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src/main/java/xyz/wbsite/ai/TestRagNativeChat.java

@@ -0,0 +1,122 @@
+package xyz.wbsite.ai;
+
+import dev.langchain4j.data.document.Document;
+import dev.langchain4j.data.document.DocumentParser;
+import dev.langchain4j.data.document.DocumentSplitter;
+import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
+import dev.langchain4j.data.document.parser.TextDocumentParser;
+import dev.langchain4j.data.document.splitter.DocumentSplitters;
+import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.message.ChatMessage;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.memory.chat.MessageWindowChatMemory;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.model.embedding.onnx.bgesmallenv15q.BgeSmallEnV15QuantizedEmbeddingModel;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
+import dev.langchain4j.rag.content.Content;
+import dev.langchain4j.rag.content.retriever.ContentRetriever;
+import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
+import dev.langchain4j.rag.query.Query;
+import dev.langchain4j.service.AiServices;
+import dev.langchain4j.service.TokenStream;
+import dev.langchain4j.store.embedding.EmbeddingStore;
+import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
+
+import java.util.List;
+
+import static dev.langchain4j.data.document.loader.FileSystemDocumentLoader.loadDocument;
+
+/**
+ * 主函数入口
+ */
+public class TestRagNativeChat {
+
+    public static void main(String[] args) {
+        OpenAiChatModel model = OpenAiChatModel.builder()
+                .baseUrl("http://36.138.207.178:11434/v1")
+                .apiKey("1")
+                .modelName("deepseek-r1:14B")
+                .build();
+
+
+
+        // Now, let's load a document that we want to use for RAG.
+        // We will use the terms of use from an imaginary car rental company, "Miles of Smiles".
+        // For this example, we'll import only a single document, but you can load as many as you need.
+        // LangChain4j offers built-in support for loading documents from various sources:
+        // File System, URL, Amazon S3, Azure Blob Storage, GitHub, Tencent COS.
+        // Additionally, LangChain4j supports parsing multiple document types:
+        // text, pdf, doc, xls, ppt.
+        // However, you can also manually import your data from other sources.
+        DocumentParser documentParser = new TextDocumentParser();
+        Document document = loadDocument("D:\\docs\\人才公共服务平台会议纪要20210720.txt", documentParser);
+
+
+        // Now, we need to split this document into smaller segments, also known as "chunks."
+        // This approach allows us to send only relevant segments to the LLM in response to a user query,
+        // rather than the entire document. For instance, if a user asks about cancellation policies,
+        // we will identify and send only those segments related to cancellation.
+        // A good starting point is to use a recursive document splitter that initially attempts
+        // to split by paragraphs. If a paragraph is too large to fit into a single segment,
+        // the splitter will recursively divide it by newlines, then by sentences, and finally by words,
+        // if necessary, to ensure each piece of text fits into a single segment.
+        DocumentSplitter splitter = DocumentSplitters.recursive(300, 0);
+        List<TextSegment> segments = splitter.split(document);
+
+
+        // Now, we need to embed (also known as "vectorize") these segments.
+        // Embedding is needed for performing similarity searches.
+        // For this example, we'll use a local in-process embedding model, but you can choose any supported model.
+        // Langchain4j currently supports more than 10 popular embedding model providers.
+        EmbeddingModel embeddingModel = new BgeSmallEnV15QuantizedEmbeddingModel();
+        List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
+
+
+        // Next, we will store these embeddings in an embedding store (also known as a "vector database").
+        // This store will be used to search for relevant segments during each interaction with the LLM.
+        // For simplicity, this example uses an in-memory embedding store, but you can choose from any supported store.
+        // Langchain4j currently supports more than 15 popular embedding stores.
+        EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+        embeddingStore.addAll(embeddings, segments);
+
+        // We could also use EmbeddingStoreIngestor to hide manual steps above behind a simpler API.
+        // See an example of using EmbeddingStoreIngestor in _01_Advanced_RAG_with_Query_Compression_Example.
+
+
+        // The content retriever is responsible for retrieving relevant content based on a user query.
+        // Currently, it is capable of retrieving text segments, but future enhancements will include support for
+        // additional modalities like images, audio, and more.
+        ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
+                .embeddingStore(embeddingStore)
+                .embeddingModel(embeddingModel)
+                .maxResults(2) // on each interaction we will retrieve the 2 most relevant segments
+                .minScore(0.1) // we want to retrieve segments at least somewhat similar to user query
+                .build();
+
+        List<Content> retrieve = contentRetriever.retrieve(new Query("参会人员有哪些人？"));
+        System.out.println();
+
+        Assistant assistant = AiServices.builder(Assistant.class)
+                .chatLanguageModel(model)
+                .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
+                .contentRetriever(contentRetriever)
+                .build();
+
+        // String chat = assistant.chat("参会人员有哪些人？");
+        //System.out.println(chat);
+    }
+
+
+    // 创建一个助手接口
+    interface Assistant {
+
+        String chat(String userMessage);
+
+        TokenStream chatStream(List<ChatMessage> messages);
+
+        TokenStream chatStream(ChatMessage message);
+
+        TokenStream chatStream(String message);
+    }
+}

src/main/java/xyz/wbsite/ai/TestTool2Chat.java

@@ -30,7 +30,6 @@ public class TestTool2Chat {
             }
         };
 
-
         Assistant agent = AiServices.builder(Assistant.class)
                 .chatLanguageModel(model)
                 .tools(weatherTools)