npx claudepluginhub qdrant/skills --plugin qdrantThis skill uses the workspace's default tool permissions.
Vectors from different models are incompatible. You cannot mix old and new embeddings in the same vector space. You also cannot add new named vector fields to an existing collection. All named vectors must be defined at collection creation time. Both migration strategies below require creating a new collection.
Guides vector database selection for embeddings and semantic search, compares managed options like Pinecone and self-hosted like pgvector/Milvus, explains ANN algorithms like HNSW.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
Engineers vector DB solutions with Pinecone, Weaviate, Qdrant, Milvus, pgvector for RAG, semantic search, recommendations, and embedding optimization.
Share bugs, ideas, or general feedback.
Vectors from different models are incompatible. You cannot mix old and new embeddings in the same vector space. You also cannot add new named vector fields to an existing collection. All named vectors must be defined at collection creation time. Both migration strategies below require creating a new collection.
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
Use when: production must stay available. Recommended for model replacement at scale.
Careful, the alias swap only redirects queries. Payloads must be re-uploaded separately.
Use when: A/B testing models, multi-modal (dense + sparse), or evaluating a new model before committing.
You cannot add a named vector to an existing collection. Create a new collection with both vector fields defined upfront:
UpdateVectors Update vectorsusing: "old_model" vs using: "new_model"Co-locating large multi-vectors (especially ColBERT) with dense vectors degrades ALL queries, even those only using dense. At millions of points, users report 13s latency dropping to 2s after removing ColBERT. Put large vectors on disk during side-by-side migration.
If you anticipate future model migrations, define both vector fields upfront at collection creation.
Use when: adding sparse/BM25 vectors to an existing dense-only collection. Most common migration pattern.
You cannot add sparse vectors to an existing dense-only collection. Must recreate:
Sparse vectors at chunk level have different TF-IDF characteristics than document level. Test retrieval quality after migration, especially for non-English text without stop-word removal.
Use when: dataset is large and re-embedding is the bottleneck.
update_mode: insert (v1.17+) for safe idempotent migration Update modewith_vectors=False, re-embed in batches, upsert into new collectionindexing_threshold_kb very high, restore after)For 400GB+ datasets, expect days. For small datasets (<25MB), re-indexing from source is faster than using the migration tool.