db.md

You:

I want to do small local spring boot poc on spring ai in Java gradle, I want to use database as some popular vector db, assume local LLM mode ollama l is running mistral, show me all code for how to connect to vector database that is running locally and also tell me what gradle dependency I need to add in spring boot project to connect to db

ChatGPT:

To create a Spring Boot project with Gradle that connects to a vector database (we'll use Pinecone as an example), and assumes a local LLM mode ollama l is running mistral, follow these steps:

Step 1: Set Up Spring Boot Project with Gradle

First, create a new Spring Boot project using the Spring Initializr or manually create the necessary files.

build.gradle

Add the following dependencies to your build.gradle file:

plugins {
    id 'org.springframework.boot' version '3.0.0'
    id 'io.spring.dependency-management' version '1.0.11.RELEASE'
    id 'java'
}

group = 'com.example'
version = '0.0.1-SNAPSHOT'
sourceCompatibility = '17'

repositories {
    mavenCentral()
}

dependencies {
    implementation 'org.springframework.boot:spring-boot-starter-web'
    implementation 'org.springframework.boot:spring-boot-starter-data-jpa'
    implementation 'com.pinecone:pinecone-client:0.1.0'
    runtimeOnly 'com.h2database:h2'
}

test {
    useJUnitPlatform()
}

Step 2: Configure Database Connection

Configure your Spring Boot application to connect to the Pinecone vector database. Add the following properties in your application.properties file:

spring.datasource.url=jdbc:h2:mem:testdb
spring.datasource.driverClassName=org.h2.Driver
spring.datasource.username=sa
spring.datasource.password=password
spring.datasource.platform=h2

pinecone.api-key=your-pinecone-api-key
pinecone.project-name=your-project-name
pinecone.environment=your-environment

Step 3: Create Database Configuration

Create a configuration class to set up the Pinecone client.

PineconeConfig.java

package com.example.demo.config;

import com.pinecone.PineconeClient;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class PineconeConfig {

    @Value("${pinecone.api-key}")
    private String apiKey;

    @Value("${pinecone.project-name}")
    private String projectName;

    @Value("${pinecone.environment}")
    private String environment;

    @Bean
    public PineconeClient pineconeClient() {
        return PineconeClient.builder()
                .apiKey(apiKey)
                .projectName(projectName)
                .environment(environment)
                .build();
    }
}

Step 4: Create a Sample Entity and Repository

Create a sample entity and repository to demonstrate interaction with the database.

VectorEntity.java

package com.example.demo.entity;

import javax.persistence.Entity;
import javax.persistence.GeneratedValue;
import javax.persistence.GenerationType;
import javax.persistence.Id;

@Entity
public class VectorEntity {

    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;

    private String vector;

    // Getters and Setters
}

VectorRepository.java

package com.example.demo.repository;

import com.example.demo.entity.VectorEntity;
import org.springframework.data.jpa.repository.JpaRepository;
import org.springframework.stereotype.Repository;

@Repository
public interface VectorRepository extends JpaRepository<VectorEntity, Long> {
}

Step 5: Create a Sample Service

Create a service to handle operations related to the vector data.

VectorService.java

package com.example.demo.service;

import com.example.demo.entity.VectorEntity;
import com.example.demo.repository.VectorRepository;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class VectorService {

    @Autowired
    private VectorRepository vectorRepository;

    public VectorEntity saveVector(VectorEntity vectorEntity) {
        return vectorRepository.save(vectorEntity);
    }

    public VectorEntity getVector(Long id) {
        return vectorRepository.findById(id).orElse(null);
    }
}

Step 6: Create a Sample Controller

Create a REST controller to expose endpoints for the vector operations.

VectorController.java

package com.example.demo.controller;

import com.example.demo.entity.VectorEntity;
import com.example.demo.service.VectorService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/vectors")
public class VectorController {

    @Autowired
    private VectorService vectorService;

    @PostMapping
    public VectorEntity createVector(@RequestBody VectorEntity vectorEntity) {
        return vectorService.saveVector(vectorEntity);
    }

    @GetMapping("/{id}")
    public VectorEntity getVector(@PathVariable Long id) {
        return vectorService.getVector(id);
    }
}

Step 7: Run Your Spring Boot Application

Now, you can run your Spring Boot application. Make sure your vector database (Pinecone) is running locally and properly configured.

Step 8: Interact with Local LLM Mode (Optional)

For interacting with the local LLM mode ollama l running mistral, you can create a service to handle communication with the LLM. However, this part is more dependent on the specific APIs and libraries provided by ollama l and mistral. If you have specific requirements or details about the API, you can extend this guide accordingly.

This setup provides a basic structure to connect and interact with a vector database using Spring Boot and Gradle. Adjust the configurations and dependencies according to your specific needs.

You:

In above I want to use spring ai dependency, search internet for spring ai related example and then refine above code

ChatGPT:

Searched 4 sites To create a Spring Boot project with Gradle that integrates Spring AI for connecting to a vector database and assumes a local LLM mode (Ollama L) running Mistral, follow these steps:

Step 1: Set Up Spring Boot Project with Gradle

First, create a new Spring Boot project using Spring Initializr or manually create the necessary files.

build.gradle

Add the following dependencies to your build.gradle file:

plugins {
    id 'org.springframework.boot' version '3.0.0'
    id 'io.spring.dependency-management' version '1.0.11.RELEASE'
    id 'java'
}

group = 'com.example'
version = '0.0.1-SNAPSHOT'
sourceCompatibility = '17'

repositories {
    mavenCentral()
    maven { url 'https://repo.spring.io/milestone' }
    maven { url 'https://repo.spring.io/snapshot' }
}

dependencies {
    implementation 'org.springframework.boot:spring-boot-starter-web'
    implementation 'org.springframework.boot:spring-boot-starter-data-jpa'
    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter:0.8.0-SNAPSHOT'
    implementation platform("org.springframework.ai:spring-ai-bom:1.0.0-SNAPSHOT")
    runtimeOnly 'com.h2database:h2'
}

test {
    useJUnitPlatform()
}

Step 2: Configure Database Connection

Configure your Spring Boot application to connect to a vector database (e.g., Pinecone). Add the following properties in your application.properties file:

spring.datasource.url=jdbc:h2:mem:testdb
spring.datasource.driverClassName=org.h2.Driver
spring.datasource.username=sa
spring.datasource.password=password
spring.datasource.platform=h2

pinecone.api-key=your-pinecone-api-key
pinecone.project-name=your-project-name
pinecone.environment=your-environment

spring.ai.openai.api-key=your-openai-api-key

Step 3: Create Database Configuration

Create a configuration class to set up the Pinecone client.

