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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".
설치로 이동 Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill exa-reference-architecture명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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jeremylongshore
jeremylongshore/claude-code-plugins-plus-skills
GitHub 저장소 열기| 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.