Example:

.. code-block:: python

from langchain_community.vectorstores import VespaStore from langchain_community.embeddings.openai import OpenAIEmbeddings from vespa.application import Vespa

Create a vespa client dependent upon your application,

e.g. either connecting to Vespa Cloud or a local deployment

such as Docker. Please refer to the PyVespa documentation on

how to initialize the client.

vespa_app = Vespa(url="...", port=..., application_package=...)

You need to instruct LangChain on which fields to use for embeddings

vespa_config = dict( page_content_field="text", embedding_field="embedding", input_field="query_embedding", metadata_fields=["date", "rating", "author"] )

embedding_function = OpenAIEmbeddings() vectorstore = VespaStore(vespa_app, embedding_function, **vespa_config)