_async(context) elapsed = int((time.time() - start_time) * 1000) + target_node_id = _resolve_handoff_target( + current_node_id, + result, + edge_map, + parallel_edge_map, + node_map, + ) # 记录日志 logs.append(_make_execution_log_entry( @@ -666,6 +832,7 @@ class AIWorkflowService: tokens_used=result.tokens_used, metadata=_node_result_metadata(result), inputs=copy.deepcopy(node_inputs), + target_node_id=target_node_id, )) total_tokens += result.tokens_used @@ -819,6 +986,13 @@ class AIWorkflowService: start_time = time.time() result = await node_instance.execute_async(branch_context) elapsed = int((time.time() - start_time) * 1000) + target_node_id = _resolve_handoff_target( + current_id, + result, + edge_map, + parallel_edge_map, + node_map, + ) branch_logs.append(_make_execution_log_entry( node_map, @@ -832,6 +1006,7 @@ class AIWorkflowService: metadata=_node_result_metadata(result), branch=branch_id, branch_label=branch_labels.get(branch_id, branch_id), + target_node_id=target_node_id, )) branch_tokens += result.tokens_used @@ -1072,6 +1247,7 @@ class AIWorkflowService: 'output': result.output, 'output_variables': result.metadata.get('frontend_output_variables', result.output_variables), }, + 'target_node_id': target_node_id, }, result), node_map, current_id, node_type, result) if not result.success: @@ -1782,6 +1958,13 @@ class AIWorkflowService: result = await node_instance.execute_async(context) elapsed = int((time.time() - start_time) * 1000) + target_node_id = _resolve_handoff_target( + current_node_id, + result, + edge_map, + parallel_edge_map, + node_map, + ) # 处理节点事件（如消息节点发送消息） if result.events: @@ -1815,7 +1998,9 @@ class AIWorkflowService: status='waiting', output=result.output, elapsed_time=elapsed, + metadata=_node_result_metadata(result), inputs=copy.deepcopy(node_inputs), + target_node_id=target_node_id, ) logs.append(log_entry) @@ -1838,6 +2023,7 @@ class AIWorkflowService: 'node_id': current_node_id, 'node_type': node_type, 'config': result.waiting_config, + 'waiting_config': result.waiting_config, }, node_map, current_node_id, node_type, result) # 暂停工作流执行，等待用户输入后续流 @@ -1855,6 +2041,7 @@ class AIWorkflowService: tokens_used=result.tokens_used, metadata=_node_result_metadata(result), inputs=copy.deepcopy(node_inputs), + target_node_id=target_node_id, ) logs.append(log_entry) @@ -1882,6 +2069,7 @@ class AIWorkflowService: 'output': result.output, 'output_variables': result.metadata.get('frontend_output_variables', result.output_variables), }, + 'target_node_id': target_node_id, }, result), node_map, current_node_id, node_type, result) if not result.success: @@ -2335,6 +2523,7 @@ class AIWorkflowService: output=result.output, elapsed_time=elapsed, metadata=_node_result_metadata(result), + target_node_id=target_node_id, ) logs.append(log_entry) @@ -2353,6 +2542,7 @@ class AIWorkflowService: 'node_id': current_node_id, 'node_type': node_type, 'config': result.waiting_config, + 'waiting_config': result.waiting_config, }, node_map, current_node_id, node_type, result) # 如果在循环中，添加迭代信息 if loop_state: @@ -2371,6 +2561,7 @@ class AIWorkflowService: elapsed_time=elapsed, tokens_used=result.tokens_used, metadata=_node_result_metadata(result), + target_node_id=target_node_id, ) logs.append(log_entry) @@ -2401,6 +2592,7 @@ class AIWorkflowService: 'output': result.output, 'output_variables': result.metadata.get('frontend_output_variables', result.output_variables), }, + 'target_node_id': target_node_id, }, result), node_map, current_node_id, node_type, result) # 如果在循环中，添加迭代信息 if loop_state: diff --git a/web/apps/web-ele/src/api/ai-platform/ai-platform.ts b/web/apps/web-ele/src/api/ai-platform/ai-platform.ts index e01bf69..7318a08 100644 --- a/web/apps/web-ele/src/api/ai-platform/ai-platform.ts +++ b/web/apps/web-ele/src/api/ai-platform/ai-platform.ts @@ -281,6 +281,9 @@ export interface ExecutionLogEntry { error?: string; elapsed_time?: number; tokens_used?: number; + branch?: string; + branch_label?: string; + metadata?: Record; } export interface WorkflowRunListItem { diff --git a/web/apps/web-ele/src/components/ChatBox/MessageBubble.vue b/web/apps/web-ele/src/components/ChatBox/MessageBubble.vue index 3945a7b..d9e56f8 100644 --- a/web/apps/web-ele/src/components/ChatBox/MessageBubble.vue +++ b/web/apps/web-ele/src/components/ChatBox/MessageBubble.vue @@ -194,6 +194,21 @@ const getStepColor = (step: ReasoningStep) => { const getStepMetaItems = (step: ReasoningStep) => { const items: string[] = []; + const communication = step.communication; + const actor = communication?.actor || {}; + const target = communication?.target || {}; + const actorName = + actor.agent_name || + actor.agent_code || + actor.node_label || + step.agent_name || + step.agent_code; + const targetName = target.agent_name || target.agent_code || target.node_label; + if (actorName && targetName) { + items.push(`交接: ${actorName} -> ${targetName}`); + } else if (communication?.channel) { + items.push(`通道: ${communication.channel}`); + } if (step.agent_name || step.agent_code) { items.push(`智能体: ${step.agent_name || step.agent_code}`); } @@ -206,6 +221,9 @@ const getStepMetaItems = (step: ReasoningStep) => { if (step.model || step.model_id) { items.push(`模型: ${step.model || step.model_id}`); } + if (communication?.message && communication.message !== step.content) { + items.push(`消息: ${communication.message}`); + } return items; }; diff --git a/web/apps/web-ele/src/components/ChatBox/types.ts b/web/apps/web-ele/src/components/ChatBox/types.ts index c199d64..7e4e527 100644 --- a/web/apps/web-ele/src/components/ChatBox/types.ts +++ b/web/apps/web-ele/src/components/ChatBox/types.ts @@ -72,6 +72,16 @@ export interface ReasoningStep { from_subflow?: boolean; collaboration_role?: string; collaboration_mode?: string; + communication?: { + actor?: Record; + branch_id?: string; + branch_label?: string; + channel?: string; + event?: string; + message?: string; + status?: string; + target?: Record; + }; output?: any; /** 步骤状态：running 执行中，completed 已完成 */ status?: 'completed' | 'running'; diff --git a/web/apps/web-ele/src/components/ai-chat-panel/AiChatPanel.vue b/web/apps/web-ele/src/components/ai-chat-panel/AiChatPanel.vue index d20cd97..64e2c2c 100644 --- a/web/apps/web-ele/src/components/ai-chat-panel/AiChatPanel.vue +++ b/web/apps/web-ele/src/components/ai-chat-panel/AiChatPanel.vue @@ -750,6 +750,7 @@ onUnmounted(() => { :enable-image="enableImage" :enable-voice="enableVoice" :enable-feedback="enableFeedback" + :show-reasoning-steps="true" :show-clear-button="showClearButton" :empty-text="emptyText" :welcome-config="welcomeConfig" diff --git a/web/apps/web-ele/src/components/ai-chat-panel/composables/useChatApi.ts b/web/apps/web-ele/src/components/ai-chat-panel/composables/useChatApi.ts index 1ac6996..75ff199 100644 --- a/web/apps/web-ele/src/components/ai-chat-panel/composables/useChatApi.ts +++ b/web/apps/web-ele/src/components/ai-chat-panel/composables/useChatApi.ts @@ -231,6 +231,17 @@ export function useChatApi(props: AiChatPanelProps): { params: step.params, node_id: step.node_id, node_type: step.node_type, + branch_id: step.branch_id, + branch_label: step.branch_label, + agent_code: step.agent_code, + agent_name: step.agent_name, + model: step.model, + model_id: step.model_id, + subflow_name: step.subflow_name, + from_subflow: step.from_subflow, + collaboration_role: step.collaboration_role, + collaboration_mode: step.collaboration_mode, + communication: step.communication, output: step.output, status: step.status, timestamp: step.timestamp, diff --git a/web/apps/web-ele/src/components/ai-chat-panel/composables/useEventHandler.ts b/web/apps/web-ele/src/components/ai-chat-panel/composables/useEventHandler.ts index a54f292..c3168c8 100644 --- a/web/apps/web-ele/src/components/ai-chat-panel/composables/useEventHandler.ts +++ b/web/apps/web-ele/src/components/ai-chat-panel/composables/useEventHandler.ts @@ -172,6 +172,7 @@ export function useEventHandler(config: EventHandlerConfig) { const buildReasoningMeta = (event: StreamEvent): Partial => { const collaboration = event.collaboration || {}; + const communication = event.communication || {}; return { branch_id: event.branch_id, branch_label: event.branch_label, @@ -187,6 +188,7 @@ export function useEventHandler(config: EventHandlerConfig) { collaboration_role: collaboration.collaboration_role || collaboration.role || event.collaboration_role, collaboration_mode: event.collaboration_mode || collaboration.collaboration_mode, + communication, }; }; @@ -230,6 +232,35 @@ export function useEventHandler(config: EventHandlerConfig) { break; } + case 'annotation_reply': + case 'knowledge_retrieval': + case 'observation': + case 'thought': + case 'tool_call': + case 'tool_result': { + currentSteps.value.push({ + type: event.type as ReasoningStep['type'], + content: + event.content || + event.message || + event.tool || + event.type, + tool: event.tool, + params: event.params, + result: event.result, + node_id: event.node_id, + node_type: event.node_type, + ...buildReasoningMeta(event), + status: 'completed', + timestamp: