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

Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 6 days ago

AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / about 1 month ago

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / about 2 months ago

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / about 2 months ago

I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / 2 months ago