doc in docs]# create the open-source embedding functionembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")# load it in sqlite-vss in a table named state_union.# the db_file parameter is the name of the file you want# as your sqlite database.db = SQLiteVSS.from_texts(    texts=texts,    embedding=embedding_function,    table="state_union",    db_file="/tmp/vss.db")# query itquery = "What did the president say about Ketanji Brown Jackson"data = db.similarity_search(query)# print resultsdata[0].page_content    'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.'Using existing sqlite connection‚Äãfrom langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import SQLiteVSSfrom langchain.document_loaders import TextLoader# load the document and split it into chunksloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()# split it into chunkstext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)texts = [doc.page_content for doc in docs]# create the open-source embedding functionembedding_function =
sqlite-vss is an SQLite extension designed for vector search, emphasizing local-first operations and easy integration into applications without external servers. Leveraging the Faiss library, it offers efficient similarity search and clustering capabilities.
sqlite-vss is an SQLite extension designed for vector search, emphasizing local-first operations and easy integration into applications without external servers. Leveraging the Faiss library, it offers efficient similarity search and clustering capabilities. ->: doc in docs]# create the open-source embedding functionembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")# load it in sqlite-vss in a table named state_union.# the db_file parameter is the name of the file you want# as your sqlite database.db = SQLiteVSS.from_texts(    texts=texts,    embedding=embedding_function,    table="state_union",    db_file="/tmp/vss.db")# query itquery = "What did the president say about Ketanji Brown Jackson"data = db.similarity_search(query)# print resultsdata[0].page_content    'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.'Using existing sqlite connection‚Äãfrom langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import SQLiteVSSfrom langchain.document_loaders import TextLoader# load the document and split it into chunksloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()# split it into chunkstext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)texts = [doc.page_content for doc in docs]# create the open-source embedding functionembedding_function =