AI endpoints doc: structured output & function calling

pages/index.md

@@ -1090,6 +1090,8 @@
                 + [AI Endpoints - Features, Capabilities and Limitations](public_cloud/ai_machine_learning/endpoints_guide_02_capabilities)
                 + [AI Endpoints - Troubleshooting](public_cloud/ai_machine_learning/endpoints_guide_03_troubleshooting)
                 + [AI Endpoints - Billing and lifecycle](public_cloud/ai_machine_learning/endpoints_guide_04_billing_concept)
+                + [AI Endpoints - Structured Output](public_cloud/ai_machine_learning/endpoints_guide_05_structured_output)
+                + [AI Endpoints - Function Calling](public_cloud/ai_machine_learning/endpoints_guide_06_function_calling)
             + [Tutorials](public-cloud-ai-and-machine-learning-ai-endpointstutorials)
                 + [AI Endpoints - Create your own audio summarizer](public_cloud/ai_machine_learning/endpoints_tuto_01_audio_summarizer)
                 + [AI Endpoints - Create your own voice assistant](public_cloud/ai_machine_learning/endpoints_tuto_02_voice_virtual_assistant)
@@ -1103,6 +1105,8 @@
                 + [AI Endpoints - Create a Memory Chatbot with LangChain4j](public_cloud/ai_machine_learning/endpoints_tuto_10_memory_chatbot_langchain4j)
                 + [AI Endpoints - Build a RAG Chatbot with LangChain](public_cloud/ai_machine_learning/endpoints_tuto_11_rag_chatbot_langchain)
                 + [AI Endpoints - Build a RAG Chatbot with LangChain4j](public_cloud/ai_machine_learning/endpoints_tuto_12_rag_chatbot_langchain4j)
+                + [AI Endpoints - Using Structured Output with LangChain4j](public_cloud/ai_machine_learning/endpoints_tuto_13_structured_output_langchain4j)
+                + [AI Endpoints - Using Function Calling with LangChain4j](public_cloud/ai_machine_learning/endpoints_tuto_14_function_calling_langchain4j)
         + [AI Partners Ecosystem](products/public-cloud-ai-and-machine-learning-ai-ecosystem)
             + [AI Partners - Guides](public-cloud-ai-and-machine-learning-ai-ecosystem-guides)
                 + [AI Partners Ecosystem - Lettria - Models features, capabilities and billing](public_cloud/ai_machine_learning/ecosystem_01_lettria_billing_features_capabilities)

pages/public_cloud/ai_machine_learning/endpoints_guide_05_structured_output/meta.yaml

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+id: 32dd1918-ef24-47a6-a291-e13f04bafdb8
+full_slug: public-cloud-ai-endpoints-structured-output

pages/public_cloud/ai_machine_learning/endpoints_guide_06_function_calling/meta.yaml

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+id: 3e2d11ce-3d51-4bb0-b221-7e66bd1685d2
+full_slug: public-cloud-ai-endpoints-structured-output

pages/public_cloud/ai_machine_learning/endpoints_tuto_13_structured_output_langchain4j/guide.en-gb.md

