757 lines
No EOL
45 KiB
JSON
757 lines
No EOL
45 KiB
JSON
{
|
||
"id": "f734656f-ea4b-40fc-a445-a1bdfd844086",
|
||
"mandate_id": 1,
|
||
"user_id": 1,
|
||
"name": "Workflow 15.4.2025, 14:25:54",
|
||
"status": "completed",
|
||
"started_at": "2025-04-15T14:25:59.086488",
|
||
"last_activity": "2025-04-15T14:26:46.550562",
|
||
"prompt": "bitte schreibe mir einen bericht darüber, wie das backend der applikation aufgebaut ist. hier die daten dazu.",
|
||
"messages": [
|
||
{
|
||
"id": "msg_393f28a1-be68-49d6-82c0-d17caafb2736",
|
||
"workflow_id": "f734656f-ea4b-40fc-a445-a1bdfd844086",
|
||
"parent_message_id": null,
|
||
"started_at": "2025-04-15T14:25:59.170133",
|
||
"finished_at": null,
|
||
"sequence_no": 1,
|
||
"status": "pending",
|
||
"role": "user",
|
||
"data_stats": {
|
||
"processing_time": 0.0,
|
||
"token_count": 0,
|
||
"bytes_sent": 0,
|
||
"bytes_received": 0
|
||
},
|
||
"documents": [
|
||
{
|
||
"id": "doc_5b5cb24a-4b13-409e-864f-25b0730befcc",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_9a781ba9-e396-4042-8783-5fd1b27ba5e1",
|
||
"name": "agentservice_dataextraction.py",
|
||
"content_type": "text/x-python",
|
||
"size": 31676,
|
||
"upload_date": "2025-04-15T14:25:59.414568"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nRefactored helper function for intelligent data extraction (continued).\r\n\"\"\"\r\n\r\nimport logging\r\nimport json\r\nfrom typing import List, Dict, Any, Optional, Tuple\r\nimport asyncio\r\nfrom datetime import datetime\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nasync def data_extraction(\r\n prompt: str, \r\n files: List[Dict[str, Any]], \r\n messages: List[Dict[str, Any]], \r\n ai_service,\r\n lucydom_interface = None,\r\n workflow_id: str = None,\r\n add_log_func = None,\r\n document_handler = None # Add this parameter\r\n) -> Dict[str, Any]:\r\n \"\"\"\r\n Performs AI-driven data extraction with support for the document handler.\r\n \r\n Args:\r\n prompt: Specification of what data to extract\r\n files: List of all available files with metadata\r\n messages: List of all messages in the workflow\r\n ai_service: Service for AI requests\r\n lucydom_interface: Interface for database access (optional)\r\n workflow_id: Optional workflow ID for logging\r\n ... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_127aff27-6ff1-4976-a637-583c5aa2a624",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_1ed36216-e13e-4f74-8171-fa2faf9c0f2d",
|
||
"name": "agentservice_document_handler.py",
|
||
"content_type": "text/x-python",
|
||
"size": 21838,
|
||
"upload_date": "2025-04-15T14:25:59.414568"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nEnhanced document handling module for the Agentservice (continued).\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\nimport uuid\r\nfrom datetime import datetime\r\nfrom typing import List, Dict, Any, Optional, Tuple, Union\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass DocumentHandler:\r\n \"\"\"\r\n Centralized document handler for consistent document management across the system.\r\n \"\"\"\r\n \r\n def __init__(self, workflow_id: str = None, lucydom_interface = None, ai_service = None):\r\n \"\"\"Initialize the document handler.\"\"\"\r\n self.workflow_id = workflow_id\r\n self.lucydom_interface = lucydom_interface\r\n self.ai_service = ai_service\r\n \r\n # Import necessary utilities\r\n from modules.agentservice_filemanager import get_file_manager\r\n self.file_manager = get_file_manager()\r\n \r\n def set_workflow_id(self, workflow_id: str):\r\n \"\"\"Set or update the workflow ID.\"\"\"\r\n self.workflow_id = workflow_id\r\n \r\n def set_lucydom_in... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_4b89653a-a044-4d3e-8b6d-eef3b78657c4",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_4d0096f8-375a-4a7d-b7bf-aeeb706b61dd",
|
||
"name": "agentservice_filemanager.py",
|
||
"content_type": "text/x-python",
|
||
"size": 44114,
|
||
"upload_date": "2025-04-15T14:25:59.415574"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nCentral file management module for the Agentservice.\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\nimport base64\r\nimport json\r\nimport uuid\r\nfrom datetime import datetime\r\nfrom typing import List, Dict, Any, Optional, Tuple, Union, BinaryIO\r\nfrom io import BytesIO\r\n\r\n# Import utilities from agentservice_utils\r\nfrom modules.agentservice_utils import extract_text_from_file_content, is_text_extractable\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n# Helper function for adding logs\r\ndef _log(add_log_func, workflow_id, message, level=\"info\"):\r\n \"\"\"Helper function for adding logs with standardized formatting.\"\"\"\r\n if add_log_func and workflow_id:\r\n add_log_func(workflow_id, message, level)\r\n \r\n # Also log to standard logger\r\n if level == \"info\":\r\n logger.info(message)\r\n elif level == \"warning\":\r\n logger.warning(message)\r\n elif level == \"error\":\r\n logger.error(message)\r\n\r\nclass FileExtractionError(Exception):\r\n \"\"\"Exception for file extraction errors.... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_8d30384a-6c80-4b1b-9e32-aaa887e1e54c",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_5174fd99-83a3-4b71-8d3d-c10ea6697ff2",
