Meilisearch

Meilisearch

Overview

Meilisearch is a fast, open-source search engine designed for instant, typo-tolerant search experiences. Unlike Elasticsearch, it requires zero configuration to get started — add documents, start searching. This skill covers deployment, document indexing, search queries, faceted filtering, sorting, relevancy tuning, multi-tenancy with tenant tokens, and integration with frontend (InstantSearch) and backend (Node.js, Python) applications.

Instructions

Step 1: Installation and Deployment

# Docker (recommended for production)
docker run -d --name meilisearch \
  -p 7700:7700 \
  -v meili_data:/meili_data \
  -e MEILI_MASTER_KEY="your-master-key-min-16-chars" \
  getmeili/meilisearch:latest

# Binary install (Linux)
curl -L https://install.meilisearch.com | sh
./meilisearch --master-key="your-master-key-min-16-chars"

# Verify
curl http://localhost:7700/health
# {"status":"available"}

Step 2: Index Documents

# Add documents via REST API
curl -X POST 'http://localhost:7700/indexes/products/documents' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer your-master-key' \
  --data-binary '[
    {"id": 1, "title": "iPhone 15 Pro", "category": "phones", "brand": "Apple", "price": 999},
    {"id": 2, "title": "Galaxy S24 Ultra", "category": "phones", "brand": "Samsung", "price": 1199},
    {"id": 3, "title": "MacBook Pro M3", "category": "laptops", "brand": "Apple", "price": 1999}
  ]'

With the Node.js SDK:

// index_products.js — Index documents from a database into Meilisearch
import { MeiliSearch } from 'meilisearch'

const client = new MeiliSearch({
  host: 'http://localhost:7700',
  apiKey: 'your-master-key',
})

const index = client.index('products')

// Add documents (Meilisearch auto-detects the primary key)
await index.addDocuments([
  { id: 1, title: 'iPhone 15 Pro', category: 'phones', brand: 'Apple', price: 999 },
  { id: 2, title: 'Galaxy S24 Ultra', category: 'phones', brand: 'Samsung', price: 1199 },
])

// Configure searchable attributes (which fields to search)
await index.updateSearchableAttributes(['title', 'brand', 'category'])

// Configure filterable attributes (for faceted search)
await index.updateFilterableAttributes(['category', 'brand', 'price'])

// Configure sortable attributes
await index.updateSortableAttributes(['price', 'title'])

Step 3: Search Queries

// search.js — Search with filters, facets, and highlighting
const results = await index.search('iphone', {
  limit: 20,
  offset: 0,
  filter: 'price < 1500 AND category = "phones"',
  sort: ['price:asc'],
  facets: ['category', 'brand'],
  attributesToHighlight: ['title'],
  attributesToCrop: ['description'],
  cropLength: 50,
})

// results.hits — matching documents with _formatted (highlighted) versions
// results.facetDistribution — { category: { phones: 2 }, brand: { Apple: 1, Samsung: 1 } }
// results.estimatedTotalHits — total matches
// results.processingTimeMs — typically <50ms

REST API equivalent:

curl -X POST 'http://localhost:7700/indexes/products/search' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer your-search-key' \
  --data '{"q": "iphon", "filter": "price < 1500", "facets": ["category", "brand"]}'
# Note: "iphon" still matches "iPhone" — typo tolerance is on by default

Step 4: Faceted Search and Filtering

// faceted_search.js — Build an e-commerce filter sidebar
// First, configure filterable attributes
await index.updateFilterableAttributes(['category', 'brand', 'price', 'rating', 'in_stock'])

// Search with multiple filters
const results = await index.search('laptop', {
  filter: [
    'category = "laptops"',
    'brand IN ["Apple", "Lenovo", "Dell"]',
    'price >= 500 AND price <= 2000',
    'in_stock = true',
  ],
  facets: ['brand', 'category', 'rating'],
})

// Geo search (for store locators, nearby results)
await index.updateFilterableAttributes(['_geo'])
const nearby = await index.search('coffee', {
  filter: '_geoRadius(48.8566, 2.3522, 5000)',  // 5km radius from Paris center
  sort: ['_geoPoint(48.8566, 2.3522):asc'],
})

Step 5: Relevancy Tuning

// relevancy.js — Fine-tune ranking rules and synonyms
// Default ranking: words → typo → proximity → attribute → sort → exactness
await index.updateRankingRules([
  'words',
  'typo',
  'proximity',
  'attribute',
  'sort',
  'exactness',
  'price:asc',           // custom: cheaper products rank higher
])

