Meilisearch — Lightning-Fast Open Source Search Engine

What it is

Meilisearch is an open-source search engine designed for instant, typo-tolerant search results. It provides a REST API for indexing and querying documents with sub-50ms response times. Meilisearch supports hybrid search combining traditional keyword matching with AI-powered semantic search, making it a drop-in alternative to Algolia or ElasticSearch for product search, site search, and documentation search.

Meilisearch targets developers building search features for web applications, e-commerce sites, documentation portals, and mobile apps who want fast setup without the operational complexity of ElasticSearch.

How it saves time or tokens

Meilisearch deploys as a single binary with zero configuration. Index your data via the REST API, and search works out of the box with typo tolerance, faceted filtering, and relevancy ranking. There is no need to configure analyzers, tokenizers, or relevancy tuning for most use cases. The hybrid search feature adds semantic understanding without requiring a separate vector database.

How to use

1. Start Meilisearch with Docker:

docker run -d --name meilisearch \
  -p 7700:7700 \
  -e MEILI_MASTER_KEY=your-master-key \
  -v meili-data:/meili_data \
  getmeili/meilisearch:latest

1. Index documents:

curl -X POST 'http://localhost:7700/indexes/products/documents' \
  -H 'Authorization: Bearer your-master-key' \
  -H 'Content-Type: application/json' \
  --data '[{"id": 1, "name": "Wireless Mouse", "category": "Electronics", "price": 29.99}]'

1. Search with typo tolerance:

curl 'http://localhost:7700/indexes/products/search' \
  -H 'Authorization: Bearer your-master-key' \
  -H 'Content-Type: application/json' \
  --data '{"q": "wireles mous"}'
# Returns 'Wireless Mouse' despite typos

Example

Faceted search with filters:

curl 'http://localhost:7700/indexes/products/search' \
  -H 'Authorization: Bearer your-master-key' \
  -H 'Content-Type: application/json' \
  --data '{
    "q": "mouse",
    "filter": "category = Electronics AND price < 50",
    "facets": ["category"]
  }'

Related on TokRepo

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Common pitfalls

• Meilisearch loads the entire index into RAM. Large datasets (millions of documents) require proportional memory. Plan server sizing based on index size, not document count.
• The master key must be set in production. Without it, anyone can modify your indexes. Use API keys with restricted permissions for frontend search requests.
• Indexing is asynchronous. After adding documents, queries may not return new results immediately. Poll the task API to confirm indexing completion.