When I did my static site search function some time ago, I used Elasticlunr. I was able to pregenerate the index file as a big json file that is loaded at the client. http://elasticlunr.com/. - Source: Hacker News / 4 months ago

If your content is mostly static, you might want to consider pre-building an index and shipping it as a whole. You could look into something like * https://stork-search.net/ (Rust/WASM) * tinysearch: https://github.com/tinysearch/tinysearch (JS, simple, stable) * http://elasticlunr.com/ - based on the former, slightly more sophisticated tuning options. - Source: Hacker News / 8 months ago

There are a few client-side libraries like Lunr [1] or Elasticlunr [2]. For my recent project I went with a server-side approach using Stork [3]. It also provides a script to be used on the client. [1] https://lunrjs.com/ [2] http://elasticlunr.com/ [3] https://stork-search.net/. - Source: Hacker News / almost 2 years ago

Very nice! Seems to perform very well. I'm curious, have you compared Fuse with other search engines? Like flex search or elasticlunr? Why did you choose fuse ? Source: almost 2 years ago

There's also Elasticlunr which is based off of lunr.js and is what mdBook uses http://elasticlunr.com/. - Source: Hacker News / over 2 years ago

Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 4 months ago

Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 4 months ago

Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 5 months ago

A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 6 months ago

To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 7 months ago