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21 22const client = new Client({ token: "<QSTASH_TOKEN>" }); 23 24const res = await client.publishJSON({ 25 url: "https://my-api...", 26 body: { hello: "world" }, 27 retries: 2, 28}); 29 `},{language:"py",code:` 30from qstash import QStash 31 32client = QStash("<QSTASH_TOKEN>") 33 34client.message.publish_json( 35 url="https://my-api...", 36 body={"hello": "world"}, 37 retries=2, 38) 39 `}]},{title:"Queues",snippets:[{language:"js",code:` 40import { Client } from "@upstash/qstash"; 41 42const client = new Client({ token: "<QSTASH_TOKEN>" }); 43 44const queue = client.queue({ 45 queueName: "my-queue" 46}) 47 48await queue.enqueueJSON({ 49 url: "https://example.com", 50 body: { 51 "Hello": "World" 52 } 53}) 54 `},{language:"py",code:` 55from qstash import QStash 56 57client = QStash("<QSTASH_TOKEN>") 58 59queue_name = "my-queue" 60client.queue.upsert(queue_name) 61 62client.message.enqueue_json( 63 queue=queue_name, 64 url="https://example.com", 65 body={"Hello": "World"} 66) 67 `}]},{title:"Callbacks",snippets:[{language:"js",code:` 68import { Client } from "@upstash/qstash"; 69 70const client = new Client({ token: "<QSTASH_TOKEN>" }); 71 72const res = await client.publishJSON({ 73 url: "https://my-api...", 74 body: { hello: "world" }, 75 callback: "https://my-callback...", 76}); 77 `},{language:"py",code:` 78from qstash import QStash 79 80client = QStash("<QSTASH_TOKEN>") 81 82client.message.publish_json( 83 url="https://my-api...", 84 body={"hello": "world"}, 85 callback="https://my-callback...", 86) 87 `}]}];function te(){return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(eJ,{groups:e5[eU.Product.QSTASH]}),(0,s.jsx)(e8,{})]})}let tt=()=>(0,s.jsx)(e7.CodeSnippets,{data:ts}),ts=[{title:"Caching",snippets:[{language:"js",code:` 88import { Redis } from "@upstash/redis"; 89 90const redis = Redis.fromEnv(); 91 92const item = await redis.get("item:123"); 93 94if (item) { 95 return item; 96} 97`},{language:"py",code:` 98from upstash_redis import Redis 99 100redis = Redis.from_env() 101 102item = redis.get("item:123") 103if item: 104 return item 105`}]},{title:"Session Management",snippets:[{language:"js",code:` 106import { Redis } from "@upstash/redis"; 107 108const redis = Redis.fromEnv(); 109 110const getSession = async (key: string) => { 111 const sessionId = await getSessionId(); 112 return redis.hget(\`s:\${sessionId}\`, key); 113}; 114 115const setSession = async (key: string, value: string) => { 116 const sessionId = await getSessionIdAndCreateIfMissing(); 117 const sessionKey = \`s:\${sessionId}\`; 118 await redis.hset(sessionKey, { [key]: value }); 119 await redis.expire(sessionKey, 900); 120 return sessionId; 121}; 122`},{language:"py",code:` 123from upstash_redis import Redis 124 125redis = Redis.from_env() 126 127def get_session(key: str) -> str: 128 session_id = get_session_id() 129 return redis.hget(f"s:{session_id}", key) 130 131def set_session(key: str, value: str) -> str: 132 session_id = get_session_id_and_create_if_missing() 133 redis.hset(f"s:{session_id}", key, value) 134 return session_id 135`}]},{title:"Rate Limiting",snippets:[{language:"js",code:` 136import { Redis } from "@upstash/redis"; 137import { Ratelimit } from "@upstash/ratelimit"; 138 139const ratelimit = new Ratelimit({ 140 redis: Redis.fromEnv(), 141 limiter: Ratelimit.slidingWindow(10, "10 s"), 142}); 143 144const identifier = getIpAddress(); 145const { success } = await ratelimit.limit(identifier); 146 147if (!success) { 148 return res.status(429).json({ "error": "Too many requests" }); 149} 150`},{language:"py",code:` 151from upstash_redis import Redis 152from upstash_ratelimit import Ratelimit, SlidingWindow 153 154redis = Redis.from_env() 155 156ratelimit = Ratelimit( 157 redis=redis,
