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/c3_attribute_only.py
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experiments/conditions/c3_attribute_only.py
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"""
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Condition 3: Attribute-Only (No Experts)
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Uses attribute decomposition to break down the query into
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structured categories, then generates ideas from each attribute.
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No expert perspectives involved.
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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.prompts.attribute_prompt import get_step1_dynamic_attributes_prompt
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from experiments.config import (
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MODEL, TEMPERATURE, FIXED_CATEGORIES, PROMPT_LANGUAGE
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)
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def get_attribute_idea_generation_prompt(
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query: str,
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category: str,
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attribute: 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 attribute-based idea generation."""
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if lang == "en":
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return f"""/no_think
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Generate {idea_count} creative ideas for "{query}" focusing on the attribute "{attribute}" (Category: {category}).
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Requirements:
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1. Each idea should be directly inspired by the attribute "{attribute}"
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2. Think about how this attribute could be improved, reimagined, or applied in new ways
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3. Ideas should be specific and actionable (15-30 words each)
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4. Be creative while maintaining relevance to the attribute
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Return JSON only:
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{{"ideas": ["idea 1", "idea 2", ...]}}
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Generate exactly {idea_count} ideas based on the attribute "{attribute}"."""
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else:
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return f"""/no_think
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為「{query}」生成 {idea_count} 個創意點子，聚焦於屬性「{attribute}」（類別：{category}）。
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要求：
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1. 每個點子要直接受屬性「{attribute}」啟發
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2. 思考如何改進、重新想像或以新方式應用這個屬性
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3. 點子要具體可行（每個 15-30 字）
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4. 保持創意同時與屬性相關
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只回傳 JSON：
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{{"ideas": ["點子1", "點子2", ...]}}
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基於屬性「{attribute}」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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categories: List[str] = None,
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ideas_per_attribute: int = 1,
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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 attribute decomposition only (C3).
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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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categories: Categories to use for decomposition
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ideas_per_attribute: Ideas to generate per attribute
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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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categories = categories or FIXED_CATEGORIES
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lang = lang or PROMPT_LANGUAGE
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# Step 1: Generate attributes using existing prompt
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# Build category definitions for the prompt
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category_defs = [
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{"name": cat, "description": f"Related {cat.lower()} of the object", "order": i}
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for i, cat in enumerate(categories)
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]
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attr_prompt = get_step1_dynamic_attributes_prompt(
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query=query,
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categories=category_defs,
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lang=lang
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)
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attr_response = await ollama_provider.generate(
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prompt=attr_prompt,
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model=model,
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temperature=temperature
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)
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attributes_by_category = extract_json_from_response(attr_response)
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# Step 2: Generate ideas for each attribute
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all_ideas = []
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attribute_details = []
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for category in categories:
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attrs = attributes_by_category.get(category, [])
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for attr in attrs:
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prompt = get_attribute_idea_generation_prompt(
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query=query,
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category=category,
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attribute=attr,
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idea_count=ideas_per_attribute,
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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 attribute 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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"category": category,
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"attribute": attr
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})
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attribute_details.append({
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"category": category,
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"attribute": attr,
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"ideas_generated": len(ideas)
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})
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return {
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"condition": "c3_attribute_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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"categories": categories,
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"attributes_by_category": attributes_by_category,
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"attribute_count": sum(len(v) for v in attributes_by_category.values()),
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"ideas_per_attribute": ideas_per_attribute,
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"attributes": attribute_details,
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"mechanism": "attribute_decomposition_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 {result['metadata']['attribute_count']} attributes:")
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for cat, attrs in result['metadata']['attributes_by_category'].items():
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print(f" {cat}: {', '.join(attrs)}")
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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['category']}/{item['attribute']}] {item['idea']}")
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asyncio.run(test())
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