|
||
"name": "agentservice_protocol.py",
|
||
"content_type": "text/x-python",
|
||
"size": 11569,
|
||
"upload_date": "2025-04-15T14:25:59.415574"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nAgent Communication Protocol module for the Agentservice.\r\nDefines a standardized format for agents to exchange information.\r\n\"\"\"\r\n\r\nimport json\r\nimport uuid\r\nfrom typing import Dict, Any, List, Optional\r\nfrom datetime import datetime\r\n\r\nclass AgentMessage:\r\n \"\"\"\r\n Standard message format for inter-agent communication.\r\n Includes content, metadata, and document references.\r\n \"\"\"\r\n \r\n def __init__(\r\n self,\r\n content: str,\r\n sender_id: str,\r\n receiver_id: Optional[str] = None,\r\n message_type: str = \"text\",\r\n metadata: Optional[Dict[str, Any]] = None,\r\n documents: Optional[List[Dict[str, Any]]] = None,\r\n context_id: Optional[str] = None\r\n ):\r\n \"\"\"\r\n Initialize an agent message.\r\n \r\n Args:\r\n content: The main message content\r\n sender_id: ID of the sending agent\r\n receiver_id: Optional ID of the receiving agent\r\n message_type: Type of mess... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_ad08db1b-b4eb-4d88-a0da-59a2acfecca1",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_791168f1-9651-414b-8462-019e71481a86",
|
||
"name": "agentservice_registry.py",
|
||
"content_type": "text/x-python",
|
||
"size": 11404,
|
||
"upload_date": "2025-04-15T14:25:59.415574"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nUpdated registry for all available agents in the system.\r\nProvides centralized agent registration and access with improved error handling.\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\nimport importlib\r\nfrom typing import Dict, Any, List, Optional\r\n\r\n# Import direct base agent module\r\nfrom modules.agentservice_base import BaseAgent\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass AgentRegistry:\r\n \"\"\"Registry for all available agents in the system\"\"\"\r\n \r\n _instance = None\r\n \r\n @classmethod\r\n def get_instance(cls):\r\n \"\"\"Get a singleton instance of the Agent Registry\"\"\"\r\n if cls._instance is None:\r\n cls._instance = cls()\r\n return cls._instance\r\n \r\n def __init__(self):\r\n \"\"\"Initialize the Agent Registry\"\"\"\r\n if AgentRegistry._instance is not None:\r\n raise RuntimeError(\"Singleton instance already exists - use get_instance()\")\r\n self.agents = {}\r\n self.ai_service = None\r\n self.document_handler = N... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_40d4cc8e-bc41-434a-b23c-751da70f3961",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_9e99c4bd-885b-4d34-a1c1-f5fd6020556d",
|
||
"name": "agentservice_utils.py",
|
||
"content_type": "text/x-python",
|
||
"size": 29021,
|
||
"upload_date": "2025-04-15T14:25:59.416575"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nCentralized utility functions for the Agentservice (continued).\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\nimport json\r\nimport uuid\r\nfrom datetime import datetime\r\nfrom typing import List, Dict, Any, Optional, Tuple, Union, Callable\r\nfrom io import BytesIO\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass WorkflowUtils:\r\n \"\"\"\r\n Utility class for workflow operations.\r\n Centralizes common workflow-related functions.\r\n \"\"\"\r\n \r\n def __init__(self, workflow_id: str = None):\r\n \"\"\"Initialize with optional workflow ID\"\"\"\r\n self.workflow_id = workflow_id\r\n \r\n def set_workflow_id(self, workflow_id: str):\r\n \"\"\"Set or update the workflow ID\"\"\"\r\n self.workflow_id = workflow_id\r\n \r\n def get_documents(self, workflow: Dict[str, Any]) -> List[Dict[str, Any]]:\r\n \"\"\"\r\n Get all documents from a workflow across all messages.\r\n \r\n Args:\r\n workflow: The workflow object\r\n \r\n Returns:\r\n Lis... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_87ad78bc-7ff3-4e11-94f1-4fb1dc1a7bfc",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_90fcb4c3-44fd-4ae4-96dc-aa18a9a6db16",
|
||
"name": "agentservice_workflow_execution.py",
|
||
"content_type": "text/x-python",
|
||
"size": 30461,
|
||
