Metadata feature allows you to store context with your vectors to make a connection. There can be a couple of uses of this:

  1. You can put the source of the vector in the metadata to use in your application from the query response.
  2. You can put some metadata to further filter the results upon the query.

You can set metadata with your vector as follows:

from upstash_vector import Index

index = Index(
    url="UPSTASH_VECTOR_REST_URL",
    token="UPSTASH_VECTOR_REST_TOKEN",
)

index.upsert(
    [("id-0", [0.9215, 0.3897]), {"url": "https://imgur.com/z9AVZLb"}],
)

When you do a query or fetch, you can opt-in to retrieve the metadata as follows:

  • Query Example
from upstash_vector import Index

index = Index(
    url="UPSTASH_VECTOR_REST_URL",
    token="UPSTASH_VECTOR_REST_TOKEN",
)

index.query(
    [0.9215, 0.3897],
    top_k=5,
    include_metadata=True,
)
{
  "result": [
    {
      "id": "id-0",
      "score": 1,
      "metadata": {
        "url": "https://imgur.com/z9AVZLb"
      }
    },
    {
      "id": "id-3",
      "score": 0.99961007,
      "metadata": {
        "url": "https://imgur.com/zfOPmnI"
      }
    }
  ]
}

Also, you can filter the results further by providing a metadata filter:

from upstash_vector import Index

index = Index(
    url="UPSTASH_VECTOR_REST_URL",
    token="UPSTASH_VECTOR_REST_TOKEN",
)

index.query(
    [0.9215, 0.3897],
    top_k=5,
    include_metadata=True,
    filter="url GLOB '*imgur.com*'",
)

See Metadata Filtering documentation for more details.

  • Range Example
from upstash_vector import Index

index = Index(
    url="UPSTASH_VECTOR_REST_URL",
    token="UPSTASH_VECTOR_REST_TOKEN",
)

index.range(
    cursor="0",
    limit=3,
    include_metadata=True,
)
{
  "result": {
    "nextCursor": "4",
    "vectors": [
      { "id": "id-0", "metadata": { "url": "https://imgur.com/z9AVZLb" } },
      { "id": "id-1", "metadata": { "url": "https://imgur.com/a2nCEIt" } },
      { "id": "id-2", "metadata": { "url": "https://imgur.com/zfOPmnI" } }
    ]
  }
}