chore: Add README.md files

This commit is contained in:
Roque Caballero 2025-03-28 17:36:22 +01:00
commit 4ceec902a3
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demo-06/README.md Normal file
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Demo 06 - RAG Part 2
===============================================
Retrieval Augmented Generation (RAG) is a way to extend the knowledge of the LLM used in the AI service.
The RAG pattern is composed of two parts:
* Ingestion: This is the part that stores data in the knowledge base.
* Augmentation: This is the part that adds the retrieved information to the input of the LLM.
# Embedding model
One of the core components of the RAG pattern is the embedding model. The embedding model is used to transform the
text into numerical vectors. These vectors are used to compare the text and find the most relevant segments.
# Vector store
In the previous step, we used an in memory store. Now we will use a persistent store to keep the embeddings between restarts.
# Ingesting documents into the vector store
While you are editing the `src/main/resources/application.properties` file, add the following configuration:
```
rag.location=src/main/resources/rag
```

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demo-06/pom.xml Normal file
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<?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 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>ch.phoenixtechnologies</groupId>
<artifactId>ai-lc4j-workshop</artifactId>
<version>0.0.1-SNAPSHOT</version>
</parent>
<artifactId>demo-06</artifactId>
<properties>
<java.version>21</java.version>
<maven.compiler.source>${java.version}</maven.compiler.source>
<maven.compiler.target>${java.version}</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<!-- Plugin versions -->
<compiler-plugin.version>3.14.0</compiler-plugin.version>
<enforcer-plugin.version>3.5.0</enforcer-plugin.version>
<surefire-plugin.version>3.5.2</surefire-plugin.version>
<spotless-maven-plugin.version>2.44.3</spotless-maven-plugin.version>
<palantir-java-format.version>2.50.0</palantir-java-format.version>
<release-plugin.version>3.1.1</release-plugin.version>
<!-- Quarkus version -->
<quarkus.platform.artifact-id>quarkus-bom</quarkus.platform.artifact-id>
<quarkus.platform.group-id>io.quarkus.platform</quarkus.platform.group-id>
<quarkus.platform.version>3.19.2</quarkus.platform.version>
<!-- LangChain4j-->
<quarkus-langchain4j.version>0.25.0</quarkus-langchain4j.version>
<!-- Test dependency versions -->
<assertj.version>3.27.3</assertj.version>
</properties>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>${quarkus.platform.group-id}</groupId>
<artifactId>${quarkus.platform.artifact-id}</artifactId>
<version>${quarkus.platform.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
<dependency>
<groupId>io.quarkiverse.langchain4j</groupId>
<artifactId>quarkus-langchain4j-bom</artifactId>
<version>${quarkus-langchain4j.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<dependency>
<groupId>io.quarkus</groupId>
<artifactId>quarkus-config-yaml</artifactId>
</dependency>
<!-- langchain4j -->
<dependency>
<groupId>io.quarkiverse.langchain4j</groupId>
<artifactId>quarkus-langchain4j-openai</artifactId>
</dependency>
<dependency>
<groupId>io.quarkiverse.langchain4j</groupId>
<artifactId>quarkus-langchain4j-pgvector</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-embeddings</artifactId>
</dependency>
<dependency>
<groupId>io.quarkus</groupId>
<artifactId>quarkus-rest</artifactId>
</dependency>
<dependency>
<groupId>io.quarkus</groupId>
<artifactId>quarkus-websockets-next</artifactId>
</dependency>
<!-- UI -->
<dependency>
<groupId>org.mvnpm</groupId>
<artifactId>importmap</artifactId>
<version>1.0.8</version>
</dependency>
<dependency>
<groupId>org.mvnpm.at.mvnpm</groupId>
<artifactId>vaadin-webcomponents</artifactId>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>org.mvnpm</groupId>
<artifactId>es-module-shims</artifactId>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>org.mvnpm</groupId>
<artifactId>wc-chatbot</artifactId>
<version>0.2.0</version>
<scope>runtime</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>${quarkus.platform.group-id}</groupId>
<artifactId>quarkus-maven-plugin</artifactId>
<version>${quarkus.platform.version}</version>
<extensions>true</extensions>
<executions>
<execution>
<goals>
<goal>build</goal>
<goal>generate-code</goal>
<goal>generate-code-tests</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>${compiler-plugin.version}</version>
<configuration>
<compilerArgs>
<arg>-parameters</arg>
</compilerArgs>
<release>${java.version}</release>
</configuration>
</plugin>
<plugin>
<artifactId>maven-surefire-plugin</artifactId>
<version>${surefire-plugin.version}</version>
<configuration>
<systemPropertyVariables>
<java.util.logging.manager>org.jboss.logmanager.LogManager</java.util.logging.manager>
<maven.home>${maven.home}</maven.home>
</systemPropertyVariables>
</configuration>
</plugin>
<plugin>
<artifactId>maven-enforcer-plugin</artifactId>
<version>${enforcer-plugin.version}</version>
<executions>
<execution>
<id>ban-bad-log4j-versions</id>
<phase>validate</phase>
<goals>
<goal>enforce</goal>
</goals>
<configuration>
<rules>
<bannedDependencies>
<excludes>
<exclude>org.apache.logging.log4j:log4j-core:(,2.17.1)</exclude>
</excludes>
</bannedDependencies>
</rules>
<fail>true</fail>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>com.diffplug.spotless</groupId>
<artifactId>spotless-maven-plugin</artifactId>
<version>${spotless-maven-plugin.version}</version>
<executions>
<execution>
<goals>
<goal>check</goal>
</goals>
<phase>compile</phase>
</execution>
</executions>
<configuration>
<java>
<removeUnusedImports />
<palantirJavaFormat>
<version>${palantir-java-format.version}</version>
<style>PALANTIR</style>
<formatJavadoc>false</formatJavadoc>
</palantirJavaFormat>
</java>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-release-plugin</artifactId>
<version>${release-plugin.version}</version>
<configuration>
<tagNameFormat>@{project.version}</tagNameFormat>
<checkModificationExcludes>mvnw</checkModificationExcludes>
<scmReleaseCommitComment>chore: release @{releaseLabel}</scmReleaseCommitComment>
<scmDevelopmentCommitComment>chore: prepare for next development iteration [skip ci]</scmDevelopmentCommitComment>
</configuration>
</plugin>
</plugins>
</build>
</project>

