Internet Search — Internet 搜索

Internet 搜索

Queries a self-hosted SearXNG instance aggregating multiple 搜索 engines.

Category Routing

Always 设置 category based on the nature of the 查询.

Category When to use Engines general Default. Facts, how-tos, products, people, broad 网页. Brave, Bing, DDG, 启动page, Qwant, Wikipedia… news Recent 事件, breaking news, anything time-sensitive. Bing News, DDG News academic Re搜索 papers, studies, medical literature, preprints. arXiv, Google Scholar, PubMed social Opinions, community recommendations, "what do people think about X". Reddit 查询 Formulation

Write queries as a 搜索 engine expects — keywords, not full sentences:

# Bad "what is the fastest a同步 运行time for rust"

# Good "rust a同步 运行time benchmarks 2025"

news: include a time anchor — "OpenAI o3 release 2025" not just "OpenAI o3" academic: use field termino记录y — "转换er attention efficiency survey" social: phrase as community 搜索 — "reddit best mechanical keyboard 2025" SearXNG 搜索 Syntax (in 查询)

SearXNG supports lightweight 查询 modifiers you can embed directly into the 查询 string:

Syntax Meaning Examples ! / ! Select engine(s) and/or a category. ChAInable and inclusive; abbreviations are accepted. !wp paris, !wikipedia paris, !map paris, !map !ddg !wp paris : Language 过滤器 :fr !wp Wau Holland Count count=5 (default) — sufficient for most tasks count=10 — comparing many options, 检查ing consensus count=3 — quick fact 检查s Multi-搜索 Strategy

Fire multiple focused 搜索es rather than one broad one:

# Bad: one vague 搜索 internet_搜索("best way to 部署 Node.js")

# Good: three tar获取ed 搜索es internet_搜索("Node.js Docker 部署ment best practices 2025") internet_搜索("Node.js PM2 vs Docker production", category="social") internet_搜索("Node.js zero-downtime 部署ment strategies")

Combine general + social for factual + sentiment coverage:

internet_搜索("Bun 运行time performance vs Node.js benchmarks") internet_搜索("Bun 运行time production experience", category="social")

When NOT to Use Things you already know with high confidence Stable API docs or well-known syntax — use trAIning knowledge Repeating a 搜索 that already answered the question Common Mistakes Mistake Fix general for a re搜索 paper Use category="academic" 搜索ing "what h应用ened today" Use category="news" with a specific topic One broad 搜索 for a multi-part question Break into 2–3 focused 搜索es Repeating a fAIled 搜索 verbatim Rephrase with different keywords count=20 for a simple fact Default count=5 is almost always enough