Upsert

Adds or updates vector embeddings in the index. Accepts a list of dictionaries, where each dictionary represents a vector with its ID.

def upsert(self, vectors: List[Dict[str, Union[int, List[float], bytes, Dict[str, Union[str, int, bool, float]]]]])

Parameters

Parameter	Type	Description
vectors	List[Dict[str, Union[int, List[float], bytes, Dict[str, Union[str, int, bool, float]]]]]	A list of dictionaries, where each dictionary has the format {"id": int, "vector": List[float], "item": bytes}.

Where each vector dictionary has the following fields:

• id (int): Unique integer identifier for the vector.
• vector (List[float]): Embedding vector as a list of floats.
• item (bytes): Item contents in bytes (optional)

Exceptions

ValueError

• Throws if the vector dimensions are incompatible with the index configuration.
• Throws if the index was not created or loaded yet.

RuntimeError

• Throws if the vectors could not be upserted.

Example Usage

# Initial configuration already done...

# Load Index
index = client.load_index(index_name=index_name, index_key=index_key)

# Single upsert (wrapped in a list)
index.upsert([{"id": 1, "vector": [0.1, 0.2, 0.3]}])

# Batch upsert
index.upsert([
    {"id": 1, "vector": [0.1, 0.2, 0.3]},
    {"id": 2, "vector": [0.4, 0.5, 0.6]}
])

# Upsert with items
index.upsert([
    {"id": 1, "vector": [0.1, 0.1, 0.1, 0.1], "item": b'item_contents_here...'},
    {"id": 2, "vector": [0.2, 0.2, 0.2, 0.2], "item": b'item_contents_here...'}
])

You can pass the id and vector fields as tuples, if you wish to skip the dictionary. On big datasets, this can make a signficant difference in memory usage.

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Upsert Secondary Overload: NumPy Array Format

This format is optimal for large batches due to its memory efficiency and compatibility with batch processing optimizations.

def upsert(self, ids: np.ndarray, vectors: np.ndarray)

Accepts two NumPy arrays:

• A 2D array of floats for the vector embeddings.
• A 1D array of integers for the unique IDs.

This structure is suited for efficient handling of large batches, with type safety for IDs and embeddings.

Parameters

Parameter	Type	Description
ids	np.ndarray	1D NumPy array of shape (num_items,) with dtype=int, containing unique integer identifiers for each vector. Length must match vectors.
vectors	np.ndarray	2D NumPy array of shape (num_items, vector_dim) with dtype=float, representing vector embeddings.

Exceptions

ValueError

• Throws if the vector dimensions are incompatible with the index configuration.
• Throws if the index was not created or loaded yet.

RuntimeError

• Throws if the vectors could not be upserted.

Example Usage

import numpy as np

# Load index
index = client.load_index(index_name=index_name, index_key=index_key)

# NumPy-based upsert with two arrays
vectors = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=float)  # 2 vectors of dimension 3
ids = np.array([101, 102], dtype=int)  # Unique integer IDs for each vector

index.upsert(vectors=vectors, ids=ids)