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package ch.phoenixtechnologies.lc4j.openai.runtime.config;
import io.smallrye.config.ConfigMapping;
@ConfigMapping(prefix = "l4j.custom-embedding-model")
public interface CustomEmbeddingModelConfig {
String baseUrl();
String apiKey();
String modelName();
boolean logRequests();
boolean logResponses();
// int dimensions();
}

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package ch.phoenixtechnologies.lc4j.openai.runtime.config;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
import jakarta.enterprise.context.ApplicationScoped;
import jakarta.enterprise.inject.Produces;
@ApplicationScoped
public class CustomEmbeddingModelProducer {
private final CustomEmbeddingModelConfig config;
public CustomEmbeddingModelProducer(CustomEmbeddingModelConfig config) {
this.config = config;
}
@Produces
public EmbeddingModel getModel() {
return OpenAiEmbeddingModel.builder()
.apiKey(config.apiKey())
.baseUrl(config.baseUrl())
.modelName(config.modelName())
.logRequests(config.logRequests())
.logResponses(config.logResponses())
// .dimensions(config.dimensions())
.build();
}
}

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package ch.phoenixtechnologies.lc4j.workshop;
import dev.langchain4j.service.SystemMessage;
import io.quarkiverse.langchain4j.RegisterAiService;
import io.smallrye.mutiny.Multi;
import jakarta.enterprise.context.SessionScoped;
@SessionScoped
@RegisterAiService
public interface CustomerSupportAgent {
@SystemMessage(
"""
You are a customer support agent of a IT company 'Phoenix Technologies AG based in Switzerland.
You are friendly, polite and concise.
If the question is unrelated to IT services, you should politely redirect the customer to the right department.
""")
Multi<String> chat(String userMessage);
}

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package ch.phoenixtechnologies.lc4j.workshop;
import io.quarkus.websockets.next.OnOpen;
import io.quarkus.websockets.next.OnTextMessage;
import io.quarkus.websockets.next.WebSocket;
import io.smallrye.mutiny.Multi;
@WebSocket(path = "/customer-support-agent")
public class CustomerSupportAgentWebSocket {
private final CustomerSupportAgent agent;
public CustomerSupportAgentWebSocket(CustomerSupportAgent agent) {
this.agent = agent;
}
@OnOpen
public String onOpen() {
return "Welcome to Phoenix Technologies AI!";
}
@OnTextMessage
public Multi<String> onTextMessage(String message) {
return agent.chat(message);
}
}

