wd666/orchestrator/research_manager.py

from typing import List, Dict, Generator
from dataclasses import dataclass
from agents.research_agent import ResearchAgent
from utils.llm_client import LLMClient
import config

@dataclass
class ResearchConfig:
    topic: str
    context: str = ""
    expert_a_model: str = "gpt-4o"
    expert_b_model: str = "gemini-1.5-pro"
    expert_c_model: str = "claude-3-5-sonnet-20241022"

class ResearchManager:
    """Manages the Multi-Model Council workflow"""
    
    def __init__(self, api_key: str, base_url: str = None, provider: str = "aihubmix"):
        self.api_key = api_key
        self.base_url = base_url
        self.provider = provider
        self.agents = {}
        
    def _get_client(self, model: str) -> LLMClient:
        return LLMClient(
            provider=self.provider,
            api_key=self.api_key,
            base_url=self.base_url,
            model=model
        )

    def create_agents(self, config: ResearchConfig):
        """Initialize agents with specific models"""
        self.agents["expert_a"] = ResearchAgent("expert_a", self._get_client(config.expert_a_model))
        self.agents["expert_b"] = ResearchAgent("expert_b", self._get_client(config.expert_b_model))
        self.agents["expert_c"] = ResearchAgent("expert_c", self._get_client(config.expert_c_model))

    def collaborate(self, topic: str, context: str) -> Generator[Dict[str, str], None, None]:
        """
        Execute the collaborative research process:
        1. Expert A: Propose Analysis
        2. Expert B: Critique
        3. Expert C: Synthesis & Final Plan
        """
        
        # Step 1: Expert A Analysis
        findings_a = ""
        yield {"type": "step_start", "step": "Expert A Analysis", "agent": self.agents["expert_a"].name, "model": self.agents["expert_a"].model_name}
        prompt_a = f"Please provide a comprehensive analysis and initial proposal for the topic: '{topic}'.\nContext: {context}"
        for chunk in self.agents["expert_a"].generate(prompt_a, context):
            findings_a += chunk
            yield {"type": "content", "content": chunk}
        yield {"type": "step_end", "output": findings_a}

        # Step 2: Expert B Critique
        findings_b = ""
        yield {"type": "step_start", "step": "Expert B Critique", "agent": self.agents["expert_b"].name, "model": self.agents["expert_b"].model_name}
        prompt_b = f"Review Expert A's proposal on '{topic}'. Critique it, find gaps, and suggest improvements.\nExpert A's Proposal:\n{findings_a}"
        for chunk in self.agents["expert_b"].generate(prompt_b, context):
            findings_b += chunk
            yield {"type": "content", "content": chunk}
        yield {"type": "step_end", "output": findings_b}

        # Step 3: Expert C Synthesis
        findings_c = ""
        yield {"type": "step_start", "step": "Expert C Synthesis", "agent": self.agents["expert_c"].name, "model": self.agents["expert_c"].model_name}
        prompt_c = f"Synthesize a final comprehensive plan for '{topic}' based on Expert A's proposal and Expert B's critique.\nExpert A:\n{findings_a}\nExpert B:\n{findings_b}"
        for chunk in self.agents["expert_c"].generate(prompt_c, context):
            findings_c += chunk
            yield {"type": "content", "content": chunk}
        yield {"type": "step_end", "output": findings_c}