​
Metadata

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" } }
    ]
  }
}

​
Data

Data is another kind of information you can store per vector to attribute some context to it. Compared to metadata, it is not structured, and it can only be fetched in queries, not used to further filter them.

It is especially useful when you upsert raw text data, so that you would have access to the textual form of vector along with the embedded vector values.

It can save you from storing contextual information per vector in a separate database.

You can set both the metadata and data, or only one of them while upserting your vectors as follows:

from upstash_vector import Index

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

index.upsert(
    [
        {
            "id": "id-0",
            "vector": [0.9215, 0.3897],
            "metadata": {"url": "https://imgur.com/z9AVZLb"},
            "data": "data-0",
        },
        {
            "id": "id-1",
            "vector": [0.3897, 0.9215],
            "data": "data-1",
        },
    ],
)

When a raw text data is upserted, the data will be set to the raw text data automatically:

from upstash_vector import Index

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

index.upsert(
    [
        {
            "id": "id-2",
            "data": "Upstash is a serverless data platform.",
        },
    ],
)

from upstash_vector import Index

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

result = index.query(
    data="What is Upstash?",
    include_data=True,
)

for res in result:
    print(f"{res.id}: {res.data}")

Similar to metadata, the data field can be requested in queries, range iterator, and fetch requests, by setting the includeData to true as shown above.

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