Bi-Stage RAG

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Bi-Stage RAG
Bi-Stage Adaptive RAG

Adapts group-recommendation's two-stage pipeline to RAG: Stage 1 generates per-aspect candidate pools with BM25, Stage 2 adaptively reranks using attention weights computed from aspect–query similarity. No hand-tuning of weights needed — they are computed on-the-fly from the query content. RAG applicability: Multi-faceted queries (e.g. 'fast Python RAG for beginners') decompose into aspects; attention routing ensures the most relevant aspects dominate the final ranking without static per-query configuration.

## Reference implementation (Vector Ridge Labs, POC-007)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-007
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/bi_stage_rag

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures retrieval quality (recall@k / nDCG on a labelled sample of my queries) and latency, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.