Fabric AI Functions Enhancements (Generally Available)
Transform your data with AI in an instant by using Fabric AI functions. These powerful functions allow you to turn data into insights with just a single line of code.
- ai.analyze_sentiment() – detect emotional state
- ai.classify() – categorize according to your labels
- [NEW] ai.embed() – convert text into vector embeddings
- ai.extract() – extract specific types of information
- ai.fix_grammar() – correct spelling, grammar, and punctuation
- ai.generate_response() – use your own custom prompt!
- ai.similarity() – compare meaning with a single text value or with text in another column
- ai.summarize() – summarize input text
- ai.translate() – translate into another language
AI functions have been saving customers hours, and even weeks’ worth of work. Read on to learn about the latest updates!
What’s New?
Since releasing AI Functions to preview earlier this year, we have expanded the functionality to include new optional parameters for additional control, added a new ai.embed() function, implemented support for advanced configurations when using gpt-5, reduced default execution time, and added support for additional models beyond OpenAI.
ai.analyze_sentiment() parameters
ai.analyze_sentiment() now has configurable labels. You can continue using the function with the default parameters of ‘positive’, ‘negative’, ‘neutral’, and ‘mixed’, or you can pass in your own labels.
ai.extract() parameters
ai.extract() now has ExtractLabel, a custom object that lets users set detailed instructions about the information to be extracted. It includes the following parameters:
- label – The name of the extracted column.
- description – Natural language description to add extra context for the AI model. It can include requirements, context, or instructions for the AI to consider while performing the extraction.
- max_items – Maximum number of items to extract for this label.
- type – JSON schema type for the extracted value. Supported types for this class include ‘string’, ‘number’, ‘integer’, ‘boolean’, ‘object’, and ‘array’
- properties – Additional JSON schema properties for the type as a dictionary. It can include supported properties like ‘items’ for arrays, ‘properties’ for objects, ‘enum’ for enum types, and more. For example usage, refer to the documentation on Supported Schemas.
ai.generate_response() parameters
ai.generate_response() now has a response_format parameter for more control over the output format. You can choose text, json, or specify the json_schema, including with pydantic model.
ai.summarize() parameters
ai.summarize() has a new instructions parameter that allows you to provide additional context to the AI model, such as specifying output length.
New function: ai.embed()
This brand-new function allows you to convert text into numeric vectors that capture its meaning and context — a process known as embedding. These vectors let AI understand relationships between texts, so you can search, group, and compare content based on meaning rather than exact wording.
Advanced configurations for gpt-5
For more complicated tasks, AI functions can be configured to use the gpt-5 model, which has a configurable reasoning_effort parameter, to regulate the number of reasoning tokens the AI model should use, and a verbosity parameter, to regulate the number of reasoning tokens the AI model should use.
To learn more, refer to the custom configurations documentation.
Faster execution
The concurrency parameter allows you to configure the maximum number of rows to process in parallel with asynchronous requests to the model. We have increased the default concurrency to 200 so you can enjoy faster execution out-of-the-box.
Refer to the custom configurations documentation.
Support for additional models
AI functions give you more flexibility than ever when choosing models. You can use the default model, any of the models supported in Fabric, bring your own Azure OpenAI LLM resource, and you can now also bring an AI Foundry resource. This allows you to access models beyond OpenAI, such as Claude, LLaMA, and more, with both PySpark and pandas.
For more details, refer to the custom configurations documentation.
Next Steps
- These updates will be generally available across all geographies in the coming weeks.
- Want to dive deeper? Explore the AI functions documentation to learn more.
- Submit your feedback on Fabric Ideas and join the conversation in the Fabric Community.
