n8n-docs VS Typesense

n8n (27.2k)

n8n documentation

Focus on Documentation We get more traffic to our documentation than our main website. A well-documented project is always loved by the community. Open-source projects like Docusaurus make it super easy to build documentation portals that look great just out of the box. Adding links to the repository from the documentation can drive more visitors to your repository. What to include in documentation How to install/deploy the project If the project has a compiled software as the final product, make sure to add installation instructions. If the project is the codebase for a library such as an npm package or a Ruby gem, include details on how to import and use the library. If the project needs to be or can be deployed on platforms like Kubernetes, Docker, Heroku, and others, include separate guides for each of the options. Contributing guide Apart from the contributing guide doc in the codebase, add one to the documentation, too. It should include guides for setting up a local environment on different platforms like Docker, Mac OS, Ubuntu, Windows, and so on. Tutorials and code examples If this is applicable, it can be really helpful. How to guides on using using the project will show other devs how they can actually get started. It can be code examples if the project is a library. Architecture reference It will be helpful for the contributors if the documentation has details on different components of the project. For example, if the project has server and client components, include a diagram on how everything works together. Here are some projects with great documentation: https://docs.nestjs.com/ https://docs.n8n.io/ https://guides.rubyonrails.org/ https://plotly.com/python/ https://docs.mapbox.com/ https://www.github-stars.com or Github24.

Here are some projects with great documentation: a) https://docs.nestjs.com/ b) https://docs.n8n.io/ c) https://guides.rubyonrails.org/ d) https://plotly.com/python/ e) https://docs.mapbox.com/

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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.

Try tigris | typesense for faster search

I’m also checking out Typesense as a possibility for replacing Elastic: https://typesense.org/