langchain-elasticsearch

import os from langchain_elasticsearch import ElasticsearchStore from langchain_openai import OpenAIEmbeddings # Or any other Embedding class from langchain_core.documents import Document # Set up Elasticsearch client URL and OpenAI API Key ELASTICSEARCH_URL = os.environ.get("ELASTICSEARCH_URL", "http://localhost:9200") OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "") if not OPENAI_API_KEY: print("Warning: OPENAI_API_KEY not set. Using a placeholder for demonstration.") # In a real application, you would ensure this is set or use a mock. embeddings = None # Prevent actual API calls else: embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) if embeddings: # Initialize ElasticsearchStore # Ensure Elasticsearch is running and accessible at ELASTICSEARCH_URL vectorstore = ElasticsearchStore( es_url=ELASTICSEARCH_URL, index_name="langchain-test-index", embedding=embeddings, # num_dimensions is crucial for correct vector mapping num_dimensions=1536 # For OpenAI embeddings ) # Add documents docs = [ Document(page_content="The quick brown fox jumps over the lazy dog", metadata={"source": "lorem"}), Document(page_content="A dog barks at the moon", metadata={"source": "nature"}), ] vectorstore.add_documents(docs) # Perform a similarity search query = "What is a fox?" results = vectorstore.similarity_search(query, k=1) print(f"\nSimilarity search results for '{query}':") for doc in results: print(f"- Content: {doc.page_content}, Metadata: {doc.metadata}") else: print("Embeddings not initialized due to missing API key. Skipping vector store example.")