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package ch.phoenixtechnologies.lc4j.workshop;
import jakarta.annotation.PostConstruct;
import jakarta.enterprise.context.ApplicationScoped;
import jakarta.ws.rs.GET;
import jakarta.ws.rs.Path;
import jakarta.ws.rs.Produces;
import org.mvnpm.importmap.Aggregator;
@ApplicationScoped
@Path("/_importmap")
public class ImportmapResource {
private static final String JAVASCRIPT_CODE =
"""
const im = document.createElement('script');
im.type = 'importmap';
im.textContent = JSON.stringify(%s);
document.currentScript.after(im);
""";
private String importmap;
@PostConstruct
void init() {
Aggregator aggregator = new Aggregator();
// Add our own mappings
aggregator.addMapping("icons/", "/icons/");
aggregator.addMapping("components/", "/components/");
aggregator.addMapping("fonts/", "/fonts/");
this.importmap = aggregator.aggregateAsJson();
}
@GET
@Path("/dynamic.importmap")
@Produces("application/importmap+json")
public String importMap() {
return this.importmap;
}
@GET
@Path("/dynamic-importmap.js")
@Produces("application/javascript")
public String importMapJson() {
return JAVASCRIPT_CODE.formatted(this.importmap);
}
}

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package ch.phoenixtechnologies.lc4j.workshop;
import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.embedding.onnx.HuggingFaceTokenizer;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import io.quarkus.logging.Log;
import io.quarkus.runtime.StartupEvent;
import jakarta.enterprise.context.ApplicationScoped;
import jakarta.enterprise.event.Observes;
import java.nio.file.Path;
import java.util.List;
import org.eclipse.microprofile.config.inject.ConfigProperty;
@ApplicationScoped
public class RagIngestion {
/**
* Ingests the documents from the given location into the embedding store.
*
* @param ev the startup event to trigger the ingestion when the application starts
* @param store the embedding store the embedding store (PostGreSQL in our case)
* @param embeddingModel the embedding model to use for the embedding (BGE-Small-EN-Quantized in our case)
* @param documents the location of the documents to ingest
*/
public void ingest(
@Observes StartupEvent ev,
EmbeddingStore store,
EmbeddingModel embeddingModel,
@ConfigProperty(name = "rag.location") Path documents) {
store.removeAll(); // cleanup the store to start fresh (just for demo purposes)
List<Document> list = FileSystemDocumentLoader.loadDocumentsRecursively(documents);
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.embeddingStore(store)
.embeddingModel(embeddingModel)
.documentSplitter(recursive(100, 25, new HuggingFaceTokenizer()))
.build();
ingestor.ingest(list);
Log.info("Documents ingested successfully");
}
}

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package ch.phoenixtechnologies.lc4j.workshop;
import dev.langchain4j.data.message.UserMessage;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.DefaultRetrievalAugmentor;
import dev.langchain4j.rag.RetrievalAugmentor;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import jakarta.enterprise.context.ApplicationScoped;
import jakarta.enterprise.inject.Produces;
public class RagRetriever {
@Produces
@ApplicationScoped
public RetrievalAugmentor create(EmbeddingStore store, EmbeddingModel model) {
var contentRetriever = EmbeddingStoreContentRetriever.builder()
.embeddingModel(model)
.embeddingStore(store)
.maxResults(3)
.build();
return DefaultRetrievalAugmentor.builder()
.contentRetriever(contentRetriever)
.contentInjector((contents, userMessage) -> {
StringBuffer prompt = new StringBuffer(userMessage.singleText());
//prompt.append("\nPlease, only use the following information:\n");
contents.forEach(content -> prompt.append("- ")
.append(content.textSegment().text())
.append("\n"));
return new UserMessage(prompt.toString());
})
.build();
}
}

