使用spring-ai-alibaba重构项目
This commit is contained in:
123
pom.xml
123
pom.xml
@@ -5,7 +5,7 @@
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<parent>
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<parent>
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<groupId>org.springframework.boot</groupId>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-parent</artifactId>
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<artifactId>spring-boot-starter-parent</artifactId>
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<version>3.5.0</version>
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<version>3.4.5</version>
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<relativePath/> <!-- lookup parent from repository -->
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<relativePath/> <!-- lookup parent from repository -->
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</parent>
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</parent>
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<groupId>com.qingqiu</groupId>
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<groupId>com.qingqiu</groupId>
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@@ -28,6 +28,9 @@
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</scm>
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</scm>
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<properties>
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<properties>
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<java.version>17</java.version>
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<java.version>17</java.version>
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<spring-ai.version>1.0.0</spring-ai.version>
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<spring-ai-alibaba.version>1.0.0.2</spring-ai-alibaba.version>
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<spring-boot.version>3.4.5</spring-boot.version>
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</properties>
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</properties>
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<dependencies>
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<dependencies>
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<!-- MyBatis-Plus -->
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<!-- MyBatis-Plus -->
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@@ -53,26 +56,56 @@
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<artifactId>spring-boot-starter-webflux</artifactId> <!-- 用于 WebClient -->
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<artifactId>spring-boot-starter-webflux</artifactId> <!-- 用于 WebClient -->
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</dependency>
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</dependency>
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<!-- <!– Spring AI Dependencies –>-->
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<!-- <dependency>-->
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<!-- <dependency>-->
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<!-- <groupId>org.springframework.ai</groupId>-->
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<!-- <groupId>org.springframework.ai</groupId>-->
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<!-- <artifactId>spring-ai-openai-spring-boot-starter</artifactId>-->
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<!-- <artifactId>spring-ai-openai-spring-boot-starter</artifactId>-->
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<!-- <exclusions>-->
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<!-- <version>1.0.0-SNAPSHOT</version>-->
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<!-- <exclusion>-->
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<!-- <groupId>com.alibaba.cloud.ai</groupId>-->
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<!-- <artifactId>spring-ai-alibaba-autoconfigure</artifactId>-->
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<!-- </exclusion>-->
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<!-- <exclusion>-->
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<!-- <groupId>org.springframework.ai</groupId>-->
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<!-- <artifactId>spring-ai-core</artifactId>-->
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<!-- </exclusion>-->
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<!-- </exclusions>-->
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<!-- </dependency>-->
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<!-- </dependency>-->
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<dependency>
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<dependency>
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<groupId>com.alibaba</groupId>
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<groupId>org.springframework.ai</groupId>
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<artifactId>dashscope-sdk-java</artifactId>
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<artifactId>spring-ai-starter-model-openai</artifactId>
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<version>2.21.5</version>
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</dependency>
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</dependency>
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<dependency>
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<groupId>com.alibaba.cloud.ai</groupId>
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<artifactId>spring-ai-alibaba-starter-dashscope</artifactId>
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</dependency>
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<dependency>
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<groupId>com.alibaba.cloud.ai</groupId>
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<artifactId>spring-ai-alibaba-starter-memory</artifactId>
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</dependency>
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<dependency>
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<groupId>com.alibaba.cloud.ai</groupId>
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<artifactId>spring-ai-alibaba-starter-memory-jdbc</artifactId>
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</dependency>
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<dependency>
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<groupId>com.alibaba.cloud.ai</groupId>
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<artifactId>spring-ai-alibaba-starter-memory-redis</artifactId>
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</dependency>
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<dependency>
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<groupId>redis.clients</groupId>
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<artifactId>jedis</artifactId>
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<version>5.2.0</version>
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</dependency>
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<!-- 集成redis依赖 -->
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-data-redis</artifactId>
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</dependency>
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<!-- <dependency>-->
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<!-- <groupId>com.alibaba</groupId>-->
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<!-- <artifactId>dashscope-sdk-java</artifactId>-->
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<!-- <version>2.21.5</version>-->
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<!-- </dependency>-->
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<!-- https://mvnrepository.com/artifact/cn.hutool/hutool-all -->
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<!-- https://mvnrepository.com/artifact/cn.hutool/hutool-all -->
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<dependency>
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<dependency>
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<groupId>cn.hutool</groupId>
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<groupId>cn.hutool</groupId>
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@@ -129,16 +162,28 @@
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</dependency>
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</dependency>
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</dependencies>
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</dependencies>
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<dependencyManagement>
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<dependencyManagement>
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<!-- <dependencies>-->
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<!-- <dependency>-->
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<!-- <groupId>org.springframework.ai</groupId>-->
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<!-- <artifactId>spring-ai-bom</artifactId>-->
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<!-- <version>1.0.0-M5</version> <!– 或最新版本 –>-->
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<!-- <type>pom</type>-->
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<!-- <scope>import</scope>-->
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<!-- </dependency>-->
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<!-- </dependencies>-->
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<dependencies>
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<dependencies>
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<dependency>
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<groupId>com.alibaba.cloud.ai</groupId>
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<artifactId>spring-ai-alibaba-bom</artifactId>
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<version>${spring-ai-alibaba.version}</version>
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<type>pom</type>
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<scope>import</scope>
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</dependency>
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-dependencies</artifactId>
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<version>${spring-boot.version}</version>
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<type>pom</type>
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<scope>import</scope>
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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-bom</artifactId>
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<version>${spring-ai.version}</version>
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<type>pom</type>
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<scope>import</scope>
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</dependency>
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<dependency>
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<dependency>
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<groupId>com.baomidou</groupId>
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<groupId>com.baomidou</groupId>
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<artifactId>mybatis-plus-bom</artifactId>
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<artifactId>mybatis-plus-bom</artifactId>
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@@ -147,6 +192,7 @@
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<scope>import</scope>
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<scope>import</scope>
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</dependency>
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</dependency>
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</dependencies>
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</dependencies>
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</dependencyManagement>
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</dependencyManagement>
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<build>
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<build>
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@@ -167,33 +213,6 @@
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</plugin>
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</plugin>
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</plugins>
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</plugins>
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</build>
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</build>
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<repositories>
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<repository>
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<id>spring-snapshots</id>
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<name>Spring Snapshots</name>
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<url>https://repo.spring.io/snapshot</url>
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<releases>
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<enabled>false</enabled>
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</releases>
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</repository>
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<repository>
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<id>spring-milestones</id>
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<name>Spring Milestones</name>
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<url>https://repo.spring.io/milestone</url>
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<snapshots>
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<enabled>false</enabled>
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</snapshots>
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</repository>
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</repositories>
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<pluginRepositories>
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<pluginRepository>
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<id>spring-snapshots</id>
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<name>Spring Snapshots</name>
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<url>https://repo.spring.io/snapshot</url>
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<releases>
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<enabled>false</enabled>
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</releases>
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</pluginRepository>
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</pluginRepositories>
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</project>
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</project>
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@@ -1,22 +0,0 @@
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package com.qingqiu.interview.ai.entity;
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import lombok.AllArgsConstructor;
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import lombok.Data;
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import lombok.NoArgsConstructor;
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import lombok.experimental.Accessors;
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import java.io.Serial;
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import java.io.Serializable;
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@Data
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@Accessors(chain = true)
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@AllArgsConstructor
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@NoArgsConstructor
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public class Message implements Serializable {
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@Serial
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private static final long serialVersionUID = 1L;
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private String role;
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private String content;
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}
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@@ -1,11 +0,0 @@
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package com.qingqiu.interview.ai.factory;
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import com.qingqiu.interview.common.enums.LLMProvider;
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import com.qingqiu.interview.ai.service.AIClientService;
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public interface AIClientFactory {
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AIClientService createAIClient();
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// 支持的提供商
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LLMProvider getSupportedProvider();
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}
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@@ -1,33 +0,0 @@
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package com.qingqiu.interview.ai.factory;
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import com.qingqiu.interview.common.enums.LLMProvider;
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import com.qingqiu.interview.ai.service.AIClientService;
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import org.springframework.stereotype.Service;
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import java.util.List;
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import java.util.Map;
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import java.util.function.Function;
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import java.util.stream.Collectors;
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@Service
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public class AIClientManager {
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private final Map<LLMProvider, AIClientFactory> factories;
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public AIClientManager(List<AIClientFactory> strategies) {
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this.factories = strategies.stream()
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.collect(Collectors.toMap(
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AIClientFactory::getSupportedProvider,
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Function.identity()
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));
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}
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public AIClientService getClient(LLMProvider provider) {
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// String factoryName = aiType + "ClientFactory";
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AIClientFactory factory = factories.get(provider);
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if (factory == null) {
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throw new IllegalArgumentException("不支持的AI type: " + provider);
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}
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return factory.createAIClient();
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}
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}
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@@ -1,20 +0,0 @@
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package com.qingqiu.interview.ai.factory;
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import com.qingqiu.interview.common.enums.LLMProvider;
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import com.qingqiu.interview.ai.service.AIClientService;
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import com.qingqiu.interview.ai.service.impl.DeepSeekClientServiceImpl;
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import com.qingqiu.interview.common.utils.SpringApplicationContextUtil;
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import org.springframework.stereotype.Service;
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@Service
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public class DeepSeekClientFactory implements AIClientFactory{
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@Override
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public AIClientService createAIClient() {
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return SpringApplicationContextUtil.getBean(DeepSeekClientServiceImpl.class);
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}
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@Override
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public LLMProvider getSupportedProvider() {
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return LLMProvider.DEEPSEEK;
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|
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}
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|
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}
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@@ -1,20 +0,0 @@
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package com.qingqiu.interview.ai.factory;
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|
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import com.qingqiu.interview.common.enums.LLMProvider;
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|
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import com.qingqiu.interview.ai.service.AIClientService;
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import com.qingqiu.interview.ai.service.impl.QwenClientServiceImpl;
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import com.qingqiu.interview.common.utils.SpringApplicationContextUtil;
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import org.springframework.stereotype.Service;
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@Service
