Tldr;

Use FAISS for massive datasets & high-performance search (e.g., billions of vectors)

Use Chroma for metadata-rich use cases & simpler, real-time deployments. 5/5 🎉

However, it's querying options are limited, so if you're looking for some very complex query in your retriever, you might find it difficulties. 4/5

Chroma: Ideal for apps requiring metadata like conversational AI, or doc search

It's also beginner-friendly with built-in persistence, great for small-to-medium projects needing rapid prototyping & metadata-based filtering. 3/5

FAISS: Good when you're looking for speed & scalability, perfect for large-scale ML apps like recommendations or image indexing.

However, it neither supports Metadata nor built-in persistence. But both can be added with little of work-arounds. 2/5

if you're using LLM to get these, it might increase the time significantly RAG will take to process query

3. Fusion and step-back technique can get messy if they're not well integrated in the flow