--- type: docs title: "How to: Author and manage Dapr Conversation AI in the Java SDK" linkTitle: "How to: Author and manage Conversation AI" weight: 20000 description: How to get up and running with Conversation AI using the Dapr Java SDK --- As part of this demonstration, we will look at how to use the Conversation API to converse with a Large Language Model (LLM). The API will return the response from the LLM for the given prompt. With the [provided conversation ai example](https://github.com/dapr/java-sdk/tree/master/examples/src/main/java/io/dapr/examples/conversation), you will: - You will provide a prompt using the [Conversation AI example](https://github.com/dapr/java-sdk/blob/master/examples/src/main/java/io/dapr/examples/conversation/DemoConversationAI.java) - Filter out Personally identifiable information (PII). This example uses the default configuration from `dapr init` in [self-hosted mode](https://github.com/dapr/cli#install-dapr-on-your-local-machine-self-hosted). ## Prerequisites - [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started). - Java JDK 11 (or greater): - [Oracle JDK](https://www.oracle.com/java/technologies/downloads), or - OpenJDK - [Apache Maven](https://maven.apache.org/install.html), version 3.x. - [Docker Desktop](https://www.docker.com/products/docker-desktop) ## Set up the environment Clone the [Java SDK repo](https://github.com/dapr/java-sdk) and navigate into it. ```bash git clone https://github.com/dapr/java-sdk.git cd java-sdk ``` Run the following command to install the requirements for running the Conversation AI example with the Dapr Java SDK. ```bash mvn clean install -DskipTests ``` From the Java SDK root directory, navigate to the examples' directory. ```bash cd examples ``` Run the Dapr sidecar. ```sh dapr run --app-id conversationapp --dapr-grpc-port 51439 --dapr-http-port 3500 --app-port 8080 ``` > Now, Dapr is listening for HTTP requests at `http://localhost:3500` and gRPC requests at `http://localhost:51439`. ## Send a prompt with Personally identifiable information (PII) to the Conversation AI API In the `DemoConversationAI` there are steps to send a prompt using the `converse` method under the `DaprPreviewClient`. ```java public class DemoConversationAI { /** * The main method to start the client. * * @param args Input arguments (unused). */ public static void main(String[] args) { try (DaprPreviewClient client = new DaprClientBuilder().buildPreviewClient()) { System.out.println("Sending the following input to LLM: Hello How are you? This is the my number 672-123-4567"); ConversationInput daprConversationInput = new ConversationInput("Hello How are you? " + "This is the my number 672-123-4567"); // Component name is the name provided in the metadata block of the conversation.yaml file. Mono responseMono = client.converse(new ConversationRequest("echo", List.of(daprConversationInput)) .setContextId("contextId") .setScrubPii(true).setTemperature(1.1d)); ConversationResponse response = responseMono.block(); System.out.printf("Conversation output: %s", response.getConversationOutputs().get(0).getResult()); } catch (Exception e) { throw new RuntimeException(e); } } } ``` Run the `DemoConversationAI` with the following command. ```sh java -jar target/dapr-java-sdk-examples-exec.jar io.dapr.examples.conversation.DemoConversationAI ``` ### Sample output ``` == APP == Conversation output: Hello How are you? This is the my number ``` As shown in the output, the number sent to the API is obfuscated and returned in the form of . The example above uses an ["echo"](https://docs.dapr.io/developing-applications/building-blocks/conversation/howto-conversation-layer/#set-up-the-conversation-component) component for testing, which simply returns the input message. When integrated with LLMs like OpenAI or Claude, you’ll receive meaningful responses instead of echoed input. ## Next steps - [Learn more about Conversation AI]({{< ref conversation-overview.md >}}) - [Conversation AI API reference]({{< ref conversation_api.md >}})