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public class QwenClientFactory implements AIClientFactory{
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@Override
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public AIClientService createAIClient() {
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return SpringApplicationContextUtil.getBean(QwenClientServiceImpl.class);
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|
||||||
}
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|
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@Override
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|
||||||
public LLMProvider getSupportedProvider() {
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|
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return LLMProvider.QWEN;
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|
||||||
}
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|
||||||
}
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|
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@@ -1,13 +0,0 @@
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package com.qingqiu.interview.ai.service;
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|
||||||
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|
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import com.alibaba.dashscope.common.Message;
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|
||||||
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|
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import java.util.List;
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|
||||||
|
|
||||||
public abstract class AIClientService {
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|
||||||
public abstract String chatCompletion(String prompt);
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|
||||||
|
|
||||||
public String chatCompletion(List<Message> messages) {
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||||||
return null;
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|
||||||
}
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|
||||||
}
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@@ -1,65 +0,0 @@
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package com.qingqiu.interview.ai.service.impl;
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|
||||||
|
|
||||||
import com.alibaba.dashscope.common.Message;
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|
||||||
import com.alibaba.fastjson2.JSONArray;
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|
||||||
import com.alibaba.fastjson2.JSONObject;
|
|
||||||
import com.qingqiu.interview.ai.service.AIClientService;
|
|
||||||
import com.qingqiu.interview.common.service.HttpService;
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|
||||||
import lombok.RequiredArgsConstructor;
|
|
||||||
import org.apache.commons.lang3.StringUtils;
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|
||||||
import org.springframework.beans.factory.annotation.Value;
|
|
||||||
import org.springframework.stereotype.Service;
|
|
||||||
|
|
||||||
import java.util.Collections;
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|
||||||
import java.util.List;
|
|
||||||
import java.util.Map;
|
|
||||||
|
|
||||||
import static com.qingqiu.interview.common.utils.AIUtils.createUserMessage;
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|
||||||
|
|
||||||
/**
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|
||||||
* deepseek 接入
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|
||||||
*/
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|
||||||
@Service
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|
||||||
@RequiredArgsConstructor
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|
||||||
public class DeepSeekClientServiceImpl extends AIClientService {
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|
||||||
|
|
||||||
private final HttpService httpService;
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|
||||||
|
|
||||||
@Value("${deepseek.api-url}")
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|
||||||
private String apiUrl;
|
|
||||||
|
|
||||||
@Value("${deepseek.api-key}")
|
|
||||||
private String apiKey;
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|
||||||
|
|
||||||
|
|
||||||
@Override
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|
||||||
public String chatCompletion(String prompt) {
|
|
||||||
return chatCompletion(Collections.singletonList(createUserMessage(prompt)));
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String chatCompletion(List<Message> messages) {
|
|
||||||
JSONObject jsonObject = new JSONObject();
|
|
||||||
jsonObject.put("type", "json_object");
|
|
||||||
Map<String, Object> requestBody = Map.of(
|
|
||||||
"model", "deepseek-chat",
|
|
||||||
"messages", messages,
|
|
||||||
"max_tokens", 8192,
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|
||||||
"response_format", Map.of("type", "json_object")
|
|
||||||
);
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|
||||||
String res = httpService.postWithAuth(
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|
||||||
apiUrl,
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|
||||||
requestBody,
|
|
||||||
String.class,
|
|
||||||
"Bearer " + apiKey
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|
||||||
).block();
|
|
||||||
if (StringUtils.isNotBlank(res)) {
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|
||||||
JSONObject jsonRes = JSONObject.parse(res);
|
|
||||||
JSONArray choices = jsonRes.getJSONArray("choices");
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|
||||||
JSONObject resContent = choices.getJSONObject(0);
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|
||||||
JSONObject message = resContent.getJSONObject("message");
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|
||||||
return message.getString("content");
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|
||||||
}
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|
||||||
return null;
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|
||||||
}
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|
||||||
}
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|
||||||
@@ -1,62 +0,0 @@
|
|||||||
package com.qingqiu.interview.ai.service.impl;
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|
||||||
|
|
||||||
import com.alibaba.dashscope.aigc.generation.Generation;
|
|
||||||
import com.alibaba.dashscope.aigc.generation.GenerationParam;
|
|
||||||
import com.alibaba.dashscope.aigc.generation.GenerationResult;
|
|
||||||
import com.alibaba.dashscope.common.Message;
|
|
||||||
import com.alibaba.dashscope.common.ResponseFormat;
|
|
||||||
import com.alibaba.dashscope.exception.ApiException;
|
|
||||||
import com.alibaba.dashscope.exception.InputRequiredException;
|
|
||||||
import com.alibaba.dashscope.exception.NoApiKeyException;
|
|
||||||
import com.qingqiu.interview.ai.service.AIClientService;
|
|
||||||
import com.qingqiu.interview.common.res.ResultCode;
|
|
||||||
import lombok.RequiredArgsConstructor;
|
|
||||||
import lombok.extern.slf4j.Slf4j;
|
|
||||||
import org.springframework.beans.factory.annotation.Value;
|
|
||||||
import org.springframework.stereotype.Service;
|
|
||||||
|
|
||||||
import java.util.Collections;
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|
||||||
import java.util.List;
|
|
||||||
|
|
||||||
import static com.qingqiu.interview.common.constants.QwenModelConstant.QWEN_PLUS_LATEST;
|
|
||||||
import static com.qingqiu.interview.common.utils.AIUtils.createUserMessage;
|
|
||||||
|
|
||||||
@Slf4j
|
|
||||||
@Service
|
|
||||||
@RequiredArgsConstructor
|
|
||||||
public class QwenClientServiceImpl extends AIClientService {
|
|
||||||
|
|
||||||
@Value("${dashscope.api-key}")
|
|
||||||
private String apiKey;
|
|
||||||
|
|
||||||
private final Generation generation;
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String chatCompletion(String prompt) {
|
|
||||||
return chatCompletion(Collections.singletonList(createUserMessage(prompt)));
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String chatCompletion(List<Message> messages) {
|
|
||||||
|
|
||||||
GenerationParam param = GenerationParam.builder()
|
|
||||||
.model(QWEN_PLUS_LATEST) // 可根据需要更换模型
|
|
||||||
.messages(messages)
|
|
||||||
.resultFormat(GenerationParam.ResultFormat.MESSAGE)
|
|
||||||
.responseFormat(ResponseFormat.builder().type(ResponseFormat.JSON_OBJECT).build())
|
|
||||||
.apiKey(apiKey)
|
|
||||||
.build();
|
|
||||||
|
|
||||||
GenerationResult result = null;
|
|
||||||
try {
|
|
||||||
result = generation.call(param);
|
|
||||||
return result.getOutput().getChoices().get(0).getMessage().getContent();
|
|
||||||
} catch (NoApiKeyException e) {
|
|
||||||
log.error("没有api key，请先确认配置！");
|
|
||||||
throw new com.qingqiu.interview.common.ex.ApiException(ResultCode.INTERNAL);
|
|
||||||
} catch (ApiException | InputRequiredException e) {
|
|
||||||
log.error("调用AI服务失败", e);
|
|
||||||
throw new com.qingqiu.interview.common.ex.ApiException(ResultCode.INTERNAL);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -0,0 +1,44 @@
|
|||||||
|
package com.qingqiu.interview.common.constants;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 聊天相关常量类
|
||||||
|
* 定义了聊天功能中使用的各种常量值
|
||||||
|
*/
|
||||||
|
public class ChatConstant {
|
||||||
|
|
||||||
|
/**
|
||||||
|
* MySQL聊天记录存储的Bean名称
|
||||||
|
*/
|
||||||
|
public static final String MYSQL_CHAT_MEMORY_BEAN_NAME = "mysql-chat-memory";
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Redis聊天记录存储的Bean名称
|
||||||
|
*/
|
||||||
|
public static final String REDIS_CHAT_MEMORY_BEAN_NAME = "redis-chat-memory";
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 聊天记录最大保存消息数
|
||||||
|
*/
|
||||||
|
public static final Integer MAX_MESSAGES = 100;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* DashScope聊天模型的Bean名称
|
||||||
|
*/
|
||||||
|
public static final String DASH_SCOPE_CHAT_MODEL_BEAN_NAME = "dash-scope-chat-model";
|
||||||
|
public static final String OPEN_AI_CHAT_MODEL_BEAN_NAME = "open-ai-chat-model";
|
||||||
|
|
||||||
|
/**
|
||||||
|
* DashScope聊天客户端的Bean名称
|
||||||
|
*/
|
||||||
|
public static final String DASH_SCOPE_CHAT_CLIENT_BEAN_NAME = "dash-scope-chat-client";
|
||||||
|
|
||||||
|
/**
|
||||||
|
* OpenAI聊天客户端的Bean名称
|
||||||
|
*/
|
||||||
|
public static final String OPEN_AI_CHAT_CLIENT_BEAN_NAME = "open-ai-chat-client";
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 最大补全token数量
|
||||||
|
*/
|
||||||
|
public static final Integer MAX_COMPLETION_TOKENS = 8192;
|
||||||
|
}
|
||||||
@@ -1,41 +0,0 @@
|
|||||||
package com.qingqiu.interview.common.utils;
|
|
||||||
|
|
||||||
import com.alibaba.dashscope.common.Message;
|
|
||||||
import com.alibaba.dashscope.common.Role;
|
|
||||||
import com.alibaba.dashscope.tokenizers.Tokenizer;
|
|
||||||
import com.alibaba.dashscope.tokenizers.TokenizerFactory;
|
|
||||||
|
|
||||||
import java.util.List;
|
|
||||||
|
|
||||||
public class AIUtils {
|
|
||||||
|
|
||||||
public static Message createMessage(String role, String content) {
|
|
||||||
return Message.builder()
|
|
||||||
.role(role)
|
|
||||||
.content(content)
|
|
||||||
.build();
|
|
||||||
}
|
|
||||||
|
|
||||||
public static Message createUserMessage(String prompt) {
|
|
||||||
return createMessage(Role.USER.getValue(), prompt);
|
|
||||||
}
|
|
||||||
|
|
||||||
public static Message createAIMessage(String prompt) {
|
|
||||||
return createMessage(Role.ASSISTANT.getValue(), prompt);
|
|
||||||
}
|
|
||||||
|
|
||||||
public static Message createSystemMessage(String prompt) {
|
|
||||||
return createMessage(Role.SYSTEM.getValue(), prompt);
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* 获取prompt的token数
|
|
||||||
* @param prompt 输入
|
|
||||||
* @return tokens
|
|
||||||
*/
|
|
||||||
public static Integer getPromptTokens(String prompt) {
|
|
||||||
Tokenizer tokenizer = TokenizerFactory.qwen();
|
|
||||||
List<Integer> integers = tokenizer.encodeOrdinary(prompt);
|
|
||||||
return integers.size();
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -0,0 +1,430 @@
|
|||||||
|
package com.qingqiu.interview.common.utils;
|
||||||
|
|
||||||
|
import org.springframework.ai.chat.messages.SystemMessage;
|
||||||
|
import org.springframework.ai.chat.messages.UserMessage;
|
||||||
|
import org.springframework.ai.chat.prompt.Prompt;
|
||||||
|
import org.springframework.ai.chat.prompt.PromptTemplate;
|
||||||
|
import org.springframework.ai.chat.prompt.SystemPromptTemplate;
|
||||||
|
|
||||||
|
import java.util.List;
|
||||||
|
import java.util.Map;
|
||||||
|
import java.util.Objects;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 提示模板工具类
|
||||||
|
*/
|
||||||
|
public class PromptTemplateUtils {
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 获取提取技能的提示
|
||||||
|
*
|
||||||
|
* @param resumeContent 简历内容
|
||||||
|
* @return 提示
|
||||||
|
*/
|
||||||
|
public static Prompt getExtractSkillsPrompt(String resumeContent) {
|
||||||
|
SystemMessage systemMessage = new SystemMessage("""
|
||||||
|
你是一位资深的技术面试官，以严格和深入著称。
|
||||||
|
你需要从提供的简历内容中，提取出所有与职位相关的技能。
|
||||||
|
请按照以下JSON格式返回：
|
||||||
|
{"skills": ["技能1", "技能2", "..."]}
|
||||||
|
""");
|
||||||
|
UserMessage userMessage = new UserMessage(resumeContent);
|
||||||
|
return new Prompt(List.of(userMessage, systemMessage));
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 获取AI面试官的提示
|
||||||
|
*
|
||||||
|
* @param params 参数
|
||||||
|
* @return 提示
|
||||||
|
*/
|
||||||
|
public static Prompt getAiInterviewerPrompt(Map<String, Object> params) {
|
||||||
|
SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate("""
|
||||||
|
你是一位经验丰富的软件开发技术面试官，具备以下特质：
|
||||||
|
|
||||||
|
## 专业能力
|
||||||
|
- 拥有10年以上软件开发和团队管理经验
|
||||||
|
- 熟悉各种主流技术栈和架构模式
|
||||||
|
- 擅长通过技术面试评估候选人的真实能力
|
||||||
|
- 能够设计多层次、递进式的面试问题
|
||||||
|
|
||||||
|
## 面试原则
|
||||||
|
1. **精准匹配**: 问题必须紧密结合岗位要求和候选人背景
|
||||||
|
2. **层次递进**: 从基础概念到深度应用，从理论到实践
|
||||||
|
3. **场景化考察**: 基于真实项目场景设计问题
|
||||||
|
4. **全面评估**: 涵盖技术深度、问题解决能力、系统思维
|
||||||
|
## 本岗位招聘要求
|
||||||
|
[{jobRequirements}]
|
||||||
|
|
||||||
|
## 输出要求
|
||||||
|
- 严格按照指定的JSON格式输出
|
||||||
|
- 不得包含任何JSON格式之外的内容
|
||||||
|
- 不得添加代码块标记(```json)或其他解释性文字
|
||||||
|
- 确保生成的问题数量与要求完全一致
|
||||||
|
""");
|
||||||
|
PromptTemplate userPromptTemplate = new PromptTemplate("""
|
||||||
|
请根据岗位招聘要求设计 {count} 道技术面试题。
|
||||||
|
|
||||||
|
## 候选人信息
|
||||||
|
|
||||||
|
### 技术栈
|
||||||
|
{skills}
|
||||||
|
|
||||||
|
### 简历内容
|
||||||
|
{resume}
|
||||||
|
|
||||||
|
## 面试题设计要求
|
||||||
|
|
||||||
|
### 覆盖维度
|
||||||
|
1. **基础理论** (20%): 核心概念、原理机制
|
||||||
|
2. **项目实践** (40%): 具体项目中的技术难点和解决方案
|
||||||
|
3. **系统设计** (20%): 架构思维、技术选型、性能优化
|
||||||
|
4. **问题解决** (20%): 调试能力、故障排查、代码优化
|
||||||
|
|
||||||
|
### 难度分布
|
||||||
|
- 基础题 (30%): 验证核心技能掌握情况
|
||||||
|
- 进阶题 (50%): 考察深度理解和实际应用
|
||||||
|
- 高阶题 (20%): 评估架构能力和创新思维
|
||||||
|
|
||||||
|
### 问题类型
|
||||||
|
- **概念阐述**: "请解释..."、"什么是..."
|
||||||
|
- **场景分析**: "在你的XX项目中..."、"如果遇到XX问题..."
|
||||||
|
- **方案设计**: "如何设计..."、"请给出..."
|
||||||
|
- **比较选择**: "XX和YY的区别..."、"为什么选择..."
|
||||||
|
|
||||||
|
## 输出格式
|
||||||
|
必须严格按照以下JSON格式输出，不得有任何偏差：
|
||||||
|
|
||||||
|
{jsonRes}
|
||||||
|
|
||||||
|
请立即开始生成面试题：
|
||||||
|
""");
|
||||||
|
String s = """
|
||||||
|
{
|
||||||
|
"questions": [
|
||||||
|
{
|
||||||
|
"id": "ai-gen-1",
|
||||||
|
"content": "问题内容..."
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
""";
|
||||||
|
params.put("jsonRes", s);
|
||||||
|
return new Prompt(List.of(userPromptTemplate.createMessage(params), systemPromptTemplate.createMessage(params)));
|
||||||
|
}
|
||||||
|
|
||||||
|
public static Prompt getLocalInterviewPrompt(Map<String, Object> params) {
|
||||||
|
SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate("""
|
||||||
|
你是一位资深的技术面试专家。
|
||||||
|
## 你的专业背景
|
||||||
|
- 10年+ 软件开发和技术管理经验
|
||||||
|
- 熟悉前端、后端、全栈、嵌入式、移动端、DevOps、大数据、AI等各技术领域
|
||||||
|
- 精通各种技术栈的深度和广度评估
|
||||||
|
- 擅长根据岗位特点和候选人背景进行精准匹配
|
||||||
|
## 通用岗位分类及评估重点
|
||||||
|
### 后端开发岗位
|
||||||
|
- 重点：框架熟练度、数据库设计、API设计、性能优化、并发处理
|
||||||
|
- 核心技能：编程语言基础、框架应用、系统设计、问题排查
|
||||||
|
### 前端开发岗位
|
||||||
|
- 重点：UI/UX实现、性能优化、跨浏览器兼容、现代框架使用
|
||||||
|
- 核心技能：HTML/CSS/JS、框架应用、工程化、用户体验
|
||||||
|
### 全栈开发岗位
|
||||||
|
- 重点：前后端技术栈、系统架构、DevOps流程
|
||||||
|
- 核心技能：多技术栈掌握、系统整合、项目管理
|
||||||
|
### 架构师岗位
|
||||||
|
- 重点：系统设计、技术选型、性能调优、团队技术规划
|
||||||
|
- 核心技能：架构设计、技术决策、团队领导、业务理解
|
||||||
|
## 筛选策略框架
|
||||||
|
### 匹配维度权重
|
||||||
|
1. **技术栈匹配度** (35%): 候选人技能与岗位要求的重合度
|
||||||
|
2. **项目经验相关性** (30%): 过往项目与岗位场景的匹配度
|
||||||
|
3. **技能深度评估** (20%): 根据经验年限判断技能掌握深度
|
||||||
|
4. **发展潜力考量** (15%): 学习能力和技术视野的考察
|
||||||
|
## 本岗位招聘要求
|
||||||
|
[{jobRequirements}]
|
||||||
|
|
||||||
|
## 输出规范
|
||||||
|
- 必须输出标准JSON格式：{jsonRes}
|
||||||
|
- 不得包含任何解释、注释或代码块标记
|
||||||
|
- 确保所选题目ID真实存在于题库中
|
||||||
|
- 保证题目数量与要求完全一致
|
||||||
|
""");
|
||||||
|
|
||||||
|
PromptTemplate userPromptTemplate = new PromptTemplate("""
|
||||||
|
请根据岗位招聘要求，从题库中筛选筛选 {count} 道技术面试题。
|
||||||
|
## 候选人档案
|
||||||
|
### 技术栈
|
||||||
|
{skills}
|
||||||
|
### 简历内容
|
||||||
|
{resume}
|
||||||
|
## 筛选要求
|
||||||
|
请根据以上信息，结合你的专业框架，从题库中筛选出最能评估该候选人是否适合此岗位的面试题。
|
||||||
|
### 重点考虑因素
|
||||||
|
1. 岗位核心技术要求与候选人技能的匹配度
|
||||||
|
2. 候选人项目经验与岗位应用场景的关联性
|
||||||
|
3. 题目难度与候选人经验水平的适配性
|
||||||
|
4. 题目类型的多样性（理论+实践+设计）
|
||||||
|
"""
|
||||||
|
);
|
||||||
|
params.put("jsonRes",
|
||||||
|
"""
|
||||||
|
{
|
||||||
|
"questions": [
|
||||||
|
{
|
||||||
|
"id": "1",
|
||||||
|
"content": "问题内容..."
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "3",
|
||||||
|
"content": "问题内容..."
|
||||||
|
}
|
||||||
|
...
|
||||||
|
]
|
||||||
|
}
|
||||||
|
"""
|
||||||
|
);
|
||||||
|
return new Prompt(List.of(
|
||||||
|
userPromptTemplate.createMessage(params),
|
||||||
|
systemPromptTemplate.createMessage(params)
|
||||||
|
));
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
public static Prompt getEvaluatePrompt(Map<String, Object> params) {
|
||||||
|
// SystemMessage systemMessage = new SystemMessage("""
|
||||||
|
// 你是一位经验丰富的高级技术面试官，以公正、严谨、深入的评估风格著称。
|
||||||
|
// ## 你的专业特质
|
||||||
|
// - 拥有15年+ 技术开发和面试经验
|
||||||
|
// - 善于通过追问挖掘候选人的真实技术水平
|
||||||
|
// - 能够准确识别标准答案、实际经验和深度理解的区别
|
||||||
|
// - 注重考察解决问题的思路和实际应用能力
|
||||||
|
//
|
||||||
|
// """);
|
||||||
|
|
||||||
|
PromptTemplate promptTemplate = new PromptTemplate("""
|
||||||
|
请对候选人的回答进行专业评估。
|
||||||
|
## 评估维度及权重
|
||||||
|
### 技术准确性 (30%)
|
||||||
|
- 概念理解的正确性
|
||||||
|
- 技术细节的准确程度
|
||||||
|
- 是否存在明显错误或误解
|
||||||
|
### 深度与广度 (25%)
|
||||||
|
- 知识的深入程度
|
||||||
|
- 相关技术的关联理解
|
||||||
|
- 是否能举一反三
|
||||||
|
### 实践经验 (25%)
|
||||||
|
- 是否有真实项目经验支撑
|
||||||
|
- 能否结合具体场景说明
|
||||||
|
- 对技术选型和权衡的理解
|
||||||
|
### 表达能力 (20%)
|
||||||
|
- 逻辑清晰度
|
||||||
|
- 表达的完整性
|
||||||
|
- 专业术语使用的准确性
|
||||||
|
## 评分标准
|
||||||
|
### 优秀 (85-100分)
|
||||||
|
- 回答准确、深入、有见解
|
||||||
|
- 能结合实际项目经验
|
||||||
|
- 表达清晰、逻辑严密
|
||||||
|
- 展现出深度思考和实践能力
|
||||||
|
### 良好 (70-84分)
|
||||||
|
- 回答基本正确，有一定深度
|
||||||
|
- 有实际应用经验
|
||||||
|
- 表达较为清晰
|
||||||
|
- 个别地方可能需要补充
|
||||||
|
### 及格 (60-69分)
|
||||||
|
- 回答基本正确但较浅显
|
||||||
|
- 缺乏深入理解或实践经验
|
||||||
|
- 表达尚可但不够完整
|
||||||
|
- 需要进一步考察
|
||||||
|
### 不及格 (0-59分)
|
||||||
|
- 回答错误或严重不完整
|
||||||
|
- 基础概念理解有误
|
||||||
|
- 表达混乱或逻辑不清
|
||||||
|
- 明显缺乏相关经验
|
||||||
|
## 追问策略
|
||||||
|
### 何时追问
|
||||||
|
1. **概念模糊**: 候选人给出的概念定义不够准确或完整
|
||||||
|
2. **缺少细节**: 回答过于宽泛，缺乏具体的技术细节
|
||||||
|
3. **经验质疑**: 怀疑候选人是否有真实的项目经验
|
||||||
|
4. **深度探索**: 基础回答正确，想考察更深层次的理解
|
||||||
|
### 追问类型
|
||||||
|
1. **澄清追问**: "你刚才提到XX，能具体解释一下吗？"
|
||||||
|
2. **场景追问**: "在实际项目中，你是如何处理XX问题的？"
|
||||||
|
3. **对比追问**: "XX和YY有什么区别？你会如何选择？"
|
||||||
|
4. **深度追问**: "如果遇到XX情况，你会如何优化？"
|
||||||
|
### 追问限制
|
||||||
|
- 单个问题最多追问3次
|
||||||
|
- 追问应该递进深入，不重复
|
||||||
|
- 追问后必须给出综合评估
|
||||||
|
- 避免过度纠缠细节，影响整体面试节奏
|
||||||
|
## 当前问题
|
||||||
|
{question}
|
||||||
|
|
||||||
|
## 候选人回答
|
||||||
|
{candidateAnswer}
|
||||||
|
|
||||||
|
## 评估任务
|
||||||
|
|
||||||
|
### 请分析以下方面
|
||||||
|
1. **技术准确性**: 回答是否正确，有无技术错误
|
||||||
|
2. **完整性**: 是否涵盖了问题的关键要点
|
||||||
|
3. **深度**: 是否展现了深入的理解和思考
|
||||||
|
4. **实践性**: 是否结合了实际项目经验
|
||||||
|
5. **表达质量**: 逻辑是否清晰，表达是否准确
|
||||||
|
|
||||||
|
### 追问决策逻辑
|
||||||
|
- 如果回答存在明显不足，制定一个精准的追问来深入考察
|
||||||
|
- 如果已经追问过2次，本次必须结束追问（continueAsking: false）
|
||||||
|
- 追问应该针对性强，避免过于宽泛
|
||||||
|
- 优先考察核心技术能力，避免偏离主题
|
||||||
|
|
||||||
|
### AI标准答案要求
|
||||||
|
请提供一个简洁、准确的技术答案作为参考，突出关键要点。
|
||||||
|
|
||||||
|
## 输出格式要求
|
||||||
|
严格按照以下JSON格式输出，确保字段完整且格式正确：
|
||||||
|
|
||||||
|
{jsonRes}
|
||||||
|
|
||||||
|
开始评估：
|
||||||
|
""");
|
||||||
|
params.put(
|
||||||
|
"jsonRes",
|
||||||
|
"""
|
||||||
|
{
|
||||||
|
"feedback": "对回答的具体评价，指出优点和不足",
|
||||||
|
"suggestions": "具体的改进建议和学习方向",
|
||||||
|
"aiAnswer": "简洁准确的标准答案要点",
|
||||||
|
"score": 75.5,
|
||||||
|
"continueAsking": true,
|
||||||
|
"followUpQuestion": "具体的追问问题（如果不追问则为空字符串）"
|
||||||
|
}
|
||||||
|
"""
|
||||||
|
);
|
||||||
|
return new Prompt(List.of(
|
||||||
|
promptTemplate.createMessage(params)
|
||||||
|
));
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 获取最终报告
|
||||||
|
*
|
||||||
|
* @param params 参数
|
||||||
|
* @return 提示
|
||||||
|
*/
|
||||||
|
public static Prompt getFinalReportPrompt(Map<String, Object> params) {
|
||||||
|
// SystemMessage systemMessage = new SystemMessage("""
|
||||||
|
// 你是一位资深的技术面试官和HR专家，具备丰富的候选人评估经验。你的职责是：
|
||||||
|
//
|
||||||
|
// ## 核心职能：
|
||||||
|
// - 基于面试数据进行客观、全面的技术能力评估
|
||||||
|
// - 提供标准化的面试结果分析和建议
|
||||||
|
// - 支持招聘决策制定
|
||||||
|
//
|
||||||
|
// ## 评估原则：
|
||||||
|
// 1. **客观性**：基于实际表现数据，避免主观推测
|
||||||
|
// 2. **全面性**：覆盖技术能力、沟通表达、问题解决等维度
|
||||||
|
// 3. **标准化**：使用统一的评分体系和输出格式
|
||||||
|
// 4. **实用性**：提供可执行的改进建议和明确的录用建议
|
||||||
|
//
|
||||||
|
// ## 输出要求：
|
||||||
|
// 严格按照JSON格式输出，不得包含任何markdown标记或额外解释：
|
||||||
|
//
|
||||||
|
// {
|
||||||
|
// "overallScore": <1-100整数>,
|
||||||
|
// "overallFeedback": "<综合评价，客观描述候选人整体表现，150-200字>",
|
||||||
|
// "technicalAssessment": {
|
||||||
|
// "Java基础": "掌握良好，对集合框架理解深入。",
|
||||||
|
// "Spring框架": "熟悉基本使用，但对底层原理理解不足。",
|
||||||
|
// "数据库": "能够编写常规SQL，但在索引优化方面知识欠缺。",
|
||||||
|
// "<技术领域1>": "<该领域的具体评估>",
|
||||||
|
// ...
