please refer to the PyVespa application for how to connect.Creating a Vespa vector store‚ÄãNow, let's load some documents:from langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")Here, we also set up local sentence embedder to transform the text to embedding
vectors. One could also use OpenAI embeddings, but the vector length needs to
be updated to 1536 to reflect the larger size of that embedding.To feed these to Vespa, we need to configure how the vector store should map to
fields in the Vespa application. Then we create the vector store directly from
this set of documents:vespa_config = dict(    page_content_field="text",    embedding_field="embedding",    input_field="query_embedding")from langchain.vectorstores import VespaStoredb = VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config)This creates a Vespa vector store and feeds that set of documents to Vespa.
The vector store takes care of calling the embedding function for each document
and inserts them into the database.We can now query the vector store:query = "What did the president say about Ketanji Brown Jackson"results = db.similarity_search(query)print(results[0].page_content)This will use the embedding function given above to create a representation
for the query and use that to search Vespa. Note that this will use the
default ranking function, which we set up in the application package
above. You can use the ranking argument to similarity_search to
specify which ranking function to use.Please refer to the pyvespa documentation
Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query.
Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. ->: please refer to the PyVespa application for how to connect.Creating a Vespa vector store‚ÄãNow, let's load some documents:from langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")Here, we also set up local sentence embedder to transform the text to embedding
vectors. One could also use OpenAI embeddings, but the vector length needs to
be updated to 1536 to reflect the larger size of that embedding.To feed these to Vespa, we need to configure how the vector store should map to
fields in the Vespa application. Then we create the vector store directly from
this set of documents:vespa_config = dict(    page_content_field="text",    embedding_field="embedding",    input_field="query_embedding")from langchain.vectorstores import VespaStoredb = VespaStore.from_documents(docs, embedding_function, app=vespa_app, **vespa_config)This creates a Vespa vector store and feeds that set of documents to Vespa.
The vector store takes care of calling the embedding function for each document
and inserts them into the database.We can now query the vector store:query = "What did the president say about Ketanji Brown Jackson"results = db.similarity_search(query)print(results[0].page_content)This will use the embedding function given above to create a representation
for the query and use that to search Vespa. Note that this will use the
default ranking function, which we set up in the application package
above. You can use the ranking argument to similarity_search to
specify which ranking function to use.Please refer to the pyvespa documentation