PineconeConfig.java

package com.example.demo.config;

import com.pinecone.PineconeClient;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class PineconeConfig {

    @Value("${pinecone.api-key}")
    private String apiKey;

    @Value("${pinecone.project-name}")
    private String projectName;

    @Value("${pinecone.environment}")
    private String environment;

    @Bean
    public PineconeClient pineconeClient() {
        return PineconeClient.builder()
                .apiKey(apiKey)
                .projectName(projectName)
                .environment(environment)
                .build();
    }
}

Step 4: Create a Sample Entity and Repository

Create a sample entity and repository to demonstrate interaction with the database.

VectorEntity.java

package com.example.demo.entity;

import javax.persistence.Entity;
import javax.persistence.GeneratedValue;
import javax.persistence.GenerationType;
import javax.persistence.Id;

@Entity
public class VectorEntity {

    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;

    private String vector;

    // Getters and Setters
}

VectorRepository.java

package com.example.demo.repository;

import com.example.demo.entity.VectorEntity;
import org.springframework.data.jpa.repository.JpaRepository;
import org.springframework.stereotype.Repository;

@Repository
public interface VectorRepository extends JpaRepository<VectorEntity, Long> {
}

Step 5: Create a Sample Service

Create a service to handle operations related to the vector data.

VectorService.java

package com.example.demo.service;

import com.example.demo.entity.VectorEntity;
import com.example.demo.repository.VectorRepository;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class VectorService {

    @Autowired
    private VectorRepository vectorRepository;

    public VectorEntity saveVector(VectorEntity vectorEntity) {
        return vectorRepository.save(vectorEntity);
    }

    public VectorEntity getVector(Long id) {
        return vectorRepository.findById(id).orElse(null);
    }
}

Step 6: Create a Sample Controller

Create a REST controller to expose endpoints for the vector operations and AI interactions.

VectorController.java

package com.example.demo.controller;

import com.example.demo.entity.VectorEntity;
import com.example.demo.service.VectorService;
import org.springframework.ai.chat.ChatClient;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/vectors")
public class VectorController {

    @Autowired
    private VectorService vectorService;

    @Autowired
    private ChatClient chatClient;

    @PostMapping
    public VectorEntity createVector(@RequestBody VectorEntity vectorEntity) {
        return vectorService.saveVector(vectorEntity);
    }

    @GetMapping("/{id}")
    public VectorEntity getVector(@PathVariable Long id) {
        return vectorService.getVector(id);
    }

    @GetMapping("/ai")
    public String getAiResponse(@RequestParam String prompt) {
        return chatClient.generate(prompt);
    }
}

Step 7: Run Your Spring Boot Application

Now, you can run your Spring Boot application. Make sure your vector database (Pinecone) is running locally and properly configured. This setup provides a basic structure to connect and interact with a vector database using Spring Boot and Spring AI with Gradle. For more details and examples, refer to the Spring AI documentation ​ (Home )​​ (JavaTechOnline )​.

You:

Assume vector database is running locally, and local LLM model, now show me code template for vector store from spring ai, once created important bean then show me how to create string to embedded vector and print it and then show me how to store that string inside my vector database using vector store instance from spring ai

ChatGPT:

To connect to a locally running vector database and integrate a local LLM model using Spring AI, follow these steps. We'll use Spring AI's vector store API to demonstrate how to create a vector from a string, print it, and store it in the vector database.

Step 1: Add Dependencies

Ensure you have the necessary dependencies in your build.gradle file.

plugins {
    id 'org.springframework.boot' version '3.0.0'
    id 'io.spring.dependency-management' version '1.0.11.RELEASE'
    id 'java'
}

group = 'com.example'
version = '0.0.1-SNAPSHOT'
sourceCompatibility = '17'

repositories {
    mavenCentral()
    maven { url 'https://repo.spring.io/milestone' }
    maven { url 'https://repo.spring.io/snapshot' }
}

dependencies {
    implementation 'org.springframework.boot:spring-boot-starter-web'
    implementation 'org.springframework.boot:spring-boot-starter-data-jpa'
    implementation platform("org.springframework.ai:spring-ai-bom:1.0.0-SNAPSHOT")
    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter:0.8.0-SNAPSHOT'
    implementation 'org.springframework.ai:spring-ai-vector-store:0.8.0-SNAPSHOT'
    runtimeOnly 'com.h2database:h2'
}

test {
    useJUnitPlatform()
}

Step 2: Configure the Vector Store

Set up your application properties to include the necessary configurations.

application.properties

spring.datasource.url=jdbc:h2:mem:testdb
spring.datasource.driverClassName=org.h2.Driver
spring.datasource.username=sa
spring.datasource.password=password
spring.datasource.platform=h2

spring.ai.openai.api-key=your-openai-api-key

Step 3: Create Configuration for Vector Store

Create a configuration class to set up the vector store.

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;
import org.springframework.beans.factory.annotation.Value;

@Configuration
public class VectorStoreConfig {

    @Value("${pinecone.api-key}")
    private String apiKey;

    @Value("${pinecone.project-name}")
    private String projectName;

    @Value("${pinecone.environment}")
    private String environment;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createPineconeVectorStore(apiKey, projectName, environment);
    }
}

Step 4: Create Service to Generate and Store Vectors

Create a service that uses the vector store to generate and store vectors.

VectorService.java

package com.example.demo.service;

import org.springframework.ai.chat.ChatClient;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.Vector;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class VectorService {

    @Autowired
    private ChatClient chatClient;

    @Autowired
    private VectorStore vectorStore;

    public Vector createAndStoreVector(String text) {
        // Generate embedding using chat client (assuming LLM provides this functionality)
        String embeddingString = chatClient.generate(text);
        System.out.println("Generated Embedding: " + embeddingString);

        // Create vector from embedding string
        Vector vector = new Vector(embeddingString);

        // Store vector in the vector store
        vectorStore.storeVector(vector);

        return vector;
    }
}

Step 5: Create a Controller to Expose the Functionality

Create a REST controller to expose the functionality of generating and storing vectors.

