= Clickhouse.from_documents(docs, embeddings)    Inserting data...: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 42/42 [00:00<00:00, 6939.56it/s]meta = docsearch.metadata_columnoutput = docsearch.similarity_search_with_relevance_scores(    "What did the president say about Ketanji Brown Jackson?",    k=4,    where_str=f"{meta}.doc_id<10",)for d, dist in output:    print(dist, d.metadata, d.page_content[:20] + "...")    0.6779101415357189 {'doc_id': 0} Madam Speaker, Madam...    0.6997970363474885 {'doc_id': 8} And so many families...    0.7044504914336727 {'doc_id': 1} Groups of citizens b...    0.7053558702165094 {'doc_id': 6} And I‚Äôm taking robus...Deleting your data‚Äãdocsearch.drop()PreviousClarifaiNextDashVectorSetting up envrionmentsGet connection info and data schemaClickhouse table schemaFilteringDeleting your dataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
ClickHouse is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like L2Distance) as well as approximate nearest neighbor search indexes enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL.
ClickHouse is the fastest and most resource efficient open-source database for real-time apps and analytics with full SQL support and a wide range of functions to assist users in writing analytical queries. Lately added data structures and distance search functions (like L2Distance) as well as approximate nearest neighbor search indexes enable ClickHouse to be used as a high performance and scalable vector database to store and search vectors with SQL. ->: = Clickhouse.from_documents(docs, embeddings)    Inserting data...: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 42/42 [00:00<00:00, 6939.56it/s]meta = docsearch.metadata_columnoutput = docsearch.similarity_search_with_relevance_scores(    "What did the president say about Ketanji Brown Jackson?",    k=4,    where_str=f"{meta}.doc_id<10",)for d, dist in output:    print(dist, d.metadata, d.page_content[:20] + "...")    0.6779101415357189 {'doc_id': 0} Madam Speaker, Madam...    0.6997970363474885 {'doc_id': 8} And so many families...    0.7044504914336727 {'doc_id': 1} Groups of citizens b...    0.7053558702165094 {'doc_id': 6} And I‚Äôm taking robus...Deleting your data‚Äãdocsearch.drop()PreviousClarifaiNextDashVectorSetting up envrionmentsGet connection info and data schemaClickhouse table schemaFilteringDeleting your dataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.