ai_summarize
function
Applies to: Databricks SQL Databricks Runtime
Preview
This feature is in Public Preview.
In the preview:
The underlying language model can handle several languages, however these functions are tuned for English.
There is rate limiting for the underlying Foundation Model APIs. See Foundation Model APIs limits to update these limits.
The ai_summarize()
function allows you to invoke a state-of-the-art generative AI model to generate a summary of a given text using SQL. This function uses a chat model serving endpoint made available by Databricks Foundation Model APIs.
Requirements
Important
The underlying models that might be used at this time are licensed under the Apache 2.0 License, Copyright © The Apache Software Foundation or the LLAMA 3.1 Community License Copyright © Meta Platforms, Inc. All rights reserved. Customers are responsible for ensuring compliance with applicable model licenses.
Databricks recommends reviewing these licenses to ensure compliance with any applicable terms. If models emerge in the future that perform better according to Databricks’s internal benchmarks, Databricks might change the model (and the list of applicable licenses provided on this page).
Currently, Meta-Llama-3.1-70B-Instruct is the underlying model that powers these AI functions.
This function is only available on workspaces in AI Functions using Foundation Model APIs supported regions.
This function is not available on Databricks SQL Classic.
Check the Databricks SQL pricing page.
Note
In Databricks Runtime 15.1 and above, this function is supported in Databricks notebooks, including notebooks that are run as a task in a Databricks workflow.
Arguments
content
: ASTRING
expression, the text to be summarized.max_words
: An optional non-negative integral numeric expression representing the best-effort target number of words in the returned summary text. The default value is 50. If set to 0, there is no word limit.
Examples
> SELECT ai_summarize(
'Apache Spark is a unified analytics engine for large-scale data processing. ' ||
'It provides high-level APIs in Java, Scala, Python and R, and an optimized ' ||
'engine that supports general execution graphs. It also supports a rich set ' ||
'of higher-level tools including Spark SQL for SQL and structured data ' ||
'processing, pandas API on Spark for pandas workloads, MLlib for machine ' ||
'learning, GraphX for graph processing, and Structured Streaming for incremental ' ||
'computation and stream processing.',
20
)
"Apache Spark is a unified, multi-language analytics engine for large-scale data processing
with additional tools for SQL, machine learning, graph processing, and stream computing."