"upload_date": "2025-04-15T14:25:59.416575"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nRefactored architecture for the Agentservice multi-agent system.\r\nThis module defines the revised workflow execution with improved agent handovers.\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\nimport asyncio\r\nimport uuid\r\nfrom datetime import datetime\r\nfrom typing import List, Dict, Any, Optional, Tuple, Union\r\n\r\nlogger = logging.getLogger(__name__)\r\nlogging.getLogger('matplotlib.font_manager').setLevel(logging.INFO)\r\n\r\nclass WorkflowExecution:\r\n \"\"\"\r\n Handles the execution of workflows with improved agent collaboration.\r\n Integrates planning and execution phases for better context awareness.\r\n \"\"\"\r\n \r\n def __init__(self, workflow_manager, workflow_id: str, mandate_id: int, user_id: int, ai_service, lucydom_interface):\r\n \"\"\"Initialize the workflow execution\"\"\"\r\n self.workflow_manager = workflow_manager\r\n self.workflow_id = workflow_id\r\n self.mandate_id = mandate_id\r\n self.user_id = user_id\r\n self.ai_service = ai_service\r\n se... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_d14ebe04-c3ae-4668-b647-c0f635297610",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_57ae76e5-ecb9-45a5-aa0d-b1b1b066cb18",
|
||
"name": "agentservice_workflow_manager.py",
|
||
"content_type": "text/x-python",
|
||
"size": 29007,
|
||
"upload_date": "2025-04-15T14:25:59.416575"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nRefactored WorkflowManager class for the Agentservice (continued).\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\nimport asyncio\r\nimport uuid\r\nfrom datetime import datetime\r\nfrom typing import List, Dict, Any, Optional, Tuple, Union\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass WorkflowManager:\r\n\r\n def __init__(self, mandate_id: int = None, user_id: int = None, ai_service = None, lucydom_interface = None):\r\n \"\"\"Initialize the WorkflowManager.\"\"\"\r\n self.mandate_id = mandate_id\r\n self.user_id = user_id\r\n self.ai_service = ai_service\r\n self.lucydom_interface = lucydom_interface\r\n \r\n # Cache for workflows\r\n self.workflows = {}\r\n \r\n # Directory for results\r\n self.results_dir = os.path.join(\"results\", \"workflows\")\r\n os.makedirs(self.results_dir, exist_ok=True)\r\n \r\n # Initialize document handler\r\n from modules.agentservice_document_handler import get_document_handler\r\n self.document_... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_24977ebb-4ad0-4a17-b8ae-1ad4b4d5a618",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_5f39f745-27b0-49bd-8aad-e2b31c08a199",
|
||
"name": "agentservice_agent_analyst.py",
|
||
"content_type": "text/x-python",
|
||
"size": 78144,
|
||
"upload_date": "2025-04-15T14:25:59.417577"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nDatenanalyst-Agent für die Analyse und Interpretation von Daten.\r\nAngepasst für das refaktorisierte Core-Modul mit AgentCommunicationProtocol.\r\n\"\"\"\r\n\r\nimport logging\r\nimport traceback\r\nimport json\r\nimport re\r\nimport uuid\r\nimport io\r\nimport base64\r\nfrom typing import List, Dict, Any, Optional, Union, Tuple\r\nfrom datetime import datetime\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport plotly.express as px\r\nimport plotly.graph_objects as go\r\n\r\nfrom modules.agentservice_base import BaseAgent\r\nfrom connectors.connector_aichat_openai import ChatService\r\nfrom modules.agentservice_utils import WorkflowUtils, MessageUtils, LoggingUtils, FileUtils\r\nfrom modules.agentservice_protocol import AgentMessage, AgentCommunicationProtocol\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass AnalystAgent(BaseAgent):\r\n \"\"\"Agent for data analysis and interpretation\"\"\"\r\n \r\n def __init__(self):\r\n \"\"\"Initialize the data analyst agent\"\"\"\r... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_8e18a4cd-c603-4a72-a30a-fd0462d1e5a7",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_0286d31e-b3b3-4b81-92f6-36489e655717",
|
||
"name": "agentservice_agent_coder.py",
|
||
"content_type": "text/x-python",
|
||
"size": 54617,
|
||
"upload_date": "2025-04-15T14:25:59.417775"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nCoderAgent - A unified agent for developing and executing Python code.\r\nIncludes code execution capabilities previously in separate modules.\r\nEnhanced with auto-correction loop for handling execution errors.\r\n\"\"\"\r\n\r\nimport logging\r\nimport json\r\nimport re\r\nimport uuid\r\nimport traceback\r\nimport os\r\nimport subprocess\r\nimport tempfile\r\nimport shutil\r\nimport sys\r\nimport pandas as pd\r\nfrom datetime import datetime\r\nfrom typing import List, Dict, Any, Optional, Tuple\r\n\r\nfrom modules.agentservice_base import BaseAgent\r\nfrom modules.agentservice_utils import FileUtils, WorkflowUtils, MessageUtils, LoggingUtils\r\nfrom connectors.connector_aichat_openai import ChatService\r\nfrom modules.agentservice_protocol import AgentMessage, AgentCommunicationProtocol\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n# Existing SimpleCodeExecutor class remains unchanged\r\nclass SimpleCodeExecutor:\r\n # ... existing code ...\r\n \"\"\"\r\n A simplified executor that runs Python code in isolated virtual environment... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_bdaf658f-93d2-4bea-99ca-53bd9323c91d",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_8b40b440-7eae-40ba-b054-3c6b392a5b7e",
|
||
"name": "agentservice_agent_documentation.py",
|
||
"content_type": "text/x-python",
|
||
"size": 18140,
|
||
