Go to file
2021-04-15 16:25:55 +02:00
.github Add release drafter files 2021-04-12 10:18:39 +02:00
helpers Introduce an helpers crate that export the database to stdout 2021-03-01 19:55:04 +01:00
http-ui Update Tokenizer version to v0.2.1 2021-04-14 18:54:04 +02:00
infos Simplify the output of database sizes in the infos crate 2021-03-08 18:47:33 +01:00
milli add documentation 2021-04-15 16:25:55 +02:00
search Fix error displaying of the workspace members 2021-03-01 19:48:01 +01:00
.gitignore Change the project to become a workspace with milli as a default-member 2021-02-12 16:15:09 +01:00
Cargo.lock Update Tokenizer version to v0.2.1 2021-04-14 18:54:04 +02:00
Cargo.toml Introduce an helpers crate that export the database to stdout 2021-03-01 19:55:04 +01:00
LICENSE Update LICENSE 2021-03-15 16:15:14 +01:00
qc_loop.sh Initial commit 2020-05-31 14:22:06 +02:00
README.md Fix the REAMD.md bash example 2021-03-08 18:56:22 +01:00

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 -- serve --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:

echo "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.