---
title: Storing | Developer Documentation
---

## Concept

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](https://filesystem-spec.readthedocs.io/en/latest/index.html). We have confirmed support for the following storage backends:

- Local filesystem
- AWS S3
- Cloudflare R2

![](/_astro/storage.CHky3Ivr_1DsVdz.png)

## 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](/python/framework/module_guides/storing/vector_stores/index.md) below for more details.

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

```
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.storage.index_store 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(
    docstore=SimpleDocumentStore(),
    vector_store=SimpleVectorStore(),
    index_store=SimpleIndexStore(),
)
```

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

- [Customization](/python/framework/module_guides/storing/customization/index.md)
- [Save/Load](/python/framework/module_guides/storing/save_load/index.md)

## Modules

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

- [Vector Stores](/python/framework/module_guides/storing/vector_stores/index.md)
- [Docstores](/python/framework/module_guides/storing/docstores/index.md)
- [Index Stores](/python/framework/module_guides/storing/index_stores/index.md)
- [Key-Val Stores](/python/framework/module_guides/storing/kv_stores/index.md)
- [Property Graph Stores](/python/framework/module_guides/indexing/lpg_index_guide#storage/index.md)
- [ChatStores](/python/framework/module_guides/storing/chat_stores/index.md)