[Dontizi]

Hey! Yes, that's something I was planning to do—a complete documentation on the code, its architecture, and the entire stack to allow others to develop alongside me. I just deployed a functional version, and soon, the website will have documentation with its architecture and a visualization of the entire code.

but for now here is the stack used: Core Language: Go (chosen for performance, cross-platform compatibility, and single binary distribution) CLI Framework: Cobra (for command-line interface structure) LLM Integration: Ollama API (for embeddings and completions) Storage: Local filesystem-based storage (JSON files for simplicity and portability) Vector Search: Custom implementation of cosine similarity for embedding retrieval

[PhilippGille]

Hi, if you want to keep using a Go embedded/in-process vector store, but with some additional features, you can check out my project https://github.com/philippgille/chromem-go

[dcreater]

Why not use an established open source vector db like pg_vector etc? I imagine your implementation is not going to be as performant

[Xiol32]

Defeats the point of the single binary installation if you have to set up dependencies.

[andai]

rlama requires a python install (and several dependencies via pip) to extract text.

https://github.com/DonTizi/rlama/blob/main/internal/service/...

[mentalgear]

I recommend using this hybrid vector/full text search engine that works across many runtimes: https://github.com/oramasearch/orama