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, items: List[Dict[str, Any]])

Parameters

Parameter	Type	Description
items	List[Dict[str, Any]]	A list of dictionaries, where each dictionary has the format {"id": str, "vector": List[float], "contents": Union[bytes, str], "metadata": Dict[str, str]}.

Where each item dictionary has the following fields:

Parameter	Type	Description
id	str	Unique string identifier for the item.
vector	List[float]	Embedding vector as a list of floats (optional if index is configured to compute embeddings).
contents	bytes, str or Image	Item contents (optional). Must be a str or Image if index is configured to compute embeddings.
metadata	Dict[str, any]	Dictionary of key-value metadata pairs associated with the vector (optional).

If embedding auto-generation is enabled (by setting the embedding_model parameter in create_index()), then the vector parameter is optional. If vector provided, then it will be used (its dimensionality must match that of the embedding_model). If vector is not provided, a vector embedding will be auto-generated from contents using sentence-transformers. contents must be text in this case.

For more info on metadata, see Metadata Filtering.

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": "item_1", "vector": [0.1, 0.2, 0.3]}])

# Upsert with metadata
index.upsert([
  {"id": "item_1",
   "vector": [0.1, 0.2, 0.3],
   "metadata": {"type": "dog", "temperament": "good boy"}
  }
])

# Batch upsert
index.upsert([
    {"id": "item_1", "vector": [0.1, 0.2, 0.3]},
    {"id": "item_2", "vector": [0.4, 0.5, 0.6]}
])

# Upsert with items
index.upsert([
    {"id": "item_1", "vector": [0.1, 0.1, 0.1, 0.1], "contents": b'item_contents_here...'}, # Bytes
    {"id": "item_2", "vector": [0.2, 0.2, 0.2, 0.2], "contents": "item_contents_here..."} # Text
])

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. It is primarily intended for benchmarking purposes (hence the use of int for IDs).

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)