feat: Add experiments framework and novelty-driven agent loop
- Add complete experiments directory with pilot study infrastructure - 5 experimental conditions (direct, expert-only, attribute-only, full-pipeline, random-perspective) - Human assessment tool with React frontend and FastAPI backend - AUT flexibility analysis with jump signal detection - Result visualization and metrics computation - Add novelty-driven agent loop module (experiments/novelty_loop/) - NoveltyDrivenTaskAgent with expert perspective perturbation - Three termination strategies: breakthrough, exhaust, coverage - Interactive CLI demo with colored output - Embedding-based novelty scoring - Add DDC knowledge domain classification data (en/zh) - Add CLAUDE.md project documentation - Update research report with experiment findings Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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experiments/conditions/c2_expert_only.py
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176
experiments/conditions/c2_expert_only.py
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"""
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Condition 2: Expert-Only (No Attributes)
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Uses expert perspectives to generate ideas, but without
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attribute decomposition. Each expert generates ideas directly
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for the query from their professional perspective.
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"""
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import sys
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from pathlib import Path
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# Add backend to path for imports
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "backend"))
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from typing import List, Dict, Any
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from app.services.llm_service import ollama_provider, extract_json_from_response
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from app.services.expert_source_service import expert_source_service
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from experiments.config import (
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MODEL, TEMPERATURE, EXPERT_COUNT, EXPERT_SOURCE,
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IDEAS_PER_EXPERT, PROMPT_LANGUAGE
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)
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def get_expert_idea_generation_prompt(
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query: str,
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expert_name: str,
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expert_domain: str,
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idea_count: int,
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lang: str = "en"
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) -> str:
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"""Generate prompt for expert-based idea generation."""
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if lang == "en":
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domain_text = f" ({expert_domain} field)" if expert_domain else ""
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return f"""/no_think
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You are a {expert_name}{domain_text}.
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Task: Generate {idea_count} creative and innovative ideas for "{query}" from your professional perspective.
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Requirements:
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1. Each idea should reflect your professional expertise and unique viewpoint
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2. Think about how concepts from your field could improve or reimagine "{query}"
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3. Ideas should be specific and actionable (15-30 words each)
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4. Combine your professional knowledge with creative thinking
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Return JSON only:
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{{"ideas": ["idea 1", "idea 2", "idea 3", ...]}}
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Generate exactly {idea_count} ideas from your perspective as a {expert_name}."""
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else:
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domain_text = f"（{expert_domain}領域）" if expert_domain else ""
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return f"""/no_think
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你是一位{expert_name}{domain_text}。
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任務：從你的專業角度，為「{query}」生成 {idea_count} 個創意點子。
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要求：
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1. 每個點子要反映你的專業知識和獨特觀點
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2. 思考你領域的概念如何改進或重新想像「{query}」
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3. 點子要具體可行（每個 15-30 字）
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4. 結合專業知識和創意思維
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只回傳 JSON：
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{{"ideas": ["點子1", "點子2", "點子3", ...]}}
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從你作為{expert_name}的角度生成正好 {idea_count} 個點子。"""
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async def generate_ideas(
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query: str,
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model: str = None,
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temperature: float = None,
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expert_count: int = None,
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expert_source: str = None,
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ideas_per_expert: int = None,
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lang: str = None
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) -> Dict[str, Any]:
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"""
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Generate ideas using expert perspectives only (C2).
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Args:
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query: The object/concept to generate ideas for
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model: LLM model to use
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temperature: Generation temperature
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expert_count: Number of experts to use
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expert_source: Source of experts (curated, dbpedia, etc.)
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ideas_per_expert: Ideas each expert generates
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lang: Language for prompts
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Returns:
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Dict with ideas and metadata
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"""
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model = model or MODEL
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temperature = temperature or TEMPERATURE
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expert_count = expert_count or EXPERT_COUNT
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expert_source = expert_source or EXPERT_SOURCE
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ideas_per_expert = ideas_per_expert or IDEAS_PER_EXPERT
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lang = lang or PROMPT_LANGUAGE
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# Get experts from curated source
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experts, actual_source = expert_source_service.get_experts(
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source=expert_source,
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count=expert_count,
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language=lang
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)
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all_ideas = []
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expert_details = []
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for expert in experts:
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expert_name = expert.get("name", "Expert")
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expert_domain = expert.get("domain", "")
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prompt = get_expert_idea_generation_prompt(
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query=query,
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expert_name=expert_name,
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expert_domain=expert_domain,
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idea_count=ideas_per_expert,
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lang=lang
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)
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response = await ollama_provider.generate(
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prompt=prompt,
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model=model,
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temperature=temperature
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)
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result = extract_json_from_response(response)
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ideas = result.get("ideas", [])
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# Tag ideas with expert source
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for idea in ideas:
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all_ideas.append({
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"idea": idea,
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"expert_name": expert_name,
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"expert_domain": expert_domain
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})
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expert_details.append({
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"name": expert_name,
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"domain": expert_domain,
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"ideas_generated": len(ideas)
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})
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return {
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"condition": "c2_expert_only",
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"query": query,
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"ideas": [item["idea"] for item in all_ideas],
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"ideas_with_source": all_ideas,
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"idea_count": len(all_ideas),
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"metadata": {
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"model": model,
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"temperature": temperature,
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"prompt_language": lang,
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"expert_count": expert_count,
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"expert_source": actual_source,
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"ideas_per_expert": ideas_per_expert,
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"experts": expert_details,
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"mechanism": "expert_perspectives_only"
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}
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}
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# For testing
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if __name__ == "__main__":
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import asyncio
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async def test():
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result = await generate_ideas("Chair")
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print(f"Generated {result['idea_count']} ideas from {len(result['metadata']['experts'])} experts:")
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for exp in result['metadata']['experts']:
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print(f" - {exp['name']}: {exp['ideas_generated']} ideas")
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print("\nSample ideas:")
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for i, item in enumerate(result['ideas_with_source'][:5], 1):
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print(f" {i}. [{item['expert_name']}] {item['idea']}")
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asyncio.run(test())
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