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https://dev.mysql.com/doc/x-devapi-userguide-shell-js/en/collection-find.html
The find(SearchConditionStr) function is for searching documents in a collection, similar to the SELECT statement for an SQL database. It takes a search condition string (SearchConditionStr) as a parameter to specify the documents that should be ...
https://dev.mysql.com/doc/x-devapi-userguide-shell-python/en/collection-find.html
The find(SearchConditionStr) function is for searching documents in a collection, similar to the SELECT statement for an SQL database. It takes a search condition string (SearchConditionStr) as a parameter to specify the documents that should be ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-advisor-auto-query-time-estimation.html
Auto Query Time Estimation provides: Runtimes for successfully executed queries Runtime estimates for EXPLAIN queries. Runtime estimates for queries that fail due to an out-of-memory error. Runtime data can be used for query optimization, ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-connect-to-db-sys.html
This topic provides links to relevant documents that describe how to connect to a MySQL HeatWave DB System. Connect to the DB System After you have created and configured your DB System, connect to the DB System. For MySQL HeatWave on Oracle Cloud ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-create-db-system.html
To use the additional components of MySQL HeatWave that are included with the MySQL HeatWave Cluster, you must first create a MySQL HeatWave DB System. Create a DB System For MySQL HeatWave on Oracle Cloud Infrastructure (OCI), see Creating a DB ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-genai-generate-content.html
The following sections in this topic describe how to generate new text-based content using MySQL HeatWave GenAI: Before You Begin Generate Content Run Batch Queries What's Next Before You Begin Review the MySQL HeatWave GenAI requirements and ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-genai-generate-embeddings.html
This section describes how to generate vector embeddings using the ML_EMBED_ROW MySQL HeatWave GenAI routine. Vector embeddings are a numerical representation of the text that capture the semantics of the data and relationships to other data. You ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-genai-run-chat.html
When you run MySQL HeatWave Chat, it automatically loads the default LLM. By default, MySQL HeatWave Chat searches for an answer to a query across all ingested documents by automatically discovering available vector stores, and returns the answer ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-genai-summarize-content.html
The following sections in this topic describe how to summarize exiting content using MySQL HeatWave GenAI: Before You Begin Summarize Text Run Batch Queries What's Next Before You Begin Review the MySQL HeatWave GenAI requirements and privileges.
https://dev.mysql.com/doc/heatwave/en/mys-hw-lakehouse-access-object-storage.html
MySQL HeatWave on OCI can access external data stored on Oracle Cloud Infrastructure in Object Storage. To allow MySQL HeatWave Lakehouse to process data in Amazon S3, see Creating Lakehouse Mapping.