Elasticsearch Embeddings

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

!pip install llama-index

# imports

from llama_index.embeddings.elasticsearch import ElasticsearchEmbedding
from llama_index.vector_stores import ElasticsearchStore
from llama_index import ServiceContext, StorageContext, VectorStoreIndex

# get credentials and create embeddings

import os

host = os.environ.get("ES_HOST", "localhost:9200")
username = os.environ.get("ES_USERNAME", "elastic")
password = os.environ.get("ES_PASSWORD", "changeme")
index_name = os.environ.get("INDEX_NAME", "your-index-name")
model_id = os.environ.get("MODEL_ID", "your-model-id")


embeddings = ElasticsearchEmbedding.from_credentials(
    model_id=model_id, es_url=host, es_username=username, es_password=password
)

# create service context using the embeddings

service_context = ServiceContext(embed_model=embeddings, chunk_size=512)

# usage with elasticsearch vector store

vector_store = ElasticsearchStore(
    index_name=index_name, es_url=host, es_user=username, es_password=password
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_vector_store(
    vector_store=vector_store,
    storage_context=storage_context,
    service_context=service_context,
)

query_engine = index.as_query_engine()


response = query_engine.query("hello world")