FAISS is not suitable for production. The dedicated vector search solutions solve all the issues you mentioned: you just store the metadata along with vectors in JSON format. At least, with Qdrant, it works like this: https://qdrant.tech/documentation/concepts/payload/
Readit Newsandre-z commented on Ask HN: Scaling local FAISS and LLM RAG system (356k chunks)architectural advice · Posted by u/paul2495
miniCOIL is a contextualized per-word embedding model. It generates extremely small embeddings (8dim or even 4dim) while still preserving the word's context for each word in a sentence.
GitHub https://github.com/qdrant/miniCOIL HuggingFace https://huggingface.co/Qdrant/minicoil-v1
andre-z commented on Meilisearch – search engine API bringing AI-powered hybrid search github.com/meilisearch/me... · Posted by u/modinfo
Meilisearch is faster when you reduce the dataset by filtering it. I wrote an article on this subject [1].
[1]: https://blog.kerollmops.com/meilisearch-vs-qdrant-tradeoffs-...
"Slowness can arise from a misconfigured index or if filterable attributes aren't listed." ;)
Qdrant runs on Linux/Mac/Windows and on x86/ARM processors
Any dense embedding model can become a late interaction model.