langchain_elasticsearch_vector_store_with_openai_embeddings_similarity_search.py

1import os
2from langchain_elasticsearch import ElasticsearchStore
3from langchain_openai import OpenAIEmbeddings
4
5# Environment variables for configuration
6# Ensure you have your Elastic Cloud ID/URL and API Key, and OpenAI API Key ready
7os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
8elastic_cloud_id = "your-cloud-id"
9elastic_api_key = "your-api-key"
10
11# Initialize embeddings
12embeddings = OpenAIEmbeddings()
13
14# Initialize ElasticsearchStore
15vector_store = ElasticsearchStore(
16    es_cloud_id=elastic_cloud_id,
17    es_api_key=elastic_api_key,
18    index_name="langchain-demo",
19    embedding=embeddings
20)
21
22# Add documents to the vector store
23texts = ["LangChain is a framework for developing applications powered by LMs.",
24         "Elasticsearch is a distributed, RESTful search and analytics engine.",
25         "Vector databases are used to store and retrieve high-dimensional vectors."]
26vector_store.add_texts(texts)
27
28# Perform a similarity search
29query = "What is LangChain?"
30results = vector_store.similarity_search(query, k=1)
31
32# Print results
33for doc in results:
34    print(f"Content: {doc.page_content}")