Index Stores

Index stores contains lightweight index metadata (i.e. additional state information created when building an index).

See the API Reference for more details.

Simple Index Store

By default, LlamaIndex uses a simple index store backed by an in-memory key-value store. They can be persisted to (and loaded from) disk by calling index_store.persist() (and SimpleIndexStore.from_persist_path(...) respectively).

MongoDB Index Store

Similarly to document stores, we can also use MongoDB as the storage backend of the index store.

from llama_index.storage.index_store.mongodb import MongoIndexStore
from llama_index.core import VectorStoreIndex

# create (or load) index store
index_store = MongoIndexStore.from_uri(uri="<mongodb+srv://...>")

# create storage context
storage_context = StorageContext.from_defaults(index_store=index_store)

# build index
index = VectorStoreIndex(nodes, storage_context=storage_context)

# or alternatively, load index
from llama_index.core import load_index_from_storage

index = load_index_from_storage(storage_context)

Under the hood, MongoIndexStore connects to a fixed MongoDB database and initializes new collections (or loads existing collections) for your index metadata.

Note: You can configure the db_name and namespace when instantiating MongoIndexStore, otherwise they default to db_name="db_docstore" and namespace="docstore".

Note that it’s not necessary to call storage_context.persist() (or index_store.persist()) when using an MongoIndexStore since data is persisted by default.

You can easily reconnect to your MongoDB collection and reload the index by re-initializing a MongoIndexStore with an existing db_name and collection_name.

A more complete example can be found here

Redis Index Store

We support Redis as an alternative document store backend that persists data as Node objects are ingested.

from llama_index.storage.index_store.redis import RedisIndexStore
from llama_index.core import VectorStoreIndex

# create (or load) docstore and add nodes
index_store = RedisIndexStore.from_host_and_port(
    host="127.0.0.1", port="6379", namespace="llama_index"
)

# create storage context
storage_context = StorageContext.from_defaults(index_store=index_store)

# build index
index = VectorStoreIndex(nodes, storage_context=storage_context)

# or alternatively, load index
from llama_index.core import load_index_from_storage

index = load_index_from_storage(storage_context)

Under the hood, RedisIndexStore connects to a redis database and adds your nodes to a namespace stored under {namespace}/index.

Note: You can configure the namespace when instantiating RedisIndexStore, otherwise it defaults namespace="index_store".

You can easily reconnect to your Redis client and reload the index by re-initializing a RedisIndexStore with an existing host, port, and namespace.

A more complete example can be found here