code example

Build Code Search with altor-vec

What this pattern solves: Semantic lookup for snippets, internal helpers, and example repositories.

Developers often remember what a function does rather than what it is called. Vector search helps queries like 'parse webhook signature' or 'build retrying fetch wrapper' land on the right snippet without exact identifier matches.

A local code index works well for docs, SDK playgrounds, and offline reference bundles where quick snippet recall matters more than repository-scale indexing or full structural analysis.

Install

npm install altor-vec

Concept explanation

In a code 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. This pattern complements rather than replaces AST-aware tooling. Use vectors for intent retrieval, then link out to a richer editor or repository browser when needed.

Runnable JavaScript example

The following snippet indexes a small in-memory dataset, performs a semantic lookup for debounced search input hook, 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": "Retrying fetch helper",
    "text": "Wrapper around fetch with backoff, timeout support, and retryable status codes.",
    "meta": "network"
  },
  {
    "title": "Debounced input hook",
    "text": "React hook that delays expensive work until typing pauses.",
    "meta": "react"
  },
  {
    "title": "Webhook signature verifier",
    "text": "Node utility for HMAC verification of incoming webhook requests.",
    "meta": "security"
  },
  {
    "title": "CSV export function",
    "text": "Serialize table rows into downloadable CSV in the browser.",
    "meta": "data"
  },
  {
    "title": "Feature flag gate",
    "text": "Small helper for checking staged rollout settings.",
    "meta": "infra"
  },
  {
    "title": "Accessible modal component",
    "text": "Dialog with focus trap, escape handling, and aria attributes.",
    "meta": "ui"
  }
];

        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('debounced search input hook')), 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": "Retrying fetch helper",
    "text": "Wrapper around fetch with backoff, timeout support, and retryable status codes.",
    "meta": "network"
  },
  {
    "title": "Debounced input hook",
    "text": "React hook that delays expensive work until typing pauses.",
    "meta": "react"
  },
  {
    "title": "Webhook signature verifier",
    "text": "Node utility for HMAC verification of incoming webhook requests.",
    "meta": "security"
  },
  {
    "title": "CSV export function",
    "text": "Serialize table rows into downloadable CSV in the browser.",
    "meta": "data"
  },
  {
    "title": "Feature flag gate",
    "text": "Small helper for checking staged rollout settings.",
    "meta": "infra"
  },
  {
    "title": "Accessible modal component",
    "text": "Dialog with focus trap, escape handling, and aria attributes.",
    "meta": "ui"
  }
];

        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 CodeSearchExample() {
          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="Describe the code you need"
              />
              <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.

• SDK docs
• Snippet libraries
• Engineering handbooks
• Offline reference apps

Limitations

Vector search is not a full code intelligence engine. It will not understand refactors, symbol definitions, or type relationships as deeply as language-server or AST tooling.

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

Can altor-vec replace grep or sourcegraph?

No. It fills a different role by matching concepts and descriptions, not exact code structure or repository-scale indexing.

What is a good dataset for browser code search?

Docs snippets, cookbook examples, or a curated helper library are great fits because the corpus is compact and safe to ship.

Can I mix semantic and exact matching?

Yes. That hybrid usually works best for code because identifiers, filenames, and API names still matter.

Get started: npm install altor-vec · GitHub