Nomic Embed Local RAG: Data Residency and Measured Status
In short: Nomic Embed is an open-weight text embedding model that converts your documents and queries into vectors entirely on local hardware, so the text you index for retrieval-augmented generation (RAG) never has to leave the machine.
Nomic Embed is an open-weight text embedding model that converts your documents and queries into vectors entirely on local hardware, so the text you index for retrieval-augmented generation (RAG) never has to leave the machine. That single property answers the privacy question directly: the embedding step, which in hosted pipelines ships raw text to a remote API, instead runs offline against weights sitting on your own disk. Whether a Nomic-Embed RAG stack is actually private is therefore decided not by the model name but by two operational facts you can audit yourself: data residency (what is written where) and log policy (what is retained and for how long).
What did Hax measure on its own stack?#
Reference numbers Hax measured directly on its own infrastructure (measured, sourced).
| Dataset item | Measured value | Date | Source |
|---|---|---|---|
| first_response_latency_ms | 120.8 ms | 2026-07-04 | bench_harness.probe_unified_latency |
| 저장된 메모리 수 | 10839 개 | 2026-07-23 | bench_harness.probe_curator (curator stats 실측) |
| 활성 메모리 수 | 10495 개 | 2026-07-23 | bench_harness.probe_curator (curator stats 실측) |
- 표본
- 3 measured metrics (Hax /data curated)
- 수집일
- 2026-07-04 ~ 2026-07-23
- 방법
- bench_harness.probe_unified_latency; bench_harness.probe_curator (curator stats 실측)
How can you reproduce these numbers?#
Follow the source column above and our open dataset at /data.
| metric | value | label |
|---|---|---|
| Curator stored memories | 10839 | measured 2026-07-23 |
| Curator active memories | 10495 | measured 2026-07-23 |
| Curator avg confidence | 0.616 | measured 2026-07-23 |
| Nomic Embed recall@5 | not measured / 측정대기 | estimated pending |
| Index disk footprint | not measured / 측정대기 | estimated pending |
Curator active memories = 10495개
What is local RAG data residency? It is the map of every artifact your pipeline writes: the embedding weights, the vector index, the original source chunks, and any cache. In a correctly configured Nomic-Embed stack all four live on local storage, and the only network calls are the ones you explicitly allow. The leak surface is narrow but real: model-download telemetry on first run, application logs that may echo query text, and any 'fallback to remote embedding' path. Judge a build by whether those three are disabled, not by the marketing claim of being local.
On our own side we do not yet have a measured Nomic-Embed retrieval benchmark, so recall@5 and index disk footprint are marked 측정대기 in the table above and any figure you see elsewhere should be treated as 추정/estimated. What we can report is measured operational drift from the same harness. Between 2026-07-04 and 2026-07-23, active memories grew from 8919 to 10495 (measured), while average curator confidence fell from 0.721 to 0.616 (measured) — a reminder that a growing local index needs pruning, not just insertion, or retrieval quality quietly decays.
Model specs commonly cited for Nomic Embed Text (768-dim output with Matryoshka truncation, an 8192-token context) are vendor-published and were not measured by Hax; treat them as 추정/estimated for your environment until you profile them.
FAQ. Does Nomic Embed phone home? The inference call does not require a network; first-run downloads and optional telemetry do, so pin weights and disable telemetry. Can I verify residency? Yes — inspect what the process writes and run it with the network cut. See internal operational evidence at Hax data.
Note: measured curator figures reflect the 2026-07-04 to 2026-07-23 window from bench_harness.probe_curator; Nomic Embed retrieval and footprint numbers remain 측정대기 and will be labeled measured only once benchmarked. Re-verify vendor specs against the current model card before relying on them.
Related reading: Nomic Embed 로컬 RAG, 클라우드 비용을 줄일까, 로컬 RAG 청킹 전략 — 문서 쪼개기가 검색 품질을 좌우한다
Full guide: 노트북에서 돌리는 AI 모델, 흔한 함정과 해결법
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