code example

Build Offline Search with altor-vec

What this pattern solves: Cache-first semantic retrieval for PWAs, field apps, and packaged reference tools.

Offline products still need more than keyword search. Field teams, sales reps, and warehouse operators often remember a description of the answer they need, not the exact phrase printed in a manual.

altor-vec is naturally aligned with offline search because the index is designed to live in the browser or packaged app. Once cached, queries stay fast even without connectivity.

Install

npm install altor-vec

Concept explanation

In a offline search workflow, users usually describe intent in their own words. That is why vector search works well here: each record is turned into an embedding, the embeddings are indexed once, and later queries retrieve the nearest semantic neighbors instead of relying only on exact tokens. In practice this means the interface can respond to paraphrases, shorthand, and partial descriptions far better than a literal-only search box.

The browser is often the right place to do this when the corpus is moderate in size and safe to ship. The instant benefit is lower latency. The architectural benefit is that you remove a whole search service from the request path. That matters for keystroke-heavy interactions, offline-capable apps, and product surfaces where search should feel like a UI primitive rather than a network round trip.

This page uses a deterministic embedding helper so the sample is runnable with only altor-vec installed. That keeps the example honest and easy to paste into a demo project. Most teams pair this with a service worker or static asset cache so the vectors download once and survive intermittent connectivity.

Runnable JavaScript example

The following snippet indexes a small in-memory dataset, performs a semantic lookup for safety checklist before opening machine panel, and prints the nearest matches. It uses the real altor-vec API, including init(), WasmSearchEngine.from_vectors(), and search().

import init, { WasmSearchEngine } from 'altor-vec';

        const dims = 12;
        const records = [
  {
    "title": "Panel safety checklist",
    "text": "Lockout, gloves, and voltage checks before opening electrical panels.",
    "meta": "safety"
  },
  {
    "title": "Battery replacement guide",
    "text": "Procedure for swapping the handheld scanner battery in the field.",
    "meta": "hardware"
  },
  {
    "title": "Cold-start troubleshooting",
    "text": "Steps when a machine fails to boot after overnight storage.",
    "meta": "maintenance"
  },
  {
    "title": "Calibration workflow",
    "text": "How to recalibrate sensors after a parts replacement.",
    "meta": "maintenance"
  },
  {
    "title": "Warranty claim intake",
    "text": "Required photos, serial numbers, and customer notes.",
    "meta": "support"
  },
  {
    "title": "Spare parts lookup",
    "text": "How to identify replacement parts from assembly diagrams.",
    "meta": "inventory"
  }
];

        function embedText(text) {
  const vector = new Float32Array(dims);
  for (const token of text.toLowerCase().split(/[^a-z0-9]+/).filter(Boolean)) {
    let hash = 2166136261;
    for (const char of token) {
      hash = Math.imul(hash ^ char.charCodeAt(0), 16777619);
    }
    const slot = Math.abs(hash) % dims;
    vector[slot] += 1;
    vector[(slot + token.length) % dims] += token.length / 10;
  }
  const magnitude = Math.hypot(...vector) || 1;
  return Array.from(vector, (value) => value / magnitude);
}

        async function main() {
          await init();

          const flat = new Float32Array(
            records.flatMap((record) => embedText(`${record.title} ${record.text} ${record.meta}`))
          );

          const engine = WasmSearchEngine.from_vectors(flat, dims, 16, 200, 64);
          const hits = JSON.parse(engine.search(new Float32Array(embedText('safety checklist before opening machine panel')), 4));

          const results = hits.map(([id, distance]) => ({
            ...records[id],
            similarity: Number((1 - distance).toFixed(3)),
          }));

          console.table(results);
          engine.free();
        }

        main();

React component version

The React version keeps the same index build but wires it into component state so the UI can query on input changes. That is usually how teams introduce semantic retrieval into an existing product: initialize once, keep the engine in memory, and map nearest-neighbor hits back to the original records.

