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|
"""
Base class for all baseline agents.
All agents must implement:
- respond(): Generate a response to user query
- end_session(): Called when a session ends (for memory updates)
- reset_user(): Reset all state for a user
"""
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
@dataclass
class AgentResponse:
"""Response from an agent."""
answer: str
debug_info: Dict[str, Any] = field(default_factory=dict)
class BaselineAgent(ABC):
"""Abstract base class for all baseline agents."""
def __init__(self, model_name: str, **kwargs):
"""
Args:
model_name: Name/path of the LLM to use
**kwargs: Additional configuration
"""
self.model_name = model_name
self.config = kwargs
@abstractmethod
def respond(
self,
user_id: str,
query: str,
conversation_history: List[Dict[str, str]],
**kwargs
) -> AgentResponse:
"""
Generate a response to the user's query.
Args:
user_id: Unique identifier for the user
query: Current user message
conversation_history: List of previous messages [{"role": "user/assistant", "content": "..."}]
**kwargs: Additional context (e.g., task info)
Returns:
AgentResponse with answer and debug info
"""
pass
@abstractmethod
def end_session(self, user_id: str, conversation: List[Dict[str, str]]):
"""
Called when a session (one task) ends.
Use this to update memory, notes, etc.
Args:
user_id: User identifier
conversation: Complete conversation from this session
"""
pass
@abstractmethod
def reset_user(self, user_id: str):
"""
Completely reset all state for a user.
Called at the start of a new experiment.
Args:
user_id: User identifier
"""
pass
def get_name(self) -> str:
"""Get a descriptive name for this agent."""
return self.__class__.__name__
|