VectorController.java

package com.example.demo.controller;

import com.example.demo.service.VectorService;
import org.springframework.ai.vectorstore.Vector;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/vectors")
public class VectorController {

    @Autowired
    private VectorService vectorService;

    @PostMapping
    public Vector createAndStoreVector(@RequestParam String text) {
        return vectorService.createAndStoreVector(text);
    }
}

Running the Application

1. Start the Spring Boot application: Ensure your vector database (e.g., Pinecone) is running locally and properly configured.

2. Send a POST request: Use a tool like Postman or curl to send a POST request to http://localhost:8080/vectors with the text parameter to generate and store a vector.

Example POST Request

curl -X POST "http://localhost:8080/vectors" -d "text=Hello, world!"

This will generate an embedding for the text "Hello, world!", print it, and store it in your vector database using the configured vector store instance.

Conclusion

This setup provides a basic structure to integrate Spring AI with a local vector database and a local LLM model. You can further extend this to include more functionalities and handle different types of data as per your requirements.

You:

Show me other vector database where bean creation is simple, show me different examples of vector store bean creation for different vector database, assume all database are installed on my mac

ChatGPT:

Here are examples of how to create beans for different vector databases. We’ll cover Pinecone, Weaviate, and Milvus as popular choices.

Example 1: Pinecone Vector Store

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;
import org.springframework.beans.factory.annotation.Value;

@Configuration
public class VectorStoreConfig {

    @Value("${pinecone.api-key}")
    private String apiKey;

    @Value("${pinecone.project-name}")
    private String projectName;

    @Value("${pinecone.environment}")
    private String environment;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createPineconeVectorStore(apiKey, projectName, environment);
    }
}

Example 2: Weaviate Vector Store

Weaviate is another popular vector database that you can run locally. Here’s how you can create a bean for it:

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;
import org.springframework.beans.factory.annotation.Value;

@Configuration
public class VectorStoreConfig {

    @Value("${weaviate.url}")
    private String weaviateUrl;

    @Value("${weaviate.api-key}")
    private String apiKey;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createWeaviateVectorStore(weaviateUrl, apiKey);
    }
}

Example 3: Milvus Vector Store

Milvus is another powerful vector database. Here’s how you can create a bean for it:

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;
import org.springframework.beans.factory.annotation.Value;

@Configuration
public class VectorStoreConfig {

    @Value("${milvus.host}")
    private String host;

    @Value("${milvus.port}")
    private int port;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createMilvusVectorStore(host, port);
    }
}

Example 4: Qdrant Vector Store

Qdrant is also a widely used vector database. Here's an example of how to create a bean for it:

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;
import org.springframework.beans.factory.annotation.Value;

@Configuration
public class VectorStoreConfig {

    @Value("${qdrant.url}")
    private String qdrantUrl;

    @Value("${qdrant.api-key}")
    private String apiKey;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createQdrantVectorStore(qdrantUrl, apiKey);
    }
}

Example 5: Vespa Vector Store

Vespa is another vector database that you might find useful. Here’s how you can create a bean for it:

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;
import org.springframework.beans.factory.annotation.Value;

@Configuration
public class VectorStoreConfig {

    @Value("${vespa.url}")
    private String vespaUrl;

    @Value("${vespa.api-key}")
    private String apiKey;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createVespaVectorStore(vespaUrl, apiKey);
    }
}

Step-by-Step Process to Create and Store Vectors

1. Create a service to generate and store vectors:

VectorService.java

package com.example.demo.service;

import org.springframework.ai.chat.ChatClient;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.Vector;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class VectorService {

    @Autowired
    private ChatClient chatClient;

    @Autowired
    private VectorStore vectorStore;

    public Vector createAndStoreVector(String text) {
        // Generate embedding using chat client (assuming LLM provides this functionality)
        String embeddingString = chatClient.generate(text);
        System.out.println("Generated Embedding: " + embeddingString);

        // Create vector from embedding string
        Vector vector = new Vector(embeddingString);

        // Store vector in the vector store
        vectorStore.storeVector(vector);

        return vector;
    }
}

2. Create a REST controller to expose the functionality:

VectorController.java

package com.example.demo.controller;

import com.example.demo.service.VectorService;
import org.springframework.ai.vectorstore.Vector;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/vectors")
public class VectorController {

    @Autowired
    private VectorService vectorService;

    @PostMapping
    public Vector createAndStoreVector(@RequestParam String text) {
        return vectorService.createAndStoreVector(text);
    }
}

Running the Application

Ensure your respective vector database is running locally. You can then start your Spring Boot application and use a tool like Postman or curl to test the endpoint.

Example POST Request

curl -X POST "http://localhost:8080/vectors" -d "text=Hello, world!"

This will generate an embedding for the text "Hello, world!", print it, and store it in your chosen vector database using the configured vector store instance.

You:

Assume I've bean of vector store and chat client and embedded model in my application context, now show me different examples of method that I can perform using these beans, show me all methods call in one class and with comments, and only code and less explain

ChatGPT:

Here's a complete class showing different methods you can perform using VectorStore and ChatClient beans, along with comments for each operation:

VectorService.java

package com.example.demo.service;

import org.springframework.ai.chat.ChatClient;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.Vector;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class VectorService {

    @Autowired
    private ChatClient chatClient;

    @Autowired
    private VectorStore vectorStore;

    /**
     * Generates an embedding vector for the given text and stores it in the vector database.
     *
     * @param text the input text to be converted to an embedding
     * @return the stored vector
     */
    public Vector createAndStoreVector(String text) {
        // Generate embedding using chat client (assuming LLM provides this functionality)
        String embeddingString = chatClient.generate(text);
        System.out.println("Generated Embedding: " + embeddingString);

        // Create vector from embedding string
        Vector vector = new Vector(embeddingString);

        // Store vector in the vector store
        vectorStore.storeVector(vector);

        return vector;
    }

    /**
     * Retrieves a vector from the vector database by its ID.
     *
     * @param id the ID of the vector to retrieve
     * @return the retrieved vector
     */
    public Vector getVectorById(String id) {
        return vectorStore.getVectorById(id);
    }

    /**
     * Searches for vectors similar to the given query vector.
     *
     * @param queryVector the vector to search for similar vectors
     * @param topK the number of similar vectors to return
     * @return a list of similar vectors
     */
    public List<Vector> searchSimilarVectors(Vector queryVector, int topK) {
        return vectorStore.searchSimilarVectors(queryVector, topK);
    }

    /**
     * Deletes a vector from the vector database by its ID.
     *
     * @param id the ID of the vector to delete
     */
    public void deleteVectorById(String id) {
        vectorStore.deleteVectorById(id);
    }

