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starter-ai
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王兵 6 months ago
parent b9bce351fd
commit 12a62b01b1
1 changed files with 60 additions and 62 deletions
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src/main/java/xyz/wbsite/ai/Test.java
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@ -1,11 +1,9 @@
package xyz.wbsite.ai;
import cn.hutool.core.collection.CollUtil;
import dev.langchain4j.agent.tool.*;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.message.AiMessage;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.SystemMessage;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.data.message.*;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.StreamingResponseHandler;
@ -17,10 +15,15 @@ import dev.langchain4j.model.output.Response;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.TokenStream;
import dev.langchain4j.service.tool.DefaultToolExecutor;
import dev.langchain4j.service.tool.ToolExecutor;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import java.util.ArrayList;
import java.util.List;
import java.util.UUID;
import java.util.function.Consumer;
/**
* 主函数入口
@ -30,8 +33,8 @@ public class Test {
public static void main(String[] args) {
// testSimpleChat(args);
// testStreamChat(args);
testRagChat(args);
// testTool(args);
// testRagChat(args);
testTool(args);
}
public static void testSimpleChat(String[] args) {
@ -138,62 +141,57 @@ public class Test {
}
public static void testTool(String[] args) {
// ChatLanguageModel model = OllamaChatModel.builder()
// .baseUrl("http://36.138.207.178:11434")
// .modelName("qwen2.5:7b")
// .logRequests(true)
// .logResponses(true)
// .build();
//
// List<ChatMessage> chatMessages = new ArrayList<>();
// chatMessages.add(UserMessage.from("请问，泰州市的天气怎么样？"));
//
// Object weatherTools = new Object() {
// @Tool("返回某一城市的天气情况")
// public String getWeather(@P("应返回天气预报的城市") String city) {
// System.out.println(city);
// return "天气阴转多云，1~6℃";
// }
// };
//
// List<ToolSpecification> toolSpecifications = ToolSpecifications.toolSpecificationsFrom(weatherTools);
//
// ChatRequest chatRequest = ChatRequest.builder()
// .messages(chatMessages)
// .parameters(ChatRequestParameters.builder()
// .toolSpecifications(toolSpecifications)
// .build())
// .build();
//
//
// ChatResponse chatResponse = model.chat(chatRequest);
// AiMessage aiMessage = chatResponse.aiMessage();
// chatMessages.add(aiMessage);
// if (aiMessage.hasToolExecutionRequests()) {
// System.out.println("LLM决定调用工具");
// System.out.println(chatResponse.aiMessage());
// List<ToolExecutionRequest> toolExecutionRequests = chatResponse.aiMessage().toolExecutionRequests();
// toolExecutionRequests.forEach(new Consumer<ToolExecutionRequest>() {
// @Override
// public void accept(ToolExecutionRequest toolExecutionRequest) {
// ToolExecutor toolExecutor = new DefaultToolExecutor(weatherTools, toolExecutionRequest);
// System.out.println("Now let's execute the tool " + toolExecutionRequest.name());
// String result = toolExecutor.execute(toolExecutionRequest, UUID.randomUUID().toString());
// ToolExecutionResultMessage toolExecutionResultMessages = ToolExecutionResultMessage.from(toolExecutionRequest, result);
// chatMessages.add(toolExecutionResultMessages);
// }
// });
// }
//
// // STEP 4: Model generates final response
// ChatRequest chatRequest2 = ChatRequest.builder()
// .messages(chatMessages)
// .parameters(ChatRequestParameters.builder()
// .toolSpecifications(toolSpecifications)
// .build())
// .build();
// ChatResponse finalChatResponse = model.chat(chatRequest2);
// System.out.println(finalChatResponse.aiMessage().text());
OpenAiChatModel model = OpenAiChatModel.builder()
.baseUrl("http://36.138.207.178:11434/v1")
.apiKey("1")
.modelName("qwen2.5:7b")
.build();
List<ChatMessage> chatMessages = new ArrayList<>();
chatMessages.add(UserMessage.from("请问，泰州市的天气怎么样？"));
Object weatherTools = new Object() {
@Tool("返回某一城市的天气情况")
public String getWeather(@P("应返回天气预报的城市") String city) {
System.out.println(city);
return "天气阴转多云，1~6℃";
}
};
List<ToolSpecification> toolSpecifications = ToolSpecifications.toolSpecificationsFrom(weatherTools);
ChatRequest chatRequest = ChatRequest.builder()
.messages(chatMessages)
.toolSpecifications(toolSpecifications)
.build();
ChatResponse chatResponse = model.chat(chatRequest);
AiMessage aiMessage = chatResponse.aiMessage();
chatMessages.add(aiMessage);
if (aiMessage.hasToolExecutionRequests()) {
System.out.println("LLM决定调用工具");
System.out.println(chatResponse.aiMessage());
List<ToolExecutionRequest> toolExecutionRequests = chatResponse.aiMessage().toolExecutionRequests();
toolExecutionRequests.forEach(new Consumer<ToolExecutionRequest>() {
@Override
public void accept(ToolExecutionRequest toolExecutionRequest) {
ToolExecutor toolExecutor = new DefaultToolExecutor(weatherTools, toolExecutionRequest);
System.out.println("Now let's execute the tool " + toolExecutionRequest.name());
String result = toolExecutor.execute(toolExecutionRequest, UUID.randomUUID().toString());
ToolExecutionResultMessage toolExecutionResultMessages = ToolExecutionResultMessage.from(toolExecutionRequest, result);
chatMessages.add(toolExecutionResultMessages);
}
});
}
// STEP 4: Model generates final response
ChatRequest chatRequest2 = ChatRequest.builder()
.messages(chatMessages)
.toolSpecifications(toolSpecifications)
.build();
ChatResponse finalChatResponse = model.chat(chatRequest2);
System.out.println(finalChatResponse.aiMessage().text());
}
// 创建一个助手接口

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