Elasticsearch

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.

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.