Elasticsearch Vector Store#
Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and keyword search. It is built on top of the Apache Lucene library.
Signup for a free trial.
Requires Elasticsearch 8.9.0 or higher and AIOHTTP.
If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
%pip install llama-index-vector-stores-elasticsearch
!pip install llama-index
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
Running and connecting to Elasticsearch#
Two ways to setup an Elasticsearch instance for use with:
Locally#
Get started with Elasticsearch by running it locally. The easiest way is to use the official Elasticsearch Docker image. See the Elasticsearch Docker documentation for more information.
docker run -p 9200:9200 \
-e "discovery.type=single-node" \
-e "xpack.security.enabled=false" \
-e "xpack.security.http.ssl.enabled=false" \
-e "xpack.license.self_generated.type=trial" \
docker.elastic.co/elasticsearch/elasticsearch:8.9.0
Configuring ElasticsearchStore#
The ElasticsearchStore class is used to connect to an Elasticsearch instance. It requires the following parameters:
- index_name: Name of the Elasticsearch index. Required.
- es_client: Optional. Pre-existing Elasticsearch client.
- es_url: Optional. Elasticsearch URL.
- es_cloud_id: Optional. Elasticsearch cloud ID.
- es_api_key: Optional. Elasticsearch API key.
- es_user: Optional. Elasticsearch username.
- es_password: Optional. Elasticsearch password.
- text_field: Optional. Name of the Elasticsearch field that stores the text.
- vector_field: Optional. Name of the Elasticsearch field that stores the
embedding.
- batch_size: Optional. Batch size for bulk indexing. Defaults to 200.
- distance_strategy: Optional. Distance strategy to use for similarity search.
Defaults to "COSINE".
Example: Connecting locally#
from llama_index.vector_stores import ElasticsearchStore
es = ElasticsearchStore(
index_name="my_index",
es_url="http://localhost:9200",
)
Example: Connecting to Elastic Cloud with username and password#
from llama_index.vector_stores import ElasticsearchStore
es = ElasticsearchStore(
index_name="my_index",
es_cloud_id="<cloud-id>", # found within the deployment page
es_user="elastic"
es_password="<password>" # provided when creating deployment. Alternatively can reset password.
)
Example: Connecting to Elastic Cloud with API Key#
from llama_index.vector_stores import ElasticsearchStore
es = ElasticsearchStore(
index_name="my_index",
es_cloud_id="<cloud-id>", # found within the deployment page
es_api_key="<api-key>" # Create an API key within Kibana (Security -> API Keys)
)
Load documents, build VectorStoreIndex with Elasticsearch#
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
INFO:numexpr.utils:Note: NumExpr detected 10 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
Note: NumExpr detected 10 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
INFO:numexpr.utils:NumExpr defaulting to 8 threads.
NumExpr defaulting to 8 threads.
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
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
# initialize without metadata filter
from llama_index.core import StorageContext
vector_store = ElasticsearchStore(
es_url="http://localhost:9200",
# Or with Elastic Cloud
# es_cloud_id="my_cloud_id",
# es_user="elastic",
# es_password="my_password",
index_name="paul_graham",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
INFO:elastic_transport.transport:GET http://localhost:9200/ [status:200 duration:0.024s]
GET http://localhost:9200/ [status:200 duration:0.024s]
INFO:elastic_transport.transport:HEAD http://localhost:9200/paul_graham [status:200 duration:0.011s]
HEAD http://localhost:9200/paul_graham [status:200 duration:0.011s]
INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.115s]
PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.115s]
INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.083s]
PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.083s]
Basic Example#
We are going to ask the query engine a question about the data we just indexed.
# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("what were his investments in Y Combinator?")
print(response)
INFO:elastic_transport.transport:POST http://localhost:9200/paul_graham/_search [status:200 duration:0.030s]
POST http://localhost:9200/paul_graham/_search [status:200 duration:0.030s]
He invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%.