158 limiter=SlidingWindow(max_requests=10, window=10) 159) 160 161identifier = get_ip_address() 162result = ratelimit.limit(identifier) 163 164if not result.allowed: 165 return Response(status_code=429) 166`}]},{title:"Leaderboards",snippets:[{language:"js",code:` 167import { Redis } from "@upstash/redis"; 168 169const redis = Redis.fromEnv(); 170 171const LEADERBOARD_KEY = "game-leaderboard"; 172 173export const updateScore = async (playerName: string, score: number) => 174 await redis.zadd(LEADERBOARD_KEY, { score, member: playerName }); 175 176export const getTopPlayers = async (top: number) => { 177 return await redis.zrange(LEADERBOARD_KEY, 0, top - 1, { rev: true, withScores: true }); 178}; 179`},{language:"py",code:` 180from upstash_redis import Redis 181 182redis = Redis.from_env() 183 184leaderboard_key = "game-leaderboard" 185 186def update_score(player_name: str, score: int) -> None: 187 redis.zadd(leaderboard_key, {player_name: score}) 188 189def get_top_players(top: int): 190 return redis.zrange(leaderboard_key, 0, top - 1, withscores=True) 191`}]},{title:"Chat",snippets:[{language:"js",code:` 192import { Redis } from "@upstash/redis"; 193 194const redis = Redis.fromEnv(); 195 196const getChatHistoryKey = (userId: string) => \`chat-history:\${userId}\`; 197 198export const saveMessage = async (userId: string, message: string) => { 199 await redis.lpush(getChatHistoryKey(userId), message); // Add message to history 200 await redis.ltrim(getChatHistoryKey(userId), 0, 99); // Keep only the latest 100 messages 201}; 202 203export const getChatHistory = async (userId: string) => 204 await redis.lrange(getChatHistoryKey(userId), 0, -1); 205`},{language:"py",code:` 206from upstash_redis import Redis 207 208redis = Redis.from_env() 209 210def save_message(user_id: str, message: str) -> None: 211 key = f"chat:{user_id}" 212 redis.lpush(key, message) 213 redis.ltrim(key, 0, 99) 214 215def get_chat_history(user_id: str) -> list[str]: 216 return redis.lrange(f"chat:{user_id}", 0, -1) 217`}]}];function tr(){return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(eJ,{groups:e5[eU.Product.REDIS]}),(0,s.jsx)(tt,{})]})}let tn=()=>(0,s.jsx)(e7.CodeSnippets,{data:ta}),ta=[{title:"RAG",snippets:[{language:"js",code:` 218import { Index } from "@upstash/vector"; 219 220const index = new Index() 221 222const question = "What is Quantum Mechanics?" 223 224// Find related documents 225const context = await index.query({ 226 data: question, 227 topK: 5, 228 includeMetadata: true, 229}); 230 231const prompt = \`Question: \${question} - Context: \${JSON.stringify(context)}\`; 232 233const response = await openai.chat.completions.create({ 234 model: "gpt-5.4", 235 messages: [ 236 { 237 role: "system", 238 content: "You are a helpful assistant, answer the question using the context." 239 }, 240 { 241 role: "user", 242 content: prompt 243 }, 244 ], 245}); 246 `},{language:"py",code:` 247from upstash_vector import Index 248 249index = Index.from_env() 250 251question = "What is Quantum Mechanics?" 252 253# Find related documents 254context = index.query( 255 data=question, 256 top_k=5, 257 include_metadata=True 258) 259 260prompt = f"Question: {question} - Context: {str(context)}" 261 262response = openai.chat.completions.create( 263 model="gpt-5.4", 264 messages=[ 265 { 266 "role": "system", 267 "content": "You are a helpful assistant, answer the question using the context." 