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exa-reference-architecture 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".
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Use when building authentication UI, customizing sign-in experience,
or implementing OAuth social login.
Trigger with phrases like "clerk sign-in", "clerk sign-up",
"clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk.
Use when managing user sessions, configuring route protection,
or implementing token refresh and custom JWT templates.
Trigger with phrases like "clerk session", "clerk middleware",
"clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management.
Use when implementing SSO integration, configuring role-based permissions,
or setting up organization-level controls.
Trigger with phrases like "clerk SSO", "clerk RBAC",
"clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
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.