|
||||||
|
// },
|
||||||
|
// "strengthsAndWeaknesses": {
|
||||||
|
// "strengths": ["<具体优势1>", "<具体优势2>", "<具体优势3>"],
|
||||||
|
// "weaknesses": ["<具体不足1>", "<具体不足2>", "<具体不足3>"]
|
||||||
|
// },
|
||||||
|
// "suggestions": [
|
||||||
|
// "<具体可执行的改进建议1>",
|
||||||
|
// "<具体可执行的改进建议2>",
|
||||||
|
// "<具体可执行的改进建议3>",
|
||||||
|
// "<具体可执行的改进建议4>",
|
||||||
|
// "<具体可执行的改进建议5>"
|
||||||
|
// ],
|
||||||
|
// "hiringRecommendation": "<强烈推荐|推荐|待考虑|不推荐>",
|
||||||
|
// "hiringReason": "<录用建议的具体理由，50-80字>"
|
||||||
|
// }
|
||||||
|
//
|
||||||
|
// ## 评分标准：
|
||||||
|
// - 90-100分：技术优秀，表达清晰，思维敏捷，超出岗位要求
|
||||||
|
// - 80-89分：技术良好，基本满足岗位要求，有培养潜力
|
||||||
|
// - 70-79分：技术一般，需要指导和培养
|
||||||
|
// - 60-69分：技术较弱，存在明显知识盲区
|
||||||
|
// - 60分以下：技术不足，不符合岗位要求
|
||||||
|
// """);
|
||||||
|
|
||||||
|
PromptTemplate promptTemplate = new PromptTemplate("""
|
||||||
|
请根据以下候选人信息和面试记录，生成标准化的面试评估报告：
|
||||||
|
## 核心职能：
|
||||||
|
- 基于面试数据进行客观、全面的技术能力评估
|
||||||
|
- 提供标准化的面试结果分析和建议
|
||||||
|
- 支持招聘决策制定
|
||||||
|
|
||||||
|
## 评估原则：
|
||||||
|
1. **客观性**：基于实际表现数据，避免主观推测
|
||||||
|
2. **全面性**：覆盖技术能力、沟通表达、问题解决等维度
|
||||||
|
3. **标准化**：使用统一的评分体系和输出格式
|
||||||
|
4. **实用性**：提供可执行的改进建议和明确的录用建议
|
||||||
|
|
||||||
|
## 输出要求：
|
||||||
|
严格按照JSON格式输出，不得包含任何markdown标记或额外解释：
|
||||||
|
|
||||||
|
{jsonRes}
|
||||||
|
|
||||||
|
## 评分标准：
|
||||||
|
- 90-100分：技术优秀，表达清晰，思维敏捷，超出岗位要求
|
||||||
|
- 80-89分：技术良好，基本满足岗位要求，有培养潜力
|
||||||
|
- 70-79分：技术一般，需要指导和培养
|
||||||
|
- 60-69分：技术较弱，存在明显知识盲区
|
||||||
|
- 60分以下：技术不足，不符合岗位要求
|
||||||
|
|
||||||
|
## 候选人简历信息：
|
||||||
|
{resume}
|
||||||
|
|
||||||
|
## 完整面试记录：
|
||||||
|
{history}
|
||||||
|
|
||||||
|
请开始评估并输出JSON格式的报告。
|
||||||
|
""");
|
||||||
|
params.put("jsonRes", """
|
||||||
|
{
|
||||||
|
"overallScore": <1-100整数>,
|
||||||
|
"overallFeedback": "<综合评价，客观描述候选人整体表现，150-200字>",
|
||||||
|
"technicalAssessment": {
|
||||||
|
"Java基础": "掌握良好，对集合框架理解深入。",
|
||||||
|
"Spring框架": "熟悉基本使用，但对底层原理理解不足。",
|
||||||
|
"数据库": "能够编写常规SQL，但在索引优化方面知识欠缺。",
|
||||||
|
"<技术领域1>": "<该领域的具体评估>",
|
||||||
|
...
|
||||||
|
},
|
||||||
|
"strengthsAndWeaknesses": {
|
||||||
|
"strengths": ["<具体优势1>", "<具体优势2>", "<具体优势3>"],
|
||||||
|
"weaknesses": ["<具体不足1>", "<具体不足2>", "<具体不足3>"]
|
||||||
|
},
|
||||||
|
"suggestions": [
|
||||||
|
"<具体可执行的改进建议1>",
|
||||||
|
"<具体可执行的改进建议2>",
|
||||||
|
"<具体可执行的改进建议3>",
|
||||||
|
"<具体可执行的改进建议4>",
|
||||||
|
"<具体可执行的改进建议5>"
|
||||||
|
],
|
||||||
|
"hiringRecommendation": "<强烈推荐|推荐|待考虑|不推荐>",
|
||||||
|
"hiringReason": "<录用建议的具体理由，50-80字>"
|
||||||
|
}
|
||||||
|
""");
|
||||||
|
return new Prompt(List.of(
|
||||||
|
promptTemplate.createMessage(params)
|
||||||
|
));
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,8 @@
|
|||||||
|
package com.qingqiu.interview.common.utils;
|
||||||
|
|
||||||
|
public class UUIDUtils {
|
||||||
|
|
||||||
|
public static String getUUID() {
|
||||||
|
return java.util.UUID.randomUUID().toString().replace("-", "");
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,73 @@
|
|||||||
|
package com.qingqiu.interview.config;
|
||||||
|
|
||||||
|
import com.alibaba.cloud.ai.memory.jdbc.MysqlChatMemoryRepository;
|
||||||
|
import com.alibaba.cloud.ai.memory.redis.RedisChatMemoryRepository;
|
||||||
|
import com.qingqiu.interview.common.constants.ChatConstant;
|
||||||
|
import jakarta.annotation.Resource;
|
||||||
|
import org.springframework.ai.chat.memory.MessageWindowChatMemory;
|
||||||
|
import org.springframework.beans.factory.annotation.Value;
|
||||||
|
import org.springframework.context.annotation.Bean;
|
||||||
|
import org.springframework.context.annotation.Configuration;
|
||||||
|
import org.springframework.context.annotation.Primary;
|
||||||
|
import org.springframework.jdbc.core.JdbcTemplate;
|
||||||
|
|
||||||
|
import javax.sql.DataSource;
|
||||||
|
|
||||||
|
import static com.qingqiu.interview.common.constants.ChatConstant.MAX_MESSAGES;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 聊天记忆相关配置
|
||||||
|
*/
|
||||||
|
@Configuration
|
||||||
|
public class ChatMemoryConfig {
|
||||||
|
|
||||||
|
@Value("${spring.ai.memory.redis.host}")
|
||||||
|
private String redisHost;
|
||||||
|
@Value("${spring.ai.memory.redis.port}")
|
||||||
|
private int redisPort;
|
||||||
|
@Value("${spring.ai.memory.redis.password}")
|
||||||
|
private String redisPassword;
|
||||||
|
@Value("${spring.ai.memory.redis.timeout}")
|
||||||
|
private int redisTimeout;
|
||||||
|
|
||||||
|
@Resource
|
||||||
|
private DataSource dataSource;
|
||||||
|
|
||||||
|
|
||||||
|
@Bean
|
||||||
|
public MysqlChatMemoryRepository mysqlChatMemoryRepository() {
|
||||||
|
JdbcTemplate jdbcTemplate = new JdbcTemplate(dataSource);
|
||||||
|
return MysqlChatMemoryRepository.mysqlBuilder()
|
||||||
|
.jdbcTemplate(jdbcTemplate)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
@Primary
|
||||||
|
@Bean(name = ChatConstant.MYSQL_CHAT_MEMORY_BEAN_NAME)
|
||||||
|
public MessageWindowChatMemory mysqlChatMemory(MysqlChatMemoryRepository mysqlChatMemoryRepository) {
|
||||||
|
return MessageWindowChatMemory.builder()
|
||||||
|
.chatMemoryRepository(mysqlChatMemoryRepository)
|
||||||
|
.maxMessages(MAX_MESSAGES)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
@Bean
|
||||||
|
public RedisChatMemoryRepository redisChatMemoryRepository() {
|
||||||
|
return RedisChatMemoryRepository.builder()
|
||||||
|
.host(redisHost)
|
||||||
|
.port(redisPort)
|
||||||
|
// 若没有设置密码则注释该项
|
||||||
|
.password(redisPassword)
|
||||||
|
.timeout(redisTimeout)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
@Bean(name = ChatConstant.REDIS_CHAT_MEMORY_BEAN_NAME)
|
||||||
|
public MessageWindowChatMemory redisChatMemory(RedisChatMemoryRepository redisChatMemoryRepository) {
|
||||||
|
return MessageWindowChatMemory.builder()
|
||||||
|
.chatMemoryRepository(redisChatMemoryRepository)
|
||||||
|
.maxMessages(MAX_MESSAGES)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
@@ -1,14 +1,217 @@
|
|||||||
package com.qingqiu.interview.config;
|
package com.qingqiu.interview.config;
|
||||||
|
|
||||||
import com.alibaba.dashscope.aigc.generation.Generation;
|
import com.alibaba.cloud.ai.dashscope.api.DashScopeApi;
|
||||||
|
import com.alibaba.cloud.ai.dashscope.api.DashScopeResponseFormat;
|
||||||
|
import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatModel;
|
||||||
|
import com.alibaba.cloud.ai.dashscope.chat.DashScopeChatOptions;
|
||||||
|
import com.qingqiu.interview.common.constants.ChatConstant;
|
||||||
|
import io.netty.channel.ChannelOption;
|
||||||
|
import jakarta.annotation.Resource;
|
||||||
|
import org.springframework.ai.chat.client.ChatClient;
|
||||||
|
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
|
||||||
|
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
|
||||||
|
import org.springframework.ai.chat.memory.MessageWindowChatMemory;
|
||||||
|
import org.springframework.ai.chat.model.ChatModel;
|
||||||
|
import org.springframework.ai.openai.OpenAiChatModel;
|
||||||
|
import org.springframework.ai.openai.OpenAiChatOptions;
|
||||||
|
import org.springframework.ai.openai.api.OpenAiApi;
|
||||||
|
import org.springframework.ai.openai.api.ResponseFormat;
|
||||||
|
import org.springframework.beans.factory.annotation.Value;
|
||||||
import org.springframework.context.annotation.Bean;
|
import org.springframework.context.annotation.Bean;
|
||||||
import org.springframework.context.annotation.Configuration;
|
import org.springframework.context.annotation.Configuration;
|
||||||
|
import org.springframework.context.annotation.Primary;
|
||||||
|
import org.springframework.http.client.ReactorClientHttpRequestFactory;
|
||||||
|
import org.springframework.http.client.reactive.ReactorClientHttpConnector;
|
||||||
|
import org.springframework.web.client.RestClient;
|
||||||
|
import org.springframework.web.reactive.function.client.WebClient;
|
||||||
|
import reactor.netty.http.client.HttpClient;
|
||||||
|
|
||||||
|
import java.time.Duration;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* DashScope相关配置类
|
||||||
|
* 主要配置阿里云百炼平台(DashScope)和OpenAI的大模型服务
|
||||||
|
* 包括API客户端、聊天模型和聊天客户端的配置
|
||||||
|
*
|
||||||
|
* @author qingqiu
|
||||||
|
*/
|
||||||
@Configuration
|
@Configuration
|
||||||
public class DashScopeConfig {
|
public class DashScopeConfig {
|
||||||
|
|
||||||
|
// 从配置文件中读取DashScope API密钥
|
||||||
|
@Value("${spring.ai.dashscope.api-key}")
|
||||||
|
private String dashScopeApiKey;
|
||||||
|
|
||||||
|
@Value("${spring.ai.dashscope.chat.options.model}")
|
||||||
|
private String dashScopeChatModelName;
|
||||||
|
|
||||||
|
@Value("${spring.ai.openai.chat.options.model}")
|
||||||
|
private String openAiChatModelName;
|
||||||
|
|
||||||
|
@Value("${spring.ai.openai.base-url}")
|
||||||
|
private String openAiBaseUrl;
|
||||||
|
|
||||||
|
@Value("${spring.ai.openai.api-key}")
|
||||||
|
private String openAiApiKey;
|
||||||
|
|
||||||
|
@Resource(name = ChatConstant.MYSQL_CHAT_MEMORY_BEAN_NAME)
|
||||||
|
private MessageWindowChatMemory mysqlChatMemory;
|
||||||
|
|
||||||
|
@Resource(name = ChatConstant.REDIS_CHAT_MEMORY_BEAN_NAME)
|
||||||
|
private MessageWindowChatMemory redisChatMemory;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 创建DashScopeApi Bean实例
|
||||||
|
* 配置了HTTP客户端连接参数，包括连接超时和响应超时时间
|
||||||
|
*
|
||||||
|
* @return DashScopeApi实例
|
||||||
|
*/
|
||||||
@Bean
|
@Bean
|
||||||
public Generation generation() {
|
public DashScopeApi dashScopeApi() {
|
||||||
return new Generation();
|
return DashScopeApi.builder()
|
||||||
|
.apiKey(dashScopeApiKey)
|
||||||
|
.restClientBuilder(getRestClientBuilder())
|
||||||
|
.webClientBuilder(getWebClientBuilder())
|
||||||
|
.build();
|
||||||
}
|
}
|
||||||
}
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 创建DashScope聊天模型Bean实例
|
||||||
|
* 配置了最大token数、使用的模型以及返回格式为JSON对象
|
||||||
|
*
|
||||||
|
* @return ChatModel实例
|
||||||
|
*/
|
||||||
|
@Bean(name = ChatConstant.DASH_SCOPE_CHAT_MODEL_BEAN_NAME)
|
||||||
|
public ChatModel dashScopeChatModel() {
|
||||||
|
return DashScopeChatModel.builder()
|
||||||
|
.dashScopeApi(dashScopeApi())
|
||||||
|
.defaultOptions(
|
||||||
|
DashScopeChatOptions.builder()
|
||||||
|
.withMaxToken(ChatConstant.MAX_COMPLETION_TOKENS)
|
||||||
|
.withModel(dashScopeChatModelName)
|
||||||
|
.withResponseFormat(
|
||||||
|
DashScopeResponseFormat.builder()
|
||||||
|
.type(DashScopeResponseFormat.Type.JSON_OBJECT)
|
||||||
|
.build()
|
||||||
|
)
|
||||||
|
.build()
|
||||||
|
)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 创建OpenAI聊天模型Bean实例（主模型）
|
||||||
|
* 配置了最大完成token数和响应格式为JSON Schema
|
||||||
|
*
|
||||||
|
* @return ChatModel实例
|
||||||
|
*/
|
||||||
|
@Bean(name = ChatConstant.OPEN_AI_CHAT_MODEL_BEAN_NAME)
|
||||||
|
@Primary
|
||||||
|
public ChatModel openAiChatModel() {
|
||||||
|
return OpenAiChatModel.builder()
|
||||||
|
.openAiApi(
|
||||||
|
OpenAiApi.builder()
|
||||||
|
.apiKey(openAiApiKey)
|
||||||
|
.baseUrl(openAiBaseUrl)
|
||||||
|
.webClientBuilder(getWebClientBuilder())
|
||||||
|
.restClientBuilder(getRestClientBuilder())
|
||||||
|
.build()
|
||||||
|
)
|
||||||
|
.defaultOptions(
|
||||||
|
OpenAiChatOptions.builder()
|
||||||
|
.model(openAiChatModelName)
|
||||||
|
.maxCompletionTokens(ChatConstant.MAX_COMPLETION_TOKENS)
|
||||||
|
|
||||||
|
.responseFormat(
|
||||||
|
ResponseFormat.builder()
|
||||||
|
.type(ResponseFormat.Type.JSON_SCHEMA)
|
||||||
|
.jsonSchema(
|
||||||
|
ResponseFormat.JsonSchema
|
||||||
|
.builder()
|
||||||
|
.build()
|
||||||
|
).build()
|
||||||
|
)
|
||||||
|
.build()
|
||||||
|
)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 创建默认的聊天客户端Bean实例（使用DashScope模型）
|
||||||
|
* 添加了日志记录功能
|
||||||
|
*
|
||||||
|
* @return ChatClient实例
|
||||||
|
*/
|
||||||
|
@Bean
|
||||||
|
@Primary
|
||||||
|
public ChatClient chatClient() {
|
||||||
|
return ChatClient
|
||||||
|
.builder(dashScopeChatModel())
|
||||||
|
.defaultAdvisors(
|
||||||
|
new SimpleLoggerAdvisor()
|
||||||
|
)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 创建基于MySQL存储聊天历史的DashScope聊天客户端Bean实例
|
||||||
|
* 配置了消息内存管理和日志记录功能
|
||||||
|
*
|
||||||
|
* @return ChatClient实例
|
||||||
|
*/
|
||||||
|
@Bean(name = ChatConstant.DASH_SCOPE_CHAT_CLIENT_BEAN_NAME)
|
||||||
|
public ChatClient dashScopeChatClient() {
|
||||||
|
return ChatClient
|
||||||
|
.builder(dashScopeChatModel())
|
||||||
|
.defaultAdvisors(
|
||||||
|
MessageChatMemoryAdvisor.builder(mysqlChatMemory).build(),
|
||||||
|
new SimpleLoggerAdvisor()
|
||||||
|
)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 创建基于MySQL存储聊天历史的OpenAI聊天客户端Bean实例
|
||||||
|
* 配置了消息内存管理和日志记录功能
|
||||||
|
*
|
||||||
|
* @return ChatClient实例
|
||||||
|
*/
|
||||||
|
@Bean(name = ChatConstant.OPEN_AI_CHAT_CLIENT_BEAN_NAME)
|
||||||
|
public ChatClient openAiChatClient() {
|
||||||
|
return ChatClient
|
||||||
|
.builder(openAiChatModel())
|
||||||
|
.defaultAdvisors(
|
||||||
|
MessageChatMemoryAdvisor.builder(mysqlChatMemory).build(),
|
||||||
|
new SimpleLoggerAdvisor()
|
||||||
|
)
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 创建全局默认的WebClient Bean实例
|
||||||
|
* 配置了连接超时和响应超时时间
|
||||||
|
*
|
||||||
|
* @return WebClient实例
|
||||||
|
*/
|
||||||
|
public WebClient.Builder getWebClientBuilder() {
|
||||||
|
HttpClient httpClient = HttpClient.create()
|
||||||
|
.option(ChannelOption.CONNECT_TIMEOUT_MILLIS, 5000) // 连接超时
|
||||||
|
.responseTimeout(Duration.ofMillis(10000)); // 读取超时
|
||||||
|
|
||||||
|
return WebClient.builder()
|
||||||
|
.clientConnector(new ReactorClientHttpConnector(httpClient))
|
||||||
|
;
|
||||||
|
}
|
||||||
|
|
||||||
|
private RestClient.Builder getRestClientBuilder() {
|
||||||
|
return RestClient.builder()
|
||||||
|
.requestFactory(
|
||||||
|
new ReactorClientHttpRequestFactory(
|
||||||
|
HttpClient.create()
|
||||||
|
.responseTimeout(Duration.ofMinutes(5))
|
||||||
|
)
|
||||||
|
);
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,57 @@
|
|||||||
|
package com.qingqiu.interview.config;
|
||||||
