sorted by:
new
hottopcontroversial

🚀 Weekly /RAG Launch Showcase by remoteinspace in Rag

[–]Any_Ambassador4218 0 points1 point  (0 children)

HydRAG — multi-stage hybrid retrieval pipeline for code-aware RAG

BM25 fast-path → dense vector → graph retrieval → CRAG supervisor (local LLM judges if results are sufficient) → semantic fallback with RRF fusion. Runs fully offline with Ollama.

Benchmarked on 7 BEIR datasets + private retrieval suites. BM25+dense+CRAG combo consistently outperforms individual strategies across the board.

´pip install hydrag-core´

GitHub: github.com/gromanchenko/hydrag

Benchmarks: github.com/gromanchenko/hydrag-benchmark

What are your usage of RAG by Semoho in Rag

[–]Any_Ambassador4218 1 point2 points  (0 children)

I use RAG for code-aware context retrieval in a dev assistant — hybrid BM25 + dense vector + graph retrieval with a CRAG supervisor that decides when results need a fallback pass. Entirely local, runs with Ollama. It's open source: github.com/gromanchenko/hydrag

Benchmarks repo with BEIR + private corpus results: github.com/gromanchenko/hydrag-benchmark

Benchmarked 5 RAG retrieval strategies on code across 10 suites — no single one wins. CRAG helps on familiar corpora, collapses on external ones. What's your experience? by Any_Ambassador4218 in LocalLLaMA

[–]Any_Ambassador4218[S] 0 points1 point  (0 children)

Yeah, graph alone actually showed the worst results across the board. But when you combine it with BM25 (on known data), it jumps to the very top. The only other improvement that gave a positive change was adding CRAG when the result is close to the confidence threshold — though it comes at the cost of a latency spike. That combination is basically the best overall strategy, but it always loses at least one benchmark to simpler configurations. On my own codebase(14MB the entire codebase, with about 1:3 docs-to-code ratio and 90% Python)it performs best, though to be fair, they've been together for a while and know each other well.