text: Vespa Cloud instance
  or a local
  Docker instance.After connecting to the service, you can set up the retriever:from langchain.retrievers.vespa_retriever import VespaRetrievervespa_query_body = {    "yql": "select content from paragraph where userQuery()",    "hits": 5,    "ranking": "documentation",    "locale": "en-us",}vespa_content_field = "content"retriever = VespaRetriever(vespa_app, vespa_query_body, vespa_content_field)This sets up a LangChain retriever that fetches documents from the Vespa application.
  Here, up to 5 results are retrieved from the content field in the paragraph document type,
  using doumentation as the ranking method. The userQuery() is replaced with the actual query
  passed from LangChain.Please refer to the pyvespa documentation
  for more information.Now you can return the results and continue using the results in LangChain.retriever.get_relevant_documents("what is vespa?")PreviousTF-IDFNextWeaviate Hybrid SearchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.