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Databricks (Service Principal) ACTION

Upsert Vector Search Index Data

Upserts (inserts/updates) data into an existing vector search index. See the documentation
  • Action
  • Writes data
  • OAuth
  • SDK
  • MCP

IMPLEMENTATION

Call this tool

Connect a user's Databricks (Service Principal) account once, then configure and run Upsert Vector Search Index Data from your backend or agent.

import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const result = await pd.actions.run({
  id: "databricks_oauth-upsert-vector-search-index-data",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    databricks_oauth: { authProvisionId: "apn_xxxxxxx" },
    endpointName: "Endpoint Name",
    indexName: "Index Name",
  },
})

console.log(result)

SCHEMA

Inputs

Pipedream supplies the connected account. Your application provides the operation-specific values below. Dynamic inputs are resolved against that user's account.

Upsert Vector Search Index Data inputs
Property Type Description
endpointName Endpoint Name string
The name of the vector search endpoint
Required Dynamic
indexName Index Name string
The name of the vector search index
Required Dynamic
rows Rows to Upsert string
Array of rows to upsert. Each row should be a JSON object string. Example: [{ "id": "1", "text": "hello world", "text_vector": [0.1, 0.2, 0.3] }]
Required

REFERENCE

Tool details

Behavior hints are published with the component in the Pipedream registry and surface as MCP tool annotations, so an agent can reason about a tool before it calls it.

Registry key
databricks_oauth-upsert-vector-search-index-data
Version
0.0.1
App
Databricks (Service Principal)
Authentication
OAuth
Read-only
No
Destructive
No
Open world
Yes