Thursday, June 20, 2024

Getting started Spring AI with OpenAI Chat Model

 Problem statement :  develop spring boot API which connect's to Open AI and will give top 5

                                       personalities of any sports. Other than sports,

                                       if you give another details should display warning message.


We have different AI Chat models as mentioned below

This is post will explain you about , how we can use Open AI model to build simple spring boot application

Step 1 :  Signup : https://platform.openai.com/signup

              generate key -  https://platform.openai.com/account/api-keys





Step 2 :           spring boot intializr : https://start.spring.io/

dependencies :  OpenAI and other related dependencies

                        Provide group  artifact and other required details

                       import project to IntelliJ

Step3 :          Write Controller 

package com.personal.openai;

import org.springframework.ai.chat.messages.Message;
import org.springframework.ai.chat.messages.SystemMessage;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class OpenAIController {


@Autowired
private OpenAiChatModel openAiChatModel;

@GetMapping("/sports")
public String getSportPersonaDetails(@RequestParam String name){
var systemMessage = new SystemMessage("Your primary function is to provide details about sports," +
" if anything else asks simply say that i can provide only sports details");
String promptMessage = String.format(" Could you please provide detailed information" +
" on the top 5 sports persons currently in %s ?",name);
Message message = new UserMessage(promptMessage);
return openAiChatModel.call(message,systemMessage);

}
}


Step 4: update the properties/yaml

spring.application.name=openai
spring.ai.openai.api-key=sk-proj-NNcDe2HRc1UUjcbIRkRsT3Blbk
spring.ai.openai.chat.enabled=true
spring.ai.openai.chat.options.model=gpt-3.5-turbo
spring.ai.openai.chat.options.temperature=0.7


Step 5 : Run the code and start the server


pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>3.2.5</version>
<relativePath/> <!-- lookup parent from repository -->
</parent>
<groupId>com.personal</groupId>
<artifactId>openai</artifactId>
<version>0.0.1-SNAPSHOT</version>
<name>openai</name>
<description>Demo project for Spring Boot ai</description>
<url/>
<licenses>
<license/>
</licenses>
<developers>
<developer/>
</developers>
<scm>
<connection/>
<developerConnection/>
<tag/>
<url/>
</scm>
<properties>
<java.version>17</java.version>
<spring-ai.version>1.0.0-M1</spring-ai.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>

<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
</dependencies>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>${spring-ai.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>

<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<configuration>
<excludes>
<exclude>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
</exclude>
</excludes>
</configuration>
</plugin>
</plugins>
</build>
<repositories>
<repository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
</repositories>

</project>


 

Step 6:  Output :   http://localhost:8080/sports?name=cricket

              http://localhost:8080/sports?name=tollywood

              
This how we can work on any Models. 



















        

Friday, April 19, 2024

How do you foster collaboration between development, operations, and other teams in a Java project?

 

Fostering collaboration between development, operations, and other teams in a Java project requires a combination of communication, tools, and processes. Here are some strategies to help facilitate collaboration:




Establish clear communication channels: Use tools like Slack, Microsoft Teams, or similar platforms for real-time communication. Also, have regular meetings, such as daily stand-ups or weekly sync-ups, to keep everyone aligned and informed about project progress.

Encourage cross-functional teams: Form cross-functional teams comprising members from development, operations, QA, and other relevant departments. This helps in sharing knowledge and understanding each other's perspectives.

Implement DevOps practices: Embrace DevOps principles to bridge the gap between development and operations. Automate processes such as continuous integration, continuous delivery, and infrastructure provisioning to streamline collaboration and reduce bottlenecks.

Adopt Agile methodologies: Agile methodologies like Scrum or Kanban promote collaboration through iterative development cycles, regular feedback, and close collaboration between team members.

Use version control and collaboration tools: Leverage version control systems like Git along with collaboration platforms like GitHub or Bitbucket. These tools facilitate collaboration by allowing teams to work on the same codebase simultaneously and track changes effectively.

Provide training and knowledge sharing: Organize workshops, brown bag sessions, or knowledge-sharing sessions to educate team members about each other's roles, technologies, and best practices. Encourage learning and cross-training to build a more cohesive team.

Promote a culture of transparency and trust: Foster an environment where team members feel comfortable sharing their ideas, concerns, and feedback openly. Encourage transparency in decision-making processes and foster trust among team members.

Establish shared goals and metrics: Define common goals and metrics that align with both development and operations objectives. This encourages teams to collaborate towards shared outcomes and promotes a sense of collective responsibility.

Implement collaborative tools for project management: Utilize project management tools like Jira, Trello, or Asana to track tasks, assign responsibilities, and monitor progress collaboratively. Ensure that these tools are accessible to all team members and updated regularly.

Celebrate successes and learn from failures: Recognize and celebrate achievements as a team, whether it's delivering a successful release or overcoming a challenging issue. Similarly, encourage a blame-free culture where failures are seen as learning opportunities, and teams work together to identify solutions and prevent recurrence.

By implementing these strategies, you can foster a culture of collaboration and cooperation among development, operations, and other teams in a Java project, ultimately leading to improved efficiency, quality, and innovation.

Monday, March 25, 2024

Different AI tools for Intellij IDE and AI Plugins

There are several AI-powered tools and plugins available in the market that you can integrate with IntelliJ IDEA to enhance your development experience. These tools use AI and machine learning techniques to provide various features such as code completion, code analysis, refactoring suggestions, and more. Here are some popular AI tools and plugins for IntelliJ IDEA:



Code Completion & Suggestions:

1. Kite:

    Website: https://www.kite.com/

     Description: Kite provides AI-powered code completions and snippets.

      Features:

            Smart completions based on your code context.

            Documentation and examples for functions and libraries.

