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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.

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Methods

The upsert method enables you to insert or update vectors in the index. You can perform upsert operations in three ways: using a vector object, a tuple, or a dictionary.

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Upsert Via Vector Object

import random

from upstash_vector import Index, Vector

index = Index.from_env()

dimension = 128  # Adjust based on your index's dimension
upsert_amount = 100

vectors = [
    Vector(
        id=f"generated-id-{i}",
        vector=[random.random() for _ in range(dimension)],
        metadata={"some_field": f"some_value-{i}"},
        data=f"some-unstructured-data-{i}",
    )
    for i in range(upsert_amount)
]

index.upsert(vectors=vectors)

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Upsert Via Tuple

import random

from upstash_vector import Index

index = Index.from_env()

dimension = 128  # Adjust based on your index's dimension
upsert_amount = 100

vectors = [
    (
        f"generated-id-{i}",
        [random.random() for _ in range(dimension)],
        {"some_field": f"some_value-{i}"},
        f"some-unstructured-data-{i}",
    )
    for i in range(upsert_amount)
]

index.upsert(vectors=vectors)

​
Upsert Via Dictionary

import random

from upstash_vector import Index

index = Index.from_env()

dimension = 128  # Adjust based on your index's dimension
upsert_amount = 100

vectors = [
    {
        "id": f"generated-id-{i}",
        "vector": [random.random() for _ in range(dimension)],
        "metadata": {"some_field": f"some_value-{i}"},
        "data": f"some-unstructured-data-{i}",
    }
    for i in range(upsert_amount)
]

index.upsert(vectors=vectors)
Also, you can specify a namespace to operate on. When no namespace is provided, the default namespace will be used. 
index.upsert(..., namespace="ns")