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Documentation Index

Fetch the complete documentation index at: https://docs.cyborg.co/llms.txt

Use this file to discover all available pages before exploring further.

Get started with CyborgDB in minutes.
1

Get an API Key

To use CyborgDB, you need an API key. You can get an API key from the CyborgDB Admin Dashboard. For more info, follow this guide.Make sure to keep your API key secure and do not share it publicly.
2

Install CyborgDB

Install CyborgDB on your machine:
# 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]
You will need to replace <token> with your token provided by Cyborg.
3

Create a Client

Create a CyborgDB client:
import cyborgdb_core as cyborgdb
import secrets

# Using `memory` storage for this example
# `redis` and `postgres` are also supported

index_location = cyborgdb.DBConfig("memory")  # Where encrypted index is stored (for queries)
config_location = cyborgdb.DBConfig("memory") # Where encrypted index config is stored (for config/loading)
items_location = cyborgdb.DBConfig("memory")  # Where item contents are stored (for upsert/get)

# Get your API key
api_key = "your_api_key_here"  # Replace with your actual API key

# Create a client
client = cyborgdb.Client(
    api_key=api_key, 
    index_location=index_location, 
    config_location=config_location, 
    items_location=items_location
)
For more info, refer to Create a Client.
4

Create an Encrypted Index

Create an encrypted index with 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
)
For more info, refer to Create an Encrypted Index.
5

Add Items to Encrypted Index

Add data to the encrypted index via Upsert:
# ... 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.upsert(items)
For more info, refer to Add Items.
6

Query Encrypted Index

Query the encrypted index for similar vectors.
# ... 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)

# Print the results
for result in results:
    print(f"ID: {result['id']}, Distance: {result['distance']}")
For more info, refer to Query an Encrypted Index.
7

Retrieve Items from Encrypted Index

Retrieve data from the encrypted index:
# ... Continuing from the previous step

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

# Print the items
for item in items:
    print(f"ID: {item['id']}, Vector: {item['vector']}, Contents: {item['contents']}")
For more info, refer to Get Items.
8

Next Steps

Learn more about CyborgDB:

Python API Reference

Explore the Python API reference to learn how to use CyborgDB in your applications.

C++ API Reference

Explore the C++ API reference to learn how to use CyborgDB in your applications.