schemats VS Typesense

This is really cool, will look into using it in future projects!

I also made a tool (https://github.com/vramework/schemats) that generates the types directly from the db, which means whenever you do a DB migration your database types automatically update. Was forked from the original schemats library a couple years ago.

I also created a lightweight library ontop of pg that is less of a query builder and more of a typed CRUD + SQL for non trivial queries (https://github.com/vramework/postgres-typed). Most queries I deal with in a day to day is usually crud so I find it a little easier, but it's much less powerful then Kysely! I fall more into the camp of writing complex queries in SQL with small helpers and writing simple ones with util functions and typescript

I'm working on a few projects, from one/two days to platforms.

The first is OS and is a simple nodeJS environment to deploy applications via lambda and express quickly. Sort of like nestJS except less decorators and more functional (https://vramework.io/). I already know of a few other colleagues that rolled their own propriety versions of this to support enterprise and cloud deployments so decided to OS it.

The other OS project is a strongly typed postgres/mysql driver. The idea is to generate typescript definitions directly from postgres (https://github.com/vramework/schemats) and then have a think layer ontop of pg-node that gives you strongly typed queries (https://github.com/vramework/postgres-typed).

An open-source project I spent a few years on the core team is https://deepstream.io/, a realtime-server that allows you to mix and match multiple streaming protocols (mqtt/websocket/others) and allow those clients to talk to each other using pub-sub and records. I'm not longer working for it but wanted to give it a shout out!

On a non OS project, I have been working on an immersive audio platform for a while now. The main goal is to allow users to pick and choose how audio books progress, and also have a live session mode which allows users to record their pulse / answer questions and a few other metrics and associate it with sentences. I pretty much built and deployed all of it but require some advice/brainstorming on how to proceed now. I built it to satisfy an itch when I was practicing shamanism during the first lockdown when I was in-between contracts / taking time off.

I also want to build a simple web-pages strategy game based around eco-education, but don't have the bandwidth . If anyone is interested in mixing together gamification and eco-village building might be a fun conversion to bounce ideas!

All the OS projects above were used to support my personal/a couple professional projects over the last few years.

Email in profile

- Elderly care / Matching platforms (marta.de)

Recently (2021) have spent my time between my own product enjamon.com (almost at launch stage), working part-time for marta.de where I built their backend office, matching algorithm and two client facing apps (as the sole developer + intern) and some open-source projects around deployment strategies and postgres/typescript node interfaces (https://github.com/vramework/schemats).

Currently looking for a job where I can work part-time (20 hours a week) or for on-demand tasks (like optimising APIs, CI pipelines, AWS infrastructure).

Contact: [email protected]

Disregarding props-drilling technique in favor of a more reliable and elegant solution we looked for inspiration elsewhere. Another project of ours .find was using Typesense/Algolia components, which looked a bit like black-box/magic, but at the same time provided a clean approach to build complex and highly customizable solutions.

Have you ever tried to look up something, only to realise your search engine doesn't recognise your typos? Typesense to the rescue! It's a fast, typo-tolerant search engine built for an easier browsing experience. The latest version comes with new features such as built-in conversational search, image search, voice search, analytics, and more. Dive into the release notes for the full list of changes and enhancements.

There are actually plenty of non-ES products that are way easier to integrate and tune (and get better results with less effort).

- Typesense (https://github.com/typesense/typesense)

- Algolia

- Google Programmable Search Engine (https://programmablesearchengine.google.com/about/)

Typesense - Open Source Alternative to Algolia

If you like your penny take a look at Typesense https://typesense.org/ - nothing to complain here. Especially nothing complain about pricing.

I work on Typesense [1] (historically considered an open source alternative to Algolia).

We then launched vector search in Jan 2023, and just last week we launched the ability to generate embeddings from within Typesense.

You'd just need to send JSON data, and Typesense can generate embeddings for your data using OpenAI, PaLM API, or built-in models like S-BERT, E-5, etc (running on a GPU if you prefer) [2]

You can then do a hybrid (keyword + semantic) search by just sending the search keywords to Typesense, and Typesense will automatically generate embeddings for you internally and return a ranked list of keyword results weaved with semantic results (using Rank Fusion).

You can also combine filtering, faceting, typo tolerance, etc - the things Typesense already had.

[1] https://github.com/typesense/typesense

[2] https://typesense.org/docs/0.25.0/api/vector-search.html

For something small with a minimal footprint, I'd recommend Typesense. https://github.com/typesense/typesense

I haven’t used Publish, but I’d assume you could use something like https://typesense.org/ to index and search the vault.

A cheaper option would be to use https://typesense.org. You can use DynamoDb streams to automatically load records. It has worked well for me.