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Changing an embedding model changes the coordinate system of your vectors. A safe migration therefore requires regenerating embeddings from the original content, writing them to a compatible index or vector field, switching query-time embedding at the same time, and keeping a rollback path until the new retrieval results are proven.
What changes when you replace an embedding model?
An embedding model maps text or other source data into a numerical vector space. A different model—or a changed model configuration, chunking rule, preprocessing pipeline, distance metric, or dimension—defines a different space. An old vector and a new vector may both be arrays of floating-point numbers, but their coordinates do not have the same meaning.
That makes a model change a data migration, not a configuration toggle. Every stored document or chunk that must be retrieved needs a new embedding, and every search query must be embedded with that same model and configuration. Mixing old document vectors with new query vectors, or querying a new vector field with an old query embedding, produces invalid or misleading similarity results. Some systems reject the request outright when the vector length is wrong; the Pinecone SDK documentation documents this dimension check.
The original content is essential. A vector is not a reversible copy of the source text. You need the raw text, records, or another authoritative representation from which to generate the new embedding. The Qdrant migration tutorial illustrates rebuilding vectors by reading text from each point’s payload.
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Prerequisites and decisions to make first
Do not start a backfill until the migration contract is written down. Record the following for the current and proposed systems:
- Database product, deployment type, exact version, SDK version, collection or index name, and schema.
- Current embedding provider, model identifier, revision if applicable, input limits, output dimensions, normalization behavior, and any instruction or task prefix.
- Chunking, cleaning, language handling, metadata extraction, and preprocessing rules used before embedding.
- Distance function and any score transformation used by retrieval or reranking.
- Where source text lives: payload fields, object properties, files, a document store, or another system of record.
- How inserts, updates, partial updates, and deletes reach the vector database, including retry and ordering behavior.
- Filters, namespaces, tenants, access-control metadata, and the application setting that selects the collection or vector field.
- A representative query set with relevance judgments or task-level success measures.
Also decide what “done” means before implementation. Examples include a complete and reconciled corpus, no increase in error rate, an acceptable latency budget, and a retrieval or downstream answer-quality threshold on the same evaluation queries used for the baseline.
Choose a migration architecture
The right pattern depends on database capabilities, allowed downtime, rollback requirements, and whether temporary or permanent duplicate vectors are acceptable.
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| Pattern | How it works | Advantages | Risks and costs | When it fits |
|---|---|---|---|---|
| Blue-green collections or indexes | Create a new collection or index with the new dimension and settings, dual-write changes, backfill existing records, validate, then switch an alias or application configuration. | Strong isolation, straightforward comparison, and clear rollback to the old collection. | Temporary duplicate payload and vector storage; deletes, updates, and partial updates must reach both targets. | General-purpose choice, especially when the database cannot safely alter an existing schema. |
| Additional named vector in one collection | Add a separately named vector, populate it for new writes, backfill old points, and direct queries to the new name. | Can avoid a second collection and may allow searches to continue during backfill. | Requires specific schema and version support; rollback is more application-dependent, and the extra vector may remain permanently. | Existing named-vector collections on a platform that documents this operation. |
| Alias-based cutover | Keep a stable logical alias while it points to the old target, then atomically or near-atomically repoint it to the new target. | Clients do not need a hard-coded physical collection name; rollback is a pointer change. | Alias semantics, write routing, and propagation behavior vary by database. | Production systems that support aliases and need a reversible switch. |
Blue-green migration
Blue-green is the safest default when support is uncertain. Build a new target with the new vector dimension, similarity function, payload schema, filters, and tenant layout. Keep the old target serving reads while the new one is populated. Once validation passes, switch the application or alias. The Qdrant guidance presents this as the route that works across collection types, and the Weaviate migration tutorial recommends aliases for a seamless, reversible production change.
Additional vector in one collection
This option is not a universal schema operation. Qdrant documents it for collections created with named vectors on version 1.18 or later; its tutorial was tested with Qdrant 1.19.0. In that documented flow, searches and inserts can continue while the new named vector is backfilled, but updates and deletes to existing points should be paused and drained to prevent stale vectors and failures.
Weaviate describes adding vectors and re-inserting objects to trigger vectorization, but warns that the added vector cannot be deleted in the described process. Treat the extra vector as continuing storage, not as a temporary field you can assume will disappear.
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Migration runbook
1. Inventory the live system and capture a baseline
Export the configuration and write-path behavior before changing anything. Include a sample of real queries that covers common traffic, long and short queries, difficult cases, supported languages, tenants, and filtered searches. Save the old model’s top results, scores, latency, and downstream task outcome where available.
