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)curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
-H "Content-Type: application/json" \
-H "X-PD-Environment: production" \
-H "Authorization: Bearer {access_token}" \
-d '{
"external_user_id": "{external_user_id}",
"id": "databricks_oauth-upsert-vector-search-index-data",
"configured_props": {
"databricks_oauth": { "authProvisionId": "apn_xxxxxxx" },
"endpointName": "Endpoint Name",
"indexName": "Index Name"
}
}'// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
new URL("https://remote.mcp.pipedream.net/v3"),
{
requestInit: {
headers: {
Authorization: `Bearer ${accessToken}`,
"x-pd-project-id": "{project_id}",
"x-pd-environment": "production",
"x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
"x-pd-app-slug": "databricks_oauth",
},
},
},
)
const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)
const { tools } = await mcp.listTools()
// listTools() hands your model this tool's input schema, so it can
// fill the arguments itself:
const result = await mcp.callTool({
name: "databricks_oauth-upsert-vector-search-index-data",
arguments: {
endpointName: "Endpoint Name",
indexName: "Index Name",
},
})SCHEMA
Inputs
Pipedream supplies the connected account. Your application provides the operation-specific values below. Dynamic inputs are resolved against that user's account.
| 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
- Source
- View on GitHub ↗