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Get a typed response with .entity()
Use .entity(Target.class) when the result maps to a concrete Java class or record. Spring AI derives a JSON Schema from the target type, includes formatting guidance in the model request, and converts the returned text. The documented high-level flow is described in the Spring AI Structured Output reference.
record ActorFilms(String actor, List<String> films) {}
ActorFilms result = chatClient.prompt()
.user("List films starring Tom Hanks")
.call()
.entity(ActorFilms.class);The precise schema and conversion behavior can vary with the Spring AI version and the Java types and annotations in use. Treat the model-produced values as untrusted input and apply application-level checks before routing, storing, or acting on them.
Handle generic types and response metadata
Lists and maps
Java erases generic type parameters at runtime, so use ParameterizedTypeReference to tell Spring AI the element or value type when converting a generic container. For example:
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List<ActorFilms> results = chatClient.prompt()
.user("Return several actors and their films")
.call()
.entity(new ParameterizedTypeReference<List<ActorFilms>>() {});
Map<String, Object> details = chatClient.prompt()
.user("Return the requested details")
.call()
.entity(new ParameterizedTypeReference<Map<String, Object>>() {});Spring AI documents this pattern for generic targets; see its typed response examples.
Keep the ChatResponse when you need metadata
If the application needs response metadata as well as the converted object, use .responseEntity(...) rather than only .entity(...). The high-level API reference covers the response-and-metadata option alongside typed conversion: Structured Output.
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Know what structured output does—and does not—guarantee
By default, schema guidance is included as instructions in the request and the result is parsed after generation. The model can still return malformed JSON, omit expected fields, add unexpected fields, or surround JSON with prose. Conversion may fail, or it may succeed while the values remain unsuitable for your application.
- Parsing answers whether response text can be converted to the requested Java type.
- Schema validation checks whether the response has the expected shape.
- Semantic validation checks application-specific rules, such as required ranges, permitted identifiers, or consistency between fields.
- Factual verification determines whether the model’s claims are true; a schema cannot establish that.
Spring AI’s typed .entity(...) overloads are call-only, not streaming: they need a completed response to convert. Streaming provides text chunks, so keep the result as streamed text or implement a separate accumulation and conversion strategy if a typed object is required. These boundaries are documented in the Spring AI structured-output API reference.
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Choose a converter for the shape you need
The converter abstraction combines Converter<String,T> with FormatProvider: it can supply formatting instructions before generation and convert response text afterward. Spring AI’s built-in options differ in the output they guide the model to produce. See Output Converters.
| Converter | Best fit | Output and conversion |
|---|---|---|
BeanOutputConverter<T> |
A class, record, or parameterized Java type | Derives JSON Schema from the target type and deserializes JSON into it. |
MapOutputConverter |
Flexible object-shaped data | Guides toward RFC 8259 JSON and converts to Map<String,Object>. |
ListOutputConverter |
A straightforward list of converted values | Guides toward comma-delimited output and converts items through a ConversionService. |
| Custom converter | A format or parsing behavior the built-ins do not fit | Implement the structured-output conversion and formatting behavior required by the application. |
For ordinary typed responses, prefer the higher-level .entity(...) API. Use lower-level converters with ChatClient or ChatModel when you need custom formatting or conversion. Structured output converters are not the mechanism Spring AI uses for LLM tool calling; tool calling is separate.
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Improve shape reliability with validation and retries
When malformed or incomplete structure has meaningful consequences, Spring AI documents validateSchema() and StructuredOutputValidationAdvisor for validation and self-correction. The advisor can retry after validation failures; the reference documents a default of three retry attempts. Confirm that default against the Spring AI version in your project, and account for the added model calls, latency, and cost in your design. Details are in Schema Validation & Self-Correction.
Validation addresses output shape, not truth or business correctness. Add normal Java validation and domain checks after conversion, and define what the application should do if retries are exhausted.
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Use provider-native structured output when supported
useProviderStructuredOutput() asks Spring AI to send a schema through a provider’s API-level structured-output feature. Unlike prompt instructions alone, the provider may enforce the schema during generation. Spring AI leaves this off by default for compatibility: unsupported or older models may reject such requests. Provider and model versions also differ in the JSON Schema features they accept. See the Provider-Native Structured Output reference.
Spring AI’s documentation flags potential limitations involving $ref, deeply nested arrays, allOf/anyOf/oneOf, regular-expression patterns, and recursive types. Ollama behavior can also vary by model and version. Test the exact provider, model version, generated schema, and response path used in deployment; do not assume that support for structured output means support for every schema feature.
Provider-native output and validation can be combined: the former asks the provider to constrain generation, while the latter gives the application a validation-and-retry path for output that still fails its checks. The right choice depends on provider compatibility, schema complexity, the cost of malformed output, whether retries are acceptable, and whether the application needs response metadata or streamed text.
Check version-specific schema changes when upgrading
Spring AI’s upgrade notes describe changes associated with the BeanOutputConverter schema-generation update: it delegates schema generation to JsonSchemaGenerator to align with tool-calling JSON Schema. These changes matter when comparing generated schemas or upgrading applications that depend on their details. Consult the Spring AI Upgrade Notes for the release-specific context.
- Kotlin optional primary-constructor properties are no longer placed in the schema’s
requiredarray. @JsonProperty(required = false)and annotations without an explicit required value are no longer treated as required.- Primitive schemas gain OpenAPI-style format hints such as
int32,int64, anddate-time. BeanOutputConverter.postProcessSchema(JsonNode)was removed.
These are migration details tied to the upgrade notes, not guarantees that apply identically across every Spring AI release. Check the notes for the version you are moving to and review generated schemas if downstream behavior depends on required fields or format hints.
Quick Recap
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