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+---
+title: AI Endpoints - Using Structured Output with LangChain4j
+excerpt: Learn how to use Structured Output with Java, LangChain4j and OVHcloud AI Endpoints
+updated: 2025-06-17
+---
+
+> [!primary]
+>
+> AI Endpoints is covered by the **[OVHcloud AI Endpoints Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/48743bf-AI_Endpoints-ALL-1.1.pdf)** and the **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**.
+>
+
+## Objective
+
+In this tutorial, we will explore how to use **Structured Output** with OVHcloud AI Endpoints.
+
+To do this, we will use **[LangChain4j](https://github.com/langchain4j/langchain4j)**, Java-based framework inspired by [LangChain](https://github.com/langchain-ai/langchain), designed to simplify the integration of LLMs (Large Language Models) into applications. Note that LangChain4j is not officially maintained by the LangChain team, despite the similar name.
+
+Combined with OVHcloud **[AI Endpoints](https://endpoints.ai.cloud.ovh.net/)** which offers both LLM and embedding models, it becomes easy to create advanced, production-ready assistants.
+
+![image](images/parrot.webp){.thumbnail}
+
+## Definition
+
+- **Structured Output**: Structured output allows you to format output data in a way that makes it easier for machines to interpret and process.
+- **[LangChain4j](https://github.com/langchain4j/langchain4j)**: Java-based framework inspired by [LangChain](https://github.com/langchain-ai/langchain), designed to simplify the integration of LLMs (Large Language Models) into applications. Note that LangChain4j is not officially maintained by the LangChain team, despite the similar name.
+- **[AI Endpoints](https://endpoints.ai.cloud.ovh.net/)**: A serverless platform by OVHcloud providing easy access to a variety of world-renowned AI models including Mistral, LLaMA, and more. This platform is designed to be simple, secure, and intuitive, with data privacy as a top priority.
+
+## Requirements
+
+- A [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account.
+- An access token for **OVHcloud AI Endpoints**. To create an API token, follow the instructions in the [AI Endpoints - Getting Started](/pages/public_cloud/ai_machine_learning/endpoints_guide_01_getting_started) guide.
+- This code example uses JBang, a Java-based tool for creating and running Java programs as scripts. For more information on JBang, please refer to the [JBang documentation](https://www.jbang.dev/documentation/guide/latest/installation.html).
+
+## Instructions
+
+Here is an excerpt of code that shows how to define a structured output format for the responses of the language model:
+
+```java
+// Json schema definition
+ResponseFormat responseFormat = ResponseFormat.builder()
+         .type(ResponseFormatType.JSON)
+         .jsonSchema(JsonSchema.builder()
+            .name("Person")
+            .rootElement(JsonObjectSchema.builder()
+               .addStringProperty("name")
+               .addIntegerProperty("age")
+               .addNumberProperty("height")
+               .addBooleanProperty("married")
+               .required("name", "age", "height", "married")
+            .build())
+         .build())
+.build();
+```
+
+In this example, we define a JSON output format with a schema that specifies the name, age, height, and married properties as required.
+Configure the model to use
+
+This example uses the Mistral AI model hosted on OVHcloud AI Endpoints.
+To configure the model, you need to set up the API key, base URL, and model name as environment variables.
+Fell free to use another model, see AI Endpoints catalog.
+
+You can find your access token, model URL, and model name in the OVHcloud AI Endpoints model dashboard.
+
+```java
+// Model definition
+ChatModel chatModel = MistralAiChatModel.builder()
+        .apiKey(System.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN"))
+        .baseUrl(System.getenv("OVH_AI_ENDPOINTS_MODEL_URL"))
+        .modelName(System.getenv("OVH_AI_ENDPOINTS_MODEL_NAME"))
+        .logRequests(false)
+        .logResponses(false)
+.build();
+```
+
+### Calling the language model
+
+Thanks to the JSON mode of the LLM, the response from the language model is received as a JSON string:
+
+```java
+// Model call with JSON mode
+UserMessage userMessage = UserMessage.from("""
+        John is 42 years old.
+        He stands 1.75 meters tall.
+        Currently unmarried.
+        """);
+
+ChatRequest chatRequest = ChatRequest.builder()
+        .responseFormat(responseFormat)
+        .messages(userMessage)
+        .build();
