Getting Started with Browser Search

This guide is for somebody who has never used a search library before and wants a usable search box quickly.

It is also the quickest way to understand what Querylight TS is good at in practice. Even if you start with a simple search box, the same library can later grow into:

• docs or site search with highlighting
• semantic “Ask the Docs” retrieval over chunked content
• related-article suggestions
• faceted or filtered discovery
• geo-aware search

That matters because beginner-friendly search tooling often becomes a dead end as soon as your requirements grow. Querylight TS is designed so the beginner path and the more advanced path stay connected.

There are two paths:

• Use the beginner helper and get something working fast.
• Build the equivalent setup manually if you want to understand what the helper does.

For most people, the right architecture is:

1. Collect your content at build time.
2. Build the search indexes in Node.js.
3. Serialize the index state to JSON.
4. Ship that JSON with your site or app.
5. Load the prebuilt index in the browser and use it to power a search field.

That avoids indexing work in the browser and gives you a fast startup path.

Pick the path that matches your use case

• If you want a practical docs or content search box fast, start with createSimpleTextSearchIndex.
• If you know you need filters, facets, semantic retrieval, or custom ranking, use the lower-level query API directly.
• If you are unsure, start simple. You can keep the same document model and move to manual queries later.

Fastest path: beginner helper

Use createSimpleTextSearchIndex when you have plain JSON documents and you just want a reasonable default.

import { createSimpleTextSearchIndex, simpleTextSearch } from "@tryformation/querylight-ts";

const documents = [
  {
    id: "range-filters",
    title: "RangeQuery Over Lexical Fields",
    description: "Use lexical ranges over sortable string values.",
    body: "RangeQuery compares string terms lexically and works well with sortable values."
  },
  {
    id: "phrase-search",
    title: "Phrase Search and Slop",
    description: "Match analyzed terms in order with optional movement tolerance.",
    body: "Phrase search checks whether analyzed terms occur in sequence."
  }
];

const search = createSimpleTextSearchIndex({
  documents,
  primaryFields: ["title"],
  secondaryFields: ["description", "body"]
});

const hits = simpleTextSearch(search, { query: "range fi", limit: 5 });

What this gives you:

• stronger ranking for primary fields such as title
• secondary matches from description and body
• prefix recovery for incomplete queries
• fuzzy recovery for typos
• reciprocal rank fusion between the lexical and fuzzy branches

If you stop here, you already have a practical site-search baseline.

If you also want result highlighting, treat it as a second step after retrieval. Querylight TS now exposes documentIndex.highlight(...) for exact and phrase highlighting against stored source text.

This beginner path is a strong fit for:

• docs and marketing sites
• blogs and magazines
• product help centers
• static app content bundled with a browser build

Equivalent manual setup

The beginner helper is just a convenience layer around the lower-level primitives.

This is the same idea done manually:

import {
  Analyzer,
  BoolQuery,
  DocumentIndex,
  EdgeNgramsTokenFilter,
  MatchPhrase,
  MatchQuery,
  NgramTokenFilter,
  OP,
  RankingAlgorithm,
  TextFieldIndex,
  reciprocalRankFusion
} from "@tryformation/querylight-ts";

const suggestAnalyzer = new Analyzer(undefined, undefined, [new EdgeNgramsTokenFilter(2, 10)]);
const fuzzyAnalyzer = new Analyzer(undefined, undefined, [new NgramTokenFilter(3)]);

const index = new DocumentIndex({
  title: new TextFieldIndex(undefined, undefined, RankingAlgorithm.BM25),
  description: new TextFieldIndex(undefined, undefined, RankingAlgorithm.BM25),
  body: new TextFieldIndex(undefined, undefined, RankingAlgorithm.BM25),
  primarySuggest: new TextFieldIndex(suggestAnalyzer, suggestAnalyzer, RankingAlgorithm.BM25),
  secondarySuggest: new TextFieldIndex(suggestAnalyzer, suggestAnalyzer, RankingAlgorithm.BM25)
});

const fuzzyIndex = new DocumentIndex({
  combined: new TextFieldIndex(fuzzyAnalyzer, fuzzyAnalyzer, RankingAlgorithm.BM25)
});

