Initial commit: Fintec AI Framework with Agent, RAG, and MCP modules
This commit is contained in:
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fintec-framework-rag-spring-boot-autoconfigure/.DS_Store
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fintec-framework-rag-spring-boot-autoconfigure/.DS_Store
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fintec-framework-rag-spring-boot-autoconfigure/pom.xml
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fintec-framework-rag-spring-boot-autoconfigure/pom.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<parent>
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<groupId>com.ccb.fintec</groupId>
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<artifactId>fintec-framework-parent</artifactId>
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<version>1.0.0</version>
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</parent>
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<artifactId>fintec-framework-rag-spring-boot-autoconfigure</artifactId>
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<dependencies>
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<!-- 核心协议层 -->
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<dependency>
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<groupId>com.ccb.fintec</groupId>
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<artifactId>fintec-framework-ai-core</artifactId>
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<version>${project.version}</version>
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</dependency>
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<!-- Spring Boot AutoConfigure -->
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-autoconfigure</artifactId>
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</dependency>
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<!-- Spring Boot Configuration Processor -->
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-configuration-processor</artifactId>
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<optional>true</optional>
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</dependency>
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<!-- Spring AI Chat Client -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-client-chat</artifactId>
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</dependency>
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<!-- Spring AI Advisors Vector Store（包含 QuestionAnswerAdvisor） -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-advisors-vector-store</artifactId>
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</dependency>
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<!-- Embedding 模型（以 OpenAI 为例，业务系统可替换） -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-starter-model-openai</artifactId>
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<optional>true</optional>
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</dependency>
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<!-- Vector Store（以 Chroma 为例，业务系统可替换） -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-starter-vector-store-chroma</artifactId>
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<optional>true</optional>
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</dependency>
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<!-- Vector Store（以 PGVector 为例，业务系统可替换） -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-starter-vector-store-pgvector</artifactId>
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<optional>true</optional>
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</dependency>
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<!-- ETL: Document Readers (PDF, Word, etc.) -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-pdf-document-reader</artifactId>
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<optional>true</optional>
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</dependency>
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-tika-document-reader</artifactId>
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<optional>true</optional>
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</dependency>
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</dependencies>
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</project>
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@@ -0,0 +1,41 @@
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package com.ccb.fintec.rag.autoconfigure.config;
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import com.ccb.fintec.core.properties.ObservabilityProperties;
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import com.ccb.fintec.core.properties.RagProperties;
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import com.ccb.fintec.core.properties.SafetyProperties;
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import org.springframework.boot.context.properties.ConfigurationProperties;
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/**
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* AI 核心配置属性绑定类（RAG 模块）
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*/
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@ConfigurationProperties(prefix = "fintec.ai")
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public class AiCoreConfigProperties {
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private RagProperties rag = new RagProperties();
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private SafetyProperties safety = new SafetyProperties();
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private ObservabilityProperties observability = new ObservabilityProperties();
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public RagProperties getRag() {
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return rag;
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}
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public void setRag(RagProperties rag) {
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this.rag = rag;
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}
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public SafetyProperties getSafety() {
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return safety;
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}
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public void setSafety(SafetyProperties safety) {
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this.safety = safety;
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}
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public ObservabilityProperties getObservability() {
