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.

# !pip install llama-index elasticsearch --quiet
# !pip install sentence-transformers
# !pip install pydantic==1.10.11

# import
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.vector_stores import ElasticsearchStore
from llama_index.storage.storage_context import StorageContext
from IPython.display import Markdown, display

# set up OpenAI
import os
import getpass

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

openai.api_key = os.environ["OPENAI_API_KEY"]

# define embedding function
embed_model = "local/BAAI/bge-small-en-v1.5"

# load documents
documents = SimpleDirectoryReader(
    "../../../examples/paul_graham_essay/data"
).load_data()

vector_store = ElasticsearchStore(
    index_name="paul_graham_essay", es_url="http://localhost:9200"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
service_context = ServiceContext.from_defaults(embed_model=embed_model)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, service_context=service_context
)

# Query Data
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))

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.