"upload_date": "2025-04-15T14:25:59.417775"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nDokumentations-Agent für die Erstellung von Dokumentation, Berichten und strukturierten Inhalten.\r\nVerwendet einen adaptiven Prozess zur Erstellung hochwertiger Dokumentation basierend auf der Komplexität des Auftrags.\r\nAngepasst für das refaktorisierte Core-Modul und AgentCommunicationProtocol.\r\n\"\"\"\r\n\r\nimport logging\r\nimport json\r\nimport re\r\nimport traceback\r\nfrom typing import List, Dict, Any, Optional, Tuple, Union\r\nfrom datetime import datetime\r\nimport uuid\r\n\r\nfrom modules.agentservice_base import BaseAgent\r\nfrom modules.agentservice_utils import WorkflowUtils, MessageUtils, LoggingUtils\r\nfrom modules.agentservice_protocol import AgentMessage, AgentCommunicationProtocol\r\nfrom modules.agentservice_filemanager import FileManager # Import the file manager\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass DocumentationAgent(BaseAgent):\r\n \"\"\"Agent for creating documentation and structured content\"\"\"\r\n \r\n def __init__(self):\r\n \"\"\"Initialize the documentation agent\"\"\"... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_6b5a915a-7138-42ee-ae2a-06d702555b91",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_19fc066e-a632-4415-838d-ccea5d9436bf",
|
||
"name": "agentservice_agent_webcrawler.py",
|
||
"content_type": "text/x-python",
|
||
"size": 24064,
|
||
"upload_date": "2025-04-15T14:25:59.417775"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nWebCrawler-Agent für die Recherche und Beschaffung von Informationen aus dem Web.\r\nAngepasst für das refaktorisierte Core-Modul.\r\n\"\"\"\r\n\r\nimport json\r\nimport logging\r\nimport time\r\nimport traceback\r\nfrom typing import List, Dict, Any, Optional\r\nfrom urllib.parse import quote_plus, unquote\r\n\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nfrom modules.agentservice_base import BaseAgent\r\nfrom modules.agentservice_utils import MessageUtils, LoggingUtils\r\nfrom modules.agentservice_protocol import AgentCommunicationProtocol\r\n\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass WebcrawlerAgent(BaseAgent):\r\n \r\n \"\"\"Agent für Web-Recherche und Informationsbeschaffung\"\"\"\r\n \r\n def __init__(self):\r\n \"\"\"Initialisiert den WebCrawler-Agenten\"\"\"\r\n super().__init__()\r\n self.id = \"webcrawler\"\r\n self.name = \"Webscraper\"\r\n self.type = \"scraper\"\r\n self.description = \"Recherchiert Informationen im Web\"\r\n self.capabilities = \"web_search,informatio... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "doc_e5767d6d-90ce-4686-b728-711e213a0815",
|
||
"source": {
|
||
"type": "file",
|
||
"id": "file_0c8f183a-9f6a-421f-b32a-439b8a11ea69",
|
||
"name": "agentservice_base.py",
|
||
"content_type": "text/x-python",
|
||
"size": 9406,
|
||
"upload_date": "2025-04-15T14:25:59.418819"
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "\"\"\"\r\nEnhanced base agent class for the Agentservice.\r\nProvides improved communication and document handling capabilities.\r\n\"\"\"\r\n\r\nimport logging\r\nimport json\r\nfrom typing import Dict, Any, List, Optional, Tuple, Union\r\nimport asyncio\r\nfrom datetime import datetime\r\nimport uuid\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass BaseAgent:\r\n \"\"\"\r\n Enhanced base agent class with improved communication capabilities.\r\n All specialized agents should inherit from this class.\r\n \"\"\"\r\n \r\n def __init__(self):\r\n \"\"\"Initialize the enhanced agent.\"\"\"\r\n self.id = \"base_agent\"\r\n self.name = \"Base Agent\"\r\n self.type = \"base\"\r\n self.description = \"Base agent for the Agentservice\"\r\n self.capabilities = \"Basic agent operations\"\r\n self.result_format = \"Text\"\r\n \r\n # New properties for document handling\r\n self.supports_documents = True\r\n self.document_capabilities = [\"read\", \"reference\"]\r\n self.required_contex... [truncated for storage]",
|
||
"is_extracted": true,
|
||
"extraction_context": null
|
||
}
|
||
]
|
||
}
|
||
],
|
||
"content": "bitte schreibe mir einen bericht darüber, wie das backend der applikation aufgebaut ist. hier die daten dazu.",
|
||
"agent_type": null
|
||
},
|
||
{
|
||
"id": "msg_1af931a4-ce76-490e-90da-6b2ddd1e70bd",
|
||
"workflow_id": "f734656f-ea4b-40fc-a445-a1bdfd844086",
|
||
"parent_message_id": null,
|
||
"started_at": "2025-04-15T14:26:25.934908",
|
||
"finished_at": null,
|
||
"sequence_no": 2,
|
||
"status": "pending",
|
||
"role": "assistant",
|
||
"data_stats": {
|
||
"processing_time": 0.0,
|
||
"token_count": 0,
|
||
"bytes_sent": 0,
|
||
"bytes_received": 0
|
||
},
|
||
"documents": [],
|
||
