spyglass VS MeiliSearch

No direct experience but I saved some links about this:

https://wiby.me/about/guide.html

https://github.com/spyglass-search/spyglass

https://yacy.net/

Interesting thread(s) on HN about the last one:

https://news.ycombinator.com/item?id=32597309

I've been working on a self-hosted personal search app (https://github.com/spyglass-search/spyglass) and have recently been playing around with how to integrate it with local LLMs. I think this would be an awesome step into having your own personal assistant that can search through all your data and give you analysis / summaries.

We have an open-source desktop app (https://github.com/spyglass-search/spyglass) and are working on a web version that has more powerful features such as conversation search, check it out!

I'm one of the devs for Spyglass ([https://github.com/spyglass-search/spyglass](https://github.com/spyglass-search/spyglass)) an open-source personal search app. We're excited about all the advancements with language models recently and wanted to try merging the two ideas together to form something even better.

Another more recently available option is spyglass; it is more tenable than YaCy but dev is mostly on MacOS with focus on a desktop interface. I like the idea of web based interface.

I'm part of small team that's been building an open-source personal search engine (https://github.com/spyglass-search/spyglass). One of the major use cases is searching your local files & their contents which aligns perfectly with how Obsidian stores it notes.

I'm one of the developers of Spyglass (https://github.com/spyglass-search/spyglass), an open-source self-hosted personal search engine. We recently added the ability to search through your Reddit saved & upvoted posts!

We needed to introduce a new service for search. As we settled on using meilisearch, we needed a way to sync updates on our models with the records in meilisearch. We could've continued to use callbacks but we needed something better.

Meilisearch

In this case, a good strategy is to use vector search only when the keyword/prefix search returns none or just a small number of results. A good candidate for this is MeiliSearch. It uses custom ranking rules to provide results as fast as the user can type.

With the VertexAI endpoint set up and tested, our next step is to work with LibreChat. LibreChat is an open-source ChatGPT clone that can integrate with various AI models, including the PaLM 2 models via the VertexAI API. It's built using React, MongoDB, and Meilisearch technologies.

Meilisearch seems like it is the best open source option.

https://www.meilisearch.com/

Starting with v1.3, you can use Meilisearch as a vector store. Meilisearch allows you to store vector embeddings alongside your documents conveniently. You will need to create the vector embeddings using your third-party tool of choice (Hugging Face, OpenAI). As we published the first v1.3 release candidate, you can try out vector search today.

I work at Meilisearch, an open-source search engine built in Rust. 🦀

https://www.meilisearch.com/ https://github.com/meilisearch