Semantic Retrieval Augmented Generation With Lume

This is a project to bring Tieto's flat-file embedding storage and semantic matching capabilities to Lume's powerful document processing, generation capabilities and data model in order to produce a semantic data store that runs happily at the (freemium) edge with few resources.

That sounds like a lot, so it's easier to describe by telling you what it will let you do instead:

Because Tieto adds no dependencies on top of Lume, it doesn't alter the existing SOC footprint for most companies. For those that can self-host the embedding model which just requires 350mb of RAM, you can have semantic search and completion without any third-party overhead.

There's also the possibility of running inference via WASM, which would be ideal for at least the embedding end of this; completion would still be theoretically huge and laggy.

Being able to set up dev environments and stay within SOC guidelines is harder than some might imagine, and was one of the motivations to make something that had basic functionality, just totally self-contained.

Requirements

If you just want semantic search, meaning you want something like a basic version of Algolia but you own 100% of the code:

If all you want is semantic search and some MCP endpoints, all you need is access to embeddings, which can be obtained at no cost.

Retrieval-only by default. BYOM for completion, too.

If you also want completion, as in RAG (retrieval-augmented generation), then you will also need to be able to provide a completion model key and provider (Featherless, OpenRouter, OpenAI, Anthropic, Etc), or host the completion model along with the embedding model.

Current Status

Still in development and not yet functional.