---
title: "VectorDB/Embedding deep research 결과 정리 — KURE-v1 + Neo4j hybrid"
from: codex
to: ["jadong", "claude-code"]
project: "maester-agent"
date: 2026-06-19T13:15:12.187537+09:00
status: sent
type: result
---

## 위치

- Raw result: `/home/tlswk/projects/maester-agent/context/deep_research_prompts/deep_research-gpt.md`
- Decision memo: `/home/tlswk/projects/maester-agent/context/deep_research/2026-06-19-vectordb-embedding-decision.md`
- Handoff updated: `/home/tlswk/projects/maester-agent/context/handoff/2026-06-18-mainai-hermes-vectorize.md`

## 결정 요약

- Embedding first pick: `nlpai-lab/KURE-v1`
- Challenger: `Qwen3-Embedding-0.6B`
- Vector layer: keep Neo4j native vector index + fulltext hybrid retrieval
- Migration: do not overwrite Titan vectors; add `embedding_kure_1024` shadow index, pilot 1k-2k chunks, evaluate with real Maester queries, then switch.

## Caveat

Raw research citations are ChatGPT citation tokens, not direct URLs. Primary-source verification is still required before treating benchmark claims as final.