---
title: Elasticsearch
 | Developer Documentation
---

> [Elasticsearch](http://www.github.com/elastic/elasticsearch) is a search database, that supports full text and vector searches.

## Basic Example

In this basic example, we take the a Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into Elasticsearch, and then query it. For an example using different retrieval strategies see [Elasticsearch Vector Store](https://docs.llamaindex.ai/en/stable/examples/vector_stores/elasticsearchindexdemo/).

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

```
%pip install -qU llama-index-vector-stores-elasticsearch llama-index-embeddings-huggingface llama-index
```

```
# import
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
from llama_index.core import StorageContext
```

```
# set up OpenAI
import os
import getpass


os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
```

Download Data

```
!mkdir -p 'data/paul_graham/'
!wget -nv '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'
```

```
2024-05-13 15:10:43 URL:https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt [75042/75042] -> "data/paul_graham/paul_graham_essay.txt" [1]
```

```
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings


# define embedding function
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)
```

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


# define index
vector_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # see Elasticsearch Vector Store for more authentication options
    index_name="paul_graham_essay",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)
```

```
# Query Data
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)
```

```
The author worked on writing and programming outside of school. They wrote short stories and tried writing programs on an IBM 1401 computer. They also built a microcomputer kit and started programming on it, writing simple games and a word processor.
```