embedding functionembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")connection = SQLiteVSS.create_connection(db_file="/tmp/vss.db")db1 = SQLiteVSS(    table="state_union",    embedding=embedding_function,    connection=connection)db1.add_texts(["Ketanji Brown Jackson is awesome"])# query it againquery = "What did the president say about Ketanji Brown Jackson"data = db1.similarity_search(query)# print resultsdata[0].page_content    'Ketanji Brown Jackson is awesome'# Cleaning upimport osos.remove("/tmp/vss.db")Previousscikit-learnNextStarRocksQuickstartUsing existing sqlite connectionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
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. ->: embedding functionembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")connection = SQLiteVSS.create_connection(db_file="/tmp/vss.db")db1 = SQLiteVSS(    table="state_union",    embedding=embedding_function,    connection=connection)db1.add_texts(["Ketanji Brown Jackson is awesome"])# query it againquery = "What did the president say about Ketanji Brown Jackson"data = db1.similarity_search(query)# print resultsdata[0].page_content    'Ketanji Brown Jackson is awesome'# Cleaning upimport osos.remove("/tmp/vss.db")Previousscikit-learnNextStarRocksQuickstartUsing existing sqlite connectionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.