gbanyan
43c025e060
- 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>
112 lines
3.1 KiB
Python
112 lines
3.1 KiB
Python
"""
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Condition 1: Direct Generation (Baseline)
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Single LLM call asking for creative ideas directly.
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No attribute decomposition, no expert perspectives.
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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 experiments.config import MODEL, TEMPERATURE, IDEAS_DIRECT, PROMPT_LANGUAGE
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def get_direct_generation_prompt(query: str, idea_count: int, lang: str = "en") -> str:
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"""Generate prompt for direct 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 and innovative ideas for "{query}".
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Requirements:
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1. Each idea should be specific and actionable
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2. Ideas should be diverse, covering different aspects and applications
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3. Include both practical improvements and creative innovations
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4. Ideas should be 15-30 words each
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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."""
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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。
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要求：
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1. 每個點子要具體可行
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2. 點子要多元，涵蓋不同面向和應用
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3. 包含實用改進和創意創新
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4. 每個點子 15-30 字
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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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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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idea_count: 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 direct LLM generation (C1 baseline).
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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 (default from config)
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temperature: Generation temperature (default from config)
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idea_count: Number of ideas to generate (default from config)
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lang: Language for prompts (default from config)
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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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idea_count = idea_count or IDEAS_DIRECT
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lang = lang or PROMPT_LANGUAGE
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prompt = get_direct_generation_prompt(query, idea_count, lang)
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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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return {
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"condition": "c1_direct",
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"query": query,
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"ideas": ideas,
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"idea_count": len(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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"mechanism": "direct_llm_generation"
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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:")
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for i, idea in enumerate(result['ideas'], 1):
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print(f" {i}. {idea}")
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asyncio.run(test())
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