Tag
#rag
Articles tagged "rag" — 12 entries.
The Machine Manages Its Own Memory — and the Bug the Mocks Slept Through
Driving the Arena recall layer end-to-end on its own corpus: reindex → score → gate, dispatched through the control plane, recall@5 measured against 44 held-out questions. The first real drain caught a bug eight mock-injected unit tests had slept through — the case for operating the thing you built.
uses fieldkit.memoryfieldkit.arenafieldkit.harnessfieldkit.eval
Three-Mode Bracket: Baselining a Reasoning Model Before Fine-Tuning, On One Spark
Before you fine-tune a small reasoning model on a domain bench you need to know where it stands. Three context modes — closed, retrieval, oracle — triangulate the model's ceiling on one Spark, no Judge backend or cluster required.
Second Brain as a Tool — Wrapping the RAG Stack in MCP for Claude Code
Closing the Second Brain arc. Four MCP tools wrap the RAG chain — embed, retrieve, optionally rerank, generate — and any Claude Code session anywhere on the box becomes a grounded research client. 200 lines of Python, one launcher, one .mcp.json entry.
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
One Rail, Three Policies — NeMo Guardrails on the Retrieval Path
NeMo Guardrails drops a policy gate between retrieval and generation. One install, three per-arc configs — PII for Second Brain, style for LLM Wiki, code-safety for Autoresearch — and a 15-query benchmark: 100% block recall, 100% clean pass. Rails are scaffolding; detectors are the content.
uses fieldkit.rag
Bigger Generator, Same Grounding — 8B vs 49B vs 70B on One Retrieval Chain
The rerank-and-fusion article bet that a bigger generator would heal the 8B Google-IPO refusal. Ran the A/B across three sizes on one retrieval chain. Bet lost: Nemotron-Super-49B over-refuses the 8B baseline; Llama 3.3 70B narrows the gap, not closes it. The refusal was the scaffold working.
uses fieldkit.rag
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
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
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.
Gates Before the Advisor — Recall Floors, Raw-Base Preflights, and the Bench That Ate Its Own Spec
Before the Advisor trained: a 182-source corpus pack with recall gates on two retrieval lanes (BM25 and live pgvector + NIM embedder), raw-base preflights that failed two NVIDIA bases honestly, and the rebuild that caught the bench's own spec contaminating its retrieval context.
RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation — Spark reproduction notes
Reproducing the RaguTeam SemEval-2026 T8 winning system on a DGX Spark — judge-orchestrated 7-LLM ensemble (Qwen3-4B-FP8 + Meno-Lite-0.1 7B local + remote members) with Qwen3-32B judge, then extracting the pattern into `fieldkit.ensemble` + `fieldkit.judge`.
Synthetic Corpus Frameworks on the Spark — From a Bespoke Pipeline to an Orchestration Layer
A bespoke synth pipeline got 200 rows into a 5000-row reasoning corpus before a fourth meta-state surface form forced a retreat. The diagnosis: a regex-floor approach cannot catch novel surface forms by construction. The fix is the open-source orchestration layer.