268 }, 269 { 270 "role": "user", 271 "content": prompt 272 } 273 ] 274) 275 `}]},{title:"Semantic Caching",snippets:[{language:"js",code:` 276import { SemanticCache } from "@upstash/semantic-cache"; 277import { Index } from "@upstash/vector"; 278 279const semanticCache = new SemanticCache({ index: new Index(), minProximity: 0.95 }); 280 281await semanticCache.set("Capital of Turkey", "Ankara"); 282await semanticCache.set("year in which the Berlin wall fell", "1989"); 283 284// Outputs: "Ankara" 285const result1 = await semanticCache.get("What is Turkey's capital?"); 286 287// Outputs: "1989" 288const result2 = await semanticCache.get("what's the year the Berlin wall destroyed?"); 289 `},{language:"py",code:` 290from upstash_vector import SemanticCache 291 292cache = SemanticCache( 293 url="<UPSTASH_VECTOR_REST_URL>", 294 token="<UPSTASH_VECTOR_REST_TOKEN>", 295 min_proximity=0.9, 296) 297 298cache.set("Capital of Turkey", "Ankara") 299cache.set("year in which the Berlin wall fell", "1989") 300 301# Outputs: "Ankara" 302result1 = cache.get("What is Turkey's capital?") 303 304# Outputs: "1989" 305result2 = cache.get("what's the year the Berlin wall destroyed?") 306 `}]},{title:"Search",snippets:[{language:"js",code:` 307import { Index } from "@upstash/vector"; 308 309const index = new Index() 310 311const documents = [ 312 { id: "doc1", data: "Upstash Vector is a scalable vector database." }, 313 { id: "doc2", data: "LangChain is a framework for building intelligent apps." }, 314]; 315 316await index.upsert(documents); 317 318// Perform a similarity search 319const query = "What is LangChain?"; 320const results = await index.query({ data: query, topK: 3 }); 321 `},{language:"py",code:` 322from upstash_vector import Index 323 324index = Index.from_env() 325 326documents = [ 327 {"id": "doc1", "data": "Upstash Vector is a scalable vector database."}, 328 {"id": "doc2", "data": "LangChain is a framework for building intelligent apps."} 329] 330 331index.upsert(documents) 332 333# Perform a similarity search 334query = "What is LangChain?" 335results = index.query(data=query, top_k=3) 336 `}]}];function to(){return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(eJ,{groups:e5[eU.Product.VECTOR]}),(0,s.jsx)(tn,{})]})}let ti=()=>(0,s.jsx)(e7.CodeSnippets,{data:tl}),tl=[{title:"AI Data Processing",snippets:[{language:"js",code:` 337// Step 1: Get the dataset URL
338const datasetUrl = await context.run("get-dataset-url", async () => { 339 return await getDatasetUrl(datasetId) 340}); 341 342// Step 2: Download the dataset 343const { body: dataset } = await context.call("download-dataset", { 344 url: datasetUrl, 345 method: "GET" 346}); 347 348// Step 3: Analyze the dataset with LLM 349const response = await context.api.openai.call( 350 "call-openai", 351 { 352 token: "OPENAI_API_KEY", 353 operation: "chat.completions.create", 354 body: { 355 model: "gpt-5.4", 356 messages: [ 357 { role: "user", content: \`Analyze this data chunk: \${JSON.stringify(dataset)}\` }, 358 ], 359 }, 360 } 361); 362 `},{language:"py",code:` 363# Step 1: Get the dataset URL 364async def _get_url(): 365 return await get_dataset_url(dataset_id) 366 367dataset_url = await context.run("get-dataset-url", _get_url) 368 369# Step 2: Download the dataset 370response = await context.call("download-dataset", url=dataset_url, method="GET") 