"content": "# Analysis Report: Backend Architecture of Agentservice\n\n## 1. Summary of Main Themes and Topics\n\nThe provided Python files outline the architecture of a backend system for an \"Agentservice\" application. The main themes and topics identified include:\n\n- **Agent Management**: The system is structured around various agents, each with specific roles and capabilities.\n- **Workflow Management**: There is a strong emphasis on managing workflows, including execution, management, and utility functions.\n- **Communication Protocols**: A standardized protocol for agent communication is implemented.\n- **Document and File Handling**: Centralized modules for handling documents and files are present.\n- **Error Handling and Logging**: Consistent logging and error handling mechanisms are integrated across modules.\n\n## 2. Key Insights and Observations\n\n- **Modular Design**: The system is highly modular, with distinct modules handling specific functionalities such as data extraction, document handling, file management, and workflow execution.\n- **Agent-Based Architecture**: The architecture is centered around agents, each inheriting from a base agent class, which provides core functionalities and communication capabilities.\n- **Asynchronous Operations**: Many operations, particularly those involving data extraction and workflow execution, are designed to be asynchronous, indicating a focus on performance and scalability.\n- **Centralized Utilities**: Utility functions are centralized in the `agentservice_utils.py` module, promoting code reuse and consistency.\n- **Enhanced Logging**: Logging is consistently applied across modules, with a helper function `_log` standardizing log messages.\n\n## 3. Analysis of Structure, Patterns, and Relationships\n\n### Key Components and Their Interactions\n\n- **BaseAgent Class**: Serves as the foundation for all specialized agents, providing basic operations and communication capabilities.\n- **Agent Registry**: Manages the registration and access of all agents, ensuring a centralized approach to agent management.\n- **Workflow Management**: \n - `WorkflowManager` and `WorkflowExecution` classes handle the creation, execution, and management of workflows.\n - `WorkflowUtils` provides utility functions to support workflow operations.\n- **Document and File Handling**:\n - `DocumentHandler` and `FileManager` manage document and file operations, ensuring consistency across the system.\n - `data_extraction` function in `agentservice_dataextraction.py` integrates document handling into data extraction processes.\n- **Communication Protocol**: `AgentMessage` class defines a standardized format for inter-agent communication, facilitating structured information exchange.\n\n### Patterns and Structures\n\n- **Singleton Pattern**: Used in the `AgentRegistry` to ensure a single instance of the registry exists.\n- **Asynchronous Programming**: Utilized in data extraction and workflow execution to enhance performance.\n- **Refactoring and Modularity**: The codebase shows evidence of refactoring, with modules being split into smaller, more manageable components.\n\n## 4. Clear Conclusions and Interpretations\n\nThe backend architecture of the Agentservice is designed to be modular, scalable, and efficient. The use of agents allows for specialized processing, while the workflow management system ensures that complex processes can be executed in a coordinated manner. The emphasis on asynchronous operations and centralized utilities indicates a focus on performance and maintainability.\n\n## 5. Recommendations or Implications\n\n- **Further Modularization**: Consider further breaking down large modules into smaller components to enhance maintainability and readability.\n- **Enhanced Documentation**: While the code is well-structured, additional inline documentation and comments could improve understanding for new developers.\n- **Testing and Validation**: Implement comprehensive testing strategies to ensure the robustness of asynchronous operations and agent interactions.\n- **Scalability Considerations**: As the system grows, consider implementing load balancing and distributed processing to handle increased demand.\n\nThis analysis provides a comprehensive overview of the backend architecture, highlighting its strengths and areas for potential improvement. The modular and agent-based design positions the system well for future expansion and adaptation.",
|
||
"agent_type": "analyst_agent",
|
||
"agent_id": "analyst_agent",
|
||
"agent_name": "Data Analyst",
|
||
"result_format": "AnalysisReport"
|
||
},
|
||
{
|
||
"id": "msg_4d988caa-a897-41f4-9287-8302957d96f9",
|
||
"workflow_id": "f734656f-ea4b-40fc-a445-a1bdfd844086",
|
||
"parent_message_id": null,
|
||
"started_at": "2025-04-15T14:26:41.117524",
|
||
"finished_at": null,
|
||
"sequence_no": 3,
|
||
"status": "pending",
|
||
"role": "assistant",
|
||
"data_stats": {
|
||
"processing_time": 0.0,
|
||
"token_count": 0,
|
||
"bytes_sent": 0,
|
||
"bytes_received": 0
|
||
},
|
||
"documents": [
|
||
{
|
||
"id": "doc_148802b5-cece-4018-bd4c-14f57a136fea",
|
||
"source": {
|
||
"type": "generated",
|
||
"id": "doc_148802b5-cece-4018-bd4c-14f57a136fea",
|
||
"name": "Comprehensive Analysis of Backend Architecture for Agentservice Application",
|
||
"content_type": "text/markdown",
|
||
"size": 4344
|
||
},
|
||
"contents": [