+
+ChatResponse chatResponse = chatModel.chat(chatRequest);
+
+String output = chatResponse.aiMessage().text();
+System.out.println("Response: \n" + output); 
+
+
+// Person is a simple record: record Person(String name, int age, double height, boolean married) {}
+Person person = new ObjectMapper().readValue(output, Person.class);
+System.out.println(person); 
+```
+
+### The full source code
+
+```java
+///usr/bin/env jbang "$0" "$@" ; exit $?
+//JAVA 21+
+//PREVIEW
+//DEPS dev.langchain4j:langchain4j:1.0.1 dev.langchain4j:langchain4j-mistral-ai:1.0.1-beta6
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import dev.langchain4j.data.message.UserMessage;
+import dev.langchain4j.model.chat.request.ChatRequest;
+import dev.langchain4j.model.chat.request.ResponseFormat;
+import dev.langchain4j.model.chat.request.ResponseFormatType;
+import dev.langchain4j.model.chat.request.json.JsonObjectSchema;
+import dev.langchain4j.model.chat.request.json.JsonSchema;
+import dev.langchain4j.model.chat.response.ChatResponse;
+import dev.langchain4j.model.mistralai.MistralAiChatModel;
+import dev.langchain4j.model.chat.ChatModel;
+
+record Person(String name, int age, double height, boolean married) {
+}
+
+void main() throws Exception {
+    ResponseFormat responseFormat = ResponseFormat.builder()
+            .type(ResponseFormatType.JSON)
+            .jsonSchema(JsonSchema.builder()
+                    .name("Person")
+                    .rootElement(JsonObjectSchema.builder()
+                            .addStringProperty("name")
+                            .addIntegerProperty("age")
+                            .addNumberProperty("height")
+                            .addBooleanProperty("married")
+                            .required("name", "age", "height", "married")
+                            .build())
+                    .build())
+            .build();
+
+    UserMessage userMessage = UserMessage.from("""
+            John is 42 years old.
+            He stands 1.75 meters tall.
+            Currently unmarried.
+            """);
+
+    ChatRequest chatRequest = ChatRequest.builder()
+            .responseFormat(responseFormat)
+            .messages(userMessage)
+            .build();
+
+    ChatModel chatModel = MistralAiChatModel.builder()
+            .apiKey(System.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN"))
+            .baseUrl(System.getenv("OVH_AI_ENDPOINTS_MODEL_URL"))
+            .modelName(System.getenv("OVH_AI_ENDPOINTS_MODEL_NAME"))
+            .logRequests(false)
+            .logResponses(false)
+            .build();
+
+    ChatResponse chatResponse = chatModel.chat(chatRequest);
+
+    System.out.println("Prompt: \n" + userMessage.singleText());
+    String output = chatResponse.aiMessage().text();
+    System.out.println("Response: \n" + output); 
+
+    Person person = new ObjectMapper().readValue(output, Person.class);
+    System.out.println(person); 
+}
+```
+
+### Running the application
+
+```java
+jbang HelloWorld.java
+[jbang] Building jar for HelloWorld.java...
+
+Prompt: 
+John is 42 years old.
+He stands 1.75 meters tall.
+Currently unmarried.
+
+Response: 
+{"age": 42, "height": 1.75, "married": false, "name": "John"}
+Person[name=John, age=42, height=1.75, married=false]
+```
+
+## Conclusion
+
+In this article, we have seen how to use Structured Output with OVHcloud AI Endpoints and LangChain4J.
+
+## Go further
+
+You can find the full code example in the [GitHub repository](https://github.com/ovh/public-cloud-examples/tree/main/ai/ai-endpoints/structured-output-langchain4j). 
+
+Browse the full [AI Endpoints documentation](/products/public-cloud-ai-and-machine-learning-ai-endpoints) to further understand the main concepts and get started.
+
+To discover how to build complete and powerful applications using AI Endpoints, explore our dedicated [AI Endpoints guides](/products/public-cloud-ai-and-machine-learning-ai-endpoints).
+
+If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](/links/professional-services) to get a quote and ask our Professional Services experts for a custom analysis of your project.
+
+## Feedback
+
+Please feel free to send us your questions, feedback, and suggestions regarding AI Endpoints and its features:
+
+- In the #ai-endpoints channel of the OVHcloud [Discord server](https://discord.gg/ovhcloud), where you can engage with the community and OVHcloud team members.
+
+If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](/links/professional-services) to get a quote and ask our Professional Services experts for a custom analysis of your project.