371dataset = response.body 372 373# Step 3: Analyze the dataset with LLM 374response = await context.call( 375 "call-openai", 376 url="https://api.openai.com/v1/chat/completions", 377 method="POST", 378 headers={"authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"}, 379 body={ 380 "model": "gpt-5.4", 381 "messages": [ 382 { 383 "role": "user", 384 "content": f"Analyze this data chunk: {json.dumps(dataset)}", 385 } 386 ], 387 }, 388) 389 `}]},{title:"Event Based Workflows",snippets:[{language:"js",code:` 390// Step 1: Request order processing 391await context.run("request-order-processing", async () => { 392 await requestProcessing(orderId); 393}); 394 395// Step 2: Wait for the order to be processed 396const { eventData, timeout } = await context.waitForEvent( 397 "wait-for-order-processing", 398 \`order-\${orderId}\`, 399 { 400 timeout: "10m" // 10 minutes timeout 401 } 402); 403 404// Step 3: Log the processed order 405await context.run("process-order", async () => { 406 console.log(\`Order \${orderId} processed:\`, eventData); 407}); 408 `}]},{title:"Parallel Runs",snippets:[{language:"js",code:` 409const [coffeeBeansAvailable, cupsAvailable, milkAvailable] = 410 await Promise.all([ 411 context.run("check-coffee-beans", () => checkInventory("coffee-beans")), 412 context.run("check-cups", () => checkInventory("cups")), 413 context.run("check-milk", () => checkInventory("milk")), 414 ]); 415 416// If all ingredients available, brew coffee 417if (coffeeBeansAvailable && cupsAvailable && milkAvailable) { 418 const price = await context.run("brew-coffee", async () => { 419 return await brewCoffee({ style: "cappuccino" }); 420 }); 421 422 await printReceipt(price); 423}; 424 `}]},{title:"Customer Onboarding",snippets:[{language:"js",code:` 425await context.run("new-signup", async () => { 426 await sendEmail("Welcome to the platform", email); 427}); 428 429await context.sleep("wait-for-3-days", "3d"); 430 431while (true) { 432 const state = await context.run("check-user-state", async () => { 433 return await getUserState(); 434 }); 435 436 if (state === "non-active") { 437 await context.run("send-email-non-active", async () => { 438 await sendEmail("Email to non-active users", email); 439 }); 440 }; 441 442 await context.sleep("wait-for-1-month", "30d"); 443}; 444 `},{language:"py",code:` 445async def _send_welcome(): 446 await send_email("Welcome to the platform", email) 447 448await context.run("new-signup", _send_welcome) 449 450await context.sleep("wait-for-3-days", "3d") 451 452while True: 453 async def _check_state(): 454 return await get_user_state() 455 456 state = await context.run("check-user-state", _check_state) 457 458 if state == "non-active": 459 async def _send_reminder(): 460 await send_email("Email to non-active users", email) 461 462 await context.run("send-email-non-active", _send_reminder) 463 464 await context.sleep("wait-for-1-month", "30d") 465 `}]}];function tc(){return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(eJ,{groups:e5[eU.Product.WORKFLOW]}),(0,s.jsx)(ti,{})]})}var td=(0,eQ.default)("outline","notes","IconNotes",[["path",{d:"M5 3m0 2a2 2 0 0 1 2 -2h10a2 2 0 0 1 2 2v14a2 2 0 0 1 -2 2h-10a2 2 0 0 1 -2 -2z",key:"svg-0"}],["path",{d:"M9 7l6 0",key:"svg-1"}],["path",{d:"M9 11l6 0",key:"svg-2"}],["path",{d:"M9 15l4 0",key:"svg-3"}]]),tu=e.i(71350),th=e.i(94432);function tp(){return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(eJ,{groups:e5[eU.Product.BLOB]}),(0,s.jsx)(th.CodeSnippetsBlob,{})]})}e.i(54807);let