Hey everyone,

If you are already running local models through Ollama, you probably want to actually *do* something useful with them, like chatting with your private PDFs, codebases, or notes without sending your data to a corporate API.

I wanted the exact same thing, but I was completely fed up with the standard Python RAG wrappers (LangChain + Chroma, etc.). They rely entirely on pure vector search, which is fine for conceptual similarity but absolutely terrible at finding exact keywords, specific variables, or version numbers. That’s exactly why your local models end up hallucinating citations.

So, I built **Gloss**. It’s a local-first NotebookLM alternative written entirely in Rust, designed to plug straight into your local models.

**How it supercharges your local setup:**

* **Custom Hybrid Search (**`semantic-memory`**):** I bypassed the off-the-shelf vector databases and wrote a custom Rust crate from scratch. It combines an HNSW index (for dense conceptual vectors) with BM25 (for exact lexical keyword matches).
* **Zero Hallucinations:** Because of that BM25 anchor, your Ollama model is forced to ground its generation in the literal text. You get hyper-accurate, auditable citations mapped directly to your local files.
* **Zero Bloat:** It runs entirely on asynchronous Rust. You just drop your files in, point it at your Ollama endpoint, and it handles the ingestion, vectorization, and routing locally without eating all your RAM.

I’ve been up all night pushing the initial v0.1 launch, but I'd love for this community to hook it up to your favorite local models and see how the retrieval quality actually holds up against the heavy corporate APIs.

**GitHub Repo:** [https://github.com/RecursiveIntell/Gloss](https://github.com/RecursiveIntell/Gloss)

I built a local NotebookLM alternative in Rust that hooks directly into Ollama (Custom HNSW + BM25 Search)