|
|
||||||
|
import com.alibaba.fastjson2.JSON;
|
||||||
|
import com.alibaba.fastjson2.JSONReader;
|
||||||
|
import com.alibaba.fastjson2.JSONWriter;
|
||||||
|
import org.springframework.data.redis.serializer.RedisSerializer;
|
||||||
|
import org.springframework.data.redis.serializer.SerializationException;
|
||||||
|
|
||||||
|
import java.nio.charset.Charset;
|
||||||
|
import java.nio.charset.StandardCharsets;
|
||||||
|
|
||||||
|
public class Fastjson2RedisSerializer<T> implements RedisSerializer<T> {
|
||||||
|
// 默认编码
|
||||||
|
public static final Charset DEFAULT_CHARSET = StandardCharsets.UTF_8;
|
||||||
|
|
||||||
|
// 泛型类型，用于反序列化
|
||||||
|
private final Class<T> clazz;
|
||||||
|
|
||||||
|
public Fastjson2RedisSerializer(Class<T> clazz) {
|
||||||
|
super();
|
||||||
|
this.clazz = clazz;
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 序列化：将对象转换为字节数组
|
||||||
|
*/
|
||||||
|
@Override
|
||||||
|
public byte[] serialize(T t) throws SerializationException {
|
||||||
|
if (t == null) {
|
||||||
|
return new byte[0];
|
||||||
|
}
|
||||||
|
try {
|
||||||
|
// 使用 Fastjson2 序列化对象，并写入字节数组
|
||||||
|
// 配置写入特性：WriteClassName 确保反序列化时能识别类型，如果不需要，可以移除。
|
||||||
|
return JSON.toJSONBytes(t, JSONWriter.Feature.WriteClassName);
|
||||||
|
} catch (Exception ex) {
|
||||||
|
throw new SerializationException("Could not serialize object with Fastjson2", ex);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 反序列化：将字节数组转换为对象
|
||||||
|
*/
|
||||||
|
@Override
|
||||||
|
public T deserialize(byte[] bytes) throws SerializationException {
|
||||||
|
if (bytes == null || bytes.length <= 0) {
|
||||||
|
return null;
|
||||||
|
}
|
||||||
|
try {
|
||||||
|
// 使用 Fastjson2 反序列化字节数组
|
||||||
|
// 配置读取特性：SupportAutoType，确保可以正确读取带有类名信息的JSON
|
||||||
|
return JSON.parseObject(bytes, clazz, JSONReader.Feature.SupportAutoType);
|
||||||
|
} catch (Exception ex) {
|
||||||
|
throw new SerializationException("Could not deserialize object with Fastjson2", ex);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
32
src/main/java/com/qingqiu/interview/config/RedisConfig.java
Normal file
32
src/main/java/com/qingqiu/interview/config/RedisConfig.java
Normal file
@@ -0,0 +1,32 @@
|
|||||||
|
package com.qingqiu.interview.config;
|
||||||
|
|
||||||
|
import org.springframework.context.annotation.Bean;
|
||||||
|
import org.springframework.context.annotation.Configuration;
|
||||||
|
import org.springframework.data.redis.connection.RedisConnectionFactory;
|
||||||
|
import org.springframework.data.redis.core.RedisTemplate;
|
||||||
|
import org.springframework.data.redis.serializer.StringRedisSerializer;
|
||||||
|
|
||||||
|
@Configuration
|
||||||
|
public class RedisConfig {
|
||||||
|
|
||||||
|
@Bean
|
||||||
|
public RedisTemplate<String, Object> redisTemplate(RedisConnectionFactory redisConnectionFactory) {
|
||||||
|
RedisTemplate<String, Object> template = new RedisTemplate<>();
|
||||||
|
template.setConnectionFactory(redisConnectionFactory);
|
||||||
|
|
||||||
|
// 使用StringRedisSerializer来序列化和反序列化redis的key值
|
||||||
|
StringRedisSerializer stringRedisSerializer = new StringRedisSerializer();
|
||||||
|
template.setKeySerializer(stringRedisSerializer);
|
||||||
|
template.setHashKeySerializer(stringRedisSerializer);
|
||||||
|
|
||||||
|
// 使用Fastjson2RedisSerializer来序列化和反序列化redis的value值
|
||||||
|
|
||||||
|
Fastjson2RedisSerializer<Object> fastjson2RedisSerializer = new Fastjson2RedisSerializer<>(Object.class);
|
||||||
|
|
||||||
|
template.setValueSerializer(fastjson2RedisSerializer);
|
||||||
|
template.setHashValueSerializer(fastjson2RedisSerializer);
|
||||||
|
|
||||||
|
template.afterPropertiesSet();
|
||||||
|
return template;
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -1,13 +1,14 @@
|
|||||||
package com.qingqiu.interview.controller;
|
package com.qingqiu.interview.controller;
|
||||||
|
|
||||||
|
|
||||||
import com.alibaba.dashscope.common.Role;
|
|
||||||
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
|
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
|
||||||
import com.qingqiu.interview.common.res.R;
|
import com.qingqiu.interview.common.res.R;
|
||||||
import com.qingqiu.interview.entity.AiSessionLog;
|
import com.qingqiu.interview.entity.AiSessionLog;
|
||||||
import com.qingqiu.interview.service.IAiSessionLogService;
|
import com.qingqiu.interview.service.IAiSessionLogService;
|
||||||
import lombok.RequiredArgsConstructor;
|
import lombok.RequiredArgsConstructor;
|
||||||
import lombok.extern.slf4j.Slf4j;
|
import lombok.extern.slf4j.Slf4j;
|
||||||
|
import org.springframework.ai.chat.messages.MessageType;
|
||||||
|
import org.springframework.ai.chat.messages.SystemMessage;
|
||||||
import org.springframework.beans.factory.annotation.Autowired;
|
import org.springframework.beans.factory.annotation.Autowired;
|
||||||
import org.springframework.context.annotation.Lazy;
|
import org.springframework.context.annotation.Lazy;
|
||||||
import org.springframework.web.bind.annotation.GetMapping;
|
import org.springframework.web.bind.annotation.GetMapping;
|
||||||
@@ -38,7 +39,7 @@ public class AiSessionLogController {
|
|||||||
return R.success(service.list(
|
return R.success(service.list(
|
||||||
new LambdaQueryWrapper<AiSessionLog>()
|
new LambdaQueryWrapper<AiSessionLog>()
|
||||||
.eq(AiSessionLog::getToken, sessionId)
|
.eq(AiSessionLog::getToken, sessionId)
|
||||||
.ne(AiSessionLog::getRole, Role.SYSTEM.getValue())
|
.ne(AiSessionLog::getRole, MessageType.SYSTEM.getValue())
|
||||||
));
|
));
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@@ -25,5 +25,10 @@ public class InterviewStartRequest {
|
|||||||
/** 生成的面试题目数量 */
|
/** 生成的面试题目数量 */
|
||||||
private Integer totalQuestions = 10;
|
private Integer totalQuestions = 10;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 岗位要求
|
||||||
|
*/
|
||||||
|
private String jobRequirements;
|
||||||
|
|
||||||
// 简历文件通过MultipartFile单独传递
|
// 简历文件通过MultipartFile单独传递
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -31,6 +31,9 @@ public class InterviewSession implements Serializable {
|
|||||||
@TableField("resume_content")
|
@TableField("resume_content")
|
||||||
private String resumeContent;
|
private String resumeContent;
|
||||||
|
|
||||||
|
@TableField("job_requirements")
|
||||||
|
private String jobRequirements;
|
||||||
|
|
||||||
@TableField("extracted_skills")
|
@TableField("extracted_skills")
|
||||||
private String extractedSkills;
|
private String extractedSkills;
|
||||||
@TableField("interview_type")
|
@TableField("interview_type")
|
||||||
|
|||||||
@@ -0,0 +1,11 @@
|
|||||||
|
package com.qingqiu.interview.entity.ai;
|
||||||
|
|
||||||
|
import com.fasterxml.jackson.annotation.JsonProperty;
|
||||||
|
|
||||||
|
public record EvaluateAiRes(@JsonProperty("feedback") String feedback,
|
||||||
|
@JsonProperty("suggestions") String suggestions,
|
||||||
|
@JsonProperty("aiAnswer") String aiAnswer,
|
||||||
|
@JsonProperty("score") double score,
|
||||||
|
@JsonProperty("continueAsking") boolean continueAsking,
|
||||||
|
@JsonProperty("followUpQuestion") String followUpQuestion) {
|
||||||
|
}
|
||||||
@@ -0,0 +1,10 @@
|
|||||||
|
package com.qingqiu.interview.entity.ai;
|
||||||
|
|
||||||
|
import com.fasterxml.jackson.annotation.JsonProperty;
|
||||||
|
|
||||||
|
import java.util.List;
|
||||||
|
|
||||||
|
public record ExtractSkillAiRes(
|
||||||
|
@JsonProperty("skills") List<String> skills
|
||||||
|
) {
|
||||||
|
}
|
||||||
@@ -0,0 +1,27 @@
|
|||||||
|
package com.qingqiu.interview.entity.ai;
|
||||||
|
|
||||||
|
import com.fasterxml.jackson.annotation.JsonProperty;
|
||||||
|
|
||||||
|
import java.util.List;
|
||||||
|
import java.util.Map;
|
||||||
|
|
||||||
|
public record InterviewReportAiRes(
|
||||||
|
@JsonProperty("overallScore") String overallScore,
|
||||||
|
@JsonProperty("overallFeedback") String overallFeedback,
|
||||||
|
|
||||||
|
@JsonProperty("technicalAssessment") Map<String, String> technicalAssessment,
|
||||||
|
@JsonProperty("strengthsAndWeaknesses") StrengthsAndWeaknesses strengthsAndWeaknesses,
|
||||||
|
|
||||||
|
@JsonProperty("suggestions") List<String> suggestions,
|
||||||
|
@JsonProperty("hiringRecommendation") String hiringRecommendation,
|
||||||
|
@JsonProperty("hiringReason") String hiringReason
|
||||||
|
) {
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 内部 Record：对应 "strengthsAndWeaknesses" 对象。
|
||||||
|
*/
|
||||||
|
public record StrengthsAndWeaknesses(
|
||||||
|
@JsonProperty("strengths") List<String> strengths,
|
||||||
|
@JsonProperty("weaknesses") List<String> weaknesses
|
||||||
|
) {}
|
||||||
|
}
|
||||||
@@ -0,0 +1,26 @@
|
|||||||
|
package com.qingqiu.interview.entity.ai;
|
||||||
|
|
||||||
|
|
||||||
|
import com.fasterxml.jackson.annotation.JsonProperty;
|
||||||
|
|
||||||
|
import java.util.List;
|
||||||
|
|
||||||
|
public class QuestionAiRes {
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 内部实体类：对应 JSON 数组中的每个元素。
|
||||||
|
* { "id": "ai-gen-1", "content": "问题内容..." }
|
||||||
|
*/
|
||||||
|
public record Question(
|
||||||
|
@JsonProperty("id") String id,
|
||||||
|
@JsonProperty("content") String content
|
||||||
|
) {}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* 外部实体类：对应整个 JSON 响应的顶层结构。
|
||||||
|
* { "questions": [...] }
|
||||||
|
*/
|
||||||
|
public record Wrapper(
|
||||||
|
@JsonProperty("questions") List<Question> questions
|
||||||
|
) {}
|
||||||
|
}
|
||||||
@@ -1,29 +0,0 @@
|
|||||||
package com.qingqiu.interview.service;
|
|
||||||
|
|
||||||
import com.qingqiu.interview.dto.ChatDTO;
|
|
||||||
import com.qingqiu.interview.dto.InterviewStartRequest;
|
|
||||||
import com.qingqiu.interview.vo.ChatVO;
|
|
||||||
import org.springframework.web.multipart.MultipartFile;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* <h1></h1>
|
|
||||||
*
|
|
||||||
* @author qingqiu
|
|
||||||
* @date 2025/9/18 12:45
|
|
||||||
*/
|
|
||||||
public interface ChatService {
|
|
||||||
|
|
||||||
/**
|
|
||||||
* 创建普通会话
|
|
||||||
* @return sessionId
|
|
||||||
*/
|
|
||||||
ChatVO createChat(ChatDTO dto);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* 创建面试会话
|
|
||||||
* @param resume 简历
|
|
||||||
* @param request 面试信息
|
|
||||||
* @return sessionId
|
|
||||||
*/
|
|
||||||
String createInterviewChat(MultipartFile resume, InterviewStartRequest request);
|
|
||||||
}
|
|
||||||
@@ -1,15 +1,17 @@
|
|||||||
package com.qingqiu.interview.service;
|
package com.qingqiu.interview.service;
|
||||||
|
|
||||||
import com.alibaba.fastjson2.JSONObject;
|
|
||||||
import com.qingqiu.interview.entity.InterviewQuestionProgress;
|
import com.qingqiu.interview.entity.InterviewQuestionProgress;
|
||||||
import com.qingqiu.interview.entity.InterviewSession;
|
import com.qingqiu.interview.entity.InterviewSession;
|
||||||
import com.qingqiu.interview.entity.Question;
|
import com.qingqiu.interview.entity.Question;
|
||||||
|
import com.qingqiu.interview.entity.ai.EvaluateAiRes;
|
||||||
|
import com.qingqiu.interview.entity.ai.InterviewReportAiRes;
|
||||||
|
import com.qingqiu.interview.entity.ai.QuestionAiRes;
|
||||||
|
|
||||||
import java.util.List;
|
import java.util.List;
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* <h1>
|
* <h1>
|
||||||
* 面试接入AI的接口
|
* 面试接入AI的接口
|
||||||
* </h1>
|
* </h1>
|
||||||
*
|
*
|
||||||
* @author qingqiu
|
* @author qingqiu
|
||||||
@@ -23,38 +25,38 @@ public interface InterviewAiService {
|
|||||||
* @param resumeContent 简历文本
|
* @param resumeContent 简历文本
|
||||||
* @return 包含技能列表的JSON对象
|
* @return 包含技能列表的JSON对象
|
||||||
*/
|
*/
|
||||||
JSONObject extractSkillsFromResume(String resumeContent);
|
List<String> extractSkillsFromResume(String resumeContent);
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* 根据技能动态生成面试题目
|
* 根据技能动态生成面试题目
|
||||||
*
|
*
|
||||||
* @param skills 技能列表
|
* @param skills 技能列表
|
||||||
* @param resumeContent 简历内容
|
* @param resumeContent 简历内容
|
||||||
* @param count 需要生成的题目数量
|
* @param count 需要生成的题目数量
|
||||||
* @return 包含问题列表的JSON对象
|
* @return 包含问题列表的JSON对象
|
||||||
*/
|
*/
|
||||||
JSONObject generateQuestionsOfAi(String sessionId, List<String> skills, String resumeContent, int count);
|
List<QuestionAiRes.Question> generateQuestionsOfAi(String sessionId, List<String> skills, String jobRequirements, String resumeContent, int count);
|
||||||
|
|
||||||
JSONObject generateQuestionOfLocal(String sessionId, List<Question> questions, List<String> skills, String resumeContent, int count);
|
List<QuestionAiRes.Question> generateQuestionOfLocal(String sessionId, List<Question> questions, List<String> skills, String jobRequirements, String resumeContent, int count);
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* 评估用户的回答
|
* 评估用户的回答
|
||||||
*
|
*
|
||||||
* @param question 问题内容
|
* @param question 问题内容
|
||||||
* @param userAnswer 用户的回答
|
* @param userAnswer 用户的回答
|
||||||
* @param context 可选的上下文(之前的问答历史)
|
* @param context 可选的上下文(之前的问答历史)
|
||||||
* @return 包含评估结果的JSON对象
|
* @return 包含评估结果的JSON对象
|
||||||
*/
|
*/
|
||||||
JSONObject evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context);
|
EvaluateAiRes evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context);
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* 生成最终的面试评估报告
|
* 生成最终的面试评估报告
|
||||||
*
|
*
|
||||||
* @param session 面试会话信息
|
* @param session 面试会话信息
|
||||||
* @param progressList 整个面试的问答记录
|
* @param progressList 整个面试的问答记录
|
||||||
* @return 包含最终报告的JSON对象
|
* @return 包含最终报告的JSON对象
|
||||||
*/
|
*/
|
||||||
JSONObject generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList);
|
InterviewReportAiRes generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList);
|
||||||
|
|
||||||
String generateFirstQuestion(String sessionId, String candidateName, String questionContent);
|
String generateFirstQuestion(String sessionId, String candidateName, String questionContent);
|
||||||
|
|
||||||
|
|||||||
@@ -3,10 +3,14 @@ package com.qingqiu.interview.service;
