Capability · Framework — rag

Meilisearch

Meilisearch is a Rust-based search engine famous for its setup-to-query speed — you can index thousands of documents in under a minute and get typo-tolerant, ranked results via a REST API. Since v1.6 it also supports vector search and hybrid (keyword + vector) retrieval with pluggable embedders, making it a popular choice for hybrid RAG without deploying a full vector DB.

Framework facts

Category: rag
Language: Rust
License: MIT
Repository: https://github.com/meilisearch/meilisearch

Install

# Docker
docker run -it --rm -p 7700:7700 getmeili/meilisearch:latest
# or
brew install meilisearch

Quickstart

curl -X POST 'http://localhost:7700/indexes/docs/documents' \
  -H 'Content-Type: application/json' \
  --data-binary '[{"id":1, "title":"MCP spec", "body":"Model Context Protocol"}]'

curl 'http://localhost:7700/indexes/docs/search?q=mcp'

Alternatives

Tantivy — library, not service
Typesense — similar DX
Elasticsearch — heavy but powerful

Frequently asked questions

Meilisearch or Typesense?

Both are excellent. Meilisearch has broader language coverage and hybrid vector support in the open-source core; Typesense is slightly faster on pure BM25 and has fewer knobs. Pick whichever feels nicer to your team — migration between them is cheap.

Do I still need a vector DB?

Not always. Meilisearch's hybrid search is solid for <10M docs. For massive corpora or advanced filtering on vectors, a dedicated vector DB like Qdrant or Vespa scales better.

Sources

Meilisearch docs — accessed 2026-04-20
Meilisearch GitHub — accessed 2026-04-20