The langchain_elasticsearch library in LangChain simplifies the integration of Elasticsearch for Retrieval-Augmented Generation (RAG).

Initialization: Use ElasticsearchStore() to create an Elasticsearch vector store.

Storing Documents: Add documents to the vector store using add_documents(documents).

Retrieving Documents: Perform similarity-based retrieval with similarity_search(query, embedding_model), which finds relevant chunks based on embeddings.

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