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