|
||
{
|
||
"type": "text",
|
||
"text": "# Comprehensive Analysis of Backend Architecture for Agentservice Application\n\n## Introduction\n\nThe purpose of this document is to provide a comprehensive analysis of the backend architecture of the Agentservice application. This report aims to detail the key components, their functions, and interactions within the system. The analysis is based on the extracted content from various Python modules and previous activity outputs. This document is structured to offer a clear understanding of the architecture, highlighting its strengths and areas for potential improvement.\n\n## Key Components of the Backend Architecture\n\n### 1. Agent Management\n\n- **BaseAgent Class**: \n - Serves as the foundation for all specialized agents.\n - Provides basic operations and communication capabilities.\n - Supports document handling with capabilities like \"read\" and \"reference\".\n\n- **Agent Registry**:\n - Manages the registration and access of all agents.\n - Implements the Singleton pattern to ensure a sing... [truncated for storage]",
|
||
"is_extracted": true
|
||
}
|
||
]
|
||
}
|
||
],
|
||
"content": "I've created a document titled 'Comprehensive Analysis of Backend Architecture for Agentservice Application' that contains the requested information. The document is attached to this message.",
|
||
"agent_type": "documentation_agent",
|
||
"agent_id": "documentation_agent",
|
||
"agent_name": "Documentation Specialist",
|
||
"result_format": "FormattedDocument"
|
||
},
|
||
{
|
||
"id": "msg_89e25ff7-2964-4633-97ce-6a136abf7716",
|
||
"workflow_id": "f734656f-ea4b-40fc-a445-a1bdfd844086",
|
||
"parent_message_id": null,
|
||
"started_at": "2025-04-15T14:26:46.533070",
|
||
"finished_at": null,
|
||
"sequence_no": 4,
|
||
"status": "pending",
|
||
"role": "assistant",
|
||
"data_stats": {
|
||
"processing_time": 0.0,
|
||
"token_count": 0,
|
||
"bytes_sent": 0,
|
||
"bytes_received": 0
|
||
},
|
||
"documents": [],
|
||
"content": "**Summary of Workflow Results**\n\nThe workflow involved two main activities focused on analyzing and documenting the backend structure of the \"Agentservice\" application.\n\n1. **Key Findings and Results:**\n - **Agent Management:** The backend system is organized around managing various agents, each with distinct roles and capabilities, indicating a modular and role-specific architecture.\n - **Workflow Management:** There is a significant focus on workflow management, highlighting the system's capabilities in executing, managing, and utilizing workflows effectively.\n\n2. **Connection to Original Task:**\n - The original task was to analyze and report on the backend structure of the application. The analysis conducted by the analyst_agent provided insights into the architecture, focusing on agent and workflow management. Subsequently, the documentation_agent compiled these findings into a detailed report, fulfilling the task requirements.\n\n3. **Conclusions and Recommendations:**\n - The analysis and subsequent report provide a comprehensive understanding of the backend architecture, emphasizing the system's modularity and workflow management capabilities. It is recommended to leverage this structure for efficient agent and workflow operations, ensuring scalability and adaptability in the application’s backend.\n\nThis summary encapsulates the workflow's objectives, findings, and outcomes, offering a clear understanding of the backend structure's analysis and documentation process.",
|
||
"agent_type": "summary",
|
||
"agent_id": "workflow_summary",
|
||
"agent_name": "Workflow Summary",
|
||
"result_format": "Text",
|
||
"workflow_complete": true
|
||
}
|
||
],
|
||
"logs": [
|
||
{
|
||
"id": "log_067e2079-9374-41f0-a5a7-448aee98df5e",
|
||
"message": "Starting workflow execution",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.154168",
|
||
"agent_id": "workflow",
|
||
"agent_name": "Workflow Management"
|
||
},
|
||
{
|
||
"id": "log_74c62116-24f9-4960-99c2-13f2dbd16c4e",
|
||
"message": "Processing 13 files",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.170133",
|
||
"agent_id": "files",
|
||
"agent_name": "File Processing"
|
||
},
|
||
{
|
||
"id": "log_5940a7a0-3186-4cd8-82b0-b9f9ed884227",
|
||
"message": "File agentservice_dataextraction.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.190132",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_b1fbe17e-3dd8-46f6-9ee3-9716b15144d4",
|
||
"message": "File agentservice_document_handler.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.207681",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_80277503-4cb2-4e84-9d4e-9a743daf79ee",
|
||
"message": "File agentservice_filemanager.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.224275",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_dea8fc22-9173-40d3-a5f7-3f91e17731cc",
|
||
"message": "File agentservice_protocol.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.241362",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_ffdad9cd-90a7-493a-bee5-a9cd38f64bdf",
|
||
"message": "File agentservice_registry.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.257954",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_f63951be-279c-4c99-909a-1ab123248bf3",
|
||
"message": "File agentservice_utils.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.274956",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_d141deb3-c3f3-4795-aac9-14e5238c809b",
|
||
"message": "File agentservice_workflow_execution.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.292958",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_9cdbad06-2b0b-4bfb-a8a9-9242af170388",
|
||
"message": "File agentservice_workflow_manager.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.310044",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_98254108-fe6b-4841-812b-188538eca4cd",
|
||
"message": "File agentservice_agent_analyst.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.328732",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_545612c8-097a-49f5-8ff8-838a384ac066",
|
||
"message": "File agentservice_agent_coder.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.345311",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_2dc564fa-f4fe-4edd-bbb7-d315913dfb2e",
|
||
"message": "File agentservice_agent_documentation.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.363316",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_a12708b7-8f5d-464a-b49b-792cdc76aba6",
|
||
"message": "File agentservice_agent_webcrawler.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.379311",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_b19c6139-4500-4ce8-9ec4-5062316c503a",
|
||
"message": "File agentservice_base.py read successfully (extracted: True)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.396974",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_6705a4cd-5e1a-4f00-91c1-0b22f36a96aa",
|
||
"message": "User message processed",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.714580",
|
||
"agent_id": "workflow",
|
||
"agent_name": "Workflow Management"
|
||
},
|
||
{
|
||
"id": "log_1b879cb9-dfcd-4616-8592-9442e8c3b246",
|
||
"message": "Creating agent-aware work plan",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:25:59.731580",
|
||
"agent_id": "planning",
|
||
"agent_name": "Activity Planning"
|
||
},
|
||
{
|
||
"id": "log_7d55a2f1-32ae-44ad-aca8-f70a06be7c96",
|
||
"message": "Work plan created with 2 activities",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:06.388515",
|
||
"agent_id": "planning",
|
||
"agent_name": "Activity Planning"
|
||
},
|
||
{
|
||
"id": "log_d63acd49-6857-454b-b5de-ddfbc7c6de4f",
|
||
"message": "Created agent-aware work plan with 2 activities",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:06.405508",
|
||
"agent_id": "planning",
|
||
"agent_name": "Activity Planning"
|
||
},
|
||
{
|
||
"id": "log_8fafa143-ea0e-4985-aee2-8e8e5a36e157",
|
||
"message": "Starting activity: Analyze Backend Structure",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:06.424064",
|
||
"agent_id": "execution",
|
||
"agent_name": "Activity Execution"
|
||
},
|
||
{
|
||
"id": "log_c6c97bf7-39d6-454f-98fc-316a910584aa",
|
||
"message": "Extraktionsplan wird erstellt...",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:06.442761",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_c3762372-47a2-4dc4-8559-0c9e09cbdea4",
|
||
"message": "Extraktionsplan erstellt für 13 Dateien",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:11.908589",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_d3126b39-78d4-4df7-ab20-fb9d7a852aef",
|
||
"message": "Processing file: agentservice_dataextraction.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:11.926570",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_588f6f7f-49f3-453e-9b7d-ce098442b406",
|
||
"message": "Processing file: agentservice_document_handler.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:11.944081",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_bf39755c-0a6e-4cfb-8f4e-8be08d0e4813",
|
||
"message": "Processing file: agentservice_filemanager.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:11.961126",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_0aa38c57-3a41-4fd7-8770-543453ebf836",
|
||
"message": "Processing file: agentservice_workflow_execution.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:11.980637",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_7858531f-385c-4ca9-8d16-48757e3b06f9",
|
||
"message": "Processing file: agentservice_workflow_manager.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:11.999635",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_8f2a4cf6-db7f-482d-af8a-b69d7c6e387e",
|
||
"message": "Processing file: agentservice_protocol.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.022159",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_f65177c1-510d-4ff6-a59a-cd2e8ac18893",
|
||