    /**
     * Updates an existing vector in the vector database.
     *
     * @param id the ID of the vector to update
     * @param newVector the new vector data to replace the old one
     */
    public void updateVector(String id, Vector newVector) {
        vectorStore.updateVector(id, newVector);
    }

    /**
     * Generates a chat response from the given prompt using the chat client.
     *
     * @param prompt the input prompt for the chat client
     * @return the generated chat response
     */
    public String generateChatResponse(String prompt) {
        return chatClient.generate(prompt);
    }

    /**
     * Stores multiple vectors in the vector database.
     *
     * @param vectors a list of vectors to store
     */
    public void storeMultipleVectors(List<Vector> vectors) {
        vectorStore.storeVectors(vectors);
    }

    /**
     * Retrieves all vectors from the vector database.
     *
     * @return a list of all stored vectors
     */
    public List<Vector> getAllVectors() {
        return vectorStore.getAllVectors();
    }
}

This class demonstrates various operations you can perform with VectorStore and ChatClient:

1. createAndStoreVector - Generates and stores a vector.

2. getVectorById - Retrieves a vector by its ID.

3. searchSimilarVectors - Searches for similar vectors.

4. deleteVectorById - Deletes a vector by its ID.

5. updateVector - Updates an existing vector.

6. generateChatResponse - Generates a chat response from a prompt.

7. storeMultipleVectors - Stores multiple vectors.

8. getAllVectors - Retrieves all stored vectors.

You:

Show me mac local setup for milvia vector store

ChatGPT:

To set up Milvus on your Mac, you can use Docker, which simplifies the installation process. Here are the steps to get Milvus up and running locally on your Mac:

Step 1: Install Docker

1. Download and Install Docker Desktop for Mac:

• Visit the Docker Desktop for Mac download page.

• Download and install Docker Desktop by following the instructions on the website.

• Start Docker Desktop after installation.

Step 2: Start Milvus Using Docker

1. Pull the Milvus Docker Image:

• Open your terminal and pull the Milvus Docker image using the following command:

docker pull milvusdb/milvus:latest

2. Run the Milvus Container:

• Create a Docker container for Milvus with the following command:

docker run -d --name milvus-standalone -p 19530:19530 -p 9091:9091 milvusdb/milvus:latest

• This command will start Milvus in standalone mode and map the necessary ports (19530 for Milvus server, 9091 for web UI).

Step 3: Verify Milvus is Running

1. Check Docker Containers:

• Verify that the Milvus container is running by executing:

docker ps

• You should see the Milvus container listed with its status as Up.

Step 4: Connect to Milvus from a Spring Boot Application

Assuming you already have a Spring Boot application with the necessary configurations, you can create a bean to connect to Milvus.

application.properties

milvus.host=localhost
milvus.port=19530

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;
import org.springframework.beans.factory.annotation.Value;

@Configuration
public class VectorStoreConfig {

    @Value("${milvus.host}")
    private String host;

    @Value("${milvus.port}")
    private int port;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createMilvusVectorStore(host, port);
    }
}

VectorService.java

package com.example.demo.service;

import org.springframework.ai.chat.ChatClient;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.Vector;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class VectorService {

    @Autowired
    private ChatClient chatClient;

    @Autowired
    private VectorStore vectorStore;

    public Vector createAndStoreVector(String text) {
        String embeddingString = chatClient.generate(text);
        System.out.println("Generated Embedding: " + embeddingString);

        Vector vector = new Vector(embeddingString);
        vectorStore.storeVector(vector);

        return vector;
    }

    public Vector getVectorById(String id) {
        return vectorStore.getVectorById(id);
    }

    public List<Vector> searchSimilarVectors(Vector queryVector, int topK) {
        return vectorStore.searchSimilarVectors(queryVector, topK);
    }

    public void deleteVectorById(String id) {
        vectorStore.deleteVectorById(id);
    }

    public void updateVector(String id, Vector newVector) {
        vectorStore.updateVector(id, newVector);
    }

    public String generateChatResponse(String prompt) {
        return chatClient.generate(prompt);
    }

    public void storeMultipleVectors(List<Vector> vectors) {
        vectorStore.storeVectors(vectors);
    }

    public List<Vector> getAllVectors() {
        return vectorStore.getAllVectors();
    }
}

Summary

1. Install Docker and start Docker Desktop.

2. Pull and run the Milvus Docker image.

3. Verify the Milvus container is running.

4. Configure your Spring Boot application to connect to Milvus using a configuration class.

By following these steps, you should have a fully functional Milvus setup on your Mac and be able to interact with it from your Spring Boot application.

You:

What are different way that we can install milvus on mac

ChatGPT:

Here are different ways to install Milvus on a Mac:

1. Using Docker

Docker is a convenient way to run Milvus without worrying about the underlying dependencies. Steps:

1. Install Docker:

• Download and install Docker Desktop from Docker's official site .

• Start Docker Desktop after installation.

2. Pull and Run Milvus Container:

• Open your terminal and pull the Milvus Docker image:

docker pull milvusdb/milvus:latest

• Run the Milvus container:

docker run -d --name milvus-standalone -p 19530:19530 -p 9091:9091 milvusdb/milvus:latest

2. Using Homebrew

Homebrew is a package manager for macOS that can simplify the installation process. Steps:

1. Install Homebrew:

• If you don’t have Homebrew installed, install it using:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

2. Add Milvus Formula:

• Add the Milvus tap (if available):

brew tap milvus-io/milvus

• Install Milvus:

brew install milvus

• Note: As of the latest information, there may not be an official Homebrew formula for Milvus. Check the official Milvus documentation for updates.

3. Using Source Code

You can also build Milvus from source if you need more control over the installation. Steps:

1. Clone Milvus Repository:

• Clone the Milvus repository from GitHub:

git clone https://github.com/milvus-io/milvus.git

2. Install Dependencies:

• Ensure you have dependencies like CMake and GCC installed. You can install them using Homebrew:

brew install cmake gcc

3. Build Milvus:

• Navigate to the Milvus directory and build:

cd milvus
./scripts/install_deps.sh
./scripts/build.sh

4. Run Milvus:

• Start Milvus using the generated binaries.

4. Using Pre-built Binaries

You can download pre-built binaries directly from the Milvus releases page. Steps:

1. Download Binaries:

• Download the latest release from the Milvus GitHub releases page .

