tidyquery VS Typesense

tidyquery

> SQL is a perfect language for analytics.

Slightly off topic, but I strongly agree with this statement and wonder why the languages used for a lot of data science work (R, Python) don't have such a strong focus on SQL.

It might just be my brain, but SQL makes so much logical sense as a query language and, with small variances, is used to directly query so many databases.

In R, why learn the data.tables (OK, speed) or dplyr paradigms, when SQL can be easily applied directly to dataframes? There are libraries to support this like sqldf[1], tidyquery[2] and duckdf[3] (author). And I'm sure the situation is similar in Python.

This is not a post against great libraries like data.table and dplyr, which I do use from time to time. It's more of a question about why SQL is not more popular as the query language de jour for data science.

[1] https://cran.r-project.org/web/packages/sqldf/index.html

[2] https://github.com/ianmcook/tidyquery

[3] https://github.com/phillc73/duckdf

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.