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return observability;
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}
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public void setObservability(ObservabilityProperties observability) {
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this.observability = observability;
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}
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}
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package com.ccb.fintec.rag.autoconfigure.config;
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import com.ccb.fintec.rag.autoconfigure.image.ImageGenerationTemplate;
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import com.ccb.fintec.rag.autoconfigure.template.RagTemplate;
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import org.springframework.ai.chat.client.ChatClient;
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import org.springframework.ai.chat.client.advisor.vectorstore.QuestionAnswerAdvisor;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.image.ImageModel;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.ai.vectorstore.SimpleVectorStore;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.boot.autoconfigure.AutoConfiguration;
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import org.springframework.boot.autoconfigure.condition.ConditionalOnBean;
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import org.springframework.boot.autoconfigure.condition.ConditionalOnClass;
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import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean;
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import org.springframework.boot.context.properties.EnableConfigurationProperties;
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import org.springframework.context.annotation.Bean;
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/**
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* RAG 自动配置类
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*
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* 提供简化的 RAG 开发体验：
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* 1. 自动创建 RagTemplate，封装文档入库和知识库问答
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* 2. 内置公司级策略：分块大小、相似度阈值、TopK
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* 3. 支持图片生成能力
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*/
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@AutoConfiguration
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@ConditionalOnClass({VectorStore.class, EmbeddingModel.class, ChatClient.class})
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@EnableConfigurationProperties(AiCoreConfigProperties.class)
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public class RagAutoConfiguration {
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/**
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* 默认向量存储：基于内存的 SimpleVectorStore
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* 适用于开发和测试环境，无需外部向量数据库
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* 生产环境可以替换为 Chroma、Milvus、PGVector 等
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*/
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@Bean
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@ConditionalOnMissingBean(VectorStore.class)
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public VectorStore vectorStore(EmbeddingModel embeddingModel) {
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return SimpleVectorStore.builder(embeddingModel).build();
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}
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/**
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* 创建 RetrievalAugmentationAdvisor（可选，供高级用户使用）
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*/
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@Bean
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@ConditionalOnMissingBean
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public QuestionAnswerAdvisor questionAnswerAdvisor(VectorStore vectorStore,
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AiCoreConfigProperties properties) {
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return QuestionAnswerAdvisor.builder(vectorStore)
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.searchRequest(SearchRequest.builder()
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.topK(properties.getRag().getTopK())
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.similarityThreshold(properties.getRag().getSimilarityThreshold())
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.build())
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.build();
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}
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/**
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* RagTemplate：业务系统的唯一入口
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* 封装了文档入库和知识库问答两个核心场景
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*/
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@Bean
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@ConditionalOnMissingBean(RagTemplate.class)
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public RagTemplate ragTemplate(ChatClient chatClient,
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VectorStore vectorStore,
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AiCoreConfigProperties properties) {
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return new RagTemplate(chatClient, vectorStore, properties);
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}
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/**
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* ImageGenerationTemplate：图片生成能力
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*/
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@Bean
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@ConditionalOnBean(ImageModel.class)
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@ConditionalOnMissingBean
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public ImageGenerationTemplate imageGenerationTemplate(ImageModel imageModel) {
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return new ImageGenerationTemplate(imageModel);
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}
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}
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package com.ccb.fintec.rag.autoconfigure.image;
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import org.springframework.ai.image.ImageModel;
