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]],
           *,
           index_key: bytes,
           user_id: bytes = None)

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, Any]}`.
`index_key`	`bytes`	(Required, keyword-only) 32-byte index KEK. See Index key.
`user_id`	`bytes`	(Optional, keyword-only) 16-byte RBAC user identifier. See Index key.

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` or `str`	Item contents (optional). Accepts strings or bytes; all are encoded to bytes and encrypted before storage. Must be a `str` if index is configured to compute embeddings.
`metadata`	`Dict[str, any]`	Dictionary of key-value metadata pairs associated with the vector (optional).

The contents field accepts strings or bytes. All contents are encoded to bytes and encrypted before storage, and will be returned as bytes when retrieved with get().

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 is generated in-process from contents using the index’s built-in model — no sentence-transformers or torch install required. contents must be a str 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.
Throws if the vectors could not be upserted.

EmbeddingModelUnavailable

Throws if contents need embedding and the index’s built-in model cannot be downloaded or loaded (offline cache miss, network failure). Often transient.

TypeError

Throws if the arguments do not match the expected types.

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]}], index_key=index_key)

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

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

# 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
], index_key=index_key)

Index key

index_key is required on every call. Pass the same 32-byte index KEK you used with create_index() / load_index(). For an RBAC user, pass the user’s KEK as index_key= together with their user_id=:

index.upsert(
    [{"id": "item_1", "vector": [0.1, 0.2, 0.3]}],
    index_key=user_kek,
    user_id=user_id,
)

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: Union[list[str], np.ndarray],
           vectors: np.ndarray,
           *,
           index_key: bytes,
           user_id: bytes = None)

Accepts a list of string IDs (or a NumPy array) and a NumPy array of vectors:

A list of strings or 1D array of identifiers for the unique IDs.
A 2D array of float32 values for the vector embeddings.

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

Parameters

Parameter	Type	Description
`ids`	`list[str]` or `np.ndarray`	A list of strings or 1D NumPy array of shape `(num_items,)` containing unique identifiers for each vector. Can be string or integer dtype. Length must match `vectors`.
`vectors`	`np.ndarray`	2D NumPy array of shape `(num_items, vector_dim)` with `dtype=float32`, 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.
Throws if the vectors could not be upserted.

TypeError

Throws if the arguments do not match the expected types.

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=np.float32)  # 2 vectors of dimension 3
ids = np.array(["item_101", "item_102"])  # Unique string IDs for each vector

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

Introduction

Client

Encrypted Index

Types & Getters

Parameters

Exceptions

Example Usage

Index key

Upsert Secondary Overload: NumPy Array Format

Parameters

Exceptions

Example Usage

​Parameters

​Exceptions

​Example Usage

​Index key

​Upsert Secondary Overload: NumPy Array Format

​Parameters

​Exceptions

​Example Usage

Parameters

Exceptions

Example Usage

Index key

Upsert Secondary Overload: NumPy Array Format

Parameters

Exceptions

Example Usage