Meilisearch

What is Meilisearch AI

Meilisearch AI is an open‑source search engine that adds AI‑powered relevance to fast, developer‑friendly full‑text search. It combines classic keyword retrieval with semantic and hybrid search so users get precise results for natural‑language queries, even with typos. Built‑in faceted navigation, filters, and geosearch help people browse large catalogs and local listings. A simple REST API, SDKs, and instant indexing make integration straightforward for web and mobile apps. Whether self‑hosted or used as a managed service, Meilisearch AI gives teams fine‑grained control over ranking, recall, and user experience.

Meilisearch AI Main Features

• Fast full‑text search: Low‑latency retrieval with typo tolerance, prefix search, and instant indexing for responsive UIs.
• Semantic and vector search: Store embeddings to match intent beyond keywords; return contextually similar results.
• Hybrid search: Combines BM25 keyword scoring with vector similarity for balanced precision and recall.
• Faceted navigation and filters: Configure filterable attributes and facets to refine results by category, brand, price, and more.
• Geosearch: Index geo‑coordinates to filter and sort by distance for local and “near me” searches.
• Relevance controls: Ranking rules, custom ranking, synonyms, stop‑words, and highlighting to tune search quality.
• Developer‑friendly API: Clean REST endpoints, lightweight schema, and SDKs for popular languages and frameworks.
• Security: API keys and tenant tokens to scope access by index or user context.
• Multilingual support: Sensible tokenization and normalization for diverse languages out of the box.
• Operational simplicity: Lightweight binary or container deployment with modest resource needs for most projects.

Who Should Use Meilisearch AI

Meilisearch AI suits product teams and developers who need relevant, low‑latency search inside websites or applications. it's ideal for e‑commerce catalogs, marketplaces, documentation portals, knowledge bases, media and news archives, SaaS dashboards, job boards, real‑estate listings, and any app that benefits from faceted filtering, semantic understanding, or location‑aware results.

How to Use Meilisearch AI

1. Deploy Meilisearch via Docker, a package manager, binary, or choose a managed cloud option.
2. Create an index and define basic settings like searchable, filterable, and faceted attributes.
3. Ingest documents in JSON (products, articles, listings) through the REST API or SDK.
4. Tune relevance with ranking rules, synonyms, stop‑words, and custom ranking fields.
5. Enable semantic or hybrid search by generating embeddings with your preferred model and storing vector fields.
6. Add geosearch by indexing latitude/longitude and enabling distance filters or sorting.
7. Integrate the frontend using the API or SDKs; implement autocomplete, facets, and result highlighting.
8. Monitor search behavior and iterate on settings to improve click‑through and conversion.

Meilisearch AI Industry Examples

An online retailer combines hybrid search with facets so shoppers find products by intent (“comfortable running shoes”) and refine by size, price, and brand. A real‑estate platform uses geosearch to surface homes within a radius and sorts by distance to transit. A documentation site powers semantic search across guides and API references, helping developers retrieve relevant topics even when queries don’t match exact terms.

Meilisearch AI Pricing

Meilisearch is open‑source software you can self‑host at no license cost. For teams that prefer not to manage infrastructure, a managed cloud service is available with tiered plans; consult the official Meilisearch website for current pricing and limits.

Meilisearch AI Pros and Cons

Meilisearch AI FAQs

• Does Meilisearch AI require embeddings?

  No. Classic full‑text search works without embeddings. Embeddings are only needed for semantic and hybrid search.

• How does hybrid search improve results?

  It combines BM25 keyword scores with vector similarity, capturing both exact matches and intent, which boosts recall while preserving precision.

• Can I use my own embedding model?

  Yes. Generate embeddings with your chosen model or service and store vectors in your documents to enable semantic or hybrid retrieval.

• Is geosearch supported?

  Yes. Add geo‑coordinates to documents to filter within a radius, apply bounding boxes, or sort results by distance.