Output Parsers

The OutputParser interface allows you to obtain structured output, for example mapping the output to a Java class or an array of values from the String based output of AI Models.

You can think of it in terms similar to Spring JDBC’s concept of a RowMapper or ResultSetExtractor. Developers want to quickly turn results from an AI model into data types that can be passed to other functions and methods in their application. The OutputParser helps achieve that goal.

API Overview

This section provides a guide to the OutputParser interface.


Here is the OutputParser interface definition

public interface OutputParser<T> extends Parser<T>, FormatProvider {


It combines the Parser<T> interface

public interface Parser<T> {
    T parse(String text);

and the FormatProvider interface

public interface FormatProvider {

	String getFormat();


The Parser interface parses text strings to produce instances of the type T.

The FormatProvider provides text instructions for the AI Model to format the output so that it an be parsed into the type T by the Parser. These text instructions are most often appended to the end of the user input to the AI Model.

Available Implementations

The OutputParser interface has the following available implementations.

  • BeanOutputParser: Specifies the JSON schema for Java class and uses DRAFT_2020_12 of the JSON schema specification as OpenAI has indicated this would give the best results. The JSON output of the AI Model is then deserialized to a Java object, aka JavaBean.

  • MapOutputParser: Similar to BeanOutputParser but the JSON payload is deserialized into a java.util.Map<String, Object> instance.

  • ListOutputParser: Specifies the output to be a comma delimited list.

There has been considerable effort in recent OpenAI models to improve the model’s ability to return JSON by simply specifying 'return in JSON', but not all models support such direct support for returning structured data.

Example Usage

You can run a fully working example that demonstrates the use of BeanOutputParser as part of the Spring AI Azure Workshop. Part of this workshop code is reproduced below.

The use case for the example is to use the AI Model to generate the filmography for an actor.

The User prompt used is

String userMessage = """
        Generate the filmography for the actor {actor}.

The class ActorsFilms shown below

public class ActorsFilms {

	private String actor;

	private List<String> movies;

    // getters and toString omitted

Here is a controller class that shows these classes in use

    public ActorsFilms generate(@RequestParam(value = "actor", defaultValue = "Jeff Bridges") String actor) {
        var outputParser = new BeanOutputParser<>(ActorsFilms.class);

        String userMessage =
                Generate the filmography for the actor {actor}.

        PromptTemplate promptTemplate = new PromptTemplate(userMessage, Map.of("actor", actor, "format", outputParser.getFormat() ));
        Prompt prompt = promptTemplate.create();
        Generation generation = chatClient.call(prompt).getResult();

        ActorsFilms actorsFilms = outputParser.parse(generation.getOutput().getContent());
        return actorsFilms;