Pinecone diagnosis: upsert rejected, vector dimension vs index dimension

TL;DR: Symptom: upsert fails saying the vector dimension does not match the index. Confirm both sides with describe_index and one embedding, then fix the model or recreate the index. Index dimension is frozen at creation; the model is the moving part. Confirm it 1.

The fix

1. Print len() of one embedding straight from your model.
2. Run pc.describe_index(name) and read dimension.
3. Check whether the model changed recently (dependency bump, config edit) or the index was created for a different model.
4. If the model is correct: create a new index with the right dimension and re-ingest everything.
5. If the index is correct: pin the embedding model back to the one the index was built for.
6. Never truncate or pad vectors to fit.

When to use this

• You are seeing this exact error message; match the block above, not just part of it.
• The failing call matches the scenario in the title: Pinecone diagnosis.
• You want the fastest verified fix before digging through logs.

When not to use this

• Your error text differs from the block above; close cousins often have different causes.
• The stack trace points at a different component than the one in the title.
• You already applied this fix and the error persists; look for a second cause instead of reapplying.

Compatibility

• Not pinned to a specific version; follows current Pinecone behavior.

Pinecone's index guides fix dimension at creation time against the embedding model; the troubleshooting pages cover upsert numeric handling. Diagnosis pattern follows the docs' support-evidence guidance. Verified against live docs 2026-09-26.