2. Extract and Run:

• Extract the tar.gz file:

tar -zxvf milvus-<version>-linux-amd64.tar.gz

• Navigate to the extracted directory and run Milvus:

cd milvus
./start.sh

Conclusion

These are the main methods to install Milvus on a Mac. The Docker method is often the simplest and most recommended for getting started quickly. For advanced users or those needing customization, building from source or using pre-built binaries might be more suitable. Always refer to the official Milvus documentation for the most up-to-date installation instructions.

You:

Show me other popular vector database that I can easily install using brew command, show me command only

ChatGPT:

Here are some popular vector databases that you can easily install using the brew command:1. Weaviate

brew install weaviate

2. Qdrant

brew install qdrant

3. Vespa

brew install vespa-cli

These commands will install the respective vector databases using Homebrew on your Mac. For more details and configuration options, you can check their official documentation.

You:

For each show me important command for crud

ChatGPT:

Here are some important commands for CRUD operations for each of the vector databases installed using Homebrew:

1. Weaviate Install:

brew install weaviate

Start Weaviate:

weaviate --config-file=<path-to-config-file>

CRUD Operations:

• Create:

curl -X POST "http://localhost:8080/v1/objects" \
     -H "Content-Type: application/json" \
     -d '{
           "class": "Article",
           "properties": {
             "title": "Vector Database",
             "content": "This is a document about vector databases."
           }
         }'

• Read:

curl -X GET "http://localhost:8080/v1/objects/{id}"

• Update:

curl -X PUT "http://localhost:8080/v1/objects/{id}" \
     -H "Content-Type: application/json" \
     -d '{
           "class": "Article",
           "properties": {
             "title": "Updated Vector Database",
             "content": "This is an updated document about vector databases."
           }
         }'

• Delete:

curl -X DELETE "http://localhost:8080/v1/objects/{id}"

2. Qdrant Install:

brew install qdrant

Start Qdrant:

qdrant

CRUD Operations:

• Create:

curl -X POST "http://localhost:6333/collections/test_collection/points" \
     -H "Content-Type: application/json" \
     -d '{
           "points": [
             {
               "id": 1,
               "vector": [0.1, 0.2, 0.3, 0.4],
               "payload": {
                 "key": "value"
               }
             }
           ]
         }'

• Read:

curl -X GET "http://localhost:6333/collections/test_collection/points/{id}"

• Update:

curl -X PUT "http://localhost:6333/collections/test_collection/points" \
     -H "Content-Type: application/json" \
     -d '{
           "points": [
             {
               "id": 1,
               "vector": [0.5, 0.6, 0.7, 0.8],
               "payload": {
                 "key": "new_value"
               }
             }
           ]
         }'

• Delete:

curl -X DELETE "http://localhost:6333/collections/test_collection/points/{id}"

3. Vespa Install:

brew install vespa-cli

Start Vespa:

vespa dev --services <services.xml> --node <nodes.xml>

CRUD Operations:

• Create:

curl -X POST "http://localhost:8080/document/v1/namespace/documenttype/docid/1" \
     -H "Content-Type: application/json" \
     -d '{
           "fields": {
             "title": "Vector Database",
             "content": "This is a document about vector databases."
           }
         }'

• Read:

curl -X GET "http://localhost:8080/document/v1/namespace/documenttype/docid/1"

• Update:

curl -X PUT "http://localhost:8080/document/v1/namespace/documenttype/docid/1" \
     -H "Content-Type: application/json" \
     -d '{
           "fields": {
             "title": "Updated Vector Database",
             "content": "This is an updated document about vector databases."
           }
         }'

• Delete:

curl -X DELETE "http://localhost:8080/document/v1/namespace/documenttype/docid/1"

These commands provide a basic overview of how to perform CRUD operations with Weaviate, Qdrant, and Vespa vector databases.

You:

For above qdrant local database show me how to create vector bean, show me only important code not full code

ChatGPT:

Here is the essential code for creating a VectorStore bean to connect to a local Qdrant database in a Spring Boot application:

Step 1: Add Configuration Properties

application.properties

qdrant.url=http://localhost:6333
qdrant.api-key=your-api-key

Step 2: Create Configuration Class

VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;

@Configuration
public class VectorStoreConfig {

    @Value("${qdrant.url}")
    private String qdrantUrl;

    @Value("${qdrant.api-key}")
    private String apiKey;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createQdrantVectorStore(qdrantUrl, apiKey);
    }
}

Step 3: Inject and Use VectorStore Bean

VectorService.java

package com.example.demo.service;

import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.Vector;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class VectorService {

    @Autowired
    private VectorStore vectorStore;

    public Vector createAndStoreVector(String text) {
        // Assume some method to convert text to embedding
        Vector vector = new Vector(convertTextToEmbedding(text));
        vectorStore.storeVector(vector);
        return vector;
    }

    public Vector getVectorById(String id) {
        return vectorStore.getVectorById(id);
    }

    public List<Vector> searchSimilarVectors(Vector queryVector, int topK) {
        return vectorStore.searchSimilarVectors(queryVector, topK);
    }

    private String convertTextToEmbedding(String text) {
        // Implement your text to embedding logic here
        return text; // Placeholder
    }
}

Notes:

• application.properties: Contains the configuration properties for connecting to the Qdrant database.

• VectorStoreConfig.java: Defines the VectorStore bean, configured to connect to the Qdrant instance.

• VectorService.java: Demonstrates basic usage of the VectorStore bean for creating, storing, and retrieving vectors.

This setup provides a minimal yet complete way to configure and use a Qdrant vector store within a Spring Boot application.

You:

What will be api key and how to get , since we are working locally qdrant db

ChatGPT:

When running Qdrant locally, you typically do not need an API key for basic operations. The API key is generally used when connecting to a managed Qdrant service or if you have configured your local Qdrant instance to require authentication.

Running Qdrant Locally Without an API Key

If you are running Qdrant locally and do not require an API key, you can modify the configuration as follows:

1. Remove the API key configuration: application.properties

qdrant.url=http://localhost:6333

2. Update your configuration class to omit the API key: VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;

@Configuration
public class VectorStoreConfig {

    @Value("${qdrant.url}")
    private String qdrantUrl;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createQdrantVectorStore(qdrantUrl, null);
    }
}

Running Qdrant with Authentication (Optional)

If you decide to secure your local Qdrant instance with an API key, you need to set up authentication within Qdrant's configuration files and obtain an API key.

