Tell me when these changes were made."response = qa_stuff.run(query)print(response)            > Entering new RetrievalQA chain...        > Finished chain.    The following changes were made to the timescaledb functions:        1. "Add compatibility layer for _timescaledb_internal functions" - This change was made on Tue Aug 29 18:13:24 2023 +0200.    2. "Move functions to _timescaledb_functions schema" - This change was made on Sun Aug 20 22:47:10 2023 +0200.    3. "Move utility functions to _timescaledb_functions schema" - This change was made on Tue Aug 22 12:01:19 2023 +0200.    4. "Move partitioning functions to _timescaledb_functions schema" - This change was made on Tue Aug 29 10:49:47 2023 +0200.Note that the context the LLM uses to compose an answer are from retrieved documents only within the specified date range. This shows how you can use Timescale Vector to enhance retrieval augmented generation by retrieving documents within time ranges relevant to your query.3. Using ANN Search Indexes to Speed Up Queries‚ÄãYou can speed up similarity queries by creating an index on the embedding column. You should only do this once you have ingested a large part of your data.Timescale Vector supports the following indexes:timescale_vector index (tsv): a disk-ann inspired graph index for fast similarity search (default).pgvector's HNSW index: a hierarchical navigable small world graph index for fast similarity search.pgvector's IVFFLAT index: an inverted file index for fast similarity search.Important note: In PostgreSQL, each table can only have one index on a particular column. So if you'd like to test the performance of different index types, you can do so either by (1) creating multiple tables with different indexes, (2) creating multiple vector columns in the same table and creating different indexes on each column, or (3) by dropping and recreating the index on the same column and comparing results.# Initialize an existing TimescaleVector storeCOLLECTION_NAME =
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. ->: Tell me when these changes were made."response = qa_stuff.run(query)print(response)            > Entering new RetrievalQA chain...        > Finished chain.    The following changes were made to the timescaledb functions:        1. "Add compatibility layer for _timescaledb_internal functions" - This change was made on Tue Aug 29 18:13:24 2023 +0200.    2. "Move functions to _timescaledb_functions schema" - This change was made on Sun Aug 20 22:47:10 2023 +0200.    3. "Move utility functions to _timescaledb_functions schema" - This change was made on Tue Aug 22 12:01:19 2023 +0200.    4. "Move partitioning functions to _timescaledb_functions schema" - This change was made on Tue Aug 29 10:49:47 2023 +0200.Note that the context the LLM uses to compose an answer are from retrieved documents only within the specified date range. This shows how you can use Timescale Vector to enhance retrieval augmented generation by retrieving documents within time ranges relevant to your query.3. Using ANN Search Indexes to Speed Up Queries‚ÄãYou can speed up similarity queries by creating an index on the embedding column. You should only do this once you have ingested a large part of your data.Timescale Vector supports the following indexes:timescale_vector index (tsv): a disk-ann inspired graph index for fast similarity search (default).pgvector's HNSW index: a hierarchical navigable small world graph index for fast similarity search.pgvector's IVFFLAT index: an inverted file index for fast similarity search.Important note: In PostgreSQL, each table can only have one index on a particular column. So if you'd like to test the performance of different index types, you can do so either by (1) creating multiple tables with different indexes, (2) creating multiple vector columns in the same table and creating different indexes on each column, or (3) by dropping and recreating the index on the same column and comparing results.# Initialize an existing TimescaleVector storeCOLLECTION_NAME =