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368 lines
14 KiB
Python
368 lines
14 KiB
Python
import logging
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import uuid
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from typing import Dict, List, Optional
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from bson.objectid import ObjectId
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from application.agents.tools.tool_action_parser import ToolActionParser
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from application.agents.tools.tool_manager import ToolManager
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from application.core.mongo_db import MongoDB
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from application.core.settings import settings
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from application.security.encryption import decrypt_credentials
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logger = logging.getLogger(__name__)
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class ToolExecutor:
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"""Handles tool discovery, preparation, and execution.
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Extracted from BaseAgent to separate concerns and enable tool caching.
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"""
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def __init__(
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self,
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user_api_key: Optional[str] = None,
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user: Optional[str] = None,
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decoded_token: Optional[Dict] = None,
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):
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self.user_api_key = user_api_key
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self.user = user
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self.decoded_token = decoded_token
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self.tool_calls: List[Dict] = []
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self._loaded_tools: Dict[str, object] = {}
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self.conversation_id: Optional[str] = None
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def get_tools(self) -> Dict[str, Dict]:
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"""Load tool configs from DB based on user context."""
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if self.user_api_key:
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return self._get_tools_by_api_key(self.user_api_key)
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return self._get_user_tools(self.user or "local")
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def _get_tools_by_api_key(self, api_key: str) -> Dict[str, Dict]:
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mongo = MongoDB.get_client()
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db = mongo[settings.MONGO_DB_NAME]
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agents_collection = db["agents"]
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tools_collection = db["user_tools"]
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agent_data = agents_collection.find_one({"key": api_key})
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tool_ids = agent_data.get("tools", []) if agent_data else []
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tools = (
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tools_collection.find(
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{"_id": {"$in": [ObjectId(tool_id) for tool_id in tool_ids]}}
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)
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if tool_ids
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else []
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)
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tools = list(tools)
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return {str(tool["_id"]): tool for tool in tools} if tools else {}
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def _get_user_tools(self, user: str = "local") -> Dict[str, Dict]:
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mongo = MongoDB.get_client()
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db = mongo[settings.MONGO_DB_NAME]
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user_tools_collection = db["user_tools"]
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user_tools = user_tools_collection.find({"user": user, "status": True})
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user_tools = list(user_tools)
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return {str(i): tool for i, tool in enumerate(user_tools)}
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def prepare_tools_for_llm(self, tools_dict: Dict) -> List[Dict]:
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"""Convert tool configs to LLM function schemas."""
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return [
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{
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"type": "function",
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"function": {
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"name": f"{action['name']}_{tool_id}",
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"description": action["description"],
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"parameters": self._build_tool_parameters(action),
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},
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}
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for tool_id, tool in tools_dict.items()
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if (
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(tool["name"] == "api_tool" and "actions" in tool.get("config", {}))
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or (tool["name"] != "api_tool" and "actions" in tool)
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)
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for action in (
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tool["config"]["actions"].values()
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if tool["name"] == "api_tool"
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else tool["actions"]
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)
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if action.get("active", True)
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]
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def _build_tool_parameters(self, action: Dict) -> Dict:
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params = {"type": "object", "properties": {}, "required": []}
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for param_type in ["query_params", "headers", "body", "parameters"]:
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if param_type in action and action[param_type].get("properties"):
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for k, v in action[param_type]["properties"].items():
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if v.get("filled_by_llm", True):
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params["properties"][k] = {
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key: value
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for key, value in v.items()
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if key not in ("filled_by_llm", "value", "required")
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}
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if v.get("required", False):
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params["required"].append(k)
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return params
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def check_pause(
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self, tools_dict: Dict, call, llm_class_name: str
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) -> Optional[Dict]:
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"""Check if a tool call requires pausing for approval or client execution.
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Returns a dict describing the pending action if pause is needed, None otherwise.
