from typing import Generator
from utils.llm_client import LLMClient
import config

class ResearchAgent:
    """研究模式专用 Agent"""
    
    def __init__(self, role: str, llm_client: LLMClient, name: str = None, language: str = "Chinese"):
        self.role = role
        self.llm_client = llm_client
        self.role_config = config.RESEARCH_MODEL_ROLES.get(role, {})
        self.name = name if name else self.role_config.get("name", role.capitalize())
        self.language = language
        
    @property
    def model_name(self) -> str:
        return self.llm_client.model

    def _get_system_prompt(self, context: str = "") -> str:
        base_prompt = ""
        if self.role == "council_member":
            base_prompt = f"""You are {self.name}, a member of the Multi-Model Decision Council.
Your goal is to participate in a round-table discussion to solve the user's problem.
Be conversational, insightful, and constructive.
Build upon others' ideas or respectfully disagree with valid reasoning.
Context: {context}"""

        elif self.role == "expert_a":
            base_prompt = f"""You are Expert A, a Senior Analyst.
You are participating in a round-table discussion.
Your goal is to analyze the topic and propose solutions.
Be conversational, direct, and responsive to other experts. 
Do not write a full final report; focus on the current discussion turn.
Context: {context}"""
        
        elif self.role == "expert_b":
            base_prompt = f"""You are Expert B, a Critical Reviewer.
You are participating in a round-table discussion.
Your goal is to critique Expert A's points and offer alternative perspectives.
Be conversational and constructive. Challenge assumptions directly.
Context: {context}"""
        
        elif self.role == "expert_c":
            base_prompt = f"""You are Expert C, a Senior Strategist and Visual Thinker.
Your goal is to synthesize the final output. 
Combine the structural strength of Expert A with the critical insights of Expert B.
Produce a final, polished, comprehensive plan or report.
CRITICAL: You MUST include a Mermaid.js diagram (using ```mermaid code block) to visualize the timeline, process, or architecture."""
        
        else:
            base_prompt = "You are a helpful assistant."
            
        return f"{base_prompt}\n\nIMPORTANT: You MUST output your response in {self.language}."

    def generate(self, prompt: str, context: str = "") -> Generator[str, None, None]:
        """Generate response stream"""
        system_prompt = self._get_system_prompt(context)
        yield from self.llm_client.chat_stream(
            system_prompt=system_prompt,
            user_prompt=prompt
        )