"message": "Processing file: agentservice_registry.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.040400",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_a1f7a39c-71ef-47ad-80a8-8658e5705fab",
|
||
"message": "Processing file: agentservice_utils.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.057062",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_ca70710c-025c-4bd1-9250-921e0f18dd3f",
|
||
"message": "Processing file: agentservice_agent_analyst.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.074164",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_9170b799-b415-480e-9d2d-5a67af8ffd88",
|
||
"message": "Processing file: agentservice_agent_coder.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.090453",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_5ec39ae9-c03f-4313-8cf8-0cfe453ddd0c",
|
||
"message": "Processing file: agentservice_agent_documentation.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.108005",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_ad459b09-4deb-4bd3-a0f3-1645a38360a8",
|
||
"message": "Processing file: agentservice_agent_webcrawler.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.126365",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_7c72753c-a15e-4216-85f1-ed93af0cc196",
|
||
"message": "Processing file: agentservice_base.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.142373",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_f01f24d4-492b-4f42-81d8-8c87c60e7d24",
|
||
"message": "Passing 13 documents from user message to analyst_agent",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.160584",
|
||
"agent_id": "agents",
|
||
"agent_name": "Agent Selection & Execution"
|
||
},
|
||
{
|
||
"id": "log_fa705d9b-9c9d-4a07-801c-da6a2a2c28d7",
|
||
"message": "Executing agent: analyst_agent",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:12.177110",
|
||
"agent_id": "agents",
|
||
"agent_name": "Agent Selection & Execution"
|
||
},
|
||
{
|
||
"id": "log_e39ebd79-5224-4da4-8827-dfebd7678fc8",
|
||
"message": "Agent analyst_agent produced 0 documents",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:25.934908",
|
||
"agent_id": "agents",
|
||
"agent_name": "Agent Selection & Execution"
|
||
},
|
||
{
|
||
"id": "log_516f9934-e2a8-4ed1-bdcb-d9cc74089d98",
|
||
"message": "Completed activity: Analyze Backend Structure",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:25.954021",
|
||
"agent_id": "execution",
|
||
"agent_name": "Activity Execution"
|
||
},
|
||
{
|
||
"id": "log_2a4e55e0-506e-40ef-ba6f-a0259f4cf73a",
|
||
"message": "Starting activity: Generate Backend Structure Report",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:26.749206",
|
||
"agent_id": "execution",
|
||
"agent_name": "Activity Execution"
|
||
},
|
||
{
|
||
"id": "log_cd8ea353-06ef-4c52-b4ed-290d8c3f3c53",
|
||
"message": "Extraktionsplan wird erstellt...",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:26.768726",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_a3a9a9ac-3707-4197-bfdc-8ad02d05fa70",
|
||
"message": "Extraktionsplan erstellt für 1 Dateien",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:29.184811",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_3b5ec00d-3532-45e0-8f80-c71650f5ef13",
|
||
"message": "Processing file: agentservice_agent_analyst.py (Extraction needed: False)",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:29.203250",
|
||
"agent_id": null,
|
||
"agent_name": null
|
||
},
|
||
{
|
||
"id": "log_ae46d8a4-5550-47e8-bc05-e28557dd0a9a",
|
||
"message": "Passing 13 documents from user message to documentation_agent",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:29.220762",
|
||
"agent_id": "agents",
|
||
"agent_name": "Agent Selection & Execution"
|
||
},
|
||
{
|
||
"id": "log_25675dfd-486f-4e1d-a896-a7ec52fc4c2b",
|
||
"message": "Executing agent: documentation_agent",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:29.239775",
|
||
"agent_id": "agents",
|
||
"agent_name": "Agent Selection & Execution"
|
||
},
|
||
{
|
||
"id": "log_159bad73-fc90-4cc2-8822-4cc1b66a3463",
|
||
"message": "Agent documentation_agent produced 1 documents",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:41.117524",
|
||
"agent_id": "agents",
|
||
"agent_name": "Agent Selection & Execution"
|
||
},
|
||
{
|
||
"id": "log_102029fa-6df7-422d-be5b-218f83890ba8",
|
||
"message": "Completed activity: Generate Backend Structure Report",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:41.166563",
|
||
"agent_id": "execution",
|
||
"agent_name": "Activity Execution"
|
||
},
|
||
{
|
||
"id": "log_bd8be98a-ddf9-4674-8190-cb9545835054",
|
||
"message": "Created workflow summary",
|
||
"type": "info",
|
||
"timestamp": "2025-04-15T14:26:46.533070",
|
||
"agent_id": "summary",
|
||
"agent_name": "Results Summary"
|
||
}
|
||
],
|
||
"data_stats": {
|
||
"total_processing_time": 48.384773,
|
||
"total_token_count": 0,
|
||
"total_bytes_sent": 0,
|
||
"total_bytes_received": 0
|
||
},
|
||
"completed_at": "2025-04-15T14:26:47.536904"
|
||
} |