Skip to main content
POST
/
upsert-data
/
{namespace}
curl $UPSTASH_VECTOR_REST_URL/upsert-data \
  -X POST \
  -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN" \
  -d '[ 
    { "id": "id-0", "data": "Upstash is a serverless data platform.", "metadata": { "link": "upstash.com" } }, 
    { "id": "id-1", "data": "Upstash Vector is a serverless vector database." }
  ]'

{
    "result": "Success"
}

Documentation Index

Fetch the complete documentation index at: https://upstash.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

To use this endpoint, the index must be created with an embedding model.
Vector embedding of the raw text data will be upserted into the default namespace by default. You can use a different namespace by specifying it in the request path.

​
Request

You can either upsert a single data, or multiple data in an array.
​
id
string
required
The id of the vector.
​
data
string
required
The raw text data to embed and upsert.
​
metadata
Object
The metadata of the vector. This makes identifying vectors on retrieval easier and can be used to with filters on queries.
Data field of the vector will be automatically set to the raw text data, so that you can access it later, during queries.

​
Path

​
namespace
string
default:""
The namespace to use. When no namespace is specified, the default namespace will be used.

​
Response

​
result
string
"Success" string.
curl $UPSTASH_VECTOR_REST_URL/upsert-data \
  -X POST \
  -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN" \
  -d '[ 
    { "id": "id-0", "data": "Upstash is a serverless data platform.", "metadata": { "link": "upstash.com" } }, 
    { "id": "id-1", "data": "Upstash Vector is a serverless vector database." }
  ]'

{
    "result": "Success"
}