tm=()=>(0,s.jsx)(e7.CodeSnippets,{data:tx,codeBodyClassName:"h-[420px]"}),tx=[{title:"Run an agent",snippets:[{language:"js",code:` 466import { Box, Agent } from "@upstash/box" 467 468const box = await Box.create({ 469 runtime: "node", 470 agent: { 471 harness: Agent.ClaudeCode, 472 model: "anthropic/claude-opus-4-6", 473 apiKey: process.env.ANTHROPIC_API_KEY, 474 }, 475}) 476 477const run = await box.agent.run({ 478 prompt: "Set up a Next.js project with Tailwind", 479}) 480 481console.log(run.result) 482`},{language:"py",code:` 483import os 484from upstash_box import Box, Agent 485 486box = Box.create( 487 runtime="node", 488 agent={ 489 "harness": Agent.CLAUDE_CODE, 490 "model": "anthropic/claude-opus-4-6", 491 "api_key": os.environ["ANTHROPIC_API_KEY"], 492 }, 493) 494 495run = box.agent.run( 496 prompt="Set up a Next.js project with Tailwind", 497) 498 499print(run.result) 500`}]},{title:"Open a PR",snippets:[{language:"js",code:` 501import { Box, Agent } from "@upstash/box" 502 503const box = await Box.create({ 504 runtime: "node", 505 agent: { 506 harness: Agent.ClaudeCode, 507 model: "anthropic/claude-opus-4-6", 508 apiKey: process.env.ANTHROPIC_API_KEY, 509 }, 510 git: { token: process.env.GITHUB_TOKEN }, 511}) 512 513await box.git.clone({ 514 repo: "https://github.com/acme/web-app", 515 branch: "main", 516}) 517 518await box.agent.run({ 519 prompt: "Fix the broken tests, commit and open a PR.", 520}) 521`},{language:"py",code:` 522import os 523from upstash_box import Box, Agent 524 525box = Box.create( 526 runtime="node", 527 agent={ 528 "harness": Agent.CLAUDE_CODE, 529 "model": "anthropic/claude-opus-4-6", 530 "api_key": os.environ["ANTHROPIC_API_KEY"], 531 }, 532 git={"token": os.environ["GITHUB_TOKEN"]}, 533) 534 535box.git.clone( 536 repo="https://github.com/acme/web-app", 537 branch="main", 538) 539 540box.agent.run( 541 prompt="Fix the broken tests, commit and open a PR.", 542) 543`}]},{title:"Upload & analyze a PDF",snippets:[{language:"js",code:` 544import { Box, Agent } from "@upstash/box" 545import { z } from "zod" 546 547const box = await Box.create({ 548 runtime: "node", 549 agent: { 550 harness: Agent.ClaudeCode, 551 model: "anthropic/claude-opus-4-6", 552 apiKey: process.env.ANTHROPIC_API_KEY, 553 }, 554}) 555 556await box.files.upload([ 557 { path: "./data/report.pdf", destination: "/work/report.pdf" }, 558]) 559 560const run = await box.agent.run({
561 prompt: "Read /work/report.pdf and extract key metrics.", 562 responseSchema: z.object({ 563 revenue: z.number(), 564 growth: z.number(), 565 }), 566}) 567 568console.log(run.result) 569`},{language:"py",code:` 570import os 571from pydantic import BaseModel 572from upstash_box import Box, Agent 573 574class Metrics(BaseModel): 575 revenue: float 576 growth: float 577 578box = Box.create( 579 runtime="node", 580 agent={ 581 "harness": Agent.CLAUDE_CODE, 582 "model": "anthropic/claude-opus-4-6", 583 "api_key": os.environ["ANTHROPIC_API_KEY"], 584 }, 585) 586 587box.files.upload([ 588 {"path": "./data/report.pdf", "destination": "/work/report.pdf"}, 589]) 590 591run = box.agent.run( 592 prompt="Read /work/report.pdf and extract key metrics.", 593 response_schema=Metrics, 594) 595 596print(run.result) 597`}]},{title:"Expose a port",snippets:[{language:"js",code:` 598import { Box } from "@upstash/box" 599 600const box = await Box.create({ runtime: "node" }) 601 602// Start a web server on port 3000 603await box.exec.command("cd /work && npm install express") 604await box.files.write({ 605 path: "/work/server.js", 