109 lines
4.0 KiB
Python
109 lines
4.0 KiB
Python
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"""
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Auto Agent Generator - 根据主题自动生成专家配置
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Uses LLM to analyze the topic and suggest appropriate expert agents.
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"""
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import json
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import re
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from typing import List, Dict
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from utils.llm_client import LLMClient
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EXPERT_GENERATION_PROMPT = """You are an expert team composition advisor. Given a research/decision topic, you need to suggest the most appropriate team of experts to analyze it.
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Instructions:
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1. Analyze the topic carefully to understand its domain and key aspects
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2. Generate {num_experts} distinct expert roles that would provide the most valuable perspectives
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3. Each expert should have a unique focus area relevant to the topic
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4. The LAST expert should always be a "Synthesizer" role who can integrate all perspectives
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Output Format (MUST be valid JSON array):
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[
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{{"name": "Expert Name", "perspective": "Brief description of their viewpoint", "focus": "Key areas they analyze"}},
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...
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]
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Examples of good expert names based on topic:
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- For "Should we launch an e-commerce platform?": "市场渠道分析师", "电商运营专家", "供应链顾问", "数字化转型综合师"
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- For "Career transition to AI field": "职业发展顾问", "AI行业专家", "技能评估分析师", "综合规划师"
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IMPORTANT:
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- Use {language} for all names and descriptions
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- Make names specific to the topic, not generic like "Expert 1"
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- The last expert MUST be a synthesizer/integrator type
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Topic: {topic}
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Generate exactly {num_experts} experts as a JSON array:"""
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def generate_experts_for_topic(
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topic: str,
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num_experts: int,
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llm_client: LLMClient,
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language: str = "Chinese"
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) -> List[Dict[str, str]]:
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"""
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Use LLM to generate appropriate expert configurations based on the topic.
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Args:
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topic: The research/decision topic
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num_experts: Number of experts to generate (2-5)
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llm_client: LLM client instance for API calls
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language: Output language (Chinese/English)
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Returns:
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List of expert dicts: [{"name": "...", "perspective": "...", "focus": "..."}, ...]
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"""
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if not topic.strip():
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return []
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prompt = EXPERT_GENERATION_PROMPT.format(
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topic=topic,
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num_experts=num_experts,
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language=language
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)
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try:
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response = llm_client.chat(
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system_prompt="You are a helpful assistant that generates JSON output only. No markdown, no explanation.",
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user_prompt=prompt,
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max_tokens=800
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)
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# Extract JSON from response (handle potential markdown wrapping)
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json_match = re.search(r'\[[\s\S]*\]', response)
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if json_match:
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experts = json.loads(json_match.group())
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# Validate structure
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if isinstance(experts, list) and len(experts) >= 1:
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validated = []
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for exp in experts[:num_experts]:
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if isinstance(exp, dict) and "name" in exp:
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validated.append({
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"name": exp.get("name", "Expert"),
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"perspective": exp.get("perspective", ""),
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"focus": exp.get("focus", "")
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})
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return validated
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except (json.JSONDecodeError, Exception) as e:
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print(f"[AutoAgentGenerator] Error parsing LLM response: {e}")
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# Fallback: return generic experts
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fallback = []
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for i in range(num_experts):
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if i == num_experts - 1:
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fallback.append({"name": f"综合分析师", "perspective": "整合视角", "focus": "综合决策"})
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else:
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fallback.append({"name": f"专家 {i+1}", "perspective": "分析视角", "focus": "专业分析"})
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return fallback
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def get_default_model_for_expert(expert_index: int, total_experts: int, available_models: list) -> str:
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"""
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Assign a default model to an expert based on their position.
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Spreads experts across available models for diversity.
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"""
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if not available_models:
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return "gpt-4o"
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return available_models[expert_index % len(available_models)]
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