AWS Bedrock integration for LangChain4j enabling Java applications to interact with various LLM providers through a unified interface
Get started with AWS Bedrock chat models in minutes.
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-bedrock</artifactId>
<version>1.11.0</version>
</dependency>import dev.langchain4j.model.bedrock.BedrockChatModel;
import software.amazon.awssdk.regions.Region;
BedrockChatModel model = BedrockChatModel.builder()
.region(Region.US_EAST_1)
.modelId("anthropic.claude-3-5-sonnet-20241022-v2:0")
.build();
String response = model.generate("What is machine learning?");
System.out.println(response);import dev.langchain4j.model.bedrock.BedrockChatRequestParameters;
BedrockChatRequestParameters params = BedrockChatRequestParameters.builder()
.temperature(0.7)
.maxOutputTokens(2048)
.topP(0.9)
.build();
BedrockChatModel model = BedrockChatModel.builder()
.region(Region.US_EAST_1)
.modelId("anthropic.claude-3-5-sonnet-20241022-v2:0")
.defaultRequestParameters(params)
.build();import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.data.message.SystemMessage;
import dev.langchain4j.model.chat.request.ChatRequest;
import dev.langchain4j.model.chat.response.ChatResponse;
ChatRequest request = ChatRequest.builder()
.messages(
SystemMessage.from("You are a helpful assistant"),
UserMessage.from("Explain quantum computing")
)
.build();
ChatResponse response = model.chat(request);
String answer = response.aiMessage().text();Next Steps: