Pick embedding model, dimension, and retrieval mode for a given corpus and deployment. Use when you need help with embedding picker.
Scanned 9/8/2026
Install to Claude Code
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---
name: embedding-picker
description: Pick embedding model, dimension, and retrieval mode for a given corpus and deployment. Use when you need help with embedding picker.
license: CC-BY-NC-SA-4.0
phase: 5
lesson: 22
metadata:
version: 1.0.0
tags: [nlp, embeddings, retrieval]
---
Given a corpus (size, languages, domain, avg length), deployment target (cloud / edge / on-prem), latency budget, and storage budget, output:
1. Model. Named checkpoint or API. One-sentence reason.
2. Dimension. Full / Matryoshka-truncated / int8-quantized. Reason tied to storage budget.
3. Mode. Dense / sparse / multi-vector / hybrid. Reason.
4. Query prefix / template if required by the model card.
5. Evaluation plan. MTEB tasks relevant to domain + held-out domain eval with nDCG@10.
Refuse recommendations that truncate Matryoshka to <64 dims without domain validation. Refuse ColBERTv2 for corpora under 10k passages (overhead not justified). Flag long-document corpora (>8k tokens) routed to models with 512-token windows.
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Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