new Date().toISOString(), + }); + updateAssistantMessage(msgId, { + reasoning_steps: [...currentSteps.value], + ...buildMessageMeta(event), + }); + break; + } + case 'answer': { const initialMsg = messages.value.find((m) => m.id === msgId); if (initialMsg && !initialMsg.content?.trim()) { diff --git a/web/apps/web-ele/src/views/ai-platform/workflow-runs/modules/detail-dialog.vue b/web/apps/web-ele/src/views/ai-platform/workflow-runs/modules/detail-dialog.vue index 4035aa8..e3625eb 100644 --- a/web/apps/web-ele/src/views/ai-platform/workflow-runs/modules/detail-dialog.vue +++ b/web/apps/web-ele/src/views/ai-platform/workflow-runs/modules/detail-dialog.vue @@ -118,11 +118,25 @@ const collaborationTimeline = computed(() => { .map((log, index) => { const meta = getLogMeta(log); const collaboration = meta.collaboration || {}; - const agentCode = meta.agent_code || collaboration.agent_code; - const agentName = meta.agent_name || collaboration.agent_name || agentCode; - const branch = log.branch_label || log.branch || collaboration.branch_label; + const communication = meta.communication || {}; + const actor = communication.actor || {}; + const target = communication.target || {}; + const agentCode = actor.agent_code || meta.agent_code || collaboration.agent_code; + const agentName = + actor.agent_name || + actor.agent_code || + meta.agent_name || + collaboration.agent_name || + agentCode; + const branch = + communication.branch_label || + log.branch_label || + log.branch || + collaboration.branch_label; const subflowName = collaboration.subflow_name || meta.subflow_name; const model = meta.model || collaboration.model || meta.model_id || collaboration.model_id; + const targetName = + target.agent_name || target.agent_code || target.node_label || ''; const title = agentName || subflowName || @@ -131,8 +145,10 @@ const collaborationTimeline = computed(() => { log.node_id || `#${index + 1}`; const summaryParts = [ + targetName ? `交接给：${targetName}` : '', branch ? `分支：${branch}` : '', subflowName ? `子流程：${subflowName}` : '', + communication.channel ? `通道：${communication.channel}` : '', model ? `模型：${model}` : '', ].filter(Boolean); @@ -143,9 +159,10 @@ const collaborationTimeline = computed(() => { subtitle: log.node_label || log.node_id, status: log.status, summary: summaryParts.join(' · '), + message: communication.message || previewValue(log.error || log.output), elapsed: log.elapsed_time, tokens: log.tokens_used || 0, - hasSignal: Boolean(agentName || branch || subflowName || model), + hasSignal: Boolean(agentName || branch || subflowName || model || communication.channel), }; }) .filter((item) => item.hasSignal); @@ -248,6 +265,35 @@ function getLogMeta(log: any) { return log?.metadata || {}; } +function getCommunication(log: any) { + return getLogMeta(log).communication || {}; +} + +function getCollaboration(log: any) { + return getLogMeta(log).collaboration || {}; +} + +function getActorName(log: any) { + const communication = getCommunication(log); + const actor = communication.actor || {}; + const collaboration = getCollaboration(log); + return ( + actor.agent_name || + actor.agent_code || + collaboration.agent_name || + collaboration.agent_code || + actor.node_label || + log.node_label || + log.node_id || + '-' + ); +} + +function getTargetName(log: any) { + const target = getCommunication(log).target || {}; + return target.agent_name || target.agent_code || target.node_label || ''; +} + function selectLog(nodeId?: string) { if (!nodeId) return; activeNodeId.value = nodeId; @@ -468,29 +514,56 @@ defineExpose({ open }); {{ log.error }}
- {{ $t('ai-platform.workflowRuns.detail.agent') }} + 执行者
- {{ getLogMeta(log).agent_name || getLogMeta(log).agent_code }} + {{ getActorName(log) }}
- {{ $t('ai-platform.workflowRuns.detail.agentCode') }} + 交接目标 +
+
+ {{ getTargetName(log) || '-' }} +
+
+
+
+ 协作通道 +
+
+ {{ getCommunication(log).channel || getCollaboration(log).collaboration_mode || '-' }} +
+
+
+
分支/子流程
+
+ {{ + getCommunication(log).branch_label || + getCollaboration(log).branch_label || + getCollaboration(log).subflow_name || + '-' + }}
-
{{ getLogMeta(log).agent_code }}
{{ $t('ai-platform.workflowRuns.detail.model') }}
- {{ getLogMeta(log).model || getLogMeta(log).model_id || '-' }} + {{ + getLogMeta(log).model || + getCollaboration(log).model || + getLogMeta(log).model_id || + getCollaboration(log).model_id || + '-' + }}
@@ -502,6 +575,15 @@ defineExpose({ open }); {{ getLogMeta(log).completion_tokens || 0 }}
+
+
通信摘要
+
+ {{ getCommunication(log).message }} +
+