28 23:26:23 2023 +0200", "change summary": "Move ddl_internal functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - chunk_constraint_add_table_constraint(_timescaledb_catalog.chunk_constraint) - chunk_drop_replica(regclass,name) - chunk_index_clone(oid) - chunk_index_replace(oid,oid) - create_chunk_replica_table(regclass,name) - drop_stale_chunks(name,integer[]) - health() - hypertable_constraint_add_table_fk_constraint(name,name,name,integer) - process_ddl_event() - wait_subscription_sync(name,name,integer,numeric) "}    --------------------------------------------------------------------------------    --------------------------------------------------------------------------------    Score:  0.18867712088363497    Date:  2023-08-27 13:20:04+0320    {"commit": " e02b1f348eb4c48def00b7d5227238b4d9d41a4a", "author": "Sven Klemm<sven@timescale.com>", "date": "Sun Aug 27 13:20:04 2023 +0200", "change summary": "Simplify schema move update script", "change details": "Use dynamic sql to create the ALTER FUNCTION statements for those functions that may not exist in previous versions. "}    --------------------------------------------------------------------------------Method 4: We can also filter for all vectors after a given date by only specifying a start date in our query.Method 5: Similarly, we can filter for or all vectors before a given date by only specify an end date in our query.# Method 4: Query all vectors after start_datedocs_with_score = db.similarity_search_with_score(query,start_date=start_dt)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print("Date: ", doc.metadata["date"])    print(doc.page_content)    print("-" *
This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries.
This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: 28 23:26:23 2023 +0200", "change summary": "Move ddl_internal functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - chunk_constraint_add_table_constraint(_timescaledb_catalog.chunk_constraint) - chunk_drop_replica(regclass,name) - chunk_index_clone(oid) - chunk_index_replace(oid,oid) - create_chunk_replica_table(regclass,name) - drop_stale_chunks(name,integer[]) - health() - hypertable_constraint_add_table_fk_constraint(name,name,name,integer) - process_ddl_event() - wait_subscription_sync(name,name,integer,numeric) "}    --------------------------------------------------------------------------------    --------------------------------------------------------------------------------    Score:  0.18867712088363497    Date:  2023-08-27 13:20:04+0320    {"commit": " e02b1f348eb4c48def00b7d5227238b4d9d41a4a", "author": "Sven Klemm<sven@timescale.com>", "date": "Sun Aug 27 13:20:04 2023 +0200", "change summary": "Simplify schema move update script", "change details": "Use dynamic sql to create the ALTER FUNCTION statements for those functions that may not exist in previous versions. "}    --------------------------------------------------------------------------------Method 4: We can also filter for all vectors after a given date by only specifying a start date in our query.Method 5: Similarly, we can filter for or all vectors before a given date by only specify an end date in our query.# Method 4: Query all vectors after start_datedocs_with_score = db.similarity_search_with_score(query,start_date=start_dt)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print("Date: ", doc.metadata["date"])    print(doc.page_content)    print("-" *