|
|||||||
import com.fasterxml.jackson.core.JsonProcessingException;
|
import com.fasterxml.jackson.core.JsonProcessingException;
|
||||||
import com.fasterxml.jackson.databind.JsonNode;
|
import com.fasterxml.jackson.databind.JsonNode;
|
||||||
import com.fasterxml.jackson.databind.ObjectMapper;
|
import com.fasterxml.jackson.databind.ObjectMapper;
|
||||||
|
import com.qingqiu.interview.common.constants.ChatConstant;
|
||||||
|
import com.qingqiu.interview.common.utils.UUIDUtils;
|
||||||
import com.qingqiu.interview.entity.Question;
|
import com.qingqiu.interview.entity.Question;
|
||||||
import com.qingqiu.interview.service.llm.LlmService;
|
import jakarta.annotation.Resource;
|
||||||
import lombok.RequiredArgsConstructor;
|
import lombok.RequiredArgsConstructor;
|
||||||
import lombok.extern.slf4j.Slf4j;
|
import lombok.extern.slf4j.Slf4j;
|
||||||
|
import org.springframework.ai.chat.client.ChatClient;
|
||||||
|
import org.springframework.ai.chat.memory.ChatMemory;
|
||||||
import org.springframework.stereotype.Service;
|
import org.springframework.stereotype.Service;
|
||||||
|
|
||||||
import java.util.ArrayList;
|
import java.util.ArrayList;
|
||||||
@@ -17,10 +21,11 @@ import java.util.List;
|
|||||||
@RequiredArgsConstructor
|
@RequiredArgsConstructor
|
||||||
public class QuestionClassificationService {
|
public class QuestionClassificationService {
|
||||||
|
|
||||||
private final LlmService llmService;
|
|
||||||
|
|
||||||
private final ObjectMapper objectMapper = new ObjectMapper();
|
private final ObjectMapper objectMapper = new ObjectMapper();
|
||||||
|
|
||||||
|
private final ChatClient chatClient;
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* 使用AI对题目进行分类
|
* 使用AI对题目进行分类
|
||||||
*/
|
*/
|
||||||
@@ -28,10 +33,14 @@ public class QuestionClassificationService {
|
|||||||
log.info("开始使用AI分类题目，内容长度: {}", rawContent.length());
|
log.info("开始使用AI分类题目，内容长度: {}", rawContent.length());
|
||||||
|
|
||||||
String prompt = buildClassificationPrompt(rawContent);
|
String prompt = buildClassificationPrompt(rawContent);
|
||||||
String aiResponse = llmService.chat(prompt);
|
String aiResponse = chatClient.prompt()
|
||||||
|
.user(prompt)
|
||||||
|
.call()
|
||||||
|
.content();
|
||||||
|
|
||||||
log.info("AI分类响应: {}", aiResponse);
|
log.info("AI分类响应: {}", aiResponse);
|
||||||
|
|
||||||
|
assert aiResponse != null;
|
||||||
return parseAiResponse(aiResponse);
|
return parseAiResponse(aiResponse);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@@ -1,95 +0,0 @@
|
|||||||
package com.qingqiu.interview.service.impl;
|
|
||||||
|
|
||||||
import cn.hutool.core.collection.CollectionUtil;
|
|
||||||
import com.alibaba.dashscope.common.Message;
|
|
||||||
import com.alibaba.dashscope.common.Role;
|
|
||||||
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
|
|
||||||
import com.qingqiu.interview.ai.factory.AIClientManager;
|
|
||||||
import com.qingqiu.interview.annotation.AiChatLog;
|
|
||||||
import com.qingqiu.interview.common.enums.LLMProvider;
|
|
||||||
import com.qingqiu.interview.common.utils.AIUtils;
|
|
||||||
import com.qingqiu.interview.dto.ChatDTO;
|
|
||||||
import com.qingqiu.interview.dto.InterviewStartRequest;
|
|
||||||
import com.qingqiu.interview.entity.AiSessionLog;
|
|
||||||
import com.qingqiu.interview.service.ChatService;
|
|
||||||
import com.qingqiu.interview.service.IAiSessionLogService;
|
|
||||||
import com.qingqiu.interview.vo.ChatVO;
|
|
||||||
import lombok.RequiredArgsConstructor;
|
|
||||||
import lombok.extern.slf4j.Slf4j;
|
|
||||||
import org.springframework.stereotype.Service;
|
|
||||||
import org.springframework.web.multipart.MultipartFile;
|
|
||||||
|
|
||||||
import java.math.BigDecimal;
|
|
||||||
import java.math.RoundingMode;
|
|
||||||
import java.util.ArrayList;
|
|
||||||
import java.util.List;
|
|
||||||
import java.util.concurrent.atomic.AtomicInteger;
|
|
||||||
|
|
||||||
import static com.qingqiu.interview.common.constants.CommonConstant.DEFAULT_TRUNCATE_RATIO;
|
|
||||||
import static com.qingqiu.interview.common.constants.CommonConstant.MAX_TOKEN;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* <h1></h1>
|
|
||||||
*
|
|
||||||
* @author qingqiu
|
|
||||||
* @date 2025/9/18 12:56
|
|
||||||
*/
|
|
||||||
|
|
||||||
@Slf4j
|
|
||||||
@Service
|
|
||||||
@RequiredArgsConstructor
|
|
||||||
public class ChatServiceImpl implements ChatService {
|
|
||||||
|
|
||||||
private final AIClientManager aiClientManager;
|
|
||||||
|
|
||||||
private final IAiSessionLogService aiSessionLogService;
|
|
||||||
|
|
||||||
@Override
|
|
||||||
@AiChatLog
|
|
||||||
public ChatVO createChat(ChatDTO dto) {
|
|
||||||
LLMProvider llmProvider = LLMProvider.fromCode(dto.getAiModel());
|
|
||||||
List<Message> messages = new ArrayList<>();
|
|
||||||
AtomicInteger tokens = new AtomicInteger();
|
|
||||||
// 如果会话id不为空 则从数据库中获取会话记录
|
|
||||||
if (dto.getSessionId() != null) {
|
|
||||||
List<AiSessionLog> list = aiSessionLogService.list(
|
|
||||||
new LambdaQueryWrapper<AiSessionLog>()
|
|
||||||
.eq(AiSessionLog::getToken, dto.getSessionId())
|
|
||||||
.eq(AiSessionLog::getDataType, dto.getDataType())
|
|
||||||
.orderByAsc(AiSessionLog::getCreatedTime)
|
|
||||||
);
|
|
||||||
if (CollectionUtil.isNotEmpty(list)) {
|
|
||||||
messages.addAll(list.stream().map(data -> {
|
|
||||||
tokens.getAndAdd(AIUtils.getPromptTokens(data.getContent()));
|
|
||||||
return AIUtils.createMessage(data.getRole(), data.getContent());
|
|
||||||
}).toList());
|
|
||||||
}
|
|
||||||
|
|
||||||
}
|
|
||||||
messages.add(AIUtils.createMessage(dto.getRole(), dto.getContent()));
|
|
||||||
List<Message> finalMessage = new ArrayList<>();
|
|
||||||
// 剪切 10%的消息
|
|
||||||
if (tokens.get() > MAX_TOKEN) {
|
|
||||||
BigDecimal size = new BigDecimal(String.valueOf(messages.size()));
|
|
||||||
size = size.multiply(DEFAULT_TRUNCATE_RATIO).setScale(0, RoundingMode.HALF_UP);
|
|
||||||
for (int i = size.intValue(); i < messages.size(); i++) {
|
|
||||||
finalMessage.add(messages.get(i));
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
finalMessage = messages;
|
|
||||||
}
|
|
||||||
String res = aiClientManager.getClient(llmProvider).chatCompletion(finalMessage);
|
|
||||||
|
|
||||||
|
|
||||||
return ChatVO.builder()
|
|
||||||
.role(Role.ASSISTANT.getValue())
|
|
||||||
.sessionId(dto.getSessionId())
|
|
||||||
.content(res)
|
|
||||||
.build();
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String createInterviewChat(MultipartFile resume, InterviewStartRequest request) {
|
|
||||||
return "";
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,24 +1,29 @@
|
|||||||
package com.qingqiu.interview.service.impl;
|
package com.qingqiu.interview.service.impl;
|
||||||
|
|
||||||
import com.alibaba.dashscope.common.Role;
|
import cn.hutool.core.map.MapUtil;
|
||||||
import com.alibaba.fastjson2.JSON;
|
import com.alibaba.fastjson2.JSON;
|
||||||
import com.alibaba.fastjson2.JSONObject;
|
import com.alibaba.fastjson2.JSONObject;
|
||||||
import com.qingqiu.interview.common.constants.CommonConstant;
|
import com.qingqiu.interview.common.constants.ChatConstant;
|
||||||
import com.qingqiu.interview.dto.ChatDTO;
|
import com.qingqiu.interview.common.utils.PromptTemplateUtils;
|
||||||
import com.qingqiu.interview.entity.InterviewQuestionProgress;
|
import com.qingqiu.interview.entity.InterviewQuestionProgress;
|
||||||
import com.qingqiu.interview.entity.InterviewSession;
|
import com.qingqiu.interview.entity.InterviewSession;
|
||||||
import com.qingqiu.interview.entity.Question;
|
import com.qingqiu.interview.entity.Question;
|
||||||
import com.qingqiu.interview.service.ChatService;
|
import com.qingqiu.interview.entity.ai.EvaluateAiRes;
|
||||||
|
import com.qingqiu.interview.entity.ai.ExtractSkillAiRes;
|
||||||
|
import com.qingqiu.interview.entity.ai.InterviewReportAiRes;
|
||||||
|
import com.qingqiu.interview.entity.ai.QuestionAiRes;
|
||||||
import com.qingqiu.interview.service.InterviewAiService;
|
import com.qingqiu.interview.service.InterviewAiService;
|
||||||
import com.qingqiu.interview.vo.ChatVO;
|
import jakarta.annotation.Resource;
|
||||||
import lombok.RequiredArgsConstructor;
|
|
||||||
import lombok.extern.slf4j.Slf4j;
|
import lombok.extern.slf4j.Slf4j;
|
||||||
import org.springframework.beans.factory.annotation.Autowired;
|
import org.jetbrains.annotations.NotNull;
|
||||||
import org.springframework.context.annotation.Lazy;
|
import org.springframework.ai.chat.client.ChatClient;
|
||||||
|
import org.springframework.ai.chat.memory.ChatMemory;
|
||||||
|
import org.springframework.ai.chat.prompt.Prompt;
|
||||||
import org.springframework.stereotype.Service;
|
import org.springframework.stereotype.Service;
|
||||||
|
|
||||||
import java.util.List;
|
import java.util.List;
|
||||||
import java.util.stream.Collectors;
|
import java.util.Map;
|
||||||
|
import java.util.UUID;
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* <h1></h1>
|
* <h1></h1>
|
||||||
@@ -28,114 +33,163 @@ import java.util.stream.Collectors;
|
|||||||
*/
|
*/
|
||||||
@Slf4j
|
@Slf4j
|
||||||
@Service
|
@Service
|
||||||
@RequiredArgsConstructor(onConstructor_ = {@Autowired, @Lazy})
|
|
||||||
public class InterviewAiServiceImpl implements InterviewAiService {
|
public class InterviewAiServiceImpl implements InterviewAiService {
|
||||||
|
|
||||||
private final ChatService chatService;
|
@Resource
|
||||||
|
private ChatClient chatClient;
|
||||||
|
|
||||||
|
@Resource(name = ChatConstant.DASH_SCOPE_CHAT_CLIENT_BEAN_NAME)
|
||||||
|
private ChatClient dashScopeChatClient;
|
||||||
|
|
||||||
|
@Resource(name = ChatConstant.OPEN_AI_CHAT_CLIENT_BEAN_NAME)
|
||||||
|
private ChatClient openAiChatClient;
|
||||||
|
|
||||||
@Override
|
@Override
|
||||||
public JSONObject extractSkillsFromResume(String resumeContent) {
|
public List<String> extractSkillsFromResume(String resumeContent) {
|
||||||
String prompt = "你是一位资深的IT技术招聘专家。" +
|
ExtractSkillAiRes entity = chatClient
|
||||||
"请仔细阅读以下简历内容，并提取出其中所有的关键技术技能。" +
|
.prompt(PromptTemplateUtils.getExtractSkillsPrompt(resumeContent))
|
||||||
"请严格按照以下JSON格式返回，不要添加任何额外的解释或说明：\n" +
|
.call()
|
||||||
"{\"skills\": [\"技能1\", \"技能2\", \"...\"]}\n\n" +
|
.entity(ExtractSkillAiRes.class);
|
||||||
"简历内容如下：\n" + resumeContent;
|
assert entity != null;
|
||||||
|
return entity.skills();
|
||||||
ChatDTO chatDTO = new ChatDTO()
|
|
||||||
.setContent(prompt)
|
|
||||||
.setRole(Role.SYSTEM.getValue())
|
|
||||||
.setDataType(CommonConstant.ONE);
|
|
||||||
ChatVO chatVO = chatService.createChat(chatDTO);
|
|
||||||
|
|
||||||
return JSONObject.parse(chatVO.getContent());
|
|
||||||
}
|
}
|
||||||
|
|
||||||
@Override
|
@Override
|
||||||
public JSONObject generateQuestionsOfAi(String sessionId, List<String> skills, String resumeContent, int count) {
|
public List<QuestionAiRes.Question> generateQuestionsOfAi(String sessionId,
|
||||||
String skillsStr = String.join(", ", skills);
|
List<String> skills,
|
||||||
String prompt = String.format(
|
String jobRequirements,
|
||||||
"你是一位专业的软件开发岗位技术面试官。" +
|
String resumeContent,
|
||||||
"请根据候选人的以下技术栈、项目经历、简历内容，生成 %d 道有深度和广度的面试题。" +
|
int count) {
|
||||||
"题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。" +
|
// String skillsStr = String.join(", ", skills);
|
||||||
"请严格按照以下JSON格式返回，question数组中必须包含 %d 个问题对象：\n" +
|
// String prompt = String.format(
|
||||||
"{\"questions\": [{\"id\": \"ai-gen-1\", \"content\": \"问题1内容...\"}, {\"id\": \"ai-gen-2\", \"content\": \"问题2内容...\"}]}\n\n" +
|
// "你是一位专业的软件开发岗位技术面试官。" +
|
||||||
"候选人技术栈：%s\n" +
|
// "请根据候选人的以下技术栈、项目经历、简历内容，生成 %d 道有深度和广度的面试题。" +
|
||||||
"候选人简历：%s",
|
// "题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。" +
|
||||||
count, count, skillsStr, resumeContent
|
// "请严格按照以下JSON格式返回，question数组中必须包含 %d 个问题对象：\n" +
|
||||||
);
|
// "{\"questions\": [{\"id\": \"ai-gen-1\", \"content\": \"问题1内容...\"}, {\"id\": \"ai-gen-2\", \"content\": \"问题2内容...\"}]}\n\n" +
|
||||||
|
// "候选人技术栈：%s\n" +
|
||||||
ChatDTO chatDTO = new ChatDTO()
|
// "候选人简历：%s",
|
||||||
.setSessionId(sessionId)
|
// count, count, skillsStr, resumeContent
|
||||||
.setContent(prompt)
|
// );
|
||||||
.setRole(Role.SYSTEM.getValue())
|
Map<String, Object> params = MapUtil.<String, Object>builder()
|
||||||
.setDataType(CommonConstant.ONE);
|
.put("count", count)
|
||||||
ChatVO chatVO = chatService.createChat(chatDTO);
|
.put("jobRequirements", jobRequirements)
|
||||||
return JSON.parseObject(chatVO.getContent());
|
.put("skills", JSONObject.toJSONString(skills))
|
||||||
|
.put("resume", resumeContent)
|
||||||
|
.build();
|
||||||
|
Prompt aiInterviewerPrompt = PromptTemplateUtils.getAiInterviewerPrompt(params);
|
||||||
|
QuestionAiRes.Wrapper entity = openAiChatClient
|
||||||
|
.prompt(aiInterviewerPrompt)
|
||||||
|
.advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
|
||||||
|
.call()
|
||||||
|
.entity(QuestionAiRes.Wrapper.class);
|
||||||
|
// String content = openAiChatClient
|
||||||
|
// .prompt(aiInterviewerPrompt)
|
||||||
|
// .advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
|
||||||
|
// .call()
|
||||||
|
//
|
||||||
|
// .content()
|
||||||
|
// ;
|
||||||
|
// ChatDTO chatDTO = new ChatDTO()
|
||||||
|
// .setSessionId(sessionId)
|
||||||
|
// .setContent(prompt)
|
||||||
|
// .setRole(Role.SYSTEM.getValue())
|
||||||
|
// .setDataType(CommonConstant.ONE);
|
||||||
|
// ChatVO chatVO = chatService.createChat(chatDTO);
|
||||||
|
return entity.questions();
|
||||||
}
|
}
|
||||||
|
|
||||||
@Override
|
@Override
|
||||||
public JSONObject generateQuestionOfLocal(String sessionId, List<Question> questions, List<String> skills, String resumeContent, int count) {
|
public List<QuestionAiRes.Question> generateQuestionOfLocal(String sessionId,
|
||||||
String skillsStr = String.join(", ", skills);
|
List<Question> questions,
|
||||||
// 2. 构建发送给AI的提示
|
List<String> skills,
|
||||||
String prompt = String.format("""
|