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2. Freeze the embedding contract
Define one versioned embedding specification for the migration:
- Exact model and provider endpoint, including revision or deployment identifier.
- Input formatting, instruction text, truncation, normalization, and language settings.
- Chunking and overlap rules. If chunking changes, treat the operation as both a re-embedding and a corpus-shape migration.
- Output dimension and similarity metric.
- Which vector field or index receives the result.
Store this specification with each migration job and in the target index metadata. A query service must select the same specification used for the document vectors; changing only the document backfill is incomplete.
3. Create and test the target
For blue-green, create the new collection or index with the target dimension, metric, payload fields, filters, replication, and access controls. For a named-vector migration, add a distinct name rather than overwriting the old vector. Insert a small controlled sample and issue queries using embeddings generated by the new model. Verify dimension validation, filter behavior, score ordering, and authorization before launching a full backfill.
If embeddings are produced outside the database, confirm that the target accepts precomputed vectors. Weaviate documents this capability in its bring-your-own-vectors guide.
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4. Make new writes dual-write before backfilling
Route every new record and every source change to both old and new representations. The new write path should generate the new embedding from the current source content, while the existing path continues to keep the old system usable during the observation period.
Dual-write is necessary but not sufficient. Define an event or version identifier so an out-of-order retry cannot let an older source revision overwrite a newer one. Make target writes idempotent using stable record or chunk identifiers. Decide how partial updates behave: if a searchable text field changes, regenerate its vector; if only metadata changes, update payloads in both targets without unnecessarily re-embedding.
5. Backfill from the source of truth in bounded, resumable batches
Read records in bounded batches, retaining each stable identifier and the metadata needed by filters and authorization. Generate the new embedding from the original content, not by applying an unvalidated mathematical transformation to the old vector. Write the result to the new vector field or target and persist a checkpoint, cursor, or equivalent progress marker.
The backfill worker should be safe to stop and restart. Record the migration version, source revision, model specification, and write timestamp for each successful item. An idempotent upsert lets a retry safely repeat work. Qdrant’s example uses scrolling through points, preserves identifiers and payloads, and notes that progress can be persisted for resumption; its example batch size of 100 is an implementation illustration, not a performance guarantee.
Handle these cases explicitly rather than allowing a whole batch to fail:
- Changed source content: compare source revisions or timestamps and embed the newest revision.
- Deleted records: propagate tombstones or delete operations to the target; do not count a legitimately deleted item as a missing backfill.
- Empty or malformed content: quarantine it with an error reason and decide whether the old vector remains searchable.
- Provider throttling or transient failures: use bounded retries and backoff, and keep failed identifiers for replay.
- Partial batch failure: commit successful items individually or in a way that allows precise retry, rather than assuming the entire batch succeeded.
- Duplicate chunks: preserve deterministic identifiers so reruns do not create additional searchable copies.
6. Reconcile completeness and consistency
Compare the expected source population with the target using a reconciliation method that accounts for legitimate updates and deletions. A raw count is useful only when the corpus has no deletions and no concurrent changes; otherwise compare identifier sets, source revisions, and migration status.
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Check that every new write observed during backfill exists in the target with the latest revision. Review dead-letter records, retry queues, provider errors, and records whose source text was unavailable. For a named-vector migration, follow the database’s documented restrictions on updates and deletes during the backfill; pausing those operations without a drain-and-replay plan can create its own inconsistency.
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A populated target is not evidence of a better search system. Run the old and new configurations against the same representative queries and compare:
- Whether relevant documents appear in the top-k results.
- Changes in ranking, score distributions, and ties.
- Filtered and multi-tenant retrieval behavior.
- Latency, timeout, and error rates under realistic concurrency.
- Reranker inputs and downstream answer quality for RAG or other applications.
- Queries in every supported language and difficult or adversarial cases.
Confirm at the same time that the query service uses the new model, the expected output dimension, the intended metric, and the correct collection or named vector. A test that accidentally embeds queries with the old model can make a broken migration appear successful.
8. Cut over with an explicit rollback plan
Switch the alias or application setting only after completeness and quality gates pass. Keep the old collection or vector and its query configuration available, and continue synchronizing changes during a defined observation window. Monitor retrieval quality, latency, error rates, ingestion lag, and operational costs.
Write the rollback action before cutover: restore the old alias or configuration, route writes to the old representation, and verify that it contains all changes made since the switch. If dual writes stop, the old target will eventually become stale and cannot provide a complete rollback without replaying those changes.
