Stardew Wiki Advisor

Note (OpenClaw sandbox): {baseDir}/venv in this repo is a macOS venv (Mach-O) and will not run inside the Linux Docker sandbox. OpenClaw will create a Linux venv at {baseDir}/.venv inside the sandbox and install deps from {baseDir}/requirements.txt.

{baseDir}/.venv/bin/python3 {baseDir}/scripts/query.py \
  --data-dir {baseDir}/data \
  --query "你的問題" \
  --embedding-model qwen3-embedding \
  --llm-model qwen3:8b

Output Format for Agents: When responding to user queries:

1. State estimated completion time (typically 5-10 seconds)
2. Execute query and return results directly
3. Do NOT describe the process or show progress messages
4. Include Wiki links and similarity scores in results

Examples

• "怎樣賺最多錢？"
• "Elliott 喜歡什麼禮物？"
• "漁場怎樣升級？"
• "哪些作物收益最高？"
• "怎樣快速提升人氣度？"
• "能源核心在哪裡找？"

Requirements

• Python 3.8+
• Ollama with models (必須已下載):
  • qwen3-embedding (4.7 GB, for embeddings)
  • qwen3:8b (5.2 GB, for LLM inference)
• Python venv 已配置完成，所有依賴已安裝（numpy, faiss-cpu, ollama, requests, beautifulsoup4）
• 30 GB 磁盤空間（模型 + 爬蟲結果 + 索引）
• 8-10 GB RAM (qwen3:8b is lighter than GLM-4.7-Flash)

How It Works

1. Crawl — BFS 爬取 zh.stardewvalleywiki.com（100+ 頁）

   • 動態發現：從首頁自動提取所有內容連結
   • 去重機制：URL 規範化 + MD5 hash 防重複

2. Chunk — 將每頁分成 500 字的段落

3. Embed — 用 qwen3-embedding 生成 4096 維向量

   • 100 頁 → 1063+ embeddings chunks
   • 總大小：~17 MB

4. Index — 用 FAISS 建立本機索引

   • 秒級相似度搜尋
   • 無需網路或 API

5. Query — 用戶提問 → 相似度搜尋 → qwen3:8b 生成答案

   • 返回可溯源的 Wiki 連結和相似度分數

Performance

• Query latency: 5-10 seconds（使用 qwen3:8b，比 glm-4.7-flash 快 5-10 倍）
  • Embedding generation: <1 sec
  • FAISS search: ~0.1 sec
  • LLM generation: ~5-8 sec
• Throughput: ~6-10 queries/minute
• Memory: ~8-10 GB
• Storage: ~17 MB (indexed data)

Troubleshooting

• "Ollama connection failed" → Make sure ollama serve is running
• "Model not found" → Download with ollama pull qwen3:8b
• "No embeddings found" → Run setup first
• "Slow response" → Check if Ollama is overloaded; try reducing --top-k

Update Data

To refresh Wiki data:

python3 {baseDir}/scripts/crawl_wiki.py --max-pages 100 --output {baseDir}/data
python3 {baseDir}/scripts/build_vectors.py --input {baseDir}/data/raw_pages.json --output {baseDir}/data