"Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates).  With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster.  A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\".  Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabr√≠zio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': 'fabriziomello@gmail.com'})]5. Working with an existing TimescaleVector vectorstore‚ÄãIn the examples above, we created a vectorstore from a collection of documents. However, often we want to work insert data into and query data from an existing vectorstore. Let's see how to initialize, add documents to, and query an existing collection of documents in a TimescaleVector vector store.To work with an existing Timescale Vector store, we need to know the name of the table we want to query (COLLECTION_NAME) and the URL of the cloud PostgreSQL database (SERVICE_URL).# Initialize the existingCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()vectorstore = TimescaleVector(    collection_name=COLLECTION_NAME,    service_url=SERVICE_URL,    embedding_function=embeddings,)To load new data into the table, we use the add_document() function. This function takes a list of documents and a list of metadata. The metadata must contain a unique id for each document. If you want your documents to be associated with the current date and time,
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. ->: "Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates).  With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster.  A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\".  Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabr√≠zio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': 'fabriziomello@gmail.com'})]5. Working with an existing TimescaleVector vectorstore‚ÄãIn the examples above, we created a vectorstore from a collection of documents. However, often we want to work insert data into and query data from an existing vectorstore. Let's see how to initialize, add documents to, and query an existing collection of documents in a TimescaleVector vector store.To work with an existing Timescale Vector store, we need to know the name of the table we want to query (COLLECTION_NAME) and the URL of the cloud PostgreSQL database (SERVICE_URL).# Initialize the existingCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()vectorstore = TimescaleVector(    collection_name=COLLECTION_NAME,    service_url=SERVICE_URL,    embedding_function=embeddings,)To load new data into the table, we use the add_document() function. This function takes a list of documents and a list of metadata. The metadata must contain a unique id for each document. If you want your documents to be associated with the current date and time,