Tag

#retrieval

Articles tagged "retrieval" — 7 entries.

Article №28 observability NIM ~3 hours — 30 min plumbing, ~20 min for the runs themselves, the rest is reading what they show
Frontier Scout

AutoResearchBench on Spark — Two NIMs, One Bench, Two Failure Modes

Two Spark-tuned NIMs run AutoResearchBench's three Deep-Research example questions. Llama-3.1-8B crashes by turn 5-6 on its 8K context; Nemotron-Nano-9B-v2 finishes cleanly at 128K. Both score 0% Accuracy@1 — for completely different reasons.

uses fieldkit.nimfieldkit.evalfieldkit.capabilities

Article №15 observability NeMo Evaluator ~60 minutes end-to-end — 40 s to ingest the blog into pgvector, 2 min for retrieval, 4 min for generation across three 8B variants, 90 s for the LoRA variant, 9 min for grading
Second Brain

Ragas, Reranked — What 44 Held-Out Questions Say About the Second Brain Stack

A Ragas-style harness written in 200 lines of stdlib Python, run locally on the DGX Spark, against four variants of the Second Brain RAG chain. Naive RAG scores 3.30 / 5. Rerank RAG scores 4.27. LoRA+RAG is a surprise — it does not beat naive. Retrieval is where the points come from.

uses fieldkit.eval

Article №09 inference Nemotron Reranker + pgvector full-text + Llama 3.1 8B NIM ~45 minutes on top of the naive-RAG chain
Foundations

Hybrid Retrieval on the Spark — BM25, Dense, Fusion, Rerank

Four retrieval modes on one corpus — naive dense, BM25, Reciprocal Rank Fusion, Nemotron rerank. Dense is already 92% recall@5; rerank adds a point at K=10 and reorders the top. The 8B generator still refuses where retrieval is perfect — grounding, not retrieval, is the new bottleneck.

uses fieldkit.rag

Article №08 inference Llama 3.1 8B NIM + Nemotron Retriever + pgvector ~30 minutes if the three endpoints are already warm
Foundations

Three Endpoints, One Answer — Naive RAG on a DGX Spark

Three endpoints in one curl chain — a query embeds through Nemotron, pgvector returns top-5 chunks in under 80 ms, and a Llama 3.1 8B NIM stuffs them into a strict-context prompt. The chain works; the 8B generator still refuses on questions its own context answers.

uses fieldkit.ragfieldkit.eval

Article №07 inference pgvector ~15 minutes first install, re-runs in seconds
Foundations

Where Your Vectors Live — pgvector on a DGX Spark

The substrate between the embed call and the retrieve call — pgvector 0.8.2 running as a Postgres 16 container on GB10, with 1000 Nemotron vectors, HNSW and ivfflat both indexed, and a planner that prefers seq scan until you tell it otherwise.

uses fieldkit.rag

Article №06 inference NeMo ~30 minutes first install, ~1 minute every restart after
Foundations

Your Own Semantic Space — a Nemotron Embedding NIM on a DGX Spark

The embedding endpoint that every downstream RAG, wiki, and agent piece will reuse — a 2048-dim Nemotron Retriever NIM running locally on GB10, ready 52 seconds after docker run and holding 28 docs/s under batched load.

uses fieldkit.rag

Upcoming agentic NIM ~30 min read
Second Brain

Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction — Spark reproduction notes

Reproducing DCI-Agent-Lite on a DGX Spark — NIM-served 8B agent + ripgrep + filesystem corpus, no embedder or vector DB; extracts the operator vocabulary as `fieldkit.rag.operators` and quantifies how much of the existing pgvector + reranker stack DCI lets you delete.