Qdrant Model Migration

QdrantOfficialPublished by Qdrant

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Cloud and DevOps
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From the author

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.

— the description by Qdrant

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to updating or changing embedding models within Qdrant collections. It covers strategies for zero-downtime migrations, side-by-side model testing, and hybrid search implementation. The guide distinguishes between version-specific capabilities—such as adding named vector fields in newer versions versus recreating collections in older ones—and offers best practices for data backfilling, dual writes, and alias management to ensure system stability during transitions.

What it does

Outlines zero-downtime migration workflows for updating embedding models.
Explains how to add new vector fields to existing collections on Qdrant v1.18+.
Provides strategies for A/B testing multiple models simultaneously.
Details the process for migrating from dense-only to hybrid search configurations.
Offers guidance on bulk re-embedding and data backfilling performance.
Defines safety protocols for alias swapping and rollback preparation.

When to use it

When you need to switch embedding models without taking your application offline.
When upgrading to a model with different dimensions or a different provider.
When implementing A/B testing for two different embedding models.
When adding sparse vectors or BM25 full-text search to an existing collection.
When you need to validate a new model's quality before fully committing to the switch.

Advantages

Minimizes production downtime through dual-write and alias-swap strategies.
Leverages version-specific features to avoid unnecessary full collection recreations.
Provides clear decision trees for handling different Qdrant server versions.
Includes performance optimization tips for large-scale data migrations.
Reduces risk by emphasizing validation and rollback observation periods.

Usage examples

“How can I switch my Qdrant collection to a new embedding model without downtime?”: The agent will guide you through setting up dual writes to your new collection or adding a new named vector field to your existing collection, followed by a backfill and final alias swap.
“Can I test a new embedding model alongside my current one in Qdrant?”: The agent will explain how to add a second named vector field to your collection (v1.18+) or create a new collection with both fields, allowing you to query both models for comparison.
“I need to add BM25 search to my existing dense vector collection, what is the best way?”: The agent will recommend using a full-text index on v1.20+ if your text is in the payload, or explain the process of adding a sparse vector field if you are on an older version.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-model-migration/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip qdrant-model-migration.zip -d .agents/skills

Documentation by Qdrant

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

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), or switching to a model with a different dimension count.

You do NOT need to re-embed existing dense vectors if:

Adding BM25 for hybrid search on v1.20+: no vectors to generate. Create a text payload index with scoring enabled on the payload field; Qdrant builds it from the stored payload BM25 scoring
Adding sparse vectors for hybrid search: generate only the sparse vectors. On v1.18+, add the sparse field to the existing collection and backfill it with UpdateVectors. On v1.17 or earlier, copy the dense vectors into the new collection instead of recomputing them Update vectors
Using Matryoshka models: use the dimensions parameter to output lower-dimensional embeddings (some recall loss, good for 100M+ datasets)
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.

Enable dual writes before backfill, preserving point IDs. Retry and reconcile failed writes to either destination Migration workflow
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 vector schema
- Write both embeddings on incoming upserts; backfill only the new field with UpdateVectors. Pause updates/deletes to existing points and drain in-flight operations first, or implement conflict handling to prevent stale backfill Named-vector migration
- After validating completeness and quality, switch the query embedding model and using together; an alias cannot select a vector field
If the cluster is v1.17 or earlier OR the collection doesn't have named vectors:
- Create a new collection with the new model's dimensions and distance metric
- Dual-write live upserts to both collections; backfill embeddings and payloads with update_mode: insert_only (v1.17+) to preserve points already written live. Pause deletes/partial updates or reconcile them so backfill cannot resurrect deleted data Blue-green migration
- Point your application at a collection alias instead of a direct collection name
- Validate completeness and quality before swapping the alias; coordinate the query-model change so requests use the matching vector space Switch collection

Keep dual writes through an observation period for rollback. Retire old vectors or collections only after all readers switch and rollback is no longer needed. Aliases redirect requests; they do not copy payloads.

Need Both Models Live (Side-by-Side)

Use when: A/B testing models, multi-modal (dense + sparse), or evaluating a new model before committing.

For a live collection, apply the write-consistency and cutover safeguards in Need Zero Downtime.

If the cluster is v1.18 or later:
- Add the new vector field directly to the existing collection Update vector schema
- Backfill new model embeddings incrementally using UpdateVectors Update vectors
If the cluster is v1.17 or earlier: You cannot add a named vector to an existing collection. Create a new collection with both vector fields defined upfront:
- Create new collection with old and new named vectors both defined Collection with multiple vectors
- Migrate data from old collection, preserving existing vectors in the old named field
- Backfill new model embeddings incrementally using UpdateVectors Update vectors
- Compare quality by querying with using: "old_model" vs using: "new_model"
- Swap alias to new collection once satisfied

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.

Dense to Hybrid Search Migration

Use when: adding sparse/BM25 vectors to an existing dense-only collection. Most common migration pattern.

If the cluster is v1.20 or later and the text is in the payload, prefer BM25 on a full-text index over sparse vectors: create a text index with scoring enabled (plus the language's stemmer and stopwords) on the text field and add a text query prefetch next to the dense one. It needs no schema change, no vector backfill, and no avg_len, and works on a collection of any age. Re-indexing a large collection can take a while BM25 on a full-text index
Use the sparse vector options below on v1.19 or earlier, or when the text isn't stored in the payload.
If the cluster is v1.18 or later, add the sparse vector field directly, even if the dense vector is unnamed Update vector schema
- Generate sparse vectors for existing points and backfill with UpdateVectors; existing dense vectors stay as they are Update vectors
If the cluster is v1.17 or earlier, you cannot add sparse vectors to an existing collection. Recreate it:
- Create new collection with both dense and sparse vector configs defined
- Scroll the old collection with with_vectors=True to copy the dense vectors, and generate only the sparse vectors
- Migrate payloads, swap alias

Sparse vectors and full-text BM25 statistics 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.

Re-embedding Is Too Slow

Use when: dataset is large and re-embedding is the bottleneck.

Use update_mode: insert_only (v1.17+) for backfill into a new collection; it skips existing destination points, so it is not a replacement for UpdateVectors when adding embeddings to existing points Update mode
Scroll the old collection with with_vectors=False, re-embed in batches, upsert into new collection
Upload in parallel batches (64-256 points per request, 2-4 parallel streams) Bulk upload
For Qdrant Cloud inference, switching models is a config change, not a pipeline change Inference docs

For 400GB+ datasets, expect days. For small datasets (<25MB), re-indexing from source is faster than using the migration tool.

What NOT to Do

Assume you can add named vectors to an existing collection on v1.17 or earlier servers; check your server version first
Delete old vectors or collections before validation, reader cutover, and the rollback observation period
Assume dual writes or insert_only alone resolve concurrent deletes and partial updates
Forget to update the query embedding model in your application code
Skip payload migration when using alias swap (aliases redirect requests, they do not copy data)
Keep ColBERT vectors co-located with dense vectors during a long migration (I/O cost degrades all queries)
Migrate to hybrid search without testing BM25 quality at chunk level

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

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qdrant/skills · skills/qdrant-model-migration · commit 9f55f6a

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