String jobRequirements,
|
||||||
你是一位专业的面试官。请根据以下候选人的技术栈、项目经历、简历内容，从提供的题库中，精心挑选出 %d 道最相关的题目进行面试。
|
String resumeContent,
|
||||||
题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。
|
int count) {
|
||||||
要求：
|
Map<String, Object> params = MapUtil.<String, Object>builder()
|
||||||
1. 题目必须严格从【题库JSON】中选择。
|
.put("count", count)
|
||||||
2. 挑选的题目应根据候选人的简历内容来抽取。
|
.put("jobRequirements", jobRequirements)
|
||||||
3. 返回一个只包含所选题目ID的JSON数组，格式为：{"question_ids": [1, 5, 23, ...]}。
|
.put("skills", JSONObject.toJSONString(skills))
|
||||||
4. 不要返回任何多余的代码，包括markdown形式的代码，我只需要JSON对象，请严格按照api接口形式返回
|
.put("resume", resumeContent)
|
||||||
5. 不要返回任何额外的解释或文字，只返回JSON对象。
|
.build();
|
||||||
6. 严格按照前后端分离的接口形式返回JSON数据给我，不要返回"```json```"
|
|
||||||
7. 请保证返回数据的完整性，不要返回不完整的数据，否则我的JSON解析会报错！！！
|
Prompt aiInterviewerPrompt = PromptTemplateUtils.getLocalInterviewPrompt(params);
|
||||||
|
QuestionAiRes.Wrapper entity = openAiChatClient.prompt(aiInterviewerPrompt)
|
||||||
【候选人技术栈】：
|
.advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
|
||||||
%s
|
.call()
|
||||||
【候选人简历】:
|
.entity(QuestionAiRes.Wrapper.class);
|
||||||
[%s]
|
assert entity != null;
|
||||||
【题库JSON】:
|
return entity.questions();
|
||||||
%s
|
// String skillsStr = String.join(", ", skills);
|
||||||
""", count, skillsStr, resumeContent, JSONObject.toJSONString(questions));
|
// // 2. 构建发送给AI的提示
|
||||||
ChatDTO chatDTO = new ChatDTO()
|
// String prompt = String.format("""
|
||||||
.setSessionId(sessionId)
|
// 你是一位专业的面试官。请根据以下候选人的技术栈、项目经历、简历内容，从提供的题库中，精心挑选出 %d 道最相关的题目进行面试。
|
||||||
.setContent(prompt)
|
// 题目应覆盖候选人的主要技术领域，并能考察其解决问题的能力。
|
||||||
.setRole(Role.SYSTEM.getValue())
|
// 要求：
|
||||||
.setDataType(CommonConstant.ONE);
|
// 1. 题目必须严格从【题库JSON】中选择。
|
||||||
ChatVO chatVO = chatService.createChat(chatDTO);
|
// 2. 挑选的题目应根据候选人的简历内容来抽取。
|
||||||
return JSON.parseObject(chatVO.getContent());
|
// 3. 返回一个只包含所选题目ID的JSON数组，格式为：{"question_ids": [1, 5, 23, ...]}。
|
||||||
|
// 4. 不要返回任何多余的代码，包括markdown形式的代码，我只需要JSON对象，请严格按照api接口形式返回
|
||||||
|
// 5. 不要返回任何额外的解释或文字，只返回JSON对象。
|
||||||
|
// 6. 严格按照前后端分离的接口形式返回JSON数据给我，不要返回"```json```"
|
||||||
|
// 7. 请保证返回数据的完整性，不要返回不完整的数据，否则我的JSON解析会报错！！！
|
||||||
|
//
|
||||||
|
// 【候选人技术栈】：
|
||||||
|
// %s
|
||||||
|
// 【候选人简历】:
|
||||||
|
// [%s]
|
||||||
|
// 【题库JSON】:
|
||||||
|
// %s
|
||||||
|
// """, count, skillsStr, resumeContent, JSONObject.toJSONString(questions));
|
||||||
|
// ChatDTO chatDTO = new ChatDTO()
|
||||||
|
// .setSessionId(sessionId)
|
||||||
|
// .setContent(prompt)
|
||||||
|
// .setRole(Role.SYSTEM.getValue())
|
||||||
|
// .setDataType(CommonConstant.ONE);
|
||||||
|
// ChatVO chatVO = chatService.createChat(chatDTO);
|
||||||
|
// return JSON.parseObject(chatVO.getContent());
|
||||||
}
|
}
|
||||||
|
|
||||||
@Override
|
@Override
|
||||||
public JSONObject evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context) {
|
public EvaluateAiRes evaluateAnswer(String sessionId, String question, String userAnswer, List<InterviewQuestionProgress> context) {
|
||||||
// 构建上下文历史
|
// 构建上下文历史
|
||||||
String history = context.stream()
|
// String history = context.stream()
|
||||||
.map(p -> String.format("Q: %s\nA: %s", p.getQuestionContent(), p.getUserAnswer()))
|
// .map(p -> String.format("Q: %s\nA: %s", p.getQuestionContent(), p.getUserAnswer()))
|
||||||
.collect(Collectors.joining("\n---\n"));
|
// .collect(Collectors.joining("\n---\n"));
|
||||||
|
//
|
||||||
String prompt = "你是一位资深的技术面试官，以严格和深入著称。" +
|
// String prompt = "你是一位资深的技术面试官，以严格和深入著称。" +
|
||||||
"你需要评估候选人对以下问题的回答。请注意：\n" +
|
// "你需要评估候选人对以下问题的回答。请注意：\n" +
|
||||||
"1. 如果回答模糊、不完整或有错误，你可以提出一个具体的追问问题（followUpQuestion）来深入考察，此时'continueAsking'应为true。\n" +
|
// "1. 如果回答模糊、不完整或有错误，你可以提出一个具体的追问问题（followUpQuestion）来深入考察，此时'continueAsking'应为true。\n" +
|
||||||
"2. 如果回答得很好，则'continueAsking'为false，'followUpQuestion'为空字符串。\n" +
|
// "2. 如果回答得很好，则'continueAsking'为false，'followUpQuestion'为空字符串。\n" +
|
||||||
"3. 'score'范围为0-100分。\n" +
|
// "3. 'score'范围为0-100分。\n" +
|
||||||
"4. 'feedback'和'suggestions'需要给出专业、有建设性的意见。\n" +
|
// "4. 'feedback'和'suggestions'需要给出专业、有建设性的意见。\n" +
|
||||||
"5. 追问最好有限制，不要无限制的向下追问，注意追问是支线而非主线！追问至多3个问题，之后必须切回主线\n" +
|
// "5. 追问最好有限制，不要无限制的向下追问，注意追问是支线而非主线！追问至多3个问题，之后必须切回主线\n" +
|
||||||
"请严格按照以下JSON格式返回，不要有任何额外说明：\n" +
|
// "请严格按照以下JSON格式返回，不要有任何额外说明：\n" +
|
||||||
"{\"feedback\": \"...\", \"suggestions\": \"...\", \"aiAnswer\": \"...\", \"score\": 85.5, \"continueAsking\": false, \"followUpQuestion\": \"...\"}\n\n" +
|
// "{\"feedback\": \"...\", \"suggestions\": \"...\", \"aiAnswer\": \"...\", \"score\": 85.5, \"continueAsking\": false, \"followUpQuestion\": \"...\"}\n\n" +
|
||||||
"面试历史上下文：\n" + history + "\n\n" +
|
// "面试历史上下文：\n" + history + "\n\n" +
|
||||||
"当前问题：\n" + question + "\n\n" +
|
// "当前问题：\n" + question + "\n\n" +
|
||||||
"候选人回答：\n" + userAnswer;
|
// "候选人回答：\n" + userAnswer;
|
||||||
|
//
|
||||||
ChatDTO chatDTO = new ChatDTO()
|
// ChatDTO chatDTO = new ChatDTO()
|
||||||
.setSessionId(sessionId)
|
// .setSessionId(sessionId)
|
||||||
.setContent(prompt)
|
// .setContent(prompt)
|
||||||
.setRole(Role.SYSTEM.getValue())
|
// .setRole(Role.SYSTEM.getValue())
|
||||||
.setDataType(CommonConstant.ONE);
|
// .setDataType(CommonConstant.ONE);
|
||||||
ChatVO chatVO = chatService.createChat(chatDTO);
|
// ChatVO chatVO = chatService.createChat(chatDTO);
|
||||||
return JSON.parseObject(chatVO.getContent());
|
Map<String, Object> params = MapUtil.<String, Object>builder()
|
||||||
|
.put("question", question)
|
||||||
|
.put("candidateAnswer", userAnswer)
|
||||||
|
.build();
|
||||||
|
Prompt prompt = PromptTemplateUtils.getEvaluatePrompt(params);
|
||||||
|
return openAiChatClient
|
||||||
|
.prompt(prompt)
|
||||||
|
.advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
|
||||||
|
.call()
|
||||||
|
.entity(EvaluateAiRes.class);
|
||||||
}
|
}
|
||||||
|
|
||||||
@Override
|
@Override
|
||||||
public JSONObject generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList) {
|
public InterviewReportAiRes generateFinalReport(InterviewSession session, List<InterviewQuestionProgress> progressList) {
|
||||||
// String transcript = progressList.stream()
|
// String transcript = progressList.stream()
|
||||||
// .map(p -> String.format("问题: %s\n回答: %s\nAI评分: %.1f\nAI反馈: %s\n",
|
// .map(p -> String.format("问题: %s\n回答: %s\nAI评分: %.1f\nAI反馈: %s\n",
|
||||||
// p.getQuestionContent(), p.getUserAnswer(), p.getScore(), p.getFeedback()))
|
// p.getQuestionContent(), p.getUserAnswer(), p.getScore(), p.getFeedback()))
|
||||||
@@ -148,23 +202,26 @@ public class InterviewAiServiceImpl implements InterviewAiService {
|
|||||||
// "{\"overallScore\": 88.0, \"summary\": \"...\", \"strengths\": [\"...\"], \"weaknesses\": [\"...\"]}\n\n" +
|
// "{\"overallScore\": 88.0, \"summary\": \"...\", \"strengths\": [\"...\"], \"weaknesses\": [\"...\"]}\n\n" +
|
||||||
// "候选人姓名：" + session.getCandidateName() + "\n" +
|
// "候选人姓名：" + session.getCandidateName() + "\n" +
|
||||||
// "面试完整记录：\n" + transcript;
|
// "面试完整记录：\n" + transcript;
|
||||||
|
// ChatDTO chatDTO = new ChatDTO()
|
||||||
String prompt = buildFinalReportPrompt(session, progressList);
|
// .setRole(Role.SYSTEM.getValue())
|
||||||
|
// .setDataType(CommonConstant.ONE)
|
||||||
ChatDTO chatDTO = new ChatDTO()
|
// .setContent(prompt);
|
||||||
.setRole(Role.SYSTEM.getValue())
|
// ChatVO chatVO = chatService.createChat(chatDTO);
|
||||||
.setDataType(CommonConstant.ONE)
|
Map<String, Object> params = getFinalReportParams(session, progressList);
|
||||||
.setContent(prompt);
|
Prompt prompt = PromptTemplateUtils.getFinalReportPrompt(params);
|
||||||
ChatVO chatVO = chatService.createChat(chatDTO);
|
return openAiChatClient.prompt(prompt)
|
||||||
return JSON.parseObject(chatVO.getContent());
|
.advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, UUID.randomUUID().toString().replace("-", "")))
|
||||||
|
.call()
|
||||||
|
.entity(InterviewReportAiRes.class);
|
||||||
}
|
}
|
||||||
|
|
||||||
private String buildFinalReportPrompt(InterviewSession session, List<InterviewQuestionProgress> progressList) {
|
@NotNull
|
||||||
|
private static Map<String, Object> getFinalReportParams(InterviewSession session, List<InterviewQuestionProgress> progressList) {
|
||||||
StringBuilder historyBuilder = new StringBuilder();
|
StringBuilder historyBuilder = new StringBuilder();
|
||||||
for (InterviewQuestionProgress progress : progressList) {
|
for (InterviewQuestionProgress progress : progressList) {
|
||||||
historyBuilder.append(
|
historyBuilder.append(
|
||||||
String.format("\n【问题】: %s\n【回答】: %s\n【AI单题反馈】: %s\n【AI单题建议】: %s\n【AI单题评分】: %s/5.0\n",
|
String.format("\n【问题】: %s\n【回答】: %s\n【AI单题反馈】: %s\n【AI单题建议】: %s\n【AI单题评分】: %s/5.0\n",
|
||||||
progress.getQuestionContent(),
|
progress.getQuestionContent(),
|
||||||
progress.getUserAnswer(),
|
progress.getUserAnswer(),
|
||||||
progress.getFeedback(),
|
progress.getFeedback(),
|
||||||
progress.getSuggestions(),
|
progress.getSuggestions(),
|
||||||
@@ -172,6 +229,27 @@ public class InterviewAiServiceImpl implements InterviewAiService {
|
|||||||
)
|
)
|
||||||
);
|
);
|
||||||
}
|
}
|
||||||
|
return MapUtil.<String, Object>builder()
|
||||||
|
.put("resume", session.getResumeContent())
|
||||||
|
.put("history", historyBuilder.toString())
|
||||||
|
.build();
|
||||||
|
}
|
||||||
|
|
||||||
|
/*
|
||||||
|
private String buildFinalReportPrompt(InterviewSession session, List<InterviewQuestionProgress> progressList) {
|
||||||
|
StringBuilder historyBuilder = new StringBuilder();
|
||||||
|
for (InterviewQuestionProgress progress : progressList) {
|
||||||
|
historyBuilder.append(
|
||||||
|
String.format("\n【问题】: %s\n【回答】: %s\n【AI单题反馈】: %s\n【AI单题建议】: %s\n【AI单题评分】: %s/5.0\n",
|
||||||
|
progress.getQuestionContent(),
|
||||||
|
progress.getUserAnswer(),
|
||||||
|
progress.getFeedback(),
|
||||||
|
progress.getSuggestions(),
|
||||||
|
progress.getScore()
|
||||||
|
)
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
return String.format("""
|
return String.format("""
|
||||||
你是一位资深的HR和技术总监。请根据以下候选人的简历、完整的面试问答历史和AI对每一题的初步评估，给出一份全面、专业、有深度的最终面试报告。
|
你是一位资深的HR和技术总监。请根据以下候选人的简历、完整的面试问答历史和AI对每一题的初步评估，给出一份全面、专业、有深度的最终面试报告。
|
||||||
@@ -206,22 +284,36 @@ public class InterviewAiServiceImpl implements InterviewAiService {
|
|||||||
%s
|
%s
|
||||||
""", session.getResumeContent(), historyBuilder.toString());
|
""", session.getResumeContent(), historyBuilder.toString());
|
||||||
}
|
}
|
||||||
|
*/
|
||||||
|
|
||||||
@Override
|
@Override
|
||||||
public String generateFirstQuestion(String sessionId, String candidateName, String questionContent) {
|
public String generateFirstQuestion(String sessionId, String candidateName, String questionContent) {
|
||||||
|
|
||||||
|
// ChatDTO chatDTO = new ChatDTO()
|
||||||
|
// .setSessionId(sessionId)
|
||||||
|
// .setRole(Role.SYSTEM.getValue())
|
||||||
|
// .setDataType(CommonConstant.ONE)
|
||||||
|
// .setContent(prompt);
|
||||||
|
// ChatVO chatVO = chatService.createChat(chatDTO);
|
||||||
|
// return chatVO.getContent();
|
||||||
String prompt = String.format("""
|
String prompt = String.format("""
|
||||||
你是一位专业的技术面试官。现在要开始面试，候选人是 %s。
|
你是一位专业的技术面试官。现在要开始面试，候选人是 %s。
|
||||||
|
\s
|
||||||
第一个问题是：%s
|
第一个问题是：%s
|
||||||
|
\s
|
||||||
请以友好但专业的语气提出这个问题，可以适当添加一些引导性的话语。
|
请以友好但专业的语气提出这个问题，可以适当添加一些引导性的话语。
|
||||||
""", candidateName, questionContent);
|
严格按照JSON格式输出，不得包含任何markdown标记或额外解释：
|
||||||
ChatDTO chatDTO = new ChatDTO()
|
请返回JSON格式的数据:\s
|
||||||
.setSessionId(sessionId)
|
{
|
||||||
.setRole(Role.SYSTEM.getValue())
|
"content": "xxx"
|
||||||
.setDataType(CommonConstant.ONE)
|
}
|
||||||
.setContent(prompt);
|
\s""", candidateName, questionContent);
|
||||||
ChatVO chatVO = chatService.createChat(chatDTO);
|
String content = openAiChatClient.prompt()
|
||||||
return chatVO.getContent();
|
.user(prompt)
|
||||||
|
.advisors(spec -> spec.param(ChatMemory.CONVERSATION_ID, sessionId))
|
||||||
|
.call()
|
||||||
|
.content();
|
||||||
|
return JSON.parseObject(content).getString("content");
|
||||||
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -37,9 +37,10 @@ public class InterviewQuestionProgressServiceImpl extends ServiceImpl<InterviewQ
|
|||||||
InterviewQuestionProgress.Status.COMPLETED.name()
|
InterviewQuestionProgress.Status.COMPLETED.name()
|
||||||
)
|
)
|
||||||
)
|
)
|
||||||
|
.orderByDesc(InterviewQuestionProgress::getCreatedTime)
|
||||||
.orderByAsc(InterviewQuestionProgress::getStatus)
|
.orderByAsc(InterviewQuestionProgress::getStatus)
|
||||||
.orderByDesc(InterviewQuestionProgress::getUpdatedTime)
|
.orderByDesc(InterviewQuestionProgress::getUpdatedTime)
|
||||||
.orderByDesc(InterviewQuestionProgress::getCreatedTime)
|
|
||||||
);
|
);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@@ -12,6 +12,9 @@ import com.qingqiu.interview.dto.InterviewReportResponse;
|
|||||||
import com.qingqiu.interview.dto.InterviewStartRequest;
|
import com.qingqiu.interview.dto.InterviewStartRequest;
|
||||||
import com.qingqiu.interview.dto.SubmitAnswerDTO;
|
import com.qingqiu.interview.dto.SubmitAnswerDTO;
|
||||||
import com.qingqiu.interview.entity.*;
|
import com.qingqiu.interview.entity.*;
|
||||||
|
import com.qingqiu.interview.entity.ai.EvaluateAiRes;
|
||||||
|
import com.qingqiu.interview.entity.ai.InterviewReportAiRes;
|
||||||
|
import com.qingqiu.interview.entity.ai.QuestionAiRes;
|
||||||
import com.qingqiu.interview.mapper.InterviewEvaluationMapper;
|
import com.qingqiu.interview.mapper.InterviewEvaluationMapper;
|
||||||
import com.qingqiu.interview.mapper.InterviewMessageMapper;
|
import com.qingqiu.interview.mapper.InterviewMessageMapper;
|
||||||
import com.qingqiu.interview.mapper.InterviewSessionMapper;
|
import com.qingqiu.interview.mapper.InterviewSessionMapper;
|
||||||
@@ -32,10 +35,9 @@ import org.springframework.transaction.annotation.Transactional;
|
|||||||
import org.springframework.web.multipart.MultipartFile;
|