606 content: \` 607 const express = require('express') 608 const app = express() 609 app.get('/', (req, res) => res.send('Hello from Box!')) 610 app.listen(3000) 611 \`, 612}) 613await box.exec.command("node /work/server.js &") 614 615// Create a public URL 616const publicUrl = await box.getPublicURL(3000) 617`},{language:"py",code:` 618from upstash_box import Box 619 620box = Box.create(runtime="node") 621 622# Start a web server on port 3000 623box.exec.command("cd /work && npm install express") 624box.files.write( 625 path="/work/server.js", 626 content=""" 627 const express = require('express') 628 const app = express() 629 app.get('/', (req, res) => res.send('Hello from Box!')) 630 app.listen(3000) 631 """, 632) 633box.exec.command("node /work/server.js &") 634 635# Create a public URL 636public_url = box.get_public_url(3000) 637`}]}];function tC(){return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(eJ,{groups:e5[eU.Product.BOX]}),(0,s.jsx)(tm,{})]})}let tf=[eU.Product.VECTOR,eU.Product.QSTASH,eU.Product.WORKFLOW,eU.Product.BOX,eU.Product.BLOB];function tg(){return(0,s.jsxs)("section",{className:"sr-only",children:[(0,s.jsx)("h2",{children:"Upstash Serverless Data Platform â products"}),tf.map(e=>(0,s.jsxs)("article",{children:[(0,s.jsxs)("h3",{children:[e,": ",e6[e].title]}),e5[e].map((e,t)=>(0,s.jsxs)("div",{children:[(0,s.jsx)("h4",{children:e.title.join(" ")}),(0,s.jsx)("ul",{children:e.bullets.map((e,t)=>(0,s.jsx)("li",{children:e},t))})]},t)),(0,s.jsxs)("p",{children:["Learn more in the"," ",(0,s.jsxs)("a",{href:e6[e].docsLink,children:[e," documentation"]}),"."]})]},e))]})}let tw=({activeProduct:e})=>{let{title:t,docsLink:r,consoleLink:n}=e6[e];return(0,s.jsxs)("div",{className:"mb-8 flex flex-col items-center gap-4 py-4",children:[(0,s.jsx)("h2",{className:"text-2xl font-medium text-emerald-800 dark:text-text",children:t}),(0,s.jsxs)("div",{className:"flex flex-col justify-center gap-3 xs:flex-row",children:[e===eU.Product.REDIS&&(0,s.jsx)(eP.default,{href:"/redis",children:(0,s.jsxs)(w.default,{variant:"default",className:"h-[42px] px-5",children:["Explore Redis",(0,s.jsx)(d.IconArrowRight,{size:24})]})}),e===eU.Product.BLOB&&(0,s.jsx)(eP.default,{href:"/blob",children:(0,s.jsxs)(w.default,{variant:"default",className:"h-[42px] px-5",children:["Explore Blob",(0,s.jsx)(d.IconArrowRight,{size:24})]})}),(0,s.jsx)("a",{href:r,target:"_blank",children:(0,s.jsxs)(w.default,{variant:"defaultDark",className:"h-[42px] px-5",children:["Documentation",(0,s.jsx)(td,{size:24})]})}),(0,s.jsx)("a",{href:n,target:"_blank",children:(0,s.jsxs)(w.default,{variant:"primary",className:"h-[42px] px-5",children:[e===eU.Product.REDIS||e===eU.Product.SEARCH?"Create Database":e===eU.Product.VECTOR?"Create Index":e===eU.Product.BOX?"Create Box":e===eU.Product.BLOB?"Create Bucket":"Upstash Console",e===eU.Product.REDIS||e===eU.Product.VECTOR||e===eU.Product.SEARCH||e===eU.Product.BOX||e===eU.Product.BLOB?(0,s.jsx)(tu.IconPlus,{size:24}):(0,s.jsx)(b.IconArrowUpRight,{size:24})]})})]})]})};function tb(){let[e,t]=(0,B.useState)(eU.Product.REDIS);return(0,s.jsxs)("section",{className:"relative z-10 sm:mt-4",children:[(0,s.jsx)(f.default,{section:"products"}),(0,s.jsxs)(n.default,{children:[(0,s.jsx)("div",{className:"md:text-ba flex items-end justify-center md:gap-1 lg:gap-2",children:(0,s.jsx)(eK,{activeProduct:e,setActiveProduct:s=>{s!==e&&(0,P.trackEvent)("home_product_switch",{product:s.toLowerCase()}),t(s)}})}
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.