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"""
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parser = ToolActionParser(llm_class_name)
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tool_id, action_name, call_args = parser.parse_args(call)
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call_id = getattr(call, "id", None) or str(uuid.uuid4())
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if tool_id is None or action_name is None or tool_id not in tools_dict:
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return None # Will be handled as error by execute()
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tool_data = tools_dict[tool_id]
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# Phase 2: client-side tools
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if tool_data.get("client_side"):
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return {
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"call_id": call_id,
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"name": getattr(call, "name", f"{action_name}_{tool_id}"),
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"tool_name": tool_data.get("name", "unknown"),
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"tool_id": tool_id,
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"action_name": action_name,
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"arguments": call_args if isinstance(call_args, dict) else {},
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"pause_type": "requires_client_execution",
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"thought_signature": getattr(call, "thought_signature", None),
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}
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# Phase 3: approval required
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if tool_data["name"] == "api_tool":
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action_data = tool_data.get("config", {}).get("actions", {}).get(
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action_name, {}
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)
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else:
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action_data = next(
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(a for a in tool_data.get("actions", []) if a["name"] == action_name),
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{},
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)
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if action_data.get("require_approval"):
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return {
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"call_id": call_id,
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"name": getattr(call, "name", f"{action_name}_{tool_id}"),
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"tool_name": tool_data.get("name", "unknown"),
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"tool_id": tool_id,
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"action_name": action_name,
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"arguments": call_args if isinstance(call_args, dict) else {},
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"pause_type": "awaiting_approval",
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"thought_signature": getattr(call, "thought_signature", None),
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}
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return None
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def execute(self, tools_dict: Dict, call, llm_class_name: str):
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"""Execute a tool call. Yields status events, returns (result, call_id)."""
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parser = ToolActionParser(llm_class_name)
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tool_id, action_name, call_args = parser.parse_args(call)
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call_id = getattr(call, "id", None) or str(uuid.uuid4())
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if tool_id is None or action_name is None:
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error_message = f"Error: Failed to parse LLM tool call. Tool name: {getattr(call, 'name', 'unknown')}"
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logger.error(error_message)
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tool_call_data = {
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"tool_name": "unknown",
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"call_id": call_id,
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"action_name": getattr(call, "name", "unknown"),
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"arguments": call_args or {},
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"result": f"Failed to parse tool call. Invalid tool name format: {getattr(call, 'name', 'unknown')}",
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}
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yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
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self.tool_calls.append(tool_call_data)
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return "Failed to parse tool call.", call_id
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if tool_id not in tools_dict:
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error_message = f"Error: Tool ID '{tool_id}' extracted from LLM call not found in available tools_dict. Available IDs: {list(tools_dict.keys())}"
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logger.error(error_message)
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tool_call_data = {
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"tool_name": "unknown",
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"call_id": call_id,
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"action_name": f"{action_name}_{tool_id}",
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"arguments": call_args,
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"result": f"Tool with ID {tool_id} not found. Available tools: {list(tools_dict.keys())}",
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}
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yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
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self.tool_calls.append(tool_call_data)
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return f"Tool with ID {tool_id} not found.", call_id
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tool_call_data = {
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"tool_name": tools_dict[tool_id]["name"],
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"call_id": call_id,
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"action_name": f"{action_name}_{tool_id}",
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"arguments": call_args,
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}
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yield {"type": "tool_call", "data": {**tool_call_data, "status": "pending"}}
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tool_data = tools_dict[tool_id]
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action_data = (
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tool_data["config"]["actions"][action_name]
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if tool_data["name"] == "api_tool"
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else next(
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action
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for action in tool_data["actions"]
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if action["name"] == action_name
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)
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)
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query_params, headers, body, parameters = {}, {}, {}, {}
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param_types = {
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"query_params": query_params,
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"headers": headers,
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"body": body,
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"parameters": parameters,
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}
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for param_type, target_dict in param_types.items():
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if param_type in action_data and action_data[param_type].get("properties"):
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for param, details in action_data[param_type]["properties"].items():
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if (
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param not in call_args
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and "value" in details
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and details["value"]
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):
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target_dict[param] = details["value"]
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for param, value in call_args.items():
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for param_type, target_dict in param_types.items():
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if param_type in action_data and param in action_data[param_type].get(
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"properties", {}
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):
