exa-search

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Chinese

Exa Search Integration

Exa搜索集成

Quick Reference

快速参考

Topic	When to Use	Reference
Search Modes	Choosing between auto, neural, and keyword search	search-modes.md
Filters	Domain, date, text, and category filtering	filters.md
Contents	Text extraction, highlights, summaries, livecrawl	contents.md
SDK Patterns	Python (exa_py) and TypeScript (exa-js) usage	sdk-patterns.md

主题	使用场景	参考链接
搜索模式	在自动、神经和关键词搜索之间做选择	search-modes.md
筛选器	域名、日期、文本和分类过滤	filters.md
内容获取	文本提取、高亮显示、摘要、实时爬取	contents.md
SDK使用模式	Python（exa_py）和TypeScript（exa-js）的用法	sdk-patterns.md

Essential Patterns

核心使用模式

Basic Search (Python)

基础搜索（Python）

python

from exa_py import Exa

exa = Exa(api_key="your-api-key")  # or set EXA_API_KEY env var

results = exa.search_and_contents(
    "latest developments in quantum computing",
    type="auto",
    num_results=10,
    text=True,
    highlights=True
)

for result in results.results:
    print(f"{result.title}: {result.url}")
    print(result.text[:500])

python

from exa_py import Exa

exa = Exa(api_key="your-api-key")  # or set EXA_API_KEY env var

results = exa.search_and_contents(
    "latest developments in quantum computing",
    type="auto",
    num_results=10,
    text=True,
    highlights=True
)

for result in results.results:
    print(f"{result.title}: {result.url}")
    print(result.text[:500])

Basic Search (TypeScript)

基础搜索（TypeScript）

typescript

import Exa from "exa-js";

const exa = new Exa(process.env.EXA_API_KEY);

const results = await exa.searchAndContents(
  "latest developments in quantum computing",
  {
    type: "auto",
    numResults: 10,
    text: true,
    highlights: true,
  }
);

results.results.forEach((result) => {
  console.log(`${result.title}: ${result.url}`);
});

typescript

import Exa from "exa-js";

const exa = new Exa(process.env.EXA_API_KEY);

const results = await exa.searchAndContents(
  "latest developments in quantum computing",
  {
    type: "auto",
    numResults: 10,
    text: true,
    highlights: true,
  }
);

results.results.forEach((result) => {
  console.log(`${result.title}: ${result.url}`);
});

Search with Filters

带筛选条件的搜索

python

results = exa.search_and_contents(
    "AI startup funding rounds",
    type="neural",
    num_results=10,
    include_domains=["techcrunch.com", "venturebeat.com"],
    start_published_date="2024-01-01",
    text={"max_characters": 2000},
    summary=True
)

python

results = exa.search_and_contents(
    "AI startup funding rounds",
    type="neural",
    num_results=10,
    include_domains=["techcrunch.com", "venturebeat.com"],
    start_published_date="2024-01-01",
    text={"max_characters": 2000},
    summary=True
)

Find Similar Links

查找相似链接

python

similar = exa.find_similar_and_contents(
    "https://example.com/interesting-article",
    num_results=10,
    exclude_source_domain=True,
    text=True
)

python

similar = exa.find_similar_and_contents(
    "https://example.com/interesting-article",
    num_results=10,
    exclude_source_domain=True,
    text=True
)

Search Mode Selection

搜索模式选择

Mode	When to Use	Notes
`auto`	Default for most queries	Exa optimizes between neural/keyword automatically
`neural`	Natural language, conceptual queries	Best for "what is...", "how to...", topic exploration
`keyword`	Exact matches, technical terms, names	Best for specific product names, error codes, proper nouns

模式	使用场景	说明
`auto`	大多数查询的默认选择	Exa会自动在神经/关键词搜索之间优化选择
`neural`	自然语言、概念类查询	最适合“什么是……”“如何……”这类问题，以及主题探索
`keyword`	精确匹配、技术术语、名称	最适合特定产品名称、错误代码、专有名词

Common Mistakes

常见错误

Using
keyword
for conceptual queries - Neural search understands intent better; use
```
auto
```
or
```
neural
```
for natural language questions
Not setting
text=True
- Search returns URLs only by default; explicitly request content with
```
text=True
```
Ignoring
highlights
- Use
```
highlights=True
```
for relevant snippets without downloading full page text
Missing API key - Set
```
EXA_API_KEY
```
environment variable or pass explicitly to constructor
Over-filtering initially - Start with broad searches, then add domain/date filters to refine
Not using
summary
- For RAG applications,
```
summary=True
```
provides concise context without full page text
Expecting scores in auto mode - Relevance scores are only returned with
```
type="neural"
```
; auto mode doesn't include them

为概念类查询使用
keyword
模式 - 神经搜索更能理解意图；针对自然语言问题请使用
```
auto
```
或
```
neural
```
模式
未设置
text=True
- 默认情况下搜索仅返回URL；需显式设置
```
text=True
```
来请求内容
忽略
highlights
参数 - 使用
```
highlights=True
```
可获取相关片段，无需下载完整页面文本
缺少API密钥 - 设置
```
EXA_API_KEY
```
环境变量，或在构造函数中显式传入
初始过滤条件过于严格 - 先从宽泛的搜索开始，再添加域名/日期筛选条件来细化结果
未使用
summary
参数 - 对于RAG应用，
```
summary=True
```
可提供简洁的上下文，无需完整页面文本
期望自动模式返回评分 - 相关性评分仅在
```
type="neural"
```
模式下返回；自动模式不提供评分",