Cyborg Vector Search Quickstart

Get started with Cyborg Vector Search in minutes.

1. Install Cyborg Vector Search

   Install Cyborg Vector Search on your machine:

   Python

   # Ensure that Python 3.11 or 3.12 is installed
   # Or create a virtual environment with Python 3.11 or 3.12:
   conda create -n cyborg-env python=3.12
   
   # Activate the virtual environment:
   conda activate cyborg-env
   
   # Install Cyborg Vector Search:
   pip install cyborg_vector_search_py -i https://dl.cloudsmith.io/<token>/cyborg/cyborg-vector-search/python/simple/
   

   You will need to replace <token> with your token provided by Cyborg.
2. Create a Client

   Create a Cyborg Vector Search client:

   Python

   import cyborg_vector_search_py as cvs
   import secrets
   
   # Using `memory` storage for this example
   # `redis` and `postgres` are also supported
   
   index_location = cvs.DBConfig("memory")  # Where encrypted index is stored (for queries)
   config_location = cvs.DBConfig("memory") # Where encrypted index config is stored (for config/loading)
   items_location = cvs.DBConfig("memory")  # Where item contents are stored (for upsert/get)
   
   # Create a client
   client = cvs.Client(index_location, config_location, items_location)
   

   For more info, refer to Create a Client.

3. Create an Encrypted Index

   Create an encrypted index with Cyborg Vector Search:

   Python

   # ... Continuing from the previous step
   
   # Create an IVFFlat index config (can also be IVF/IVFPQ)
   # Using an example vector dimension of 128, and number of lists of 1024
   index_config = cvs.IndexIVFFlat(dimension=128, n_lists=1024)
   
   # Generate an encryption key for the index
   index_key = secrets.token_bytes(32)
   
   # Create an encrypted index
   index = client.create_index("my_index", index_key, index_config)
   

   For more info, refer to Create an Encrypted Index.

4. Add Items to Encrypted Index

   Add data to the encrypted index via Upsert:

   Python

   # ... Continuing from the previous step
   
   # Add items to the encrypted index
   items = [
       {"id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "item": "Hello!"},
       {"id": 2, "vector": [0.5, 0.6, 0.7, 0.8]}, "item": "Hello!",
       {"id": 3, "vector": [0.9, 0.10, 0.11, 0.12], "item": "Hello!"}
   ]
   
   index.upsert(items)
   

   For more info, refer to Add Items.

5. Query Encrypted Index

   Query the encrypted index for similar vectors.

   Python

   # ... Continuing from the previous step
   
   # Query the encrypted index
   query_vector = [0.1, 0.2, 0.3, 0.4]
   results = index.query(query_vector)
   
   # Print the results
   for result in results:
       print(f"ID: {result.id}, Distance: {result.distance}")
   

   For more info, refer to Query an Encrypted Index.

6. Retrieve Items from Encrypted Index

   Retrieve data from the encrypted index:

   Python

   # ... Continuing from the previous step
   
   # Retrieve items from the encrypted index
   ids = [1, 2, 3]
   items = index.get_items(ids)
   
   # Print the items
   for i in range(len(ids)):
       print(f"ID: {ids[i]}, Contents: {items[i]}")
   

   For more info, refer to Get Items.

7. Next Steps

   Learn more about Cyborg Vector Search:

   About Cyborg Vector Search

   Learn about the architecture, design and deployment of Cyborg Vector Search.

   API Docs

   Explore the API reference to learn how to use Cyborg Vector Search in your applications.