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Set a retention date or operational condition for deleting the old collection or vector. Do not remove it merely because the backfill finished. Retire it only after the observation window, recovery review, quality checks, and access-control verification are complete. Preserve migration metadata and the final reconciliation report so a later incident can be explained and reproduced.
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Database-specific constraints
Qdrant
Qdrant’s embedding-model migration tutorial distinguishes two routes. Blue-green collections are the general option. The in-place additional-vector route requires a collection created with named vectors and Qdrant 1.18 or later; the published tutorial reports testing with 1.19.0. Its documented backfill allows searches and inserts to continue, but recommends pausing updates and deletes to existing points until the new vector is populated. Verify the deployed version and current documentation before relying on those behaviors.
Qdrant’s broader migration guidance lists possible sources such as Pinecone, Weaviate, Milvus/Zilliz, Elasticsearch, pgvector, and custom FAISS or ScaNN systems. That list does not mean the Qdrant procedure or tooling applies unchanged to each source; schema, export, filtering, and consistency work remains provider-specific.
Weaviate
The Weaviate migration tutorial specifies Weaviate Cloud version 1.32 or newer for its described process and recommends collection aliases for production because they provide a seamless, reversible switch. Its added-vector workflow leaves old and new vectors coexisting, and the tutorial notes that the added vector cannot be deleted through that process. Account for the resulting storage before choosing it.
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Dimension-enforcing indexes
Some indexes define vector length at creation time. The Pinecone SDK documentation shows that an upsert or query whose vector length does not match the index dimension is rejected. For any dimension-enforcing system, create a compatible target rather than trying to insert the new output into the old index, and update document and query embedding paths together.
Consistency, failure modes, and recovery
| Symptom | Likely cause | Recovery action |
|---|---|---|
| Dimension error on upsert or query | The model output and index or vector field have different lengths. | Inspect the deployed model configuration and target schema; route the operation to a matching target and reject incompatible traffic. |
| Results are populated but clearly irrelevant | Old and new models are mixed, preprocessing differs, or the wrong vector field is queried. | Log model and vector-field identifiers on every request, then rerun controlled queries with matching document and query specifications. |
| New records appear in one target only | Dual-write failure, asynchronous lag, or a write path that bypasses the migration layer. | Reconcile identifiers and source revisions, replay missing events idempotently, and block cutover until lag is zero or explicitly bounded. |
| Updates revert after backfill | An older backfill write arrived after a newer source update. | Use source revisions or event ordering, and reject writes older than the target’s recorded revision. |
| Deletes reappear | The backfill read a record before deletion and wrote it afterward. | Propagate tombstones, maintain a delete log during backfill, and replay deletes after the batch completes. |
| Quality improves for common queries but fails for a tenant or language | The baseline sample was not representative, or filtering and preprocessing differ by segment. | Expand evaluation by tenant, language, filter, and difficult-query class before promoting the new model. |
Cost and capacity planning
Estimate migration cost from the actual corpus rather than using a universal figure. Count source chunks, input tokens or characters accepted by the provider, retry volume, and the number of query and document embeddings generated during the overlap period. Include workers, queueing, network transfer, database write I/O, and any reranking or evaluation workloads.
Blue-green migration temporarily duplicates vectors and usually payload storage. An additional-vector approach may avoid a second payload copy but can leave an extra vector permanently, as described in Weaviate’s workflow. Size the target for peak backfill writes plus normal ingestion, and set provider and database rate limits so the migration cannot starve production traffic.
Batch sizes shown in vendor examples—100 in Qdrant’s example and 200 in a Weaviate code example—are illustrative parameters, not published throughput results. Tune batch size, concurrency, and checkpoint frequency against your own rate limits and recovery requirements.
Production checklist
- Source text or records are available for every item that must be re-embedded.
- Model, revision, preprocessing, dimension, metric, and vector-field names are versioned.
- A representative baseline query set and relevance measure are saved.
- The target schema has been tested with new document and query vectors.
- New writes, updates, partial updates, and deletes are synchronized to both representations.
- Backfill progress, source revisions, retries, and malformed records are observable and resumable.
- Completeness reconciliation distinguishes missing data from legitimate changes and deletions.
- Old and new retrieval are compared on the same queries, filters, languages, and tenants.
- Cutover, rollback, retention, and recovery ownership are documented.
- Storage, embedding, and operational costs are approved for the overlap period and the steady state.
When is the migration complete?
Completion is more than a full target index. It means the new representation contains the intended current corpus, all online writes use the agreed consistency rules, queries use the matching embedding contract, retrieval and downstream quality meet the decision threshold, and the team can restore the old path while its data is still current. Only then should the old vectors be treated as retired data rather than as an emergency recovery option.
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