141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Analyst Agent - Based on AgentScope ReActAgent
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Performs analysis using tools and LLM
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"""
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from typing import Any, Dict, Optional
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from agentscope.agent import ReActAgent
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from agentscope.memory import InMemoryMemory, LongTermMemoryBase
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from agentscope.message import Msg
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from ..config.constants import ANALYST_TYPES
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from ..utils.progress import progress
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from .prompt_factory import build_agent_system_prompt, clear_prompt_factory_cache
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class AnalystAgent(ReActAgent):
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"""
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Analyst Agent - Uses LLM for tool selection and analysis
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Inherits from AgentScope's ReActAgent
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"""
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def __init__(
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self,
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analyst_type: str,
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toolkit: Any,
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model: Any,
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formatter: Any,
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agent_id: Optional[str] = None,
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config: Optional[Dict[str, Any]] = None,
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long_term_memory: Optional[LongTermMemoryBase] = None,
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):
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"""
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Initialize Analyst Agent
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Args:
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analyst_type: Type of analyst (e.g., "fundamentals", etc.)
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toolkit: AgentScope Toolkit instance
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model: LLM model instance
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formatter: Message formatter instance
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agent_id: Agent ID (defaults to "{analyst_type}_analyst")
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config: Configuration dictionary
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long_term_memory: Optional ReMeTaskLongTermMemory instance
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"""
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if analyst_type not in ANALYST_TYPES:
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raise ValueError(
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f"Unknown analyst type: {analyst_type}. "
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f"Must be one of: {list(ANALYST_TYPES.keys())}",
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)
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object.__setattr__(self, "analyst_type_key", analyst_type)
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object.__setattr__(
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self,
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"analyst_persona",
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ANALYST_TYPES[analyst_type]["display_name"],
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)
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if agent_id is None:
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agent_id = analyst_type
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object.__setattr__(self, "agent_id", agent_id)
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object.__setattr__(self, "config", config or {})
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object.__setattr__(self, "toolkit", toolkit)
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sys_prompt = self._load_system_prompt()
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kwargs = {
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"name": agent_id,
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"sys_prompt": sys_prompt,
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"model": model,
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"formatter": formatter,
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"toolkit": toolkit,
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"memory": InMemoryMemory(),
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"max_iters": 10,
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}
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if long_term_memory:
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kwargs["long_term_memory"] = long_term_memory
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kwargs["long_term_memory_mode"] = "static_control"
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super().__init__(**kwargs)
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def _load_system_prompt(self) -> str:
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"""Load system prompt for analyst"""
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return build_agent_system_prompt(
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agent_id=self.agent_id,
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config_name=self.config.get("config_name", "default"),
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toolkit=self.toolkit,
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analyst_type=self.analyst_type_key,
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)
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async def reply(self, x: Msg = None) -> Msg:
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"""
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Override reply method to add progress tracking
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Args:
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x: Input message (content must be str)
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Returns:
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Response message (content is str)
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"""
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ticker = None
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if x and hasattr(x, "metadata") and x.metadata:
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ticker = x.metadata.get("tickers")
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if ticker:
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progress.update_status(
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self.name,
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ticker,
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f"Starting {self.analyst_persona} analysis",
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)
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result = await super().reply(x)
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if ticker:
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progress.update_status(
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self.name,
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ticker,
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"Analysis completed",
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)
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return result
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def reload_runtime_assets(self, active_skill_dirs: Optional[list] = None) -> None:
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"""Reload toolkit and system prompt from current run assets."""
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from .toolkit_factory import create_agent_toolkit
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clear_prompt_factory_cache()
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self.toolkit = create_agent_toolkit(
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self.agent_id,
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self.config.get("config_name", "default"),
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active_skill_dirs=active_skill_dirs,
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)
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self._apply_runtime_sys_prompt(self._load_system_prompt())
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def _apply_runtime_sys_prompt(self, sys_prompt: str) -> None:
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"""Update the prompt used by future turns and the cached system msg."""
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self._sys_prompt = sys_prompt
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for msg, _marks in self.memory.content:
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if getattr(msg, "role", None) == "system":
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msg.content = sys_prompt
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break
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