Upsert Pinecone Vectors: Pinecone action

Inputs

The fields this action accepts.

Every field can be mapped from an upstream trigger, AI step, table row, or hard-coded literal.

Field	Type	Required	Description
Namespace namespace	string	Optional	Partition within the index
Vectors (JSON) vectors	string	Required	Array of vectors: [{"id": "doc1", "values": [0.1, 0.2, ...], "metadata": {"text": "..."}}]

Sample request

{
  "namespace": "e.g. default",
  "vectors": "[{\"id\": \"doc1\", \"values\": [0.1, 0.2, 0.3], \"metadata\": {\"source\": \"article\"}}]"
}

Returns

{
  "upsertedCount": 1
}

Use these fields in downstream nodes for routing, logging, or error handling.

FAQ

Questions about Upsert Pinecone Vectors.

What does the Upsert Pinecone Vectors action do in Pinecone?

Batch-inserts up to 100 vectors with metadata and namespace. For bulk ingestion >100, pre-batch upstream. The standard "embed content → index in Pinecone" step.

What inputs does Upsert Pinecone Vectors require?

Required: Vectors (JSON). Every input accepts a static value or a variable from any upstream node in your workflow.

Can I use dynamic inputs from earlier workflow nodes?

Yes. Any field on this action can pull values from upstream nodes, whether that's a form response, a trigger payload, an AI output, or a lookup result.

What happens if Pinecone returns an error?

The workflow pauses on the failed node, the error message is captured in the run log, and you can retry the run with one click. Auto-retry policies are configurable per workflow with exponential backoff up to 5 attempts.

Does Upsert Pinecone Vectors support batch operations?

Yes. Run Upsert Pinecone Vectors inside a Loop node to process arrays. Tiny Command handles Pinecone's rate limits automatically so you don't have to throttle manually.

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