smarvin2

This section of the docs may be confusing. What you described will actually almost work. See: https://postgresml.org/docs/open-source/korvus/guides/rag#ra...

It does work by running the LLM on the database server but you can configure the LLM to run on the GPU

You can use Korvus for search and feed the results to an external model

Great question! Making calls to external services is not something we plan to support. The point of Korvus is to write SQL queries that take advantage of the pgml and pgvector extensions. Making calls to external services is something that could be done by users after retrieval.

Unfortunately the pgml extension does not work on AWS RDS so there is not.

You can totally use an existing postgres database. Just make sure to install the pgvector and pgml postgres extensions and it will work!

We would love help developing a Ruby SDK! We programmatically generate our Python, JavaScript, and C bindings from our Rust library. Check out the rust-bridge folder for more info on how we do that.

I think there are a couple things worth noting here:

Semantic search performs well at capturing documents keyword search misses. As noted in the article, when searching for exact keywords, keyword will outperform semantic search. It is when users do not know the exact phrase they are looking for that keyword search shines.

Semantic search should only be a part of your search system, not your entire search system. We find that combining keyword search + semantic search and then using a reranker gives the best results. It is best if the reranker is fine tuned on your search history, but general crossencoders perform surprisingly well.

Hey thanks for bringing this up. I'm not able to reproduce the error you are getting. I just ran `cargo install lsp-ai -F llama_cpp -F metal` on my mac and received no errors and a clean install. Can give me the full output of your command either here or in a github issue?

LSP-AI is meant to work with plugins. It can provide auto complete features without them, but to have the kind of experience Copilot provides with VS Code, you will need editor specific plugins.

LSP-AI makes writing these plugins easier. Check out "The Case for LSP-AI" https://github.com/SilasMarvin/lsp-ai?tab=readme-ov-file#the... for more info on why I think that is true

Most editors should let you use multiple language severs at once, they merge the results together which can be annoying.

LSP-AI is not meant to replace plugins. It works best when wrapped and used by a plugin. Our VS Code plugin is a great example of this: https://github.com/SilasMarvin/lsp-ai/wiki/Plugins

LSP-AI abstracts complex implementation details like maintaining document / context parity and providing different LLM backends.

I have a section no the Github Title: "The Case for LSP-AI" you might like: https://github.com/SilasMarvin/lsp-ai?tab=readme-ov-file#the...

Thank you! Let me know if there are features you are wanting that are currently missing!

You can specify FIM objective models for completion. I find that Codestral from Mistral works pretty well.

That next stage is currently what I am working. I'm building out a code splitter using TreeSitter right now and already have experimental vector search in the language server.

There are no docs for configuration for Emacs. That is something we need to add! I don't have experience using emacs, but if anyone does and wants to create an issue with some examples that would be awesome, otherwise I can find some time later to add it

Of course, let me know how it goes and if I can help with anything else

You have complete control over the prompt configuration for LSP-AI. Feel free to check out the prompt section on our wiki: https://github.com/SilasMarvin/lsp-ai/wiki/Prompting It is very much still a work in progress, but I find the prompt I have listed as the default on that page does a good job of encouraging gpt-4o / llama 70b to provide only minimal completion suggestions.

Yes! I would love to hear your thoughts on our current features and roadmap. If you have any ideas or want to contribute, feel free to make a github issue.

These are all awesome projects! After skimming them, one big difference between these and LSP-AI is that LSP-AI is a language server. That means we work out of the box with almost all popular text editors and IDEs, you don't need any plugins to get completion.

For custom use cases like chat windows and some of the things we are working on next, you will still need plugins, but making it an LSP simplifies things like synchronizing document changes and a communicating with the client.

This is actually what we are working on adding next! We are working on code crawling and a vector search for better context gathering. Stay tuned for some more info on this

Hey everyone! Thank you so much for posting and upvoting this. Just wanted to say I'm the primary author here and happy to try and answer any questions anyone might have! Feel free to ask away here! This is a very new project and we have a lot of ground we are hoping to cover. Thank you for the support!

Thanks for checking it out! Sorry you had some issues I'll look into it.

We are still actively building out our documentation. You can find the current docs here: https://postgresml.org/docs/introduction/machine-learning/sd...

I'm not sure if something like that already exists, but you could absolutely build it with this! Feel free to jump into our discord with any questions you have