import org.springframework.web.multipart.MultipartFile;
|
||||||
|
|
||||||
import java.io.IOException;
|
import java.io.IOException;
|
||||||
import java.util.ArrayList;
|
import java.math.BigDecimal;
|
||||||
import java.util.List;
|
import java.util.*;
|
||||||
import java.util.Objects;
|
import java.util.concurrent.ConcurrentHashMap;
|
||||||
import java.util.UUID;
|
|
||||||
import java.util.stream.Collectors;
|
import java.util.stream.Collectors;
|
||||||
|
|
||||||
/**
|
/**
|
||||||
@@ -61,6 +63,8 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
|
|
||||||
private final DocumentParserManager documentParserManager;
|
private final DocumentParserManager documentParserManager;
|
||||||
|
|
||||||
|
private final Map<String, Integer> flowedQuestions = new ConcurrentHashMap<>();
|
||||||
|
|
||||||
@Override
|
@Override
|
||||||
@Transactional(rollbackFor = Exception.class)
|
@Transactional(rollbackFor = Exception.class)
|
||||||
public InterviewSession startInterview(MultipartFile file, InterviewStartRequest dto) throws IOException {
|
public InterviewSession startInterview(MultipartFile file, InterviewStartRequest dto) throws IOException {
|
||||||
@@ -76,13 +80,14 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
session.setStatus(InterviewSession.Status.ACTIVE.name());
|
session.setStatus(InterviewSession.Status.ACTIVE.name());
|
||||||
session.setTotalQuestions(dto.getTotalQuestions());
|
session.setTotalQuestions(dto.getTotalQuestions());
|
||||||
session.setModel(dto.getModel());
|
session.setModel(dto.getModel());
|
||||||
|
session.setJobRequirements(dto.getJobRequirements());
|
||||||
this.baseMapper.insert(session); // 先插入以获取ID
|
this.baseMapper.insert(session); // 先插入以获取ID
|
||||||
|
|
||||||
// 2. 调用AI服务从简历提取技能
|
// 2. 调用AI服务从简历提取技能
|
||||||
JSONObject skillsJson = aiService.extractSkillsFromResume(resumeContent);
|
List<String> skills = aiService.extractSkillsFromResume(resumeContent);
|
||||||
|
|
||||||
// ---> 解析AI返回的JSON数据，获取技能列表 <---
|
// ---> 解析AI返回的JSON数据，获取技能列表 <---
|
||||||
List<String> skills = skillsJson.getList("skills", String.class);
|
session.setExtractedSkills(JSONObject.toJSONString(skills));
|
||||||
session.setExtractedSkills(skillsJson.toJSONString());
|
|
||||||
|
|
||||||
// 3. 准备面试问题（本地 + AI生成）
|
// 3. 准备面试问题（本地 + AI生成）
|
||||||
if (dto.getModel().equals("local")) {
|
if (dto.getModel().equals("local")) {
|
||||||
@@ -94,11 +99,15 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
// 4. 更新会话信息
|
// 4. 更新会话信息
|
||||||
this.baseMapper.updateById(session);
|
this.baseMapper.updateById(session);
|
||||||
InterviewQuestionProgress nextQuestion = progressService.getNextQuestion(sessionId);
|
InterviewQuestionProgress nextQuestion = progressService.getNextQuestion(sessionId);
|
||||||
aiService.generateFirstQuestion(session.getSessionId(), session.getCandidateName(), nextQuestion.getQuestionContent());
|
String aiRes = aiService.generateFirstQuestion(session.getSessionId(),
|
||||||
|
session.getCandidateName(),
|
||||||
|
nextQuestion.getQuestionContent()
|
||||||
|
);
|
||||||
saveMessage(sessionId,
|
saveMessage(sessionId,
|
||||||
InterviewMessage.MessageType.QUESTION.name(),
|
InterviewMessage.MessageType.QUESTION.name(),
|
||||||
InterviewMessage.Sender.AI.name(),
|
InterviewMessage.Sender.AI.name(),
|
||||||
nextQuestion.getQuestionContent(),
|
// nextQuestion.getQuestionContent(),
|
||||||
|
aiRes,
|
||||||
nextQuestion.getId()
|
nextQuestion.getId()
|
||||||
);
|
);
|
||||||
return session;
|
return session;
|
||||||
@@ -107,25 +116,23 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
|
|
||||||
private void aiGenerateQuestions(InterviewSession session, List<String> skills) {
|
private void aiGenerateQuestions(InterviewSession session, List<String> skills) {
|
||||||
List<InterviewQuestionProgress> progressList = new ArrayList<>();
|
List<InterviewQuestionProgress> progressList = new ArrayList<>();
|
||||||
JSONObject aiQuestionsJson = aiService.generateQuestionsOfAi(
|
List<QuestionAiRes.Question> aiQuestionRes = aiService.generateQuestionsOfAi(
|
||||||
session.getSessionId(),
|
session.getSessionId(),
|
||||||
skills,
|
skills,
|
||||||
|
session.getJobRequirements(),
|
||||||
session.getResumeContent(),
|
session.getResumeContent(),
|
||||||
session.getTotalQuestions()
|
session.getTotalQuestions()
|
||||||
);
|
);
|
||||||
// ---> 解析AI返回的JSON数据，获取问题列表 <---
|
if (CollectionUtil.isNotEmpty(aiQuestionRes)) {
|
||||||
JSONArray questions = aiQuestionsJson.getJSONArray("questions");
|
for (QuestionAiRes.Question aiQuestionRe : aiQuestionRes) {
|
||||||
if (questions != null) {
|
|
||||||
questions.forEach(item -> {
|
|
||||||
JSONObject q = (JSONObject) item;
|
|
||||||
InterviewQuestionProgress progress = new InterviewQuestionProgress();
|
InterviewQuestionProgress progress = new InterviewQuestionProgress();
|
||||||
progress.setSessionId(session.getSessionId());
|
progress.setSessionId(session.getSessionId());
|
||||||
progress.setQuestionId(0L); // AI生成的问题没有本地ID
|
progress.setQuestionId(0L); // AI生成的问题没有本地ID
|
||||||
// ---> 解析单个问题内容 <---
|
// ---> 解析单个问题内容 <---
|
||||||
progress.setQuestionContent(q.getString("content"));
|
progress.setQuestionContent(aiQuestionRe.content());
|
||||||
progress.setStatus(InterviewQuestionProgress.Status.DEFAULT.name());
|
progress.setStatus(InterviewQuestionProgress.Status.DEFAULT.name());
|
||||||
progressList.add(progress);
|
progressList.add(progress);
|
||||||
});
|
}
|
||||||
}
|
}
|
||||||
// 批量保存问题进度
|
// 批量保存问题进度
|
||||||
if (CollectionUtil.isNotEmpty(progressList)) {
|
if (CollectionUtil.isNotEmpty(progressList)) {
|
||||||
@@ -158,19 +165,19 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
);
|
);
|
||||||
}
|
}
|
||||||
// ai调用返回的内容进行提取
|
// ai调用返回的内容进行提取
|
||||||
JSONObject jsonObject = aiService.generateQuestionOfLocal(
|
List<QuestionAiRes.Question> questions = aiService.generateQuestionOfLocal(
|
||||||
session.getSessionId(),
|
session.getSessionId(),
|
||||||
localQuestionDataList,
|
localQuestionDataList,
|
||||||
skills,
|
skills,
|
||||||
|
session.getJobRequirements(),
|
||||||
session.getResumeContent(),
|
session.getResumeContent(),
|
||||||
session.getTotalQuestions()
|
session.getTotalQuestions()
|
||||||
);
|
);
|
||||||
JSONArray questionIds = jsonObject.getJSONArray("question_ids");
|
Set<String> resQuestionIds = questions.stream().map(QuestionAiRes.Question::id).collect(Collectors.toSet());
|
||||||
List<Long> list = questionIds.toList(Long.class);
|
|
||||||
// 查询返回的内容 并将其保存为问题进度的相关数据
|
// 查询返回的内容 并将其保存为问题进度的相关数据
|
||||||
List<Question> questionList = questionService.list(
|
List<Question> questionList = questionService.list(
|
||||||
new LambdaQueryWrapper<Question>()
|
new LambdaQueryWrapper<Question>()
|
||||||
.in(Question::getId, list)
|
.in(Question::getId, resQuestionIds)
|
||||||
);
|
);
|
||||||
List<InterviewQuestionProgress> progressList = new ArrayList<>();
|
List<InterviewQuestionProgress> progressList = new ArrayList<>();
|
||||||
questionList.forEach(q -> {
|
questionList.forEach(q -> {
|
||||||
@@ -256,7 +263,8 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
.eq(InterviewQuestionProgress::getStatus, InterviewQuestionProgress.Status.COMPLETED.name())
|
.eq(InterviewQuestionProgress::getStatus, InterviewQuestionProgress.Status.COMPLETED.name())
|
||||||
.orderByAsc(InterviewQuestionProgress::getId)
|
.orderByAsc(InterviewQuestionProgress::getId)
|
||||||
);
|
);
|
||||||
JSONObject evalResult = aiService.evaluateAnswer(
|
Integer flowedQuestionCount = this.flowedQuestions.getOrDefault(currentProgress.getSessionId(), 0);
|
||||||
|
EvaluateAiRes evaluateAiRes = aiService.evaluateAnswer(
|
||||||
currentProgress.getSessionId(),
|
currentProgress.getSessionId(),
|
||||||
currentProgress.getQuestionContent(),
|
currentProgress.getQuestionContent(),
|
||||||
dto.getAnswer(),
|
dto.getAnswer(),
|
||||||
@@ -264,40 +272,45 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
);
|
);
|
||||||
|
|
||||||
// 3. ---> 解析AI返回的JSON评估结果并存入数据库 <---
|
// 3. ---> 解析AI返回的JSON评估结果并存入数据库 <---
|
||||||
currentProgress.setFeedback(evalResult.getString("feedback"));
|
currentProgress.setFeedback(evaluateAiRes.feedback());
|
||||||
currentProgress.setSuggestions(evalResult.getString("suggestions"));
|
currentProgress.setSuggestions(evaluateAiRes.suggestions());
|
||||||
currentProgress.setAiAnswer(evalResult.getString("aiAnswer"));
|
currentProgress.setAiAnswer(evaluateAiRes.aiAnswer());
|
||||||
currentProgress.setScore(evalResult.getBigDecimal("score"));
|
currentProgress.setScore(BigDecimal.valueOf(evaluateAiRes.score()));
|
||||||
currentProgress.setStatus(InterviewQuestionProgress.Status.COMPLETED.name());
|
currentProgress.setStatus(InterviewQuestionProgress.Status.COMPLETED.name());
|
||||||
progressService.updateById(currentProgress);
|
progressService.updateById(currentProgress);
|
||||||
|
|
||||||
// 4. 将单题评估结果存入 evaluation 表用于分析
|
// 4. 将单题评估结果存入 evaluation 表用于分析
|
||||||
saveEvaluationRecord(currentProgress, evalResult);
|
saveEvaluationRecord(currentProgress, evaluateAiRes);
|
||||||
|
|
||||||
|
|
||||||
// 5. ---> 解析AI的是否追问判断，并处理追问逻辑 <---
|
// 5. ---> 解析AI的是否追问判断，并处理追问逻辑 <---
|
||||||
if (evalResult.getBooleanValue("continueAsking", false)) {
|
if (evaluateAiRes.continueAsking()) {
|
||||||
// 创建一个新的、状态为ACTIVE的追问问题
|
// 创建一个新的、状态为ACTIVE的追问问题
|
||||||
InterviewQuestionProgress followUp = new InterviewQuestionProgress();
|
InterviewQuestionProgress followUp = new InterviewQuestionProgress();
|
||||||
followUp.setSessionId(currentProgress.getSessionId());
|
followUp.setSessionId(currentProgress.getSessionId());
|
||||||
followUp.setQuestionId(0L); // 追问问题没有本地ID
|
followUp.setQuestionId(0L); // 追问问题没有本地ID
|
||||||
followUp.setQuestionContent(evalResult.getString("followUpQuestion"));
|
followUp.setQuestionContent(evaluateAiRes.followUpQuestion());
|
||||||
followUp.setStatus(InterviewQuestionProgress.Status.ACTIVE.name()); // 直接设为激活状态，作为下一个问题
|
followUp.setStatus(InterviewQuestionProgress.Status.ACTIVE.name()); // 直接设为激活状态，作为下一个问题
|
||||||
progressService.save(followUp);
|
progressService.save(followUp);
|
||||||
|
// 记录追问题目数量
|
||||||
|
flowedQuestionCount++;
|
||||||
|
this.flowedQuestions.put(currentProgress.getSessionId(), flowedQuestionCount);
|
||||||
return followUp; // 将这个新的追问问题返回给前端
|
return followUp; // 将这个新的追问问题返回给前端
|
||||||
}
|
}
|
||||||
|
// 清空追问题目数量
|
||||||
|
this.flowedQuestions.put(currentProgress.getSessionId(), 0);
|
||||||
|
|
||||||
return currentProgress;
|
return currentProgress;
|
||||||
}
|
}
|
||||||
|
|
||||||
private void saveEvaluationRecord(InterviewQuestionProgress progress, JSONObject evalResult) {
|
private void saveEvaluationRecord(InterviewQuestionProgress progress, EvaluateAiRes evaluateAiRes) {
|
||||||
InterviewEvaluation evaluation = new InterviewEvaluation();
|
InterviewEvaluation evaluation = new InterviewEvaluation();
|
||||||
evaluation.setSessionId(progress.getSessionId());
|
evaluation.setSessionId(progress.getSessionId());
|
||||||
evaluation.setQuestionId(progress.getQuestionId());
|
evaluation.setQuestionId(progress.getQuestionId());
|
||||||
evaluation.setUserAnswer(progress.getUserAnswer());
|
evaluation.setUserAnswer(progress.getUserAnswer());
|
||||||
// ---> 解析AI评估结果并存入分析表 <---
|
// ---> 解析AI评估结果并存入分析表 <---
|
||||||
evaluation.setAiFeedback(evalResult.getString("feedback"));
|
evaluation.setAiFeedback(evaluateAiRes.feedback());
|
||||||
evaluation.setScore(evalResult.getBigDecimal("score"));
|
evaluation.setScore(BigDecimal.valueOf(evaluateAiRes.score()));
|
||||||
evaluationMapper.insert(evaluation);
|
evaluationMapper.insert(evaluation);
|
||||||
}
|
}
|
||||||
|
|
||||||
@@ -319,12 +332,12 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
}
|
}
|
||||||
|
|
||||||
// 2. 调用AI服务生成最终报告
|
// 2. 调用AI服务生成最终报告
|
||||||
JSONObject finalReportJson = aiService.generateFinalReport(session, completedProgresses);
|
InterviewReportAiRes interviewReportAiRes = aiService.generateFinalReport(session, completedProgresses);
|
||||||
|
|
||||||
// 3. ---> 解析AI返回的最终报告JSON，更新会话状态 <---
|
// 3. ---> 解析AI返回的最终报告JSON，更新会话状态 <---
|
||||||
session.setStatus(InterviewSession.Status.COMPLETED.name());
|
session.setStatus(InterviewSession.Status.COMPLETED.name());
|
||||||
session.setScore(finalReportJson.getBigDecimal("overallScore"));
|
session.setScore(new BigDecimal(interviewReportAiRes.overallScore()));
|
||||||
session.setFinalReport(finalReportJson.toJSONString());
|
session.setFinalReport(JSONObject.toJSONString(interviewReportAiRes));
|
||||||
this.baseMapper.updateById(session);
|
this.baseMapper.updateById(session);
|
||||||
|
|
||||||
return session;
|
return session;
|
||||||
@@ -398,7 +411,7 @@ public class InterviewServiceImpl extends ServiceImpl<InterviewSessionMapper, In
|
|||||||
|
|
||||||
|
|
||||||
private InterviewMessage saveMessage(String sessionId, String messageType, String sender,
|
private InterviewMessage saveMessage(String sessionId, String messageType, String sender,
|
||||||
String content, Long questionId) {
|
String content, Long questionId) {
|
||||||
int nextOrder = messageMapper.selectMaxOrderBySessionId(sessionId) + 1;
|
int nextOrder = messageMapper.selectMaxOrderBySessionId(sessionId) + 1;
|
||||||
InterviewMessage message = new InterviewMessage()
|
InterviewMessage message = new InterviewMessage()
|
||||||
.setSessionId(sessionId)
|
.setSessionId(sessionId)
|
||||||
|
|||||||
@@ -17,7 +17,6 @@ import com.qingqiu.interview.mapper.QuestionMapper;
|
|||||||
import com.qingqiu.interview.service.IQuestionCategoryService;
|
import com.qingqiu.interview.service.IQuestionCategoryService;
|
||||||
import com.qingqiu.interview.service.QuestionClassificationService;
|
import com.qingqiu.interview.service.QuestionClassificationService;
|
||||||
import com.qingqiu.interview.service.QuestionService;
|
import com.qingqiu.interview.service.QuestionService;
|
||||||
import com.qingqiu.interview.service.llm.LlmService;
|
|
||||||
