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qdrant-model-migration

Skill

Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dim

0Testing

Install

git clone https://github.com/qdrant/skills.git ~/.claude/skills/qdrant-model-migration

What is qdrant-model-migration?

Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~88 tokens of context used while enabled, before you invoke anything

Documentation

README · ~4 min read

What to Do When Changing Embedding Models

Vectors from different models are incompatible. You cannot mix old and new embeddings in the same vector space. On v1.18+, you can add or delete named vector fields on an existing collection — migration no longer always requires a new collection. On v1.17 or earlier, all named vectors must be defined at collection creation time.

Can I Avoid Re-embedding?

Use when: looking for shortcuts before committing to full migration.

You MUST re-embed if: changing model provider (OpenAI to Cohere), changing architecture (CLIP to BGE), incompatible dimension counts across different models, or adding sparse vectors to dense-only collection.

You CAN avoid re-embedding if: using Matryoshka models (use dimensions parameter to output lower-dimensional embeddings, learn linear transformation from sample data, some recall loss, good for 100M+ datasets). Or changing quantization (binary to scalar): Qdrant re-quantizes automatically. Quantization

Need Zero Downtime

Use when: production must stay available. Recommended for model replacement at scale.

  • If the cluster is v1.18 or later AND the collection has named vectors:

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