       IntelliJ Plugin: Kite PlugIn

2. TabNine:

       Website: TabNine

        Description: TabNine offers AI-driven code completion.

         Features:

                Predictive completions based on your coding patterns.

                Support for multiple languages.

           IntelliJ Plugin: TabNine Plugin

3. Codota:

          Website: Codota

          Description: Codota provides AI-powered code completions and suggestions.

          Features:

                     Contextual code suggestions.

                      Enhanced productivity with code snippets.

          IntelliJ Plugin: Codota Plugin


Code Analysis & Refactoring:

1. DeepCode:

          Website: DeepCode 

           Description: DeepCode uses AI to identify and fix issues in your code.

            Features:

                Automated code reviews.

                Real-time code analysis for potential errors.

                IntelliJ Plugin: DeepCode Plugin

2. SonarLint:

                Website: SonarLint

                Description: SonarLint offers AI-driven static code analysis.

                Features:

                    Code quality and security checks.

                    Real-time feedback on code issues.

                IntelliJ Plugin: SonarLint


Natural Language Processing (NLP) & Documentation:

1. NLP4J:

        Description: NLP4J provides natural language processing capabilities.

        Features:

                Tokenization, parsing, and entity recognition.

                Text summarization and keyword extraction.

           IntelliJ Plugin: NLP4J Plugin

2. DocTime:

          Description: DocTime helps generate code documentation.

           Features:

                Automatic code comments and documentation.

                Summarization of code blocks.

            IntelliJ Plugin: DocTime Plugin


Other AI Tools & Integrations:

1.TensorFlow Support:

             Description: Integration with TensorFlow for machine learning projects.

              Features:

                    TensorFlow model importing and execution.

                    TensorBoard integration.

                IntelliJ Plugin: TensorFlow Support Plugin


2. PyCharm AI Assisted Review:

               Description: AI-powered code reviews for Python projects.

               Features:

                        Automated code review suggestions.

                        Code style improvements.

                IntelliJ Plugin: PyCharm AI Assisted Review Plugin

3. Chatbot Integration:

                 Description: Integration with chatbot development platforms.

                  Features:

                         Chatbot code completion and suggestions.

                        Testing and debugging chatbot code.

                 IntelliJ Plugin: Chatbot Integration Plugin

 

Monday, March 18, 2024

What are the best practices for securing communication between microservices in a Java ecosystem

 

Securing communication between microservices in a Java ecosystem is crucial to protect sensitive data, prevent unauthorized access, and ensure the integrity and confidentiality of communication. Here are some best practices for achieving secure communication between microservices:






1. Transport Layer Security (TLS):

Use TLS/SSL for encrypting data transmitted over the network.

Enable HTTPS for RESTful APIs to ensure data confidentiality and integrity.

Configure mutual TLS (mTLS) for two-way authentication between services, where both client and server authenticate each other using certificates.

2. Service-to-Service Authentication:

Implement secure authentication mechanisms between microservices.

Use tokens (like JWT) or OAuth tokens for authentication and authorization.

Validate incoming tokens for each request to ensure only authorized services can access endpoints.

3. Role-Based Access Control (RBAC):

Implement RBAC to control access to microservice endpoints.

Define roles and permissions for each microservice, allowing only authorized users or services to perform specific actions.

4. API Gateway:

Use an API gateway to centralize security concerns and provide a single entry point for microservices.

Implement authentication, authorization, rate limiting, and request validation at the API gateway level.

5. Secure Service Discovery:

When using service discovery mechanisms like Eureka or Consul, ensure that service registration and discovery are secure.

Use authentication and encryption for communication between service registry and microservices.

6. Secure Configuration Management:

Store sensitive configuration properties (such as passwords, API keys) securely.

Use tools like Spring Cloud Config Server with encryption to manage and distribute configuration securely.

7. Secure Logging and Monitoring:

Implement secure logging practices to avoid logging sensitive information.

Use log encryption and centralized log management tools to monitor and detect security incidents.

8. Implement Content Validation:

Validate and sanitize input data to prevent injection attacks (e.g., SQL injection, XSS).

Use input validation libraries like Hibernate Validator or Bean Validation.

9. Container Security:

If deploying microservices in containers, ensure container images are scanned for vulnerabilities.

Implement least privilege principles for container permissions and avoid running containers with unnecessary privileges.

10. Use of Secure Protocols:

Avoid using insecure protocols such as HTTP and use HTTPS/TLS for secure communication.

Use protocols with strong security features like OAuth 2.0 for authentication and authorization.

11. Secure Message Queues:

If using message brokers (like RabbitMQ, Apache Kafka), ensure they are secured.

Use TLS/SSL for communication with the message broker.

Implement message encryption for sensitive data.

12. Data Encryption:

Encrypt sensitive data at rest and in transit.

Use libraries like Java Cryptography Architecture (JCA) or Bouncy Castle for encryption/decryption.

13. API Versioning and Deprecation:

Implement API versioning to manage changes in microservices.

Securely deprecate and remove old APIs to prevent security vulnerabilities in outdated endpoints.

14. Regular Security Audits and Penetration Testing:

Conduct regular security audits and vulnerability scans of microservices.

Perform penetration testing to identify potential security weaknesses and address them proactively.

15. Continuous Security Monitoring:

Implement continuous security monitoring using tools like Prometheus, Grafana, or ELK stack.

Monitor for suspicious activities, abnormal behaviors, or unauthorized access attempts.

Implement OAuth 2.0 Authorization Server and Resource Server in your microservices architecture.

Use JWT tokens for authentication and authorization between microservices.

Secure endpoints based on roles and scopes defined in JWT tokens.

Configure OAuth 2.0 clients for microservices to request and validate tokens.

Use Spring Cloud Gateway or Zuul as an API Gateway for centralized security enforcement.

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