import com.qingqiu.interview.service.parser.DocumentParser;
|
import com.qingqiu.interview.service.parser.DocumentParser;
|
||||||
import com.qingqiu.interview.vo.QuestionAndCategoryTreeListVO;
|
import com.qingqiu.interview.vo.QuestionAndCategoryTreeListVO;
|
||||||
import lombok.RequiredArgsConstructor;
|
import lombok.RequiredArgsConstructor;
|
||||||
@@ -41,7 +40,6 @@ public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> i
|
|||||||
private final QuestionMapper questionMapper;
|
private final QuestionMapper questionMapper;
|
||||||
private final QuestionClassificationService classificationService;
|
private final QuestionClassificationService classificationService;
|
||||||
private final List<DocumentParser> documentParserList; // This will be injected by Spring
|
private final List<DocumentParser> documentParserList; // This will be injected by Spring
|
||||||
private final LlmService llmService;
|
|
||||||
private final IQuestionCategoryService questionCategoryService;
|
private final IQuestionCategoryService questionCategoryService;
|
||||||
|
|
||||||
/**
|
/**
|
||||||
@@ -119,30 +117,30 @@ public class QuestionServiceImpl extends ServiceImpl<QuestionMapper, Question> i
|
|||||||
@Override
|
@Override
|
||||||
@Transactional(rollbackFor = Exception.class)
|
@Transactional(rollbackFor = Exception.class)
|
||||||
public void useAiCheckQuestionData() {
|
public void useAiCheckQuestionData() {
|
||||||
// 查询数据库
|
// // 查询数据库
|
||||||
List<Question> questions = questionMapper.selectList(
|
// List<Question> questions = questionMapper.selectList(
|
||||||
new LambdaQueryWrapper<Question>()
|
// new LambdaQueryWrapper<Question>()
|
||||||
.orderByDesc(Question::getCreatedTime)
|
// .orderByDesc(Question::getCreatedTime)
|
||||||
);
|
// );
|
||||||
// 组装prompt
|
// // 组装prompt
|
||||||
if (CollectionUtil.isEmpty(questions)) {
|
// if (CollectionUtil.isEmpty(questions)) {
|
||||||
return;
|
// return;
|
||||||
}
|
// }
|
||||||
String prompt = getPrompt(questions);
|
// String prompt = getPrompt(questions);
|
||||||
log.info("发送内容: {}", prompt);
|
// log.info("发送内容: {}", prompt);
|
||||||
// 验证token上下文长度
|
// // 验证token上下文长度
|
||||||
Integer promptTokens = llmService.getPromptTokens(prompt);
|
// Integer promptTokens = llmService.getPromptTokens(prompt);
|
||||||
log.info("当前prompt长度: {}", promptTokens);
|
// log.info("当前prompt长度: {}", promptTokens);
|
||||||
String chat = llmService.chat(prompt, LLMProvider.DEEPSEEK);
|
// String chat = llmService.chat(prompt, LLMProvider.DEEPSEEK);
|
||||||
// 调用AI
|
// // 调用AI
|
||||||
log.info("AI返回内容: {}", chat);
|
// log.info("AI返回内容: {}", chat);
|
||||||
JSONObject parse = JSONObject.parse(chat);
|
// JSONObject parse = JSONObject.parse(chat);
|
||||||
JSONArray questionsIds = parse.getJSONArray("questions");
|
// JSONArray questionsIds = parse.getJSONArray("questions");
|
||||||
List<Long> list = questionsIds.toList(Long.class);
|
// List<Long> list = questionsIds.toList(Long.class);
|
||||||
questionMapper.delete(
|
// questionMapper.delete(
|
||||||
new LambdaQueryWrapper<Question>()
|
// new LambdaQueryWrapper<Question>()
|
||||||
.notIn(Question::getId, list)
|
// .notIn(Question::getId, list)
|
||||||
);
|
// );
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@@ -1,35 +0,0 @@
|
|||||||
package com.qingqiu.interview.service.llm;
|
|
||||||
|
|
||||||
import com.qingqiu.interview.common.enums.LLMProvider;
|
|
||||||
|
|
||||||
public interface LlmService {
|
|
||||||
|
|
||||||
|
|
||||||
/**
|
|
||||||
* 与模型进行单轮对话
|
|
||||||
* @param prompt 提示词
|
|
||||||
* @return ai回复
|
|
||||||
*/
|
|
||||||
String chat(String prompt);
|
|
||||||
String chat(String prompt, LLMProvider provider);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* 与模型进行多轮对话
|
|
||||||
* @param prompt 提示词
|
|
||||||
* @param token 会话token
|
|
||||||
* @return ai回复
|
|
||||||
*/
|
|
||||||
String chat(String prompt, String token);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* 与模型进行多轮对话 指定模型
|
|
||||||
* @param prompt 提示词
|
|
||||||
* @param model 模型名称
|
|
||||||
* @param token 会话token
|
|
||||||
* @return ai回复
|
|
||||||
*/
|
|
||||||
String chat(String prompt, String token, LLMProvider provider);
|
|
||||||
|
|
||||||
Integer getPromptTokens(String prompt);
|
|
||||||
}
|
|
||||||
|
|
||||||
@@ -1,191 +0,0 @@
|
|||||||
package com.qingqiu.interview.service.llm.qwen;
|
|
||||||
|
|
||||||
import cn.hutool.core.collection.CollectionUtil;
|
|
||||||
import com.alibaba.dashscope.aigc.generation.Generation;
|
|
||||||
import com.alibaba.dashscope.common.Message;
|
|
||||||
import com.alibaba.dashscope.common.Role;
|
|
||||||
import com.alibaba.dashscope.tokenizers.Tokenizer;
|
|
||||||
import com.alibaba.dashscope.tokenizers.TokenizerFactory;
|
|
||||||
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
|
|
||||||
import com.qingqiu.interview.common.enums.LLMProvider;
|
|
||||||
import com.qingqiu.interview.ai.factory.AIClientManager;
|
|
||||||
import com.qingqiu.interview.entity.AiSessionLog;
|
|
||||||
import com.qingqiu.interview.mapper.AiSessionLogMapper;
|
|
||||||
import com.qingqiu.interview.service.llm.LlmService;
|
|
||||||
import lombok.RequiredArgsConstructor;
|
|
||||||
import lombok.extern.slf4j.Slf4j;
|
|
||||||
import org.springframework.beans.factory.annotation.Value;
|
|
||||||
import org.springframework.stereotype.Service;
|
|
||||||
|
|
||||||
import java.util.ArrayList;
|
|
||||||
import java.util.List;
|
|
||||||
import java.util.stream.Collectors;
|
|
||||||
|
|
||||||
import static com.qingqiu.interview.common.utils.AIUtils.createMessage;
|
|
||||||
|
|
||||||
@Slf4j
|
|
||||||
@Service("qwenService")
|
|
||||||
@RequiredArgsConstructor
|
|
||||||
public class QwenService implements LlmService {
|
|
||||||
|
|
||||||
private final Generation generation;
|
|
||||||
|
|
||||||
private final AiSessionLogMapper aiSessionLogMapper;
|
|
||||||
|
|
||||||
@Value("${dashscope.api-key}")
|
|
||||||
private String apiKey;
|
|
||||||
|
|
||||||
private final AIClientManager aiClientManager;
|
|
||||||
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String chat(String prompt) {
|
|
||||||
// log.info("开始调用API....");
|
|
||||||
// long l = System.currentTimeMillis();
|
|
||||||
return chat(prompt, LLMProvider.DEEPSEEK);
|
|
||||||
// GenerationParam param = GenerationParam.builder()
|
|
||||||
// .model(DEEPSEEK_3) // 可根据需要更换模型
|
|
||||||
// .messages(Collections.singletonList(createMessage(Role.USER.getValue(), prompt)))
|
|
||||||
// .resultFormat(GenerationParam.ResultFormat.MESSAGE)
|
|
||||||
// .apiKey(apiKey)
|
|
||||||
// .build();
|
|
||||||
//
|
|
||||||
// GenerationResult result = null;
|
|
||||||
// try {
|
|
||||||
// result = generation.call(param);
|
|
||||||
// log.info("调用成功，耗时: {} ms", System.currentTimeMillis() - l);
|
|
||||||
// log.debug("响应结果: {}", result.getOutput().getChoices().get(0).getMessage().getContent());
|
|
||||||
// return result.getOutput().getChoices().get(0).getMessage().getContent();
|
|
||||||
// } catch (ApiException | InputRequiredException e) {
|
|
||||||
// throw new RuntimeException("调用AI服务失败", e);
|
|
||||||
// } catch (NoApiKeyException e) {
|
|
||||||
// throw new RuntimeException("请检查API密钥是否正确", e);
|
|
||||||
// }
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String chat(String prompt, LLMProvider provider) {
|
|
||||||
return aiClientManager.getClient(provider).chatCompletion(prompt);
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String chat(String prompt, String token) {
|
|
||||||
return chat(prompt, token, LLMProvider.DEEPSEEK);
|
|
||||||
|
|
||||||
// // 调用AI模型
|
|
||||||
// try {
|
|
||||||
// log.info("开始调用API....");
|
|
||||||
// long l = System.currentTimeMillis();
|
|
||||||
// GenerationParam param = GenerationParam.builder()
|
|
||||||
// .model(DEEPSEEK_3_1) // 可根据需要更换模型
|
|
||||||
// .messages(messages)
|
|
||||||
// .resultFormat(GenerationParam.ResultFormat.MESSAGE)
|
|
||||||
// .apiKey(apiKey)
|
|
||||||
// .build();
|
|
||||||
//
|
|
||||||
// GenerationResult result = generation.call(param);
|
|
||||||
// log.info("调用成功，耗时: {} ms", System.currentTimeMillis() - l);
|
|
||||||
// String aiResponse = result.getOutput().getChoices().get(0).getMessage().getContent();
|
|
||||||
// log.debug("响应结果: {}", aiResponse);
|
|
||||||
// // 存储用户提问
|
|
||||||
// AiSessionLog userLog = new AiSessionLog();
|
|
||||||
// userLog.setToken(token);
|
|
||||||
// userLog.setRole(Role.USER.getValue());
|
|
||||||
// userLog.setContent(prompt);
|
|
||||||
// aiSessionLogMapper.insert(userLog);
|
|
||||||
//
|
|
||||||
// // 存储AI回复
|
|
||||||
// AiSessionLog aiLog = new AiSessionLog();
|
|
||||||
// aiLog.setToken(token);
|
|
||||||
// aiLog.setRole(Role.ASSISTANT.getValue());
|
|
||||||
// aiLog.setContent(aiResponse);
|
|
||||||
// aiSessionLogMapper.insert(aiLog);
|
|
||||||
//
|
|
||||||
// return aiResponse;
|
|
||||||
// } catch (ApiException | NoApiKeyException | InputRequiredException e) {
|
|
||||||
// throw new RuntimeException("调用AI服务失败", e);
|
|
||||||
// }
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public String chat(String prompt, String token, LLMProvider provider) {
|
|
||||||
// 根据token查询会话记录
|
|
||||||
List<AiSessionLog> aiSessionLogs = aiSessionLogMapper.selectList(
|
|
||||||
new LambdaQueryWrapper<AiSessionLog>()
|
|
||||||
.eq(AiSessionLog::getToken, token)
|
|
||||||
.orderByDesc(AiSessionLog::getCreatedTime)
|
|
||||||
);
|
|
||||||
// 构造发给ai的消息
|
|
||||||
List<Message> messages = new ArrayList<>();
|
|
||||||
if (CollectionUtil.isNotEmpty(aiSessionLogs)) {
|
|
||||||
// 预估tokens
|
|
||||||
StringBuilder sb = new StringBuilder();
|
|
||||||
for (AiSessionLog aiSessionLog : aiSessionLogs) {
|
|
||||||
sb.append(aiSessionLog.getContent());
|
|
||||||
}
|
|
||||||
// 加上本次对话内容
|
|
||||||
sb.append(prompt);
|
|
||||||
Integer promptTokens = getPromptTokens(sb.toString());
|
|
||||||
// 如果token大于了模型上限，则执行丢弃操作
|
|
||||||
int size = aiSessionLogs.size();
|
|
||||||
log.info("当前会话id: {}, tokens: {}", token, promptTokens);
|
|
||||||
|
|
||||||
// 假设模型上限为30000个token（根据实际模型调整）
|
|
||||||
int maxTokens = 100000;
|
|
||||||
if (promptTokens > maxTokens) {
|
|
||||||
// 需要丢弃30%的会话记录（按时间倒序，丢弃最旧的）
|
|
||||||
int discardCount = (int) (size * 0.3);
|
|
||||||
// 从当前会话记录列表中移除旧的会话记录，而不是删除数据库中的记录
|
|
||||||
for (int i = 0; i < discardCount; i++) {
|
|
||||||
aiSessionLogs.remove(aiSessionLogs.size() - 1);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
// 移除旧记录后再按时间正序排序（最旧的在前面，最新的在后面）
|
|
||||||
aiSessionLogs = aiSessionLogs.stream()
|
|
||||||
.sorted((log1, log2) -> log1.getCreatedTime().compareTo(log2.getCreatedTime()))
|
|
||||||
.collect(Collectors.toList());
|
|
||||||
for (AiSessionLog aiSessionLog : aiSessionLogs) {
|
|
||||||
messages.add(
|
|
||||||
createMessage(aiSessionLog.getRole(), aiSessionLog.getContent())
|
|
||||||
);
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
messages.add(
|
|
||||||
createMessage(Role.USER.getValue(), prompt)
|
|
||||||
);
|
|
||||||
|
|
||||||
String aiResponse = aiClientManager.getClient(provider).chatCompletion(messages);
|
|
||||||
// 存储用户提问
|
|
||||||
AiSessionLog userLog = new AiSessionLog();
|
|
||||||
userLog.setToken(token);
|
|
||||||
userLog.setRole(Role.USER.getValue());
|
|
||||||
userLog.setContent(prompt);
|
|
||||||
aiSessionLogMapper.insert(userLog);
|
|
||||||
|
|
||||||
// 存储AI回复
|
|
||||||
AiSessionLog aiLog = new AiSessionLog();
|
|
||||||
aiLog.setToken(token);
|
|
||||||
aiLog.setRole(Role.ASSISTANT.getValue());
|
|
||||||
aiLog.setContent(aiResponse);
|
|
||||||
aiSessionLogMapper.insert(aiLog);
|
|
||||||
return aiResponse;
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* 获取prompt的token数
|
|
||||||
*
|
|
||||||
* @param prompt 输入
|
|
||||||
* @return tokens
|
|
||||||
*/
|
|
||||||
@Override
|
|
||||||
public Integer getPromptTokens(String prompt) {
|
|
||||||
Tokenizer tokenizer = TokenizerFactory.qwen();
|
|
||||||
List<Integer> integers = tokenizer.encodeOrdinary(prompt);
|
|
||||||
return integers.size();
|
|
||||||
}
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
@@ -1,14 +1,42 @@
|
|||||||
dashscope:
|
|
||||||
api-key: sk-58d6fed688c54e8db02e6a7ffbfc7a5f
|
|
||||||
deepseek:
|
|
||||||
api-url: https://api.deepseek.com/chat/completions
|
|
||||||
api-key: sk-faaa2a1b485442ccbf115ff1271a3480
|
|
||||||
spring:
|
spring:
|
||||||
datasource:
|
datasource:
|
||||||
url: jdbc:mysql://gz-cynosdbmysql-grp-5ai5zw7r.sql.tencentcdb.com:24944/ai_interview?createDatabaseIfNotExist=true&useUnicode=true&characterEncoding=utf8&serverTimezone=GMT%2B8
|
url: jdbc:mysql://gz-cynosdbmysql-grp-5ai5zw7r.sql.tencentcdb.com:24944/ai_interview?createDatabaseIfNotExist=true&useUnicode=true&characterEncoding=utf8&serverTimezone=GMT%2B8
|
||||||
username: qingqiu
|
username: qingqiu
|
||||||
password: 020979hP
|
password: 020979hP
|
||||||
driver-class-name: com.mysql.cj.jdbc.Driver
|
driver-class-name: com.mysql.cj.jdbc.Driver
|
||||||
|
ai:
|
||||||
|
openai:
|
||||||
|
base-url: https://api.ruyun.fun
|
||||||
|
api-key: ${RUYUN_API_KEY}
|
||||||
|
chat:
|
||||||
|
options:
|
||||||
|
model: gemini-2.5-flash-nothinking
|
||||||
|
dashscope:
|
||||||
|
api-key: ${DASHSCOPE_API_KEY}
|
||||||
|
read-timeout: 600
|
||||||
|
chat:
|
||||||
|
options:
|
||||||
|
model: qwen3-max
|
||||||
|
memory:
|
||||||
|
redis:
|
||||||
|
host: 127.0.0.1
|
||||||
|
port: 6379
|
||||||
|
password: 123456
|
||||||
|
timeout: 6000
|
||||||
|
data:
|
||||||
|
redis:
|
||||||
|
host: localhost
|
||||||
|
port: 6379
|
||||||
|
password: 123456
|
||||||
|
database: 0
|
||||||
|
timeout: 6000
|
||||||
|
jedis:
|
||||||
|
pool:
|
||||||
|
max-active: 16
|
||||||
|
max-idle: 8
|
||||||
|
min-idle: 0
|
||||||
|
max-wait: -1ms
|
||||||
|
|
||||||
# ai:
|
# ai:
|
||||||
# openai:
|
# openai:
|
||||||
# api-key: sk-faaa2a1b485442ccbf115ff1271a3480
|
# api-key: sk-faaa2a1b485442ccbf115ff1271a3480
|
||||||
|
|||||||
Reference in New Issue
Block a user