for (const doc of documents) {
  index.index({
    id: doc.id,
    fields: {
      title: [doc.title],
      description: [doc.description],
      body: [doc.body],
      primarySuggest: [doc.title],
      secondarySuggest: [doc.description, doc.body].join(" ")
    }
  });

  fuzzyIndex.index({
    id: doc.id,
    fields: {
      combined: [doc.title, doc.description, doc.body].join(" ")
    }
  });
}

function search(query: string) {
  const lexicalHits = index.searchRequest({
    query: new BoolQuery([
      new MatchPhrase("title", query, 1, 8),
      new MatchPhrase("description", query, 2, 3),
      new MatchPhrase("body", query, 2, 3),
      new MatchQuery("title", query, OP.AND, false, 6),
      new MatchQuery("description", query, OP.AND, false, 2.5),
      new MatchQuery("body", query, OP.AND, false, 2.0),
      new MatchQuery("primarySuggest", query, OP.OR, true, 4),
      new MatchQuery("secondarySuggest", query, OP.OR, true, 2)
    ]),
    limit: 20
  });

  const fuzzyHits = fuzzyIndex.searchRequest({
    query: new MatchQuery("combined", query, OP.OR, false, 1.5),
    limit: 20
  });

  return reciprocalRankFusion([lexicalHits, fuzzyHits], {
    rankConstant: 20,
    weights: [3, 1]
  });
}

The manual version is useful when:

• you want different boosts
• you want different fuzzy behavior
• you need filters or faceting
• you want to mix in vector or geo search later

It is also the path to take when your product requirements sound more like:

• “search only within this section”
• “show related articles”
• “use embeddings for semantic reranking”
• “limit results to the visible map area”
• “support strict metadata filters”

Recommended browser architecture

For a production static site or browser app, do the indexing at build time.

Build step

In a Node.js build script:

import fs from "node:fs/promises";
import { createSimpleTextSearchIndex } from "@tryformation/querylight-ts";

const documents = await loadYourDocsSomehow();
const search = createSimpleTextSearchIndex({
  documents,
  primaryFields: ["title"],
  secondaryFields: ["description", "body"]
});

const payload = {
  search: {
    idField: search.idField,
    primaryFields: search.primaryFields,
    secondaryFields: search.secondaryFields,
    ranking: search.ranking,
    documentIndexState: JSON.parse(JSON.stringify(search.documentIndex.indexState)),
    fuzzyIndexState: JSON.parse(JSON.stringify(search.fuzzyIndex.indexState))
  },
  documents
};

await fs.writeFile("dist/search-index.json", JSON.stringify(payload));

Browser step

In the browser:

import { createSimpleTextSearchIndex, DocumentIndex, RankingAlgorithm, TextFieldIndex, simpleTextSearch } from "@tryformation/querylight-ts";

const payload = await fetch("/search-index.json").then((response) => response.json());

const search = createSimpleTextSearchIndex({
  documents: payload.documents,
  primaryFields: payload.search.primaryFields,
  secondaryFields: payload.search.secondaryFields,
  idField: payload.search.idField,
  ranking: payload.search.ranking as RankingAlgorithm
});

const hydratedSearch = {
  ...search,
  documentIndex: search.documentIndex.loadState(payload.search.documentIndexState),
  fuzzyIndex: search.fuzzyIndex.loadState(payload.search.fuzzyIndexState)
};

const hits = simpleTextSearch(hydratedSearch, { query: "range fi", limit: 5 });

const query = new MatchQuery("title", "range filters");
const highlighted = hydratedSearch.documentIndex.highlight(hits[0]![0], query, {
  fields: ["title", "body"]
});

This pattern keeps the browser simple:

• fetch prebuilt JSON
• hydrate the indices
• run search on each keystroke or submit
• render matching documents from the original payload

When to move beyond the beginner helper

Stay with simpleTextSearch if you need a fast, practical default.

Move to manual queries when you need:

• filters or facets
• field-specific boosts tuned to your content
• custom analyzers for tags, IDs, or code snippets
• hybrid lexical plus vector retrieval
• geo queries

That transition is normal. Many teams start with a plain site-search box and only later add:

• semantic search over chunked docs
• recommendations or related content
• faceted navigation
• geo or region-based filtering

The important point is that the beginner path is not a dead end. It gets you to a useful search box quickly, and the same library lets you grow from there.

For highlight-specific behavior and current limitations, see Highlighting with Querylight TS.