1. Generate an API Key:

• You can generate a random string to use as an API key using various methods. For instance, you can use an online key generator or create one using command-line tools.

openssl rand -hex 32

2. Configure Qdrant to Use the API Key:

• Edit the Qdrant configuration file (e.g., config.yaml) to include the generated API key. Refer to the Qdrant documentation for details on where to place this configuration.

3. Update Application Properties: application.properties

qdrant.url=http://localhost:6333
qdrant.api-key=your-generated-api-key

4. Use the API Key in Your Configuration Class: VectorStoreConfig.java

package com.example.demo.config;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.VectorStoreFactory;

@Configuration
public class VectorStoreConfig {

    @Value("${qdrant.url}")
    private String qdrantUrl;

    @Value("${qdrant.api-key}")
    private String apiKey;

    @Bean
    public VectorStore vectorStore() {
        return VectorStoreFactory.createQdrantVectorStore(qdrantUrl, apiKey);
    }
}

Summary

For a local Qdrant setup, you typically don't need an API key unless you specifically configure Qdrant for authentication. If no API key is needed, omit the apiKey parameter in your configuration. If you secure your Qdrant instance, generate an API key and configure your Spring Boot application to use it accordingly.

You:

Once my qdrant is started, tell me how to create table and what are column I need to add and then how to store some data to that table from command

ChatGPT:

To create a collection (similar to a table in SQL databases) and insert data into a Qdrant vector database, follow these steps:

Step 1: Start Qdrant

Ensure Qdrant is running. If it's not already started, use the following command:

docker run -d --name qdrant -p 6333:6333 qdrant/qdrant

Step 2: Create a Collection

Use the Qdrant REST API to create a collection. A collection in Qdrant is analogous to a table in a relational database. Create Collection Command:

curl -X PUT "http://localhost:6333/collections/test_collection" \
     -H "Content-Type: application/json" \
     -d '{
           "vector_size": 4,
           "distance": "Cosine"
         }'

This command creates a collection named test_collection with vectors of size 4 and cosine distance metric.

Step 3: Insert Data

Insert vector data into the collection using the following command: Insert Data Command:

curl -X POST "http://localhost:6333/collections/test_collection/points" \
     -H "Content-Type: application/json" \
     -d '{
           "points": [
             {
               "id": 1,
               "vector": [0.1, 0.2, 0.3, 0.4],
               "payload": {
                 "key": "value"
               }
             },
             {
               "id": 2,
               "vector": [0.5, 0.6, 0.7, 0.8],
               "payload": {
                 "key": "another_value"
               }
             }
           ]
         }'

This command inserts two points into the test_collection collection. Each point consists of:

• id: A unique identifier for the point.

• vector: The vector data (e.g., [0.1, 0.2, 0.3, 0.4]).

• payload: Additional metadata associated with the point.

Step 4: Retrieve Data

Retrieve the inserted data using the following command: Retrieve Data Command:

curl -X GET "http://localhost:6333/collections/test_collection/points/1"

This command fetches the point with id 1 from the test_collection collection.

Step 5: Search for Similar Vectors

Search for vectors similar to a given vector: Search Command:

curl -X POST "http://localhost:6333/collections/test_collection/points/search" \
     -H "Content-Type: application/json" \
     -d '{
           "vector": [0.1, 0.2, 0.3, 0.4],
           "top": 2
         }'

This command searches for the top 2 vectors similar to the vector [0.1, 0.2, 0.3, 0.4] in the test_collection collection.

Summary

• Start Qdrant: docker run -d --name qdrant -p 6333:6333 qdrant/qdrant

• Create Collection:

curl -X PUT "http://localhost:6333/collections/test_collection" \
     -H "Content-Type: application/json" \
     -d '{"vector_size": 4, "distance": "Cosine"}'

• Insert Data:

curl -X POST "http://localhost:6333/collections/test_collection/points" \
     -H "Content-Type: application/json" \
     -d '{"points": [{"id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "payload": {"key": "value"}}, {"id": 2, "vector": [0.5, 0.6, 0.7, 0.8], "payload": {"key": "another_value"}}]}'

• Retrieve Data:

curl -X GET "http://localhost:6333/collections/test_collection/points/1"

• Search Similar Vectors:

curl -X POST "http://localhost:6333/collections/test_collection/points/search" \
     -H "Content-Type: application/json" \
     -d '{"vector": [0.1, 0.2, 0.3, 0.4], "top": 2}'

This guide should help you set up a Qdrant vector database, create a collection, insert data, and perform basic retrieval and search operations.

You:

Ca we also store long string text via command

ChatGPT:

Yes, you can store long string texts in Qdrant using the payload field. The payload field in Qdrant can hold various types of metadata, including long text strings. Below is an example of how to store a long text string in the payload when inserting data into a Qdrant collection.

Step-by-Step Example to Store Long String Text

Step 1: Start Qdrant (if not already running)

docker run -d --name qdrant -p 6333:6333 qdrant/qdrant

Step 2: Create a Collection

Create Collection Command:

curl -X PUT "http://localhost:6333/collections/test_collection" \
     -H "Content-Type: application/json" \
     -d '{
           "vector_size": 4,
           "distance": "Cosine"
         }'

Step 3: Insert Data with Long Text String

Insert Data Command:

curl -X POST "http://localhost:6333/collections/test_collection/points" \
     -H "Content-Type: application/json" \
     -d '{
           "points": [
             {
               "id": 1,
               "vector": [0.1, 0.2, 0.3, 0.4],
               "payload": {
                 "title": "Vector Database",
                 "content": "This is a long text string. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum."
               }
             },
             {
               "id": 2,
               "vector": [0.5, 0.6, 0.7, 0.8],
               "payload": {
                 "title": "Another Vector",
                 "content": "Another long text string. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum."
               }
             }
           ]
         }'

Step 4: Retrieve Data

Retrieve Data Command:

curl -X GET "http://localhost:6333/collections/test_collection/points/1"

Step 5: Search for Similar Vectors

Search Command:

curl -X POST "http://localhost:6333/collections/test_collection/points/search" \
     -H "Content-Type: application/json" \
     -d '{
           "vector": [0.1, 0.2, 0.3, 0.4],
           "top": 2
         }'

Summary

• Create a collection: Use the PUT method to create a new collection.

• Insert data with long text strings: Use the POST method to insert points with long text strings in the payload.

• Retrieve and search data: Use the GET and POST methods to retrieve and search for vectors.

By following these steps, you can store and manage long text strings in Qdrant, leveraging its vector search capabilities alongside rich metadata.

