bors[bot] 6faa87302c
Merge #220
220: Make hard separators split phrase query r=Kerollmops a=ManyTheFish

hard separators will now split a phrase query as two sequential phrases (double-quoted strings):

the query `"Radioactive (Imagine Dragons)"` would be considered equivalent to `"Radioactive" "Imagine Dragons"` which as the little disadvantage of not keeping the order of the two (or more) separate phrases.

Fix #208

Co-authored-by: many <maxime@meilisearch.com>
Co-authored-by: Many <legendre.maxime.isn@gmail.com>
2021-06-09 08:22:58 +00:00
2021-06-09 08:22:58 +00:00
2021-05-03 12:29:30 +02:00
2021-06-08 11:39:10 +02:00
2021-03-15 16:15:14 +01:00
2020-05-31 14:22:06 +02:00
2021-04-29 09:25:35 +02:00

the milli logo

a concurrent indexer combined with fast and relevant search algorithms

Introduction

This engine is a prototype, do not use it in production. This is one of the most advanced search engine I have worked on. It currently only supports the proximity criterion.

Compile and Run the server

You can specify the number of threads to use to index documents and many other settings too.

cd http-ui
cargo run --release -- --db my-database.mdb -vvv --indexing-jobs 8

Index your documents

It can index a massive amount of documents in not much time, I already achieved to index:

  • 115m songs (song and artist name) in ~1h and take 107GB on disk.
  • 12m cities (name, timezone and country ID) in 15min and take 10GB on disk.

All of that on a 39$/month machine with 4cores.

You can feed the engine with your CSV (comma-seperated, yes) data like this:

printf "name,age\nhello,32\nkiki,24\n" | http POST 127.0.0.1:9700/documents content-type:text/csv

Here ids will be automatically generated as UUID v4 if they doesn't exist in some or every documents.

Note that it also support JSON and JSON streaming, you can send them to the engine by using the content-type:application/json and content-type:application/x-ndjson headers respectively.

Querying the engine via the website

You can query the engine by going to the HTML page itself.

Description
No description provided
Readme 190 MiB
Languages
Rust 97.4%
HTML 1.3%
Shell 1.2%