CyborgDB Embedded Quickstart

# Install CyborgDB:
pip install cyborgdb-core

# For automatic embedding generation, install with:
pip install cyborgdb-core[embeddings]

# For LangChain integration, install with:
pip install cyborgdb-core[langchain]

# Or with all extras:
pip install cyborgdb-core[all]

import cyborgdb_core as cyborgdb
import secrets

# Backing store: disk (local, RocksDB-backed) for simple persistent storage.
# Use .memory() for ephemeral storage or .s3("bucket") for cloud persistence.
client = cyborgdb.Client(storage_config=cyborgdb.StorageConfig.disk("/tmp/cyborgdb"))

# ... Continuing from the previous step

# Generate an encryption key for the index
index_key = secrets.token_bytes(32)

# Create an encrypted index
index = client.create_index(
    index_name="my_index", 
    index_key=index_key
)

# ... Continuing from the previous step

# Add items 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": "Bonjour!"},
    {"id": "item_3", "vector": [0.9, 0.10, 0.11, 0.12], "contents": "Hola!"}
]

# index_key must be supplied on every data operation
index.upsert(items, index_key=index_key)

# ... Continuing from the previous step

# Query the encrypted index
query_vectors = [0.1, 0.2, 0.3, 0.4]
results = index.query(query_vectors=query_vectors, include=["distance"], index_key=index_key)

# Print the results
for result in results:
    print(f"ID: {result['id']}, Distance: {result['distance']}")

# ... Continuing from the previous step

# Retrieve items from the encrypted index
ids = ["item_1", "item_2", "item_3"]
items = index.get(ids, index_key=index_key)

# Print the items (contents are returned as bytes)
for item in items:
    print(f"ID: {item['id']}, Vector: {item['vector']}, Contents: {item['contents']}")