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Once you have created or loaded an encrypted index, you can add items to it. To do this, you can use the upsert() function: 
# Items to add to the encrypted index
items = [
    {"id": "item_1", "vector": [0.1, 0.2, 0.3, 0.4]},
    {"id": "item_2", "vector": [0.5, 0.6, 0.7, 0.8]},
    {"id": "item_3", "vector": [0.9, 0.10, 0.11, 0.12]}
]

# Add items to the encrypted index
index.upsert(items)
For more info on Array2D in C++, see the API Reference.

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Adding Items with Contents

It’s also possible to store item contents alongside vectors. To do this, include contents to the upsert() call. For Python, the contents field accepts both strings and bytes. For C++, the contents field only accepts bytes. All contents are encoded to bytes and encrypted before storage using the index key, and will be returned as bytes when retrieved with get(). 
# Items to add to the encrypted index
items = [
    {"id": "item_1", "vector": [0.1, 0.2, 0.3, 0.4], "contents": "Hello!"},
    {"id": "item_2", "vector": [0.5, 0.6, 0.7, 0.8], "contents": "World!"},
    {"id": "item_3", "vector": [0.9, 0.10, 0.11, 0.12], "contents": "Cyborg!"}
]

# Add items to the encrypted index
index.upsert(items)

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Adding Items with Metadata

CyborgDB also supports metadata storage, retrieval and filtering. To add metadata to an item, include metadata in the upsert() call. All metadata fields will be encrypted using the index key. 
# Items to add to the encrypted index
items = [
    {"id": "item_1", "vector": [0.1, 0.2, 0.3, 0.4], "metadata": {"name": "Alice", "age": 30}},
    {"id": "item_2", "vector": [0.5, 0.6, 0.7, 0.8], "metadata": {"name": "Bob", "age": 40}},
    {"id": "item_3", "vector": [0.9, 0.10, 0.11, 0.12], "metadata": {"name": "Charlie", "age": 50}}
]

# Add items to the encrypted index
index.upsert(items)
For more info on metadata storage and filtering, see Metadata Filtering.

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Automatic Embedding Generation

This feature is only available in Python. To use it, use pip install cyborgdb-core[embeddings] or pip install cyborgdb-core[embeddings]
If you provided an embedding_model during index creation, you can automatically generate embeddings for items by providing contents to the upsert() call:
Python
# ... index creation and embedding model setup
embedding_model = "all-MiniLM-L6-v2"
index = client.create_index(
    index_name="my_index", 
    index_key=index_key, 
    index_config=index_config, 
    embedding_model=embedding_model
)

# Items to add to the encrypted index
items = [
    {"id": "item_1", "contents": "Hello, World!"},
    {"id": "item_2", "contents": "Hello, Cyborg!"}
]

# Add items to the encrypted index
index.upsert(items)
In this example, the embedding_model will automatically generate embeddings for the contents field. Note that contents must be a string. It will also be converted to bytes for encrypted item storage. This feature uses sentence-transformers for embedding generation. You can use any model from the HuggingFace Model Hub that is compatible with sentence-transformers.

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API Reference

For more information on adding items to an encrypted index, refer to the API reference:

Python API Reference

API reference for upsert() in Python

C++ API Reference

API reference for Upsert() in C++