ElasticSearch

Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

This notebook shows how to use functionality related to the Elasticsearch database.

ElasticVectorSearch class

Installation

Check out Elasticsearch installation instructions.

To connect to an Elasticsearch instance that does not require login credentials, pass the Elasticsearch URL and index name along with the embedding object to the constructor.

Example:

        from langchain import ElasticVectorSearch
        from langchain.embeddings import OpenAIEmbeddings

        embedding = OpenAIEmbeddings()
        elastic_vector_search = ElasticVectorSearch(
            elasticsearch_url="http://localhost:9200",
            index_name="test_index",
            embedding=embedding
        )

To connect to an Elasticsearch instance that requires login credentials, including Elastic Cloud, use the Elasticsearch URL format https://username:password@es_host:9243. For example, to connect to Elastic Cloud, create the Elasticsearch URL with the required authentication details and pass it to the ElasticVectorSearch constructor as the named parameter elasticsearch_url.

You can obtain your Elastic Cloud URL and login credentials by logging in to the Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and navigating to the "Deployments" page.

To obtain your Elastic Cloud password for the default "elastic" user:

Log in to the Elastic Cloud console at https://cloud.elastic.co
Go to "Security" > "Users"
Locate the "elastic" user and click "Edit"
Click "Reset password"
Follow the prompts to reset the password

Format for Elastic Cloud URLs is https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.

Example:

        from langchain import ElasticVectorSearch
        from langchain.embeddings import OpenAIEmbeddings

        embedding = OpenAIEmbeddings()

        elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
        elasticsearch_url = f"https://username:password@{elastic_host}:9243"
        elastic_vector_search = ElasticVectorSearch(
            elasticsearch_url=elasticsearch_url,
            index_name="test_index",
            embedding=embedding
        )

!pip install elasticsearch

import os
import getpass

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

OpenAI API Key: ········

Example

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch
from langchain.document_loaders import TextLoader

from langchain.document_loaders import TextLoader

loader = TextLoader("../../../state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

db = ElasticVectorSearch.from_documents(
    docs, embeddings, elasticsearch_url="http://localhost:9200"
)

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)

print(docs[0].page_content)

In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. 

We cannot let this happen. 

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

ElasticKnnSearch Class

The ElasticKnnSearch implements features allowing storing vectors and documents in Elasticsearch for use with approximate kNN search

!pip install langchain elasticsearch

from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings
import elasticsearch

# Initialize ElasticsearchEmbeddings
model_id = "<model_id_from_es>"
dims = dim_count
es_cloud_id = "ESS_CLOUD_ID"
es_user = "es_user"
es_password = "es_pass"
test_index = "<index_name>"
# input_field = "your_input_field" # if different from 'text_field'

# Generate embedding object
embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id,
    # input_field=input_field,
    es_cloud_id=es_cloud_id,
    es_user=es_user,
    es_password=es_password,
)

# Initialize ElasticKnnSearch
knn_search = ElasticKnnSearch(
    es_cloud_id=es_cloud_id,
    es_user=es_user,
    es_password=es_password,
    index_name=test_index,
    embedding=embeddings,
)

Test adding vectors

# Test `add_texts` method
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)

# Test `from_texts` method
new_texts = [
    "This is a new text.",
    "Elasticsearch is powerful.",
    "Python is great for data analysis.",
]
knn_search.from_texts(new_texts, dims=dims)

Test knn search using query vector builder

# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2)
print(f"kNN search results for query '{query}': {knn_result}")
print(
    f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'"
)

# Test `hybrid_search` method
query = "Hello"
hybrid_result = knn_search.knn_hybrid_search(query=query, model_id=model_id, k=2)
print(f"Hybrid search results for query '{query}': {hybrid_result}")
print(
    f"The 'text' field value from the top hit is: '{hybrid_result['hits']['hits'][0]['_source']['text']}'"
)

Test knn search using pre generated vector

# Generate embedding for tests
query_text = "Hello"
query_embedding = embeddings.embed_query(query_text)
print(
    f"Length of embedding: {len(query_embedding)}\nFirst two items in embedding: {query_embedding[:2]}"
)

# Test knn Search
knn_result = knn_search.knn_search(query_vector=query_embedding, k=2)
print(
    f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'"
)

# Test hybrid search - Requires both query_text and query_vector
knn_result = knn_search.knn_hybrid_search(
    query_vector=query_embedding, query=query_text, k=2
)
print(
    f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'"
)

Test source option

# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2, source=False)
assert not "_source" in knn_result["hits"]["hits"][0].keys()

# Test `hybrid_search` method
query = "Hello"
hybrid_result = knn_search.knn_hybrid_search(
    query=query, model_id=model_id, k=2, source=False
)
assert not "_source" in hybrid_result["hits"]["hits"][0].keys()

Test fields option

# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2, fields=["text"])
assert "text" in knn_result["hits"]["hits"][0]["fields"].keys()

# Test `hybrid_search` method
query = "Hello"
hybrid_result = knn_search.knn_hybrid_search(
    query=query, model_id=model_id, k=2, fields=["text"]
)
assert "text" in hybrid_result["hits"]["hits"][0]["fields"].keys()

Test with es client connection rather than cloud_id

# Create Elasticsearch connection
es_connection = Elasticsearch(
    hosts=["https://es_cluster_url:port"], basic_auth=("user", "password")
)

# Instantiate ElasticsearchEmbeddings using es_connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
    model_id,
    es_connection,
)

# Initialize ElasticKnnSearch
knn_search = ElasticKnnSearch(
    es_connection=es_connection, index_name=test_index, embedding=embeddings
)

# Test `knn_search` method with model_id and query_text
query = "Hello"
knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2)
print(f"kNN search results for query '{query}': {knn_result}")
print(
    f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'"
)

ElasticSearch

ElasticVectorSearch class

Installation​

Example​

ElasticKnnSearch Class

Test adding vectors​

Test knn search using query vector builder​

Test knn search using pre generated vector​

Test source option​

Test fields option​

Test with es client connection rather than cloud_id​