Apply this guide to your situation
This answer covers “[AI-015] Embeddings do not match the vector index dimensions”. Read it with the summary and confirm that the symptom and service match what you are experiencing. Available options can vary by plan and account setup.
- Before changing anything, note what you expected, what happened and roughly when. Check whether the issue affects one page or the whole service.
- Save the full error message and note recent changes. Hide passwords, access codes, personal data and keys before sharing screenshots or logs.
- If a step requires administrator access, restarting services or editing server files, stop if you do not administer that server. Send support the service name, error and checks you have already made.
L3 technical diagnosis
User symptoms
Insertion fails or semantic search quality drops after changing the embedding model.
Observed code or message
`dimension mismatch` / vector dimension expected N, got MLikely root cause
The index was created for a different dimension, normalisation or model. Changing only the query model leaves stored documents in another vector space.
Likely-cause and resolution matrix
Follow a branch only when evidence supports it. A symptom may have more than one cause; do not change layers by guesswork.
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Prerequisites
- Authorised access to the involved runtime, provider or automation.
- A test environment and synthetic data; never use customer prompts for diagnostics.
- A technical owner who can approve network, secret, GPU or resource changes.
Important warnings
- Do not expose local inference endpoints or webhooks without appropriate authentication, TLS and limits. Never put API keys, private prompts or personal data in commands, screenshots or logs.
- Commands are diagnostic references. Production changes, Docker/firewall rules and credential rotation require service-owner authorisation.
Step-by-step walkthrough
1. Confirm the symptom and scope
curl -sS "${VECTOR_API}/collections/knowledge" -H "Authorization: Bearer ${VECTOR_TOKEN}" | jq '{name,config}'`dimension mismatch` / vector dimension expected N, got M
2. Correct the responsible component
3. Verify and close exposure
The check confirms the expected result without exposing services or data.
Troubleshooting & Solutions
`dimension mismatch` / vector dimension expected N, got M
Root cause: The index was created for a different dimension, normalisation or model. Changing only the query model leaves stored documents in another vector space.
Resolution: Use one versioned model per collection. To migrate, create a collection with correct dimensions/metric, re-embed the authorised corpus, compare canary queries and switch an alias with rollback available; never truncate vectors.
Rollback and recovery plan
Restore the last approved configuration, stop the affected workflow and revoke any exposed credential. If real data was processed through an unauthorised path, retain minimal redacted records and follow the internal incident-response process.
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