You:

Ia this database is no SQL database

ChatGPT:

Yes, Qdrant is a NoSQL database. It is specifically designed as a vector database, which is used to store, manage, and search high-dimensional vectors and associated metadata. Unlike traditional relational databases, NoSQL databases like Qdrant are optimized for specific data models and queries, providing flexible schema and scalability to handle large datasets efficiently.

Key Characteristics of Qdrant as a NoSQL Database:

1. Schema-less Design:

• Qdrant, like other NoSQL databases, does not require a fixed schema, allowing for more flexibility in data storage. You can easily add or modify fields in your data without the need for schema migration.

2. Optimized for Vector Search:

• Qdrant is built to handle vector data, making it highly efficient for similarity searches and operations on high-dimensional vectors, which are common in machine learning and AI applications.

3. Scalability:

• NoSQL databases are designed to scale horizontally, meaning you can add more nodes to your database cluster to handle increased load and larger datasets.

4. Flexible Data Model:

• You can store various types of data in the payload of vectors, including long text strings, numeric values, and other metadata, without being constrained by a rigid schema.

Comparison with Traditional SQL Databases:

• SQL Databases:

  • Fixed schema with predefined tables and columns.

  • Designed for ACID (Atomicity, Consistency, Isolation, Durability) compliance to ensure transaction reliability.

  • Optimized for complex queries and joins across multiple tables.

• NoSQL Databases (like Qdrant):

  • Flexible schema with the ability to store complex and nested data structures.

  • Often prioritize scalability and performance over strict ACID compliance.

  • Optimized for specific use cases, such as document storage, key-value pairs, or, in the case of Qdrant, vector search.

Conclusion

Qdrant's design as a vector database and its NoSQL characteristics make it particularly suited for applications involving machine learning, AI, and similarity searches on high-dimensional data. Its flexibility and scalability provide significant advantages for these use cases compared to traditional SQL databases.

fetch-html-as-text.md

Sure, here's an example of how to use OkHttp to fetch data from the given URL:

import okhttp3.OkHttpClient;
import okhttp3.Request;
import okhttp3.Response;

import java.io.IOException;

public class WebFetcher {

    private static final OkHttpClient client = new OkHttpClient();

    public static String fetchWebsiteData(String url) throws IOException {
        Request request = new Request.Builder()
                .url(url)
                .build();

        try (Response response = client.newCall(request).execute()) {
            if (!response.isSuccessful()) throw new IOException("Unexpected code " + response);

            return response.body().string();
        }
    }

    public static void main(String[] args) {
        String url = "https://docs.spring.io/spring-ai/reference/api/chatclient.html";
        try {
            String response = fetchWebsiteData(url);
            System.out.println(response);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

This code initializes an OkHttpClient, sends a GET request to the specified URL, and prints the response body as a string. This will fetch the HTML content of the specified web page.

---

Here’s how to extend the previous OkHttp code to clean the fetched HTML content using Jsoup:

Extended Code

import okhttp3.OkHttpClient;
import okhttp3.Request;
import okhttp3.Response;
import org.jsoup.Jsoup;
import org.jsoup.nodes.Document;

import java.io.IOException;

public class WebFetcher {

    private static final OkHttpClient client = new OkHttpClient();

    public static String fetchWebsiteData(String url) throws IOException {
        Request request = new Request.Builder()
                .url(url)
                .build();

        try (Response response = client.newCall(request).execute()) {
            if (!response.isSuccessful()) throw new IOException("Unexpected code " + response);

            return response.body().string();
        }
    }

    public static String cleanHtml(String html) {
        Document doc = Jsoup.parse(html);
        return doc.text();
    }

    public static void main(String[] args) {
        String url = "https://docs.spring.io/spring-ai/reference/api/chatclient.html";
        try {
            String htmlContent = fetchWebsiteData(url);
            String cleanedContent = cleanHtml(htmlContent);
            System.out.println(cleanedContent);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
}

Explanation

1. Fetch HTML Content:

   • The fetchWebsiteData method sends a GET request to the specified URL and retrieves the HTML content.

2. Clean HTML Content:

   • The cleanHtml method uses Jsoup to parse the HTML content and extract the clean text.

3. Main Method:

   • The main method fetches the HTML content, cleans it, and prints the cleaned content.

This approach fetches the web page content and cleans it to a readable format using Jsoup.

global_h.md

• Onboarding New Developers

• Create an LLM-powered chatbot or virtual assistant that can answer questions, provide guidance, and automate onboarding tasks for new developers

• Answers questions about the codebase, tools, and processes.

• Guides developers through setup and configuration tasks.

• Provides tutorials and examples of how to use company-specific technologies

---

• Problem: Onboarding new developers is time-consuming and often relies on busy senior engineers. New hires struggle to find answers, delaying their productivity.
• Solution: OnboardBot is an AI-powered chatbot that instantly answers questions about our codebase, tools, and processes. It guides new hires through setup, offers personalized learning recommendations, and frees up senior devs to focus on core work."

---

Absolutely! Here are a few ways you can frame the problem statement and project description for your hackathon:

Option 1: Concise and Impactful

"Project Name: OnboardBot - Your AI Onboarding Buddy

Problem: Onboarding new developers is time-consuming and often relies on busy senior engineers. New hires struggle to find answers, delaying their productivity.

Solution: OnboardBot is an AI-powered chatbot that instantly answers questions about our codebase, tools, and processes. It guides new hires through setup, offers personalized learning recommendations, and frees up senior devs to focus on core work."

Option 2: Focus on the Pain Points

"Project Name: DevRamp - Accelerate Developer Onboarding

Problem: New developers face a steep learning curve, wasting time searching for information, struggling with setup, and feeling overwhelmed by our complex codebase.

Solution: DevRamp is a virtual assistant that streamlines onboarding. It instantly answers questions in natural language, provides step-by-step guidance for common tasks, and offers tailored learning paths to get new hires up to speed quickly."

Option 3: Highlight the Benefits

"Project Name: CodeBuddy - Your 24/7 Coding Companion

Problem: Onboarding knowledge is often scattered and inaccessible, leading to frustration and delays for new hires. Senior developers spend too much time on repetitive onboarding tasks.

Solution: CodeBuddy is an AI-powered knowledge base and assistant that empowers new developers to find answers independently. It improves onboarding efficiency, boosts new hire productivity, and frees up senior developers for high-impact work."

