qdrant-model-migration

# 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. - Understand collection aliases before choosing a strategy [Collection aliases](https://skills.qdrant.tech/md/documentation/manage-data/collections/?s=collection-aliases) ## 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](https://skills.qdrant.tech/md/documentation/manage-data/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: - Add the new vector field directly to the existing collection [Update ve...