Multi-hop retrieval over Chroma with no LLM in the query loop: deterministic, ~100ms, benchmarked on HotpotQA and MuSiQue.
Chroma's sweet spot is local-first: embed your documents, query them
on your own machine, no infrastructure ceremony. But local-first RAG
hits a wall on multi-hop questions:
"Which university did the founder of the company that acquired Polar
Metrics study at?"
Entity-graph retrieval on top of Qdrant: Personalized PageRank + convergence scoring, zero LLM calls at query time, +5.9 pts…
Originally published at vivekpatil23.hashnode.dev The Problem With Naive RAG Nobody Talks About Most...
Google Research adds agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries.
Replace hardcoded context with real retrieval using NVIDIA's nv-embedqa-e5-v5 embedding model. Cosine similarity, the query vs…
The assumption every RAG demo makes Standard RAG assumes the user's question maps onto one...
Frameworks like LangChain are great for moving fast, but they also hide a lot of what's actually...
