---
title: Firestore Demo
 | Developer Documentation
---

This guide shows you how to directly use our `DocumentStore` abstraction backed by Google Firestore. By putting nodes in the docstore, this allows you to define multiple indices over the same underlying docstore, instead of duplicating data across indices.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/docstore/FirestoreDemo.ipynb)

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-storage-docstore-firestore
%pip install llama-index-storage-kvstore-firestore
%pip install llama-index-storage-index-store-firestore
%pip install llama-index-llms-openai
```

```
!pip install llama-index
```

```
import nest_asyncio


nest_asyncio.apply()
```

```
import logging
import sys


logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
```

```
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
```

#### Download Data

```
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
```

#### Load Documents

```
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
```

#### Parse into Nodes

```
from llama_index.core.node_parser import SentenceSplitter


nodes = SentenceSplitter().get_nodes_from_documents(documents)
```

#### Add to Docstore

```
from llama_index.storage.kvstore.firestore import FirestoreKVStore
from llama_index.storage.docstore.firestore import FirestoreDocumentStore
from llama_index.storage.index_store.firestore import FirestoreIndexStore
```

```
kvstore = FirestoreKVStore()


storage_context = StorageContext.from_defaults(
    docstore=FirestoreDocumentStore(kvstore),
    index_store=FirestoreIndexStore(kvstore),
)
```

```
storage_context.docstore.add_documents(nodes)
```

#### Define Multiple Indexes

Each index uses the same underlying Node.

```
summary_index = SummaryIndex(nodes, storage_context=storage_context)
```

```
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
```

```
keyword_table_index = SimpleKeywordTableIndex(
    nodes, storage_context=storage_context
)
```

```
# NOTE: the docstore still has the same nodes
len(storage_context.docstore.docs)
```

#### Test out saving and loading

```
# NOTE: docstore and index_store is persisted in Firestore by default
# NOTE: here only need to persist simple vector store to disk
storage_context.persist()
```

```
# note down index IDs
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
```

```
from llama_index.core import load_index_from_storage


kvstore = FirestoreKVStore()


# re-create storage context
storage_context = StorageContext.from_defaults(
    docstore=FirestoreDocumentStore(kvstore),
    index_store=FirestoreIndexStore(kvstore),
)


# load indices
summary_index = load_index_from_storage(
    storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
    storage_context=storage_context, vector_id=vector_id
)
keyword_table_index = load_index_from_storage(
    storage_context=storage_context, keyword_id=keyword_id
)
```

#### Test out some Queries

```
chatgpt = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = chatgpt
Settings.chunk_size = 1024
```

```
query_engine = summary_index.as_query_engine()
list_response = query_engine.query("What is a summary of this document?")
```

```
display_response(list_response)
```

```
query_engine = vector_index.as_query_engine()
vector_response = query_engine.query("What did the author do growing up?")
```

```
display_response(vector_response)
```

```
query_engine = keyword_table_index.as_query_engine()
keyword_response = query_engine.query(
    "What did the author do after his time at YC?"
)
```

```
display_response(keyword_response)
```