// Synonyms
await index.updateSynonyms({
  'phone': ['smartphone', 'mobile', 'cell phone'],
  'laptop': ['notebook', 'ultrabook'],
  'tv': ['television', 'monitor', 'display'],
})

// Stop words (ignored in search)
await index.updateStopWords(['the', 'a', 'an', 'is', 'at', 'of'])

// Typo tolerance settings
await index.updateTypoTolerance({
  enabled: true,
  minWordSizeForTypos: { oneTypo: 4, twoTypos: 8 },
  disableOnAttributes: ['sku', 'isbn'],    // exact match for codes
})

Step 6: Multi-Tenancy with Tenant Tokens

// tenant_tokens.js — Secure multi-tenant search
// Each tenant can only search their own data
import { MeiliSearch } from 'meilisearch'
import crypto from 'crypto'

function generateTenantToken(apiKeyUid, tenantId, searchRules) {
  /**
   * Generate a JWT token that restricts search to a specific tenant.
   * Args:
   *   apiKeyUid: UID of the API key (not the key itself)
   *   tenantId: The tenant/organization ID to restrict access to
   *   searchRules: Index-level filter rules
   */
  const client = new MeiliSearch({ host: 'http://localhost:7700', apiKey: 'your-master-key' })

  return client.generateTenantToken(apiKeyUid, searchRules, {
    expiresAt: new Date(Date.now() + 3600 * 1000),  // 1 hour
  })
}

// Usage: frontend gets a token that auto-filters by their org_id
const token = generateTenantToken('key-uid', 'org_123', {
  products: { filter: 'org_id = org_123' },
})

Step 7: Frontend Integration with InstantSearch

// SearchUI.jsx — React component with Meilisearch InstantSearch
import { InstantSearch, SearchBox, Hits, RefinementList, Pagination } from 'react-instantsearch'
import { instantMeiliSearch } from '@meilisearch/instant-meilisearch'

const { searchClient } = instantMeiliSearch('http://localhost:7700', 'your-search-key')

export function SearchPage() {
  return (
    <InstantSearch indexName="products" searchClient={searchClient}>
      <SearchBox placeholder="Search products..." />
      <div style={{ display: 'flex', gap: '2rem' }}>
        <div>
          <h3>Brand</h3>
          <RefinementList attribute="brand" />
          <h3>Category</h3>
          <RefinementList attribute="category" />
        </div>
        <div>
          <Hits hitComponent={ProductHit} />
          <Pagination />
        </div>
      </div>
    </InstantSearch>
  )
}

function ProductHit({ hit }) {
  return (
    <div>
      <h4>{hit.title}</h4>
      <p>${hit.price} — {hit.brand}</p>
    </div>
  )
}

Examples

Example 1: Add instant search to an e-commerce product catalog

User prompt: "I have a Next.js e-commerce app with 50,000 products in PostgreSQL. Add a search bar with instant results, typo tolerance, and category/brand filters."

The agent will:

1. Deploy Meilisearch via Docker with a master key.
2. Write a sync script that reads products from PostgreSQL and indexes them in Meilisearch.
3. Configure searchable, filterable, and sortable attributes.
4. Add synonyms for common product terms.
5. Build a React search component using InstantSearch and @meilisearch/instant-meilisearch.
6. Set up a cron job or webhook to re-sync products when the database changes.

Example 2: Build a documentation search for a developer portal

User prompt: "Add search to our documentation site. We have 500+ markdown files. Users should be able to search by title and content with highlighted results."

The agent will:

1. Write a script to parse all markdown files, extract frontmatter and content, and index them as documents.
2. Configure searchable attributes with title ranked above content.
3. Enable highlighting and content cropping for search result snippets.
4. Build a search modal component that shows results as the user types.

Guidelines

• Meilisearch is designed for end-user-facing search (product catalogs, documentation, content). For log analytics or time-series data, use Elasticsearch or ClickHouse instead.
• Always set a master key in production — without it, anyone can modify your indexes.
• Use the search API key (not master key) on the frontend. Generate it via the keys API or use tenant tokens for multi-tenant apps.
• Index updates are asynchronous — addDocuments returns a task ID. Poll the task status or use webhooks for sync confirmation.
• Meilisearch stores all data in memory-mapped files. For 1M documents with moderate fields, expect ~1-4 GB RAM. Plan capacity accordingly.
• Re-index from your source of truth (database) on a schedule rather than trying to keep Meilisearch in perfect sync — it's simpler and more reliable.