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LlamaIndex provides a high-level interface for ingesting, indexing, and querying your external data.

Under the hood, LlamaIndex also supports swappable storage components that allows you to customize:

  • Document stores: where ingested documents (i.e., Node objects) are stored,
  • Index stores: where index metadata are stored,
  • Vector stores: where embedding vectors are stored.
  • Property Graph stores: where knowledge graphs are stored (i.e. for PropertyGraphIndex).
  • Chat Stores: where chat messages are stored and organized.

The Document/Index stores rely on a common Key-Value store abstraction, which is also detailed below.

LlamaIndex supports persisting data to any storage backend supported by fsspec. We have confirmed support for the following storage backends:

  • Local filesystem
  • AWS S3
  • Cloudflare R2

Usage Pattern#

Many vector stores (except FAISS) will store both the data as well as the index (embeddings). This means that you will not need to use a separate document store or index store. This also means that you will not need to explicitly persist this data - this happens automatically. Usage would look something like the following to build a new index / reload an existing one.

## build a new index
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.deeplake import DeepLakeVectorStore

# construct vector store and customize storage context
vector_store = DeepLakeVectorStore(dataset_path="<dataset_path>")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# Load documents and build index
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context

## reload an existing one
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

See our Vector Store Module Guide below for more details.

Note that in general to use storage abstractions, you need to define a StorageContext object:

from import SimpleDocumentStore
from import SimpleIndexStore
from llama_index.core.vector_stores import SimpleVectorStore
from llama_index.core import StorageContext

# create storage context using default stores
storage_context = StorageContext.from_defaults(

More details on customization/persistence can be found in the guides below.


We offer in-depth guides on the different storage components.