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target_dict[param] = value
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# Load tool (with caching)
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tool = self._get_or_load_tool(
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tool_data, tool_id, action_name,
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headers=headers, query_params=query_params,
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)
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resolved_arguments = (
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{"query_params": query_params, "headers": headers, "body": body}
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if tool_data["name"] == "api_tool"
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else parameters
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)
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if tool_data["name"] == "api_tool":
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logger.debug(
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f"Executing api: {action_name} with query_params: {query_params}, headers: {headers}, body: {body}"
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)
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result = tool.execute_action(action_name, **body)
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else:
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logger.debug(f"Executing tool: {action_name} with args: {call_args}")
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result = tool.execute_action(action_name, **parameters)
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get_artifact_id = (
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getattr(tool, "get_artifact_id", None)
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if tool_data["name"] != "api_tool"
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else None
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)
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artifact_id = None
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if callable(get_artifact_id):
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try:
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artifact_id = get_artifact_id(action_name, **parameters)
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except Exception:
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logger.exception(
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"Failed to extract artifact_id from tool %s for action %s",
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tool_data["name"],
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action_name,
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)
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artifact_id = str(artifact_id).strip() if artifact_id is not None else ""
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if artifact_id:
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tool_call_data["artifact_id"] = artifact_id
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result_full = str(result)
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tool_call_data["resolved_arguments"] = resolved_arguments
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tool_call_data["result_full"] = result_full
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tool_call_data["result"] = (
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f"{result_full[:50]}..." if len(result_full) > 50 else result_full
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)
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stream_tool_call_data = {
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key: value
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for key, value in tool_call_data.items()
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if key not in {"result_full", "resolved_arguments"}
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}
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yield {"type": "tool_call", "data": {**stream_tool_call_data, "status": "completed"}}
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self.tool_calls.append(tool_call_data)
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return result, call_id
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def _get_or_load_tool(
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self, tool_data: Dict, tool_id: str, action_name: str,
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headers: Optional[Dict] = None, query_params: Optional[Dict] = None,
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):
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"""Load a tool, using cache when possible."""
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cache_key = f"{tool_data['name']}:{tool_id}:{self.user or ''}"
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if cache_key in self._loaded_tools:
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return self._loaded_tools[cache_key]
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tm = ToolManager(config={})
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if tool_data["name"] == "api_tool":
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action_config = tool_data["config"]["actions"][action_name]
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tool_config = {
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"url": action_config["url"],
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"method": action_config["method"],
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"headers": headers or {},
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"query_params": query_params or {},
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}
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if "body_content_type" in action_config:
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tool_config["body_content_type"] = action_config.get(
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"body_content_type", "application/json"
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)
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tool_config["body_encoding_rules"] = action_config.get(
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"body_encoding_rules", {}
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)
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else:
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tool_config = tool_data["config"].copy() if tool_data["config"] else {}
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if tool_config.get("encrypted_credentials") and self.user:
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decrypted = decrypt_credentials(
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tool_config["encrypted_credentials"], self.user
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)
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tool_config.update(decrypted)
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tool_config["auth_credentials"] = decrypted
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tool_config.pop("encrypted_credentials", None)
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tool_config["tool_id"] = str(tool_data.get("_id", tool_id))
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if self.conversation_id:
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tool_config["conversation_id"] = self.conversation_id
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if tool_data["name"] == "mcp_tool":
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tool_config["query_mode"] = True
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tool = tm.load_tool(
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tool_data["name"],
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tool_config=tool_config,
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user_id=self.user,
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)
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# Don't cache api_tool since config varies by action
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if tool_data["name"] != "api_tool":
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self._loaded_tools[cache_key] = tool
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return tool
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def get_truncated_tool_calls(self) -> List[Dict]:
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return [
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{
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"tool_name": tool_call.get("tool_name"),
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"call_id": tool_call.get("call_id"),
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"action_name": tool_call.get("action_name"),
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"arguments": tool_call.get("arguments"),
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"artifact_id": tool_call.get("artifact_id"),
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"result": (
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f"{str(tool_call['result'])[:50]}..."
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if len(str(tool_call["result"])) > 50
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else tool_call["result"]
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),
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"status": "completed",
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}
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for tool_call in self.tool_calls
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]
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