> ## Documentation Index
> Fetch the complete documentation index at: https://cyborg-rid-of-c---conan.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# CyborgDB Embedded Quickstart

Get started with CyborgDB in minutes.

<Steps>
  <Step title="Install CyborgDB">
    Install CyborgDB on your machine:

    ```bash Python theme={null}
    # 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]
    ```
  </Step>

  <Step title="Create a Client">
    Create a CyborgDB client:

    ```python Python icon="python" theme={null}
    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"))
    ```

    <Note>
      Without an API key the client runs in free-tier mode, capped at 1,000,000 items per index. Pass a key from the [CyborgDB Admin Dashboard](https://cyborgdb.co) as the first argument (`cyborgdb.Client(api_key, storage_config=...)`) for unlimited usage. See [Get an API Key](../../../intro/get-api-key).
    </Note>

    For more info, refer to [Create a Client](../encrypted-indexes/create-client).
  </Step>

  <Step title="Create an Encrypted Index">
    Create an encrypted index with CyborgDB:

    ```python Python icon="python" theme={null}
    # ... 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](../encrypted-indexes/create-index).
  </Step>

  <Step title="Add Items to Encrypted Index">
    Add data to the encrypted index via Upsert:

    ```python Python icon="python" theme={null}
    # ... 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)
    ```

    For more info, refer to [Add Items](../data-operations/add-items).
  </Step>

  <Step title="Query Encrypted Index">
    Query the encrypted index for similar vectors.

    ```python Python icon="python" theme={null}
    # ... 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']}")
    ```

    For more info, refer to [Query an Encrypted Index](../data-operations/query).
  </Step>

  <Step title="Retrieve Items from Encrypted Index">
    Retrieve data from the encrypted index:

    ```python Python icon="python" theme={null}
    # ... 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']}")
    ```

    For more info, refer to [Get Items](../data-operations/get-items).
  </Step>

  <Step title="Next Steps">
    Learn more about CyborgDB:

    <CardGroup>
      <Card title="Python API Reference" href="../../python/introduction" icon="python">
        Explore the Python API reference to learn how to use CyborgDB in your applications.
      </Card>
    </CardGroup>
  </Step>
</Steps>