diff --git a/content/en/docs/appstore/use-content/platform-supported-content/modules/snowflake/snowflake-rest-sql.md b/content/en/docs/appstore/use-content/platform-supported-content/modules/snowflake/snowflake-rest-sql.md index 440cc29121d..438c7ce7975 100644 --- a/content/en/docs/appstore/use-content/platform-supported-content/modules/snowflake/snowflake-rest-sql.md +++ b/content/en/docs/appstore/use-content/platform-supported-content/modules/snowflake/snowflake-rest-sql.md @@ -14,21 +14,22 @@ The [Snowflake REST SQL connector](https://marketplace.mendix.com/link/component The Snowflake REST SQL connector provides a way to first setup key-pair authentication with an RSA key pair according to PKCS #8 standard, and then execute SQL statements on Snowflake via a REST call from within your Mendix application. These statements allow you to perform the following tasks: -* Read data from Snowflake -* Write data to Snowflake -* Trigger [Snowflake Cortex ML functions](https://docs.snowflake.com/en/guides-overview-ml-functions) - * [Forecasting](https://docs.snowflake.com/en/user-guide/ml-functions/forecasting) predicts future metric values from past trends in time-series data. - * [Anomaly Detection](https://docs.snowflake.com/en/user-guide/ml-functions/anomaly-detection) flags metric values that differ from typical expectations. -* Use [Snowflake Cortex LLM functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions). Some very helpful use cases and examples for all of these functions, written by the Head of Snowflake Tech Consulting, can be found [here](https://medium.com/@karthiksraman). - * [CLASSIFY_TEXT](https://docs.snowflake.com/en/sql-reference/functions/classify_text-snowflake-cortex): Given a piece of text, classifies it into one of the categories that you define. - * [EXTRACT_ANSWER](https://docs.snowflake.com/en/sql-reference/functions/extract_answer-snowflake-cortex): Given a question and unstructured data, returns the answer to the question if it can be found in the data. - * [PARSE_DOCUMENT](https://docs.snowflake.com/en/sql-reference/functions/parse_document-snowflake-cortex): Given an internal or external stage with documents, returns an object that contains extracted text content using OCR mode, or the extracted text and layout elements using LAYOUT mode. - * [SENTIMENT](https://docs.snowflake.com/en/sql-reference/functions/sentiment-snowflake-cortex): Returns a sentiment score, from -1 to 1, representing the detected positive or negative sentiment of the given text. - * [SUMMARIZE](https://docs.snowflake.com/en/sql-reference/functions/summarize-snowflake-cortex): Returns a summary of the given text. - * [TRANSLATE](https://docs.snowflake.com/en/sql-reference/functions/translate-snowflake-cortex): Translates given text from any supported language to any other. - * [EMBED_TEXT_768](https://docs.snowflake.com/en/sql-reference/functions/embed_text-snowflake-cortex): Given a piece of text, returns a vector embedding of 768 dimensions that represents that text. - * [EMBED_TEXT_1024](https://docs.snowflake.com/en/sql-reference/functions/embed_text_1024-snowflake-cortex): Given a piece of text, returns a vector embedding of 1024 dimensions that represents that text. -* Use [Snowflake Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst): This Snowflake Cortex feature is used to get information/insights out of structured data sets using natural language instead of sql. +* Read data from Snowflake. +* Write data to Snowflake. +* Trigger [Snowflake Cortex ML functions](https://docs.snowflake.com/en/guides-overview-ml-functions): + * [Forecasting](https://docs.snowflake.com/en/user-guide/ml-functions/forecasting) - Predicts future metric values from past trends in time-series data. + * [Anomaly Detection](https://docs.snowflake.com/en/user-guide/ml-functions/anomaly-detection) - Flags metric values that differ from typical expectations. + * [CLASSIFY_TEXT](https://docs.snowflake.com/en/sql-reference/functions/classify_text-snowflake-cortex) - Given a piece of text, classifies it into one of the categories that you define. + * [EXTRACT_ANSWER](https://docs.snowflake.com/en/sql-reference/functions/extract_answer-snowflake-cortex) - Given a question and unstructured data, returns the answer to the question if it can be found in the data. + * [PARSE_DOCUMENT](https://docs.snowflake.com/en/sql-reference/functions/parse_document-snowflake-cortex) - Given an internal or external stage with documents, returns an object that contains extracted text content using OCR mode, or the extracted text and layout elements using LAYOUT mode. + * [SENTIMENT](https://docs.snowflake.com/en/sql-reference/functions/sentiment-snowflake-cortex) - Returns a sentiment score, from -1 to 1, representing the detected positive or negative sentiment of the given text. + * [SUMMARIZE](https://docs.snowflake.com/en/sql-reference/functions/summarize-snowflake-cortex) - Returns a summary of the given text. + * [TRANSLATE](https://docs.snowflake.com/en/sql-reference/functions/translate-snowflake-cortex) - Translates given text from any supported language to any other. + * [EMBED_TEXT_768](https://docs.snowflake.com/en/sql-reference/functions/embed_text-snowflake-cortex) - Given a piece of text, returns a vector embedding of 768 dimensions that represents that text. + * [EMBED_TEXT_1024](https://docs.snowflake.com/en/sql-reference/functions/embed_text_1024-snowflake-cortex) - Given a piece of text, returns a vector embedding of 1024 dimensions that represents that text. +* Use [Snowflake Cortex Analyst](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst) - This Snowflake Cortex feature is used to get information/insights out of structured data sets using natural language instead of sql. + +For more use cases and examples for [Snowflake Cortex LLM functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions), written by the Head of Snowflake Tech Consulting, see [Karthik S Raman's Medium profile](https://medium.com/@karthiksraman). The current version of the connector supports the following: