52 lines
2.0 KiB
Java
52 lines
2.0 KiB
Java
package com.ai.config;
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import com.ai.function.MyCalculator;
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import com.ai.service.Assist;
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import dev.langchain4j.community.model.dashscope.QwenChatModel;
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import dev.langchain4j.data.document.Document;
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import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.memory.chat.MessageWindowChatMemory;
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import dev.langchain4j.model.chat.ChatLanguageModel;
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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import dev.langchain4j.service.AiServices;
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import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
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import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
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import lombok.RequiredArgsConstructor;
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import org.springframework.beans.factory.annotation.Value;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Configuration;
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import java.util.List;
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@Configuration
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@RequiredArgsConstructor
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public class AssistantInit {
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// All files in a directory, txt seems to be faster
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@Value("${langchain4j.api-key}")
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private String apiKey;
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@Value("${langchain4j.model}")
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private String model;
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@Bean
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public Assist init() {
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ChatLanguageModel qwenModel = QwenChatModel.builder()
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.apiKey(apiKey)
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.modelName(model)
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.build();
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List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:\\ideaProject\\liang-ai");
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// for simplicity, we will use an in-memory one:
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InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
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EmbeddingStoreIngestor.ingest(documents, embeddingStore);
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return AiServices.builder(Assist.class)
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.chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10))
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.chatLanguageModel(qwenModel)
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.contentRetriever(EmbeddingStoreContentRetriever.from(embeddingStore))
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// .tools(new MyCalculator())
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.build();
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}
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}
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