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perplexity-webhooks-events Build event-driven architectures around Perplexity Sonar API with streaming,
batch pipelines, and scheduled search monitoring.
Trigger with phrases like "perplexity streaming", "perplexity events",
"perplexity batch search", "perplexity news monitor", "perplexity SSE".
インストールへ移動 Skills Marketplace コミュニティが作成したAIスキルを発見・探索
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langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
name perplexity-webhooks-events description Build event-driven architectures around Perplexity Sonar API with streaming,
batch pipelines, and scheduled search monitoring.
Trigger with phrases like "perplexity streaming", "perplexity events",
"perplexity batch search", "perplexity news monitor", "perplexity SSE".
allowed-tools Read, Write, Edit, Bash(curl:*) version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","perplexity","webhooks"] compatibility Designed for Claude Code
Perplexity Events & Async Patterns
Overview
Build event-driven architectures around Perplexity Sonar API. Perplexity does not have webhooks -- all interactions are request/response. Event patterns are built using streaming SSE, job queues for batch processing, and cron-triggered monitoring.
Event Patterns
Pattern Trigger Use Case Streaming SSE Client request Real-time search with progressive rendering Batch queue Job submission Research automation, report generation Scheduled search Cron job News monitoring, trend alerts, competitive intel Citation pipeline Post-processing Source verification, link validation
Prerequisites
openai package installed
PERPLEXITY_API_KEY set
Queue system (BullMQ, SQS) for batch patterns
Cron scheduler for monitoring patterns
Instructions
Step 1: Streaming Search (Server-Sent Events)
import OpenAI from "openai" ;
import express from "express" ;
const perplexity = new OpenAI ({
apiKey : process.env .PERPLEXITY_API_KEY !,
baseURL : "https://api.perplexity.ai" ,
});
const app = express ();
app.use (express.json ());
app.post ("/api/search/stream" , async (req, res) => {
const { query, model = "sonar" } = req.body ;
res. ( , {
: ,
: ,
: ,
});
{
stream = perplexity. . . ({
model,
: [{ : , : query }],
: ,
: ,
});
fullText = ;
( chunk stream) {
text = chunk. [ ]?. ?. || ;
fullText += text;
res. ( );
citations = (chunk ). ;
(citations) {
res. ( );
}
}
res. ( );
} ( : ) {
res. ( );
}
res. ();
});
writeHead
200
"Content-Type"
"text/event-stream"
"Cache-Control"
"no-cache"
Connection
"keep-alive"
try
const
await
chat
completions
create
messages
role
"user"
content
stream
true
max_tokens
2048
let
""
for
await
const
of
const
choices
0
delta
content
""
write
`data: ${JSON .stringify({ type : "text" , content: text })} \n\n`
const
as
any
citations
if
write
`data: ${JSON .stringify({ type : "citations" , urls: citations })} \n\n`
write
`data: ${JSON .stringify({ type : "done" , totalLength: fullText.length })} \n\n`
catch
err
any
write
`data: ${JSON .stringify({ type : "error" , message: err.message })} \n\n`
end
Step 2: Batch Research Pipeline import { Queue , Worker } from "bullmq" ;
const searchQueue = new Queue ("perplexity-research" , {
connection : { host : "localhost" , port : 6379 },
});
async function submitResearchBatch (
queries : string [],
callbackUrl : string ,
model : string = "sonar-pro"
) {
const batchId = crypto.randomUUID ();
for (const query of queries) {
await searchQueue.add ("search" , { batchId, query, callbackUrl, model }, {
attempts : 3 ,
backoff : { type : "exponential" , delay : 2000 },
});
}
return { batchId, totalQueries : queries.length };
}
const worker = new Worker ("perplexity-research" , async (job) => {
const { query, callbackUrl, batchId, model } = job.data ;
const response = await perplexity.chat .completions .create ({
model,
messages : [{ role : "user" , content : query }],
max_tokens : 2048 ,
});
const result = {
event : "perplexity.search.completed" ,
batchId,
query,
answer : response.choices [0 ].message .content ,
citations : (response as any ).citations || [],
model : response.model ,
tokens : response.usage ?.total_tokens ,
};
await fetch (callbackUrl, {
method : "POST" ,
headers : { "Content-Type" : "application/json" },
body : JSON .stringify (result),
});
}, {
connection : { host : "localhost" , port : 6379 },
concurrency : 3 ,
limiter : { max : 40 , duration : 60000 },
});
Step 3: Scheduled News Monitor
async function monitorTopics (
topics : string [],
webhookUrl : string
) {
for (const topic of topics) {
const response = await perplexity.chat .completions .create ({
model : "sonar" ,
messages : [{
role : "system" ,
content : "Summarize the latest developments. Be concise. Include only new information." ,
}, {
role : "user" ,
content : `Latest developments about "${topic} " in the past 24 hours` ,
}],
search_recency_filter : "day" ,
max_tokens : 500 ,
} as any );
const answer = response.choices [0 ].message .content || "" ;
const citations = (response as any ).citations || [];
if (citations.length > 0 && answer.length > 100 ) {
await fetch (webhookUrl, {
method : "POST" ,
headers : { "Content-Type" : "application/json" },
body : JSON .stringify ({
event : "perplexity.monitor.update" ,
topic,
summary : answer,
citations,
timestamp : new Date ().toISOString (),
}),
});
}
await new Promise ((r ) => setTimeout (r, 2000 ));
}
}
Step 4: Client-Side SSE Consumer
function consumeSearchStream (
query : string ,
onText : (text: string ) => void ,
onCitations : (urls: string []) => void ,
onDone : () => void
) {
fetch ("/api/search/stream" , {
method : "POST" ,
headers : { "Content-Type" : "application/json" },
body : JSON .stringify ({ query }),
}).then (async (response) => {
const reader = response.body !.getReader ();
const decoder = new TextDecoder ();
while (true ) {
const { done, value } = await reader.read ();
if (done) break ;
const lines = decoder.decode (value).split ("\n" );
for (const line of lines) {
if (!line.startsWith ("data: " )) continue ;
const event = JSON .parse (line.slice (6 ));
if (event.type === "text" ) onText (event.content );
if (event.type === "citations" ) onCitations (event.urls );
if (event.type === "done" ) onDone ();
}
}
});
}
Error Handling Issue Cause Solution Stream stalls Complex search taking too long Set per-chunk timeout (10s) 429 in batch Too many concurrent workers Reduce concurrency, add rate limiter Empty monitor alerts Topic too niche Broaden topic or reduce recency filter Callback fails Webhook URL down Retry with exponential backoff
Output
Streaming SSE endpoint for real-time search
Batch research pipeline with queue-based processing
Scheduled news monitoring with alerting
Client-side stream consumer
Resources
Next Steps For deployment setup, see perplexity-deploy-integration.