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import org.springframework.ai.image.ImageOptions;
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import org.springframework.ai.image.ImageOptionsBuilder;
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import org.springframework.ai.image.ImagePrompt;
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import org.springframework.ai.image.ImageResponse;
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import org.springframework.core.io.Resource;
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/**
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* 图片生成模板 - 文本生成图片
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*/
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public class ImageGenerationTemplate {
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private final ImageModel imageModel;
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public ImageGenerationTemplate(ImageModel imageModel) {
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this.imageModel = imageModel;
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}
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/**
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* 根据文本描述生成图片
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* @param prompt 图片描述
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* @return 生成的图片URL字符串
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*/
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public String generate(String prompt) {
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ImageOptions options = ImageOptionsBuilder.builder()
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.build();
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ImagePrompt imagePrompt = new ImagePrompt(prompt, options);
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ImageResponse response = imageModel.call(imagePrompt);
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return response.getResult().getOutput().getUrl();
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}
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/**
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* 根据文本描述生成图片(自定义参数)
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* @param prompt 图片描述
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* @param width 图片宽度(如: 1024)
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* @param height 图片高度(如: 1024)
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* @return 生成的图片URL字符串
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*/
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public String generateWithSize(String prompt, int width, int height) {
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ImageOptions options = ImageOptionsBuilder.builder()
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.width(width)
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.height(height)
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.build();
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ImagePrompt imagePrompt = new ImagePrompt(prompt, options);
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ImageResponse response = imageModel.call(imagePrompt);
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return response.getResult().getOutput().getUrl();
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}
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/**
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* 批量生成图片
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* @param prompt 图片描述
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* @param numImages 生成数量
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* @return 生成的图片URL数组
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*/
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public String[] generateMultiple(String prompt, int numImages) {
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ImageOptions options = ImageOptionsBuilder.builder()
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.N(numImages)
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.build();
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ImagePrompt imagePrompt = new ImagePrompt(prompt, options);
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ImageResponse response = imageModel.call(imagePrompt);
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return response.getResults().stream()
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.map(result -> result.getOutput().getUrl())
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.toArray(String[]::new);
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}
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}
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@@ -0,0 +1,192 @@
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package com.ccb.fintec.rag.autoconfigure.template;
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import com.ccb.fintec.core.dto.AiResponse;
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import com.ccb.fintec.rag.autoconfigure.config.AiCoreConfigProperties;
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import org.springframework.ai.chat.client.ChatClient;
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import org.springframework.ai.chat.client.advisor.vectorstore.QuestionAnswerAdvisor;
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import org.springframework.ai.chat.memory.ChatMemory;
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import org.springframework.ai.chat.model.ChatResponse;
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import org.springframework.ai.document.Document;
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import org.springframework.ai.reader.pdf.PagePdfDocumentReader;
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import org.springframework.ai.transformer.splitter.TokenTextSplitter;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.core.io.Resource;
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import reactor.core.publisher.Flux;
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import java.util.List;
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import java.util.Map;
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import java.util.UUID;
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/**
|
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* RAG 核心入口
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* 封装：文档入库 + 知识库问答两个核心场景
|
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*/
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public class RagTemplate {
|
