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"""
Use case for processing user messages through the AI agent system.
"""
from abc import ABC, abstractmethod
from typing import Dict, Any, Optional, List
from uuid import UUID
import time
import logging
from src.core.entities.agent import Agent, AgentState, AgentType
from src.core.entities.message import Message, MessageType
from src.core.interfaces.agent_repository import AgentRepository
from src.core.interfaces.message_repository import MessageRepository
from src.core.interfaces.agent_executor import AgentExecutor
from src.core.interfaces.logging_service import LoggingService
from src.shared.exceptions import DomainException, ValidationException
from src.shared.types import AgentConfig
class ProcessMessageUseCase:
"""
Use case for processing user messages through the AI agent system.
This use case orchestrates the entire message processing workflow,
including agent selection, execution, and response generation.
"""
def __init__(
self,
agent_repository: AgentRepository,
message_repository: MessageRepository,
agent_executor: AgentExecutor,
logging_service: LoggingService,
config: AgentConfig
):
self.agent_repository = agent_repository
self.message_repository = message_repository
self.agent_executor = agent_executor
self.logging_service = logging_service
self.config = config
self.logger = logging.getLogger(__name__)
async def execute(
self,
user_message: str,
session_id: Optional[UUID] = None,
user_id: Optional[UUID] = None,
context: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Execute the message processing workflow.
Args:
user_message: The user's input message
session_id: Optional session identifier
user_id: Optional user identifier
context: Optional context information
Returns:
Dictionary containing the processing result
Raises:
ValidationException: If input validation fails
DomainException: If business rules are violated
"""
start_time = time.time()
try:
# 1. Validate input
self._validate_input(user_message)
# 2. Create message entity
message = Message(
content=user_message,
message_type=MessageType.USER,
session_id=session_id,
user_id=user_id,
context=context or {}
)
# 3. Save message
saved_message = await self.message_repository.save(message)
# 4. Select appropriate agent
agent = await self._select_agent(user_message, context)
# 5. Execute agent
result = await self._execute_agent(agent, saved_message)
# 6. Create response message
response_message = Message(
content=result["response"],
message_type=MessageType.AGENT,
session_id=session_id,
user_id=user_id,
context={"agent_id": str(agent.id), "execution_time": result["execution_time"]}
)
# 7. Save response
saved_response = await self.message_repository.save(response_message)
# 8. Update agent metrics
await self._update_agent_metrics(agent, result)
# 9. Log interaction
await self._log_interaction(saved_message, saved_response, result)
execution_time = time.time() - start_time
return {
"success": True,
"response": result["response"],
"agent_id": str(agent.id),
"agent_type": agent.agent_type.value,
"execution_time": execution_time,
"message_id": str(saved_message.id),
"response_id": str(saved_response.id),
"confidence": result.get("confidence", 0.0),
"tools_used": result.get("tools_used", []),
"metadata": result.get("metadata", {})
}
except Exception as e:
execution_time = time.time() - start_time
self.logger.error(f"Message processing failed: {str(e)}", exc_info=True)
# Log error
await self.logging_service.log_error(
"message_processing_failed",
str(e),
{"execution_time": execution_time, "session_id": str(session_id) if session_id else None}
)
return {
"success": False,
"error": str(e),
"execution_time": execution_time
}
def _validate_input(self, user_message: str) -> None:
"""Validate user input."""
if not user_message or not user_message.strip():
raise ValidationException("Message cannot be empty")
if len(user_message) > self.config.max_input_length:
raise ValidationException(
f"Message too long. Maximum length is {self.config.max_input_length} characters"
)
# Check for potentially malicious content
if self._contains_malicious_content(user_message):
raise ValidationException("Message contains potentially malicious content")
def _contains_malicious_content(self, message: str) -> bool:
"""Check for potentially malicious content."""
malicious_patterns = [
r"ignore\s+previous\s+instructions",
r"disregard\s+all\s+prior",
r"system\s*:\s*you\s+are",
r"<\|im_start\|>",
r"<\|im_end\|>",
r"\[INST\]",
r"\[/INST\]"
]
import re
for pattern in malicious_patterns:
if re.search(pattern, message, re.IGNORECASE):
return True
return False
async def _select_agent(self, message: str, context: Optional[Dict[str, Any]]) -> Agent:
"""Select the most appropriate agent for the message."""
# For now, get the first available agent
# In the future, this could implement more sophisticated selection logic
available_agents = await self.agent_repository.find_available()
if not available_agents:
raise DomainException("No available agents found")
# Simple selection: prefer FSM_REACT agents
fsm_agents = [a for a in available_agents if a.agent_type == AgentType.FSM_REACT]
if fsm_agents:
return fsm_agents[0]
return available_agents[0]
async def _execute_agent(self, agent: Agent, message: Message) -> Dict[str, Any]:
"""Execute the agent with the given message."""
# Update agent state
agent.start_task(f"Processing message: {message.content[:100]}...")
await self.agent_repository.update_state(agent.id, agent.state)
try:
# Execute agent
result = await self.agent_executor.execute(agent, message)
# Update agent state
agent.complete_task(success=True)
await self.agent_repository.update_state(agent.id, agent.state)
return result
except Exception as e:
# Update agent state
agent.enter_error_state(str(e))
await self.agent_repository.update_state(agent.id, agent.state)
raise
async def _update_agent_metrics(self, agent: Agent, result: Dict[str, Any]) -> None:
"""Update agent performance metrics."""
metrics = {
"total_requests": agent.total_requests,
"successful_requests": agent.successful_requests,
"failed_requests": agent.failed_requests,
"average_response_time": agent.average_response_time,
"last_active": agent.last_active.isoformat()
}
await self.agent_repository.update_performance_metrics(agent.id, metrics)
async def _log_interaction(
self,
user_message: Message,
response_message: Message,
result: Dict[str, Any]
) -> None:
"""Log the interaction for analytics."""
await self.logging_service.log_interaction(
user_message_id=str(user_message.id),
response_message_id=str(response_message.id),
session_id=str(user_message.session_id) if user_message.session_id else None,
user_id=str(user_message.user_id) if user_message.user_id else None,
execution_time=result.get("execution_time", 0.0),
agent_id=result.get("agent_id"),
tools_used=result.get("tools_used", []),
confidence=result.get("confidence", 0.0)
) |