Metadata Filters#
Here we are going to index a few documents with metadata so that we can apply filters to the query engine.
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text="The Shawshank Redemption",
metadata={
"author": "Stephen King",
"theme": "Friendship",
},
),
TextNode(
text="The Godfather",
metadata={
"director": "Francis Ford Coppola",
"theme": "Mafia",
},
),
TextNode(
text="Inception",
metadata={
"director": "Christopher Nolan",
},
),
]
# initialize the vector store
vector_store_metadata_example = ElasticsearchStore(
index_name="movies_metadata_example",
es_url="http://localhost:9200",
)
storage_context = StorageContext.from_defaults(
vector_store=vector_store_metadata_example
)
index = VectorStoreIndex(nodes, storage_context=storage_context)
# Metadata filter
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
filters = MetadataFilters(
filters=[ExactMatchFilter(key="theme", value="Mafia")]
)
retriever = index.as_retriever(filters=filters)
retriever.retrieve("What is inception about?")
INFO:elastic_transport.transport:GET http://localhost:9200/ [status:200 duration:0.012s]
GET http://localhost:9200/ [status:200 duration:0.012s]
INFO:elastic_transport.transport:HEAD http://localhost:9200/movies_metadata_example [status:404 duration:0.022s]
HEAD http://localhost:9200/movies_metadata_example [status:404 duration:0.022s]
INFO:elastic_transport.transport:PUT http://localhost:9200/movies_metadata_example [status:200 duration:0.099s]
PUT http://localhost:9200/movies_metadata_example [status:200 duration:0.099s]
INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.053s]
PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.053s]
INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.023s]
PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.023s]
INFO:elastic_transport.transport:POST http://localhost:9200/movies_metadata_example/_search [status:200 duration:0.034s]
POST http://localhost:9200/movies_metadata_example/_search [status:200 duration:0.034s]
[NodeWithScore(node=TextNode(id_='3b47c6b6-f01b-44fe-8f88-2249aad2a615', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='81cf4b9e847ba42e83fc401e31af8e17d629f0d5cf9c0c320ec7ac69dd0257e1', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.88513875)]
Custom Filters and overriding Query#
llama-index supports ExactMatchFilters only at the moment. Elasticsearch supports a wide range of filters, including range filters, geo filters, and more. To use these filters, you can pass them in as a list of dictionaries to the es_filter parameter.
def custom_query(query, query_str):
print("custom query", query)
return query
query_engine = index.as_query_engine(
vector_store_kwargs={
"es_filter": [{"match": {"content": "growing up"}}],
"custom_query": custom_query,
}
)
response = query_engine.query("what were his investments in Y Combinator?")
print(response)
custom query {'knn': {'filter': [{'match': {'content': 'growing up'}}], 'field': 'embedding', 'query_vector': [0.002520269714295864, -0.03282919153571129, 0.016138022765517235, -0.029537975788116455, -0.006744919344782829, 0.01626248098909855, -0.03703309968113899, 0.002381983445957303, -0.003031929489225149, -0.003616189584136009, 0.032746221870183945, 0.030201751738786697, 0.011726687662303448, 0.005043996497988701, 0.0030665011145174503, 0.016207166016101837, 0.018115518614649773, -0.008539185859262943, 0.020825933665037155, -0.011595315299928188, -0.027754081413149834, -0.004622223321348429, -0.004750138148665428, -0.015363619662821293, -0.006496003828942776, 0.012860636226832867, 0.02331508882343769, -0.009368903934955597, -0.002686213469132781, 0.0029818005859851837, 0.032441992312669754, 0.0015107790241017938, -0.0023059258237481117, 0.02384057641029358, -0.029233746230602264, 0.003574703587219119, 0.0048296526074409485, 0.019401581957936287, 0.01830912008881569, 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INFO:elastic_transport.transport:POST http://localhost:9200/paul_graham/_search [status:200 duration:0.034s]
POST http://localhost:9200/paul_graham/_search [status:200 duration:0.034s]
He invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%.