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Implement Exa reference architecture for search pipelines, RAG, and content discovery.
Use when designing new Exa integrations, reviewing project structure,
or establishing architecture standards for neural search applications.
Trigger with phrases like "exa architecture", "exa project structure",
"exa RAG pipeline", "exa reference design", "exa search pipeline".
インストールへ移動 Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill exa-reference-architectureコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか?SkillsMP が現在取得できるファイルをダウンロードできます。
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Use when implementing SSO integration, configuring role-based permissions,
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jeremylongshore
jeremylongshore/claude-code-plugins-plus-skills
GitHub リポジトリを開く| name | exa-reference-architecture |
| description | Implement Exa reference architecture for search pipelines, RAG, and content discovery.
Use when designing new Exa integrations, reviewing project structure,
or establishing architecture standards for neural search applications.
Trigger with phrases like "exa architecture", "exa project structure",
"exa RAG pipeline", "exa reference design", "exa search pipeline".
|
| allowed-tools | Read, Grep |
| version | 1.11.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","exa","architecture","rag"] |
| compatibility | Designed for Claude Code, also compatible with Codex and OpenClaw |
Exa Reference Architecture
Overview
Production architecture for Exa neural search integration. Covers search service design, content extraction pipeline, RAG integration, domain-scoped search profiles, and caching strategy.
Architecture Diagram
┌──────────────────────────────────────────────────────────┐
│ Application Layer │
│ RAG Pipeline | Research Agent | Content Discovery │
└──────────┬──────────────┬───────────────┬────────────────┘
│ │ │
▼ ▼ ▼
┌──────────────────────────────────────────────────────────┐
│ Exa Search Service Layer │
│ ┌────────────┐ ┌────────────┐ ┌──────────────────┐ │
│ │ search() │ │ findSimilar│ │ getContents() │ │
│ │ neural/ │ │ (URL seed) │ │ (known URLs) │ │
│ │ keyword/ │ └────────────┘ └──────────────────┘ │
│ │ auto/fast │ │
│ └────────────┘ ┌──────────────────┐ │
│ │ answer() / │ │
│ Content Options: │ streamAnswer() │ │
│ text | highlights | summary └──────────────────┘ │
│ │
│ ┌────────────────────────────────────────────────────┐ │
│ │ Result Cache (LRU + Redis) │ │
│ └────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────┐
│ api.exa.ai — Exa Neural Search API │
│ Auth: x-api-key header | Rate: 10 QPS default │
└──────────────────────────────────────────────────────────┘
Instructions
Step 1: Search Service Layer
import Exa from "exa-js";
const exa = new Exa(process.env.EXA_API_KEY);
interface SearchRequest {
query: string;
type?: "auto" | "neural" | "keyword" | "fast" | ;
?: ;
?: ;
?: ;
?: [];
?: [];
?: | | | | ;
}
{
?: | { ?: };
?: | { ?: ; ?: };
?: | { ?: };
}
() {
exa.(req., {
: req. || ,
: req. || ,
: req.,
: req.,
: req.,
: req.,
: req.,
...content,
});
}
() {
exa.(url, {
numResults,
: { : },
: ,
});
}
"instant"
numResults
number
startDate
string
endDate
string
includeDomains
string
excludeDomains
string
category
"company"
"research paper"
"news"
"tweet"
"people"
interface
ContentOptions
text
boolean
maxCharacters
number
highlights
boolean
maxCharacters
number
query
string
summary
boolean
query
string
export
async
function
searchWithContents
req: SearchRequest,
content: ContentOptions = { text: { maxCharacters: 2000 } }
return
searchAndContents
query
type
type
"auto"
numResults
numResults
10
startPublishedDate
startDate
endPublishedDate
endDate
includeDomains
includeDomains
excludeDomains
excludeDomains
category
category
export
async
function
findRelated
url: string, numResults = 5
return
findSimilarAndContents
text
maxCharacters
1000
excludeSourceDomain
true
Step 2: Research Pipeline
export async function researchTopic(topic: string) {
const sources = await exa.searchAndContents(topic, {
type: "neural",
numResults: 15,
text: { maxCharacters: 2000 },
highlights: { maxCharacters: 500, query: topic },
startPublishedDate: "2024-01-01T00:00:00.000Z",
});
const topUrl = sources.results[0]?.url;
const similar = topUrl
? await exa.findSimilarAndContents(topUrl, {
numResults: 5,
text: { maxCharacters: 1500 },
excludeSourceDomain: true,
})
: { results: [] };
const answer = await exa.answer(
`Based on recent research, summarize: ${topic}`,
{ text: true }
);
return {
primary: sources.results,
related: similar.results,
aiSummary: answer.answer,
sources: answer.results.map(r => ({ title: r.title, url: r.url })),
};
}
Step 3: RAG Integration Pattern
export async function ragSearch(userQuery: string, contextWindow = 5) {
const results = await exa.searchAndContents(userQuery, {
type: "neural",
numResults: contextWindow,
text: { maxCharacters: 2000 },
highlights: { maxCharacters: 500, query: userQuery },
});
const context = results.results
.map((r, i) =>
`[Source ${i + 1}] ${r.title}\n` +
`URL: ${r.url}\n` +
`Content: ${r.text}\n` +
`Key points: ${r.highlights?.join(" | ")}`
)
.join("\n\n---\n\n");
return {
context,
sources: results.results.map(r => ({
title: r.title,
url: r.url,
score: r.score,
})),
};
}
Step 4: Domain-Specific Search Profiles
const SEARCH_PROFILES = {
technical: {
includeDomains: [
"github.com", "stackoverflow.com", "arxiv.org",
"developer.mozilla.org", "docs.python.org",
],
},
news: {
category: "news" as const,
includeDomains: ["techcrunch.com", "theverge.com", "arstechnica.com"],
},
research: {
category: "research paper" as const,
includeDomains: ["arxiv.org", "nature.com", "science.org"],
},
companies: {
category: "company" as const,
},
};
export async function profiledSearch(
query: string,
profile: keyof typeof SEARCH_PROFILES
) {
const config = SEARCH_PROFILES[profile];
return searchWithContents({ query, ...config, numResults: 10 });
}
Step 5: Competitor Discovery
export async function discoverCompetitors(companyUrl: string) {
const similar = await exa.findSimilarAndContents(companyUrl, {
numResults: 10,
excludeSourceDomain: true,
text: { maxCharacters: 500 },
summary: { query: "What does this company do?" },
});
return similar.results.map(r => ({
name: r.title,
url: r.url,
description: r.summary || r.text?.substring(0, 200),
score: r.score,
}));
}
Error Handling
| Issue | Cause | Solution |
|---|
| No results | Query too specific | Broaden query, switch to neural search |
| Low relevance | Wrong search type | Use auto type for hybrid results |
| Empty text/highlights | Site blocks scraping | Use livecrawl: "preferred" or try summary |
| Rate limit | Too many concurrent requests | Add request queue with 8-10 concurrency |
Resources
Next Steps
For architecture variants at different scales, see exa-architecture-variants.