Update VectorDB Connection

Endpoint

PUT /apiops/projects/{projectName}/vector-dbs/{vectorDbName}/

Authentication

Requires a Personal API Access Token.

Header

Authorization: Bearer YOUR_TOKEN

Request

Headers

Header	Value	Required
Authorization	Bearer {token}	Yes
Content-Type	application/json	Yes

Path Parameters

Parameter	Type	Required	Description
projectName	string	Yes	Project name
vectorDbName	string	Yes	Vector DB connection name (must exist)

Query Parameters

None

Request Body

Same shape as Create: the body must include "_class": "vector-db" and a nested transport object with its own "family" discriminator. If name is blank in the body it falls back to vectorDbName from the path; otherwise the two must match.

To preserve a stored secret, omit it or send it blank. To rotate a secret, send the new plaintext value.

Full JSON Body Example - JDBC transport, preserving the stored password

{
  "_class": "vector-db",
  "name": "pgvector-primary",
  "description": "Primary pgvector store for RAG (updated)",
  "enabled": true,
  "dbType": "PGVECTOR",
  "defaultCollectionName": "documents",
  "embeddingDimension": 1536,
  "distanceMetric": "COSINE",
  "maxBatchSize": 100,
  "transport": {
    "family": "jdbc",
    "connectTimeoutMs": 5000,
    "requestTimeoutMs": 30000,
    "jdbcUrl": "jdbc:postgresql://db.internal:5432/vectors",
    "username": "apinizer",
    "password": "",
    "pool": {
      "minimumIdle": 2,
      "maximumPoolSize": 10
    }
  }
}

Full JSON Body Example - HTTP transport, rotating the apiKey

{
  "_class": "vector-db",
  "name": "qdrant-cloud",
  "description": "Qdrant Cloud cluster",
  "enabled": true,
  "dbType": "QDRANT",
  "defaultCollectionName": "documents",
  "embeddingDimension": 1536,
  "distanceMetric": "COSINE",
  "maxBatchSize": 100,
  "transport": {
    "family": "http",
    "endpoint": "https://xyz.eu-central.aws.cloud.qdrant.io:6333",
    "apiVersion": "v1",
    "authScheme": "API_KEY_HEADER",
    "authHeaderName": "api-key",
    "apiKey": "new-rotated-qdrant-key",
    "regionCode": "eu-central"
  }
}

Request Body Fields

The fields are identical to Create VectorDB Connection. The only behavioral difference is secret handling:

Field	Type	Required	Default	Description
_class	string	Yes	-	Connection subtype discriminator — must be "vector-db"
name	string	No	path value	Connection name — must match vectorDbName if provided; falls back to the path value when blank
description	string	No	-	Free-text description
enabled	boolean	No	true	Whether the connection is enabled
dbType	enum	Yes	-	Vector store type (PGVECTOR, QDRANT, WEAVIATE, MILVUS, CHROMA, PINECONE, REDIS, OTHER)
defaultCollectionName	string	No	-	Default collection / index / table name
embeddingDimension	integer	No	-	Embedding vector dimension
distanceMetric	enum	No	vendor default	COSINE, EUCLIDEAN, DOT_PRODUCT — filled from dbType when omitted
maxBatchSize	integer	No	vendor default	Maximum upsert/query batch size — filled from dbType when omitted
transport	object	Yes	-	Polymorphic transport sub-document (jdbc / http / redis) — see Create for full transport field tables
transport.password / transport.apiKey / transport.apiSecret	string	No	(preserved)	Secret fields — a blank/omitted value preserves the currently stored secret (INV-06); a non-blank value rotates it

Notes

• The connection must already exist; updating a non-existent name returns 400 Bad Request.
• Changing dbType to a different transport family means the previously stored secret cannot be preserved (family mismatch) — supply the new credentials in full.

Response

Success Response (200 OK)

{
  "status": "SUCCESS",
  "deploymentResult": {
    "success": true
  }
}

Error Response (400 Bad Request)

{
  "status": "FAILURE",
  "resultMessage": "Vector DB connection (name: pgvector-primary) was not found!"
}

or

{
  "status": "FAILURE",
  "resultMessage": "Vector DB connection name in path (pgvector-primary) does not match name in body (pgvector-2)!"
}

Error Response (401 Unauthorized)

{
  "status": "FAILURE",
  "resultMessage": "Token is not valid!"
}

cURL Example

curl -X PUT \
  "https://demo.apinizer.com/apiops/projects/MyProject/vector-dbs/pgvector-primary/" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "_class": "vector-db",
    "name": "pgvector-primary",
    "description": "Primary pgvector store for RAG (updated)",
    "enabled": true,
    "dbType": "PGVECTOR",
    "defaultCollectionName": "documents",
    "embeddingDimension": 1536,
    "distanceMetric": "COSINE",
    "maxBatchSize": 100,
    "transport": {
      "family": "jdbc",
      "jdbcUrl": "jdbc:postgresql://db.internal:5432/vectors",
      "username": "apinizer",
      "password": ""
    }
  }'

Notes and Warnings

• Blank-secret Preserve (INV-06): Sending a blank or omitted secret (JDBC password, HTTP apiKey/apiSecret, Redis password) keeps the currently stored, encrypted value — the read response already masks these, so you never need to re-send them just to make an unrelated edit.
• Rotate a Secret: Send the new plaintext value to replace the stored one.
• Family Change Loses Preservation: If you change dbType to a different transport family, blank-secret preservation does not apply — provide the new credentials in full.
• Discriminators: Keep "_class": "vector-db" at the top level and the matching transport.family value.
• Save = encrypt + deploy: A successful update re-encrypts changed secrets and redeploys the connection to the workers.

Related Documentation

• Create VectorDB Connection - Create or upsert a connection (full transport field tables)
• Get VectorDB Connection - Read a connection (masked secrets)
• VectorDB Connections API - Resource overview
• AI Gateway API - Full AI Gateway APIops surface