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|
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private final ChatClient chatClient;
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private final VectorStore vectorStore;
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private final int chunkSize;
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private final int chunkOverlap;
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private final int topK;
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private final double similarityThreshold;
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public RagTemplate(ChatClient chatClient, VectorStore vectorStore, AiCoreConfigProperties properties) {
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this.chatClient = chatClient;
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this.vectorStore = vectorStore;
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this.chunkSize = properties.getRag().getChunkSize();
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this.chunkOverlap = properties.getRag().getChunkOverlap();
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this.topK = properties.getRag().getTopK();
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this.similarityThreshold = properties.getRag().getSimilarityThreshold();
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}
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// ========== 文档入库 ==========
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/** PDF 文档入库 */
|
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public void ingest(Resource pdfResource) {
|
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var reader = new PagePdfDocumentReader(pdfResource);
|
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var splitter = new TokenTextSplitter(chunkSize, chunkOverlap, 5, 10000, true);
|
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List<Document> docs = splitter.apply(reader.read());
|
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vectorStore.add(docs);
|
||||
}
|
||||
|
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/** 直接传入 Document 列表入库（支持任意格式） */
|
||||
public void ingest(List<Document> documents) {
|
||||
var splitter = new TokenTextSplitter(chunkSize, chunkOverlap, 5, 10000, true);
|
||||
vectorStore.add(splitter.apply(documents));
|
||||
}
|
||||
|
||||
/** 直接传入纯文本入库 */
|
||||
public void ingest(String content, String source) {
|
||||
Document doc = new Document(content, Map.of("source", source));
|
||||
vectorStore.add(List.of(doc));
|
||||
}
|
||||
|
||||
// ========== 知识库问答 ==========
|
||||
|
||||
/** RAG 问答：自动检索相关文档注入 prompt */
|
||||
public String ask(String question) {
|
||||
return chatClient.prompt()
|
||||
.user(question)
|
||||
.advisors(QuestionAnswerAdvisor.builder(vectorStore)
|
||||
.searchRequest(SearchRequest.builder()
|
||||
.topK(topK)
|
||||
.similarityThreshold(similarityThreshold)
|
||||
.build())
|
||||
.build())
|
||||
.call()
|
||||
.content();
|
||||
}
|
||||
|
||||
/** RAG 问答 + 会话记忆 */
|
||||
public String ask(String question, String conversationId) {
|
||||
return chatClient.prompt()
|
||||
.user(question)
|
||||
.advisors(QuestionAnswerAdvisor.builder(vectorStore)
|
||||
.searchRequest(SearchRequest.builder()
|
||||
.topK(topK)
|
||||
.similarityThreshold(similarityThreshold)
|
||||
.build())
|
||||
.build())
|
||||
.advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
|
||||
.call()
|
||||
.content();
|
||||
}
|
||||
|
||||
/** RAG 流式输出 */
|
||||
public Flux<String> stream(String question) {
|
||||
return chatClient.prompt()
|
||||
.user(question)
|
||||
.advisors(QuestionAnswerAdvisor.builder(vectorStore)
|
||||
.searchRequest(SearchRequest.builder()
|
||||
.topK(topK)
|
||||
.similarityThreshold(similarityThreshold)
|
||||
.build())
|
||||
.build())
|
||||
.stream()
|
||||
.content();
|
||||
}
|
||||
|
||||
/** 自定义相似度阈值和 TopK */
|
||||
public String askWithConfig(String question, double similarityThreshold, int topK) {
|
||||
return chatClient.prompt()
|
||||
.user(question)
|
||||
.advisors(QuestionAnswerAdvisor.builder(vectorStore)
|
||||
.searchRequest(SearchRequest.builder()
|
||||
.similarityThreshold(similarityThreshold)
|
||||
.topK(topK)
|
||||
.build())
|
||||
.build())
|
||||
.call()
|
||||
.content();
|
||||
}
|
||||
|
||||
// ========== 元数据收集 ==========
|
||||
|
||||
/** RAG 问答返回 AiResponse（包含 Token、耗时、检索文档数） */
|
||||
public AiResponse askWithMetadata(String question) {
|
||||
String requestId = UUID.randomUUID().toString();
|
||||
long startTime = System.currentTimeMillis();
|
||||
|
||||
try {
|
||||
ChatResponse response = chatClient.prompt()
|
||||
.user(question)
|
||||
.advisors(QuestionAnswerAdvisor.builder(vectorStore)
|
||||
.searchRequest(SearchRequest.builder()
|
||||
.topK(topK)
|
||||
.similarityThreshold(similarityThreshold)
|
||||
.build())
|
||||
.build())
|
||||
.call()
|
||||
.chatResponse();
|
||||
|
||||
long duration = System.currentTimeMillis() - startTime;
|
||||
|
||||
AiResponse aiResponse = new AiResponse();
|
||||
aiResponse.setRequestId(requestId);
|
||||
aiResponse.setContent(response.getResult().getOutput().getText());
|
||||
aiResponse.setDuration(duration);
|
||||
|
||||
// 设置 Token 使用量
|
||||
if (response.getMetadata() != null && response.getMetadata().getUsage() != null) {
|
||||
var usage = response.getMetadata().getUsage();
|
||||
AiResponse.TokenUsage tokenUsage = new AiResponse.TokenUsage(
|
||||
usage.getPromptTokens(),
|
||||
usage.getCompletionTokens(),
|
||||
usage.getTotalTokens()
|
||||
);
|
||||
aiResponse.setTokenUsage(tokenUsage);
|
||||
}
|
||||
|
||||
// 添加 RAG 特有的元数据：检索到的文档数
|
||||
aiResponse.getMetadata().put("retrievedDocuments",
|
||||
response.getResults() != null ? response.getResults().size() : 0);
|
||||
|
||||
return aiResponse;
|
||||
} catch (Exception e) {
|
||||
long duration = System.currentTimeMillis() - startTime;
|
||||
AiResponse errorResponse = new AiResponse();
|
||||
errorResponse.setRequestId(requestId);
|
||||
errorResponse.setDuration(duration);
|
||||
|
||||
AiResponse.ErrorInfo errorInfo = new AiResponse.ErrorInfo(
|
||||
"RAG_CALL_FAILED",
|
||||
e.getMessage(),
|
||||
e.getClass().getSimpleName()
|
||||
);
|
||||
errorResponse.setError(errorInfo);
|
||||
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
|
||||
// ========== 逃生通道 ==========
|
||||
|
||||
public VectorStore getVectorStore() {
|
||||
return vectorStore;
|
||||
}
|
||||
|
||||
public ChatClient getChatClient() {
|
||||
return chatClient;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
com.ccb.fintec.rag.autoconfigure.config.RagAutoConfiguration
|
||||
Reference in New Issue
Block a user