llama-index-storage-index-store-azure 0.1.2

llama-index index_store azure integration

LlamaIndex Index_Store Integration: Azure Table Storage

AzureIndexStore utilizes Azure Table Storage and Cosmos DB to provide an index storage solution for indexing documents or data.

Installation

Before using the AzureIndexStore, ensure you have Python installed and then proceed to install the required packages:

pip install llama-index-storage-index-store-azure
pip install azure-data-tables
pip install azure-identity  # Only needed for AAD token authentication

Initializing AzureIndexStore

AzureIndexStore can be initialized in several ways depending on the authentication method and the Azure service (Table Storage or Cosmos DB) you are using:

1. Using a Connection String

from llama_index.storage.index_store.azure import AzureIndexStore
from llama_index.storage.kvstore.azure.base import ServiceMode

store = AzureIndexStore.from_connection_string(
    connection_string="your_connection_string_here",
    namespace="your_namespace",
    service_mode=ServiceMode.STORAGE,  # or ServiceMode.COSMOS
)

2. Using Account Name and Key

store = AzureIndexStore.from_account_and_key(
    account_name="your_account_name",
    account_key="your_account_key",
    service_mode=ServiceMode.STORAGE,  # or ServiceMode.COSMOS
)

3. Using SAS Token

store = AzureIndexStore.from_sas_token(
    endpoint="your_endpoint",
    sas_token="your_sas_token",
    service_mode=ServiceMode.STORAGE,  # or ServiceMode.COSMOS
)

4. Using Azure Active Directory (AAD) Token

store = AzureIndexStore.from_aad_token(
    endpoint="your_endpoint",
    service_mode=ServiceMode.STORAGE,  # or ServiceMode.COSMOS
)

End-to-end example:

from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import VectorStoreIndex

reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
nodes = SentenceSplitter().get_nodes_from_documents(documents)

storage_context = StorageContext.from_defaults(
    index_store=AzureIndexStore.from_account_and_key(
        "your_account_name",
        "your_account_key",
        service_mode=ServiceMode.STORAGE,
    ),
)

storage_context.docstore.add_documents(nodes)

keyword_table_index = SimpleKeywordTableIndex(
    nodes, storage_context=storage_context
)

vector_index = VectorStoreIndex(nodes, storage_context=storage_context)