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Adds new vectors to the index or updates existing ones. Supports both dictionary format and separate arrays for IDs and vectors. 
# Option 1: Dictionary format
index.upsert(items)

# Option 2: Separate arrays  
index.upsert(ids, vectors)

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Parameters

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Option 1: Dictionary Format

ParameterTypeDefaultDescription
itemsList[Dict]-List of dictionaries containing vector data
Where each dictionary can contain: 
[
  {
    "id": str, # Unique identifier for the vector
    "vector": List[float], # The vector data
    "contents": str | bytes, # Optional content (accepts both strings and bytes). Bytes are base64-encoded, strings are passed directly. All contents are encrypted before storage.
    "metadata": Dict # Optional key-value pairs for filtering and retrieval
  },
  ...
]
The contents field accepts both strings and bytes. Bytes are automatically base64-encoded before encryption, while strings are passed as-is. Contents are returned in their original format (string or bytes) when retrieved with get().

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Option 2: Separate Arrays

ParameterTypeDefaultDescription
idsList[str]-List of unique vector identifiers
vectorsList[List[float]] or np.ndarray-Vector data as 2D array

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Returns

None

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Example Usage

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Dictionary Format

# Basic vector upsert
items = [
    {
        'id': 'doc1',
        'vector': [0.1, 0.2, 0.3, 0.4]
    },
    {
        'id': 'doc2', 
        'vector': [0.5, 0.6, 0.7, 0.8]
    }
]

index.upsert(items)

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Separate Arrays

import numpy as np

# Using separate arrays
ids = ['vec1', 'vec2', 'vec3']
vectors = np.random.rand(3, 128).astype(np.float32)

index.upsert(ids, vectors)