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

Couchbase Index Store

Couchbase can be used as the storage backend for the index store.

from llama_index.storage.index_store.couchbase import CouchbaseIndexStore
from llama_index.core import VectorStoreIndex

from couchbase.cluster import Cluster
from couchbase.auth import PasswordAuthenticator
from couchbase.options import ClusterOptions
from datetime import timedelta

# create couchbase client
auth = PasswordAuthenticator("DB_USERNAME", "DB_PASSWORD")
options = ClusterOptions(authenticator=auth)

cluster = Cluster("couchbase://localhost", options)

# Wait until the cluster is ready for use.
cluster.wait_until_ready(timedelta(seconds=5))

# create (or load) docstore and add nodes
index_store = CouchbaseIndexStore.from_couchbase_client(
    client=cluster,
    bucket_name="llama-index",
    scope_name="_default",
    namespace="default",
)

# 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, CouchbaseIndexStore connects to a Couchbase operational database and adds your nodes to a collection named {namespace}_index in the specified {bucket_name} and {scope_name}.

Note: You can configure the namespace, bucket and scope when instantiating CouchbaseIndexStore. By default, the collection used is index_store_data. Apart from alphanumeric characters, -, _ and % are only allowed as part of the collection name. The store will automatically convert other special characters to _.

You can easily reconnect to your Couchbase client and reload the index by re-initializing a CouchbaseIndexStore with an existing client, bucket_name, scope_name and namespace.

Tablestore Index Store

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

from llama_index.storage.index_store.tablestore import TablestoreIndexStore
from llama_index.core import StorageContext, VectorStoreIndex

# create (or load) index store
index_store = TablestoreIndexStore.from_config(
    endpoint="<tablestore_end_point>",
    instance_name="<tablestore_instance_name>",
    access_key_id="<tablestore_access_key_id>",
    access_key_secret="<tablestore_access_key_secret>",
)

# 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, TablestoreIndexStore connects to a Tablestore database and adds your nodes to a table named under {namespace}_data.

Note: You can configure the namespace when instantiating TablestoreIndexStore.

You can easily reconnect to your Tablestore database and reload the index by re-initializing a TablestoreIndexStore with an existing endpoint, instance_name, access_key_id and access_key_secret.

A more complete example can be found here

Google AlloyDB Index Store

Similarly to document stores, we can also use AlloyDB as the storage backend of the index store. This tutorial demonstrates the synchronous interface. All synchronous methods have corresponding asynchronous methods.

pip install llama-index
pip install llama-index-alloydb-pg
pip install llama-index-llms-vertex

from llama_index_alloydb_pg import AlloyDBEngine, AlloyDBIndexStore
from llama_index.core import StorageContext, VectorStoreIndex

# create an AlloyDB Engine for connection pool
engine = AlloyDBEngine.from_instance(
    project_id=PROJECT_ID,
    region=REGION,
    cluster=CLUSTER,
    instance=INSTANCE,
    database=DATABASE,
    user=USER,
    password=PASSWORD,
)

# initialize a new table in AlloyDB
engine.init_index_store_table(
    table_name=TABLE_NAME,
)

index_store = AlloyDBIndexStore.create_sync(
    engine=engine,
    table_name=TABLE_NAME,
)

# 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)

Note: You can configure the schema_name along with the table_name when initializing a new table and instantiating AlloyDBIndexStore. By default the schema_name is public.

Under the hood, AlloyDBIndexStore connects to the alloydb database in Google Cloud and adds your nodes to a table under the schema_name.

You can easily reconnect to your AlloyDB database and reload the index by re-initializing a AlloyDBIndexStore with an AlloyDBEngine without initializing a new table.

A more detailed guide can be found here

Google Cloud SQL for PostgreSQL Index Store

Similarly to document stores, we can also use Cloud SQL for PostgreSQL as the storage backend of the index store. This tutorial demonstrates the synchronous interface. All synchronous methods have corresponding asynchronous methods.

pip install llama-index
pip install llama-index-cloud-sql-pg

from llama_index_cloud_sql_pg import PostgresEngine, PostgresIndexStore
from llama_index.core import StorageContext, VectorStoreIndex

# create an Postgres Engine for connection pool
engine = PostgresEngine.from_instance(
    project_id=PROJECT_ID,
    region=REGION,
    instance=INSTANCE,
    database=DATABASE,
    user=USER,
    password=PASSWORD,
)

# initialize a new table in cloud sql postgres
engine.init_index_store_table(
    table_name=TABLE_NAME,
)

index_store = PostgresIndexStore.create_sync(
    engine=engine,
    table_name=TABLE_NAME,
)

# 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)

Note: You can configure the schema_name along with the table_name when initializing a new table and instantiating PostgresIndexStore. By default the schema_name is public.

Under the hood, PostgresIndexStore connects to the cloud sql postgres database in Google Cloud and adds your nodes to a table under the schema_name.

You can easily reconnect to your cloud sql postgres database and reload the index by re-initializing a PostgresIndexStore with an PostgresEngine without initializing a new table.

A more detailed guide can be found here