import { useEffect, useState } from 'react';
        import init, { WasmSearchEngine } from 'altor-vec';

        const dims = 12;
        const records = [
  {
    "title": "Panel safety checklist",
    "text": "Lockout, gloves, and voltage checks before opening electrical panels.",
    "meta": "safety"
  },
  {
    "title": "Battery replacement guide",
    "text": "Procedure for swapping the handheld scanner battery in the field.",
    "meta": "hardware"
  },
  {
    "title": "Cold-start troubleshooting",
    "text": "Steps when a machine fails to boot after overnight storage.",
    "meta": "maintenance"
  },
  {
    "title": "Calibration workflow",
    "text": "How to recalibrate sensors after a parts replacement.",
    "meta": "maintenance"
  },
  {
    "title": "Warranty claim intake",
    "text": "Required photos, serial numbers, and customer notes.",
    "meta": "support"
  },
  {
    "title": "Spare parts lookup",
    "text": "How to identify replacement parts from assembly diagrams.",
    "meta": "inventory"
  }
];

        function embedText(text) {
  const vector = new Float32Array(dims);
  for (const token of text.toLowerCase().split(/[^a-z0-9]+/).filter(Boolean)) {
    let hash = 2166136261;
    for (const char of token) {
      hash = Math.imul(hash ^ char.charCodeAt(0), 16777619);
    }
    const slot = Math.abs(hash) % dims;
    vector[slot] += 1;
    vector[(slot + token.length) % dims] += token.length / 10;
  }
  const magnitude = Math.hypot(...vector) || 1;
  return Array.from(vector, (value) => value / magnitude);
}

        export function OfflineSearchExample() {
          const [engine, setEngine] = useState(null);
          const [query, setQuery] = useState('');
          const [results, setResults] = useState([]);

          useEffect(() => {
            let cancelled = false;
            let instance;

            (async () => {
              await init();
              const flat = new Float32Array(
                records.flatMap((record) => embedText(`${record.title} ${record.text} ${record.meta}`))
              );
              instance = WasmSearchEngine.from_vectors(flat, dims, 16, 200, 64);
              if (!cancelled) setEngine(instance);
            })();

            return () => {
              cancelled = true;
              instance?.free();
            };
          }, []);

          useEffect(() => {
            if (!engine || query.trim().length < 2) {
              setResults([]);
              return;
            }

            const hits = JSON.parse(engine.search(new Float32Array(embedText(query)), 5));
            setResults(
              hits.map(([id, distance]) => ({
                ...records[id],
                similarity: Number((1 - distance).toFixed(3)),
              }))
            );
          }, [engine, query]);

          return (
            <section>
              <input
                value={query}
                onChange={(event) => setQuery(event.target.value)}
                placeholder="Search while offline"
              />
              <ul>
                {results.map((result) => (
                  <li key={result.title}>
                    <strong>{result.title}</strong> — {result.meta} (score {result.similarity})
                  </li>
                ))}
              </ul>
            </section>
          );
        }

How this example works

The pattern has three moving parts. First, you choose what text represents each record: title, description, metadata, or a chunk of content. Second, you turn that text into vectors and flatten them into one Float32Array. Third, you build the HNSW graph and query it with a vector created from the user input. The library returns nearest-neighbor IDs and distances, and your app decides how to display or post-process them.

Because the retrieval step is approximate nearest-neighbor search, it stays fast even as the dataset grows beyond trivial linear scans. The most important quality lever is still the embedding model. Better vectors usually matter more than micro-optimizing ANN parameters, so teams should benchmark retrieval quality on real user phrasing before shipping the experience widely.

When to use this pattern

This is a practical fit when the search corpus is small to medium, shipped with the app, and searched frequently enough that backend latency would be noticeable. Common examples include docs portals, embedded support widgets, local-first assistants, and curated catalogs.

• PWAs
• Field manuals
• Kiosk apps
• Sales enablement bundles

Limitations

Offline search only covers the content already synced to the device. If the source material changes often, you need a reliable refresh story and clear UI to indicate staleness.

Be especially careful about corpus size, update frequency, and data sensitivity. Browser vector search is excellent when those three constraints are favorable, but it is not the right answer when the dataset is huge, private, or changing constantly for every user.

FAQ

Does altor-vec need a network connection after load?

No. Once the WebAssembly and vector data are on device, search can run entirely offline.

What else do I need for a real offline product?

Usually a cache strategy, asset versioning, and a refresh policy so users know when local content is current.

When is offline vector search a bad fit?

When the corpus is too large to ship or must reflect sensitive server-only data in real time.

Get started: npm install altor-vec · GitHub