import pytz from flask_login import current_user from core.app.app_config.easy_ui_based_app.agent.manager import AgentConfigManager from core.tools.tool_manager import ToolManager from extensions.ext_database import db from models.account import Account from models.model import App, Conversation, EndUser, Message, MessageAgentThought class AgentService: @classmethod def get_agent_logs(cls, app_model: App, conversation_id: str, message_id: str) -> dict: """ Service to get agent logs """ conversation: Conversation = ( db.session.query(Conversation) .filter( Conversation.id == conversation_id, Conversation.app_id == app_model.id, ) .first() ) if not conversation: raise ValueError(f"Conversation not found: {conversation_id}") message: Message = ( db.session.query(Message) .filter( Message.id == message_id, Message.conversation_id == conversation_id, ) .first() ) if not message: raise ValueError(f"Message not found: {message_id}") agent_thoughts: list[MessageAgentThought] = message.agent_thoughts if conversation.from_end_user_id: # only select name field executor = ( db.session.query(EndUser, EndUser.name).filter(EndUser.id == conversation.from_end_user_id).first() ) else: executor = ( db.session.query(Account, Account.name).filter(Account.id == conversation.from_account_id).first() ) if executor: executor = executor.name else: executor = "Unknown" timezone = pytz.timezone(current_user.timezone) result = { "meta": { "status": "success", "executor": executor, "start_time": message.created_at.astimezone(timezone).isoformat(), "elapsed_time": message.provider_response_latency, "total_tokens": message.answer_tokens + message.message_tokens, "agent_mode": app_model.app_model_config.agent_mode_dict.get("strategy", "react"), "iterations": len(agent_thoughts), }, "iterations": [], "files": message.files, } agent_config = AgentConfigManager.convert(app_model.app_model_config.to_dict()) agent_tools = agent_config.tools def find_agent_tool(tool_name: str): for agent_tool in agent_tools: if agent_tool.tool_name == tool_name: return agent_tool for agent_thought in agent_thoughts: tools = agent_thought.tools tool_labels = agent_thought.tool_labels tool_meta = agent_thought.tool_meta tool_inputs = agent_thought.tool_inputs_dict tool_outputs = agent_thought.tool_outputs_dict tool_calls = [] for tool in tools: tool_name = tool tool_label = tool_labels.get(tool_name, tool_name) tool_input = tool_inputs.get(tool_name, {}) tool_output = tool_outputs.get(tool_name, {}) tool_meta_data = tool_meta.get(tool_name, {}) tool_config = tool_meta_data.get("tool_config", {}) if tool_config.get("tool_provider_type", "") != "dataset-retrieval": tool_icon = ToolManager.get_tool_icon( tenant_id=app_model.tenant_id, provider_type=tool_config.get("tool_provider_type", ""), provider_id=tool_config.get("tool_provider", ""), ) if not tool_icon: tool_entity = find_agent_tool(tool_name) if tool_entity: tool_icon = ToolManager.get_tool_icon( tenant_id=app_model.tenant_id, provider_type=tool_entity.provider_type, provider_id=tool_entity.provider_id, ) else: tool_icon = "" tool_calls.append( { "status": "success" if not tool_meta_data.get("error") else "error", "error": tool_meta_data.get("error"), "time_cost": tool_meta_data.get("time_cost", 0), "tool_name": tool_name, "tool_label": tool_label, "tool_input": tool_input, "tool_output": tool_output, "tool_parameters": tool_meta_data.get("tool_parameters", {}), "tool_icon": tool_icon, } ) result["iterations"].append( { "tokens": agent_thought.tokens, "tool_calls": tool_calls, "tool_raw": { "inputs": agent_thought.tool_input, "outputs": agent_thought.observation, }, "thought": agent_thought.thought, "created_at": agent_thought.created_at.isoformat(), "files": agent_thought.files, } ) return result