"author": "Dmitry Simonenko<dmitry@timescale.com>", "date": "Thu Aug 3 14:36:39 2023 +0300", "change summary": "Support CREATE INDEX ONLY ON main table", "change details": "This PR adds support for CREATE INDEX ONLY ON clause which allows to create index only on the main table excluding chunks.  Fix #5908 "}    --------------------------------------------------------------------------------    --------------------------------------------------------------------------------    Score:  0.21698051691055298    Date:  2023-08-2 20:24:14+0140    {"commit": " 3af0d282ea71d9a8f27159a6171e9516e62ec9cb", "author": "Lakshmi Narayanan Sreethar<lakshmi@timescale.com>", "date": "Wed Aug 2 20:24:14 2023 +0100", "change summary": "PG16: ExecInsertIndexTuples requires additional parameter", "change details": "PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value.  Adapt these changes by updating the compat function to accomodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value.  postgres/postgres@19d8e23 "}    --------------------------------------------------------------------------------Once again, notice how we get results within the specified time filter, different from the previous query.Method 3: Filter within a provided end date and a time delta earlier.# Method 3: Query for vectors between end_dt and a time delta td earlier# Most relevant vectors between 30 August and 7 days earlierdocs_with_score = db.similarity_search_with_score(query, end_date=end_dt, time_delta=td)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print("Date: ", doc.metadata["date"])    print(doc.page_content)    print("-" * 80)
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. ->: "author": "Dmitry Simonenko<dmitry@timescale.com>", "date": "Thu Aug 3 14:36:39 2023 +0300", "change summary": "Support CREATE INDEX ONLY ON main table", "change details": "This PR adds support for CREATE INDEX ONLY ON clause which allows to create index only on the main table excluding chunks.  Fix #5908 "}    --------------------------------------------------------------------------------    --------------------------------------------------------------------------------    Score:  0.21698051691055298    Date:  2023-08-2 20:24:14+0140    {"commit": " 3af0d282ea71d9a8f27159a6171e9516e62ec9cb", "author": "Lakshmi Narayanan Sreethar<lakshmi@timescale.com>", "date": "Wed Aug 2 20:24:14 2023 +0100", "change summary": "PG16: ExecInsertIndexTuples requires additional parameter", "change details": "PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value.  Adapt these changes by updating the compat function to accomodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value.  postgres/postgres@19d8e23 "}    --------------------------------------------------------------------------------Once again, notice how we get results within the specified time filter, different from the previous query.Method 3: Filter within a provided end date and a time delta earlier.# Method 3: Query for vectors between end_dt and a time delta td earlier# Most relevant vectors between 30 August and 7 days earlierdocs_with_score = db.similarity_search_with_score(query, end_date=end_dt, time_delta=td)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print("Date: ", doc.metadata["date"])    print(doc.page_content)    print("-" * 80)