Additional Tips:

• Use a Catchy Name: Make it memorable and relevant to the project's purpose.
• Focus on the Value Proposition: Clearly articulate how your solution solves a real problem and benefits both new hires and the company.
• Keep it Concise: Use clear and concise language to convey your message effectively.
• Visuals: If possible, include a mockup or diagram to illustrate the user interface and demonstrate how the chatbot/virtual assistant works.

Let me know if you'd like any further refinements or want to explore other ways to frame the problem statement!

vaadin.md

package com.example.application.views;

import com.vaadin.flow.component.button.Button;
import com.vaadin.flow.component.notification.Notification;
import com.vaadin.flow.component.orderedlayout.VerticalLayout;
import com.vaadin.flow.component.textfield.TextField;
import com.vaadin.flow.router.Route;

@Route("")
public class MainView extends VerticalLayout {

    public MainView() {
        // Create a text field
        TextField textField = new TextField("Enter your name");

        // Create a button
        Button button = new Button("Say Hello");

        // Add a click listener to the button
        button.addClickListener(event -> {
            String name = textField.getValue();
            Notification.show("Hello, " + name + "!");
        });

        // Add the text field and button to the layout
        add(textField, button);
    }
}

---

Yes, you can show Markdown content in an HTML view in Vaadin. You can use a library like Flexmark to convert the Markdown to HTML and then display it using Vaadin's Html component.

Here is a simple example demonstrating how to achieve this:

Maven Dependency

First, add the Flexmark dependency to your build.gradle file to handle Markdown to HTML conversion:

dependencies {
    implementation 'com.vladsch.flexmark:flexmark-all:0.62.2'
}

Service Layer

Create a service class that returns the Markdown content as a string.

package com.example.chatapp.service;

import org.springframework.stereotype.Service;

@Service
public class MarkdownService {

    public String getMarkdownContent() {
        return "# Hello, Markdown!\nThis is a sample **Markdown** content.";
    }
}

Vaadin View

Create a package named view and inside it, create a class called MarkdownView.

package com.example.chatapp.view;

import com.example.chatapp.service.MarkdownService;
import com.vaadin.flow.component.html.Div;
import com.vaadin.flow.component.html.Html;
import com.vaadin.flow.component.orderedlayout.VerticalLayout;
import com.vaadin.flow.router.Route;
import com.vladsch.flexmark.html2md.converter.FlexmarkHtmlConverter;
import com.vladsch.flexmark.parser.Parser;
import com.vladsch.flexmark.util.ast.Node;
import com.vladsch.flexmark.util.data.MutableDataSet;
import org.springframework.beans.factory.annotation.Autowired;

@Route("markdown")
public class MarkdownView extends VerticalLayout {

    private final MarkdownService markdownService;

    @Autowired
    public MarkdownView(MarkdownService markdownService) {
        this.markdownService = markdownService;

        // Get Markdown content from the service
        String markdownContent = markdownService.getMarkdownContent();

        // Convert Markdown to HTML
        String htmlContent = convertMarkdownToHtml(markdownContent);

        // Create an Html component to display the converted content
        Html html = new Html("<div>" + htmlContent + "</div>");

        // Add the Html component to the layout
        add(html);
    }

    private String convertMarkdownToHtml(String markdown) {
        MutableDataSet options = new MutableDataSet();
        Parser parser = Parser.builder(options).build();
        Node document = parser.parse(markdown);
        return com.vladsch.flexmark.html.HtmlRenderer.builder(options).build().render(document);
    }
}

Explanation:

• MarkdownService Class:

  • This class provides the Markdown content. You can modify the getMarkdownContent method to return any Markdown string.

• MarkdownView Class:

  • Annotated with @Route("markdown"), mapping it to the /markdown URL.
  • The markdownService is injected to fetch the Markdown content.
  • The convertMarkdownToHtml method uses Flexmark to convert the Markdown string to HTML.
  • The Html component is used to display the converted HTML content in the Vaadin view.

By following this approach, you can render Markdown content in your Vaadin application.

---

package com.example.application.views;

import com.vaadin.flow.component.button.Button;
import com.vaadin.flow.component.html.Div;
import com.vaadin.flow.component.orderedlayout.VerticalLayout;
import com.vaadin.flow.component.textfield.TextField;
import com.vaadin.flow.router.Route;

@Route("")
public class MainView extends VerticalLayout {

    public MainView() {
        // Create a text field for user input
        TextField textField = new TextField("Enter your input:");

        // Create a button to trigger the action
        Button button = new Button("Print Input");

        // Create a Div to display the output
        Div output = new Div();

        // Add a click listener to the button
        button.addClickListener(event -> {
            String userInput = textField.getValue();
            output.setText("User input: " + userInput);
        });

        // Add the components to the layout
        add(textField, button, output);
    }
}

$ docker info [19:53:12]
Client:
Version: 26.1.0-rd
Context: default
Debug Mode: false
Plugins:
buildx: Docker Buildx (Docker Inc.)
Version: v0.14.1
Path: /Users/hariom.yadav/.docker/cli-plugins/docker-buildx
compose: Docker Compose (Docker Inc.)
Version: v2.27.1
Path: /Users/hariom.yadav/.docker/cli-plugins/docker-compose

Server:
ERROR: Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?
errors pretty printing info

To find the correct Docker socket path for Rancher Desktop, follow these steps:

Steps to Find the Docker Socket Path in Rancher Desktop

1. Open Rancher Desktop:
   Launch the Rancher Desktop application on your computer.

2. Navigate to Settings:

   • Look for settings or preferences in the Rancher Desktop application menu.
   • This might be under a gear icon, or in the top menu bar under Rancher Desktop > Preferences.

3. Check Docker Configuration:

   • In the settings, find the section related to Docker or the container runtime.
   • Look for the Docker daemon or socket configuration details.

4. Note the Socket Path:

   • The configuration should mention the Unix socket path that Rancher Desktop uses.
   • Common paths might be /var/run/docker.sock or a Rancher Desktop-specific path like /var/run/rancher-desktop.sock.

Example Configuration

If Rancher Desktop uses a specific socket path, configure your environment variable accordingly. For example, if the path is /var/run/rancher-desktop.sock, set the environment variable:

export DOCKER_HOST=unix:///var/run/rancher-desktop.sock

Add this line to your shell profile (~/.bashrc, ~/.zshrc, etc.) and reload the profile:

source ~/.bashrc  # or source ~/.zshrc

Verification

Run a Docker command to verify the connection:

docker ps

This command should list running Docker containers if the configuration is correct. If you continue to encounter issues, ensure Rancher Desktop is running properly and no other Docker services are conflicting.