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import {css, LitElement} from 'lit';
import '@vaadin/icon';
import '@vaadin/button';
import '@vaadin/text-field';
import '@vaadin/text-area';
import '@vaadin/form-layout';
import '@vaadin/progress-bar';
import '@vaadin/checkbox';
import '@vaadin/horizontal-layout';
import '@vaadin/grid';
import '@vaadin/grid/vaadin-grid-sort-column.js';
export class DemoChat extends LitElement {
_stripHtml(html) {
const div = document.createElement("div");
div.innerHTML = html;
return div.textContent || div.innerText || "";
}
connectedCallback() {
const chatBot = document.getElementsByTagName("chat-bot")[0];
const protocol = (window.location.protocol === 'https:') ? 'wss' : 'ws';
const socket = new WebSocket(protocol + '://' + window.location.host + '/customer-support-agent');
const that = this;
socket.onmessage = function (event) {
chatBot.hideLastLoading();
// LLM response
let lastMessage;
if (chatBot.messages.length > 0) {
lastMessage = chatBot.messages[chatBot.messages.length - 1];
}
if (lastMessage && lastMessage.sender.name === "Bot" && ! lastMessage.loading) {
if (! lastMessage.msg) {
lastMessage.msg = "";
}
lastMessage.msg += event.data;
let bubbles = chatBot.shadowRoot.querySelectorAll("chat-bubble");
let bubble = bubbles.item(bubbles.length - 1);
if (lastMessage.message) {
bubble.innerHTML = that._stripHtml(lastMessage.message) + lastMessage.msg;
} else {
bubble.innerHTML = lastMessage.msg;
}
chatBot.body.scrollTo({ top: chatBot.body.scrollHeight, behavior: 'smooth' })
} else {
chatBot.sendMessage(event.data, {
right: false,
sender: {
name: "Bot"
}
});
}
}
chatBot.addEventListener("sent", function (e) {
if (e.detail.message.sender.name !== "Bot") {
// User message
const msg = that._stripHtml(e.detail.message.message);
socket.send(msg);
chatBot.sendMessage("", {
right: false,
loading: true
});
}
});
}
}
customElements.define('demo-chat', DemoChat);

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import {LitElement, html, css} from 'lit';
import '@vaadin/icon';
import '@vaadin/button';
import '@vaadin/text-field';
import '@vaadin/text-area';
import '@vaadin/form-layout';
import '@vaadin/progress-bar';
import '@vaadin/checkbox';
import '@vaadin/grid';
import '@vaadin/grid/vaadin-grid-sort-column.js';
export class DemoTitle extends LitElement {
static styles = css`
h2 {
font-family: "Red Hat Mono", monospace;
font-size: 60px;
font-style: normal;
font-variant: normal;
font-weight: 700;
line-height: 26.4px;
color: var(--main-highlight-text-color);
}
.title {
text-align: center;
padding: 1em;
background: var(--main-bg-color);
}
.explanation {
margin-left: auto;
margin-right: auto;
width: 50%;
text-align: justify;
font-size: 20px;
}
.explanation img {
max-width: 60%;
display: block;
float:left;
margin-right: 2em;
margin-top: 1em;
}
`
render() {
return html`
<div class="title">
<h2>Phoenix Technologies</h2>
</div>
<div class="explanation">
<p>Welcome to Phoenix Technologies!</p>
<p>Please click the button on the bottom right to start the conversation
with an LLM-powered customer support agent.</p>
</div>
`
}
}
customElements.define('demo-title', DemoTitle);

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// import './font-awesome-brands.js';
// import './font-awesome-regular.js';
import './font-awesome-solid.js';
// export * from './font-awesome-brands.js';
// export * from './font-awesome-regular.js';
export * from './font-awesome-solid.js';

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<link rel="shortcut icon" type="image/png" href="favicon.ico">
<script src="/_importmap/dynamic-importmap.js"></script>
<script type="module">
import 'icons/font-awesome.js';
import 'components/demo-title.js';
import 'components/demo-chat.js';
import 'wc-chatbot';
</script>
<link rel="stylesheet" href="fonts/red-hat-font.min.css">
<title>Miles of Smiles</title>
<style>
:root {
--main-bg-color: rgb(246, 242, 242);
--main-highlight-text-color: rgba(237, 98, 128);
}
body {
margin: 0;
width: 100%;
height: 100vh;
font-family: 'Red Hat Text', sans-serif;
overflow: hidden;
display: flex;
flex-direction: column;
justify-content: center;
color: var(--lumo-body-text-color);
background: var(--main-bg-color);
}
chat-bot {
--chatbot-avatar-bg-color: rgba(237, 98, 128);
--chatbot-avatar-margin: 10%;
--chatbot-header-bg-color: rgba(237, 98, 128);
--chatbot-header-title-color: #FFFFFF;
--chatbot-body-bg-color: var(--main-bg-color);
--chatbot-send-button-color: rgba(237, 98, 128);
}
chat-bot::part(chat-bubble) {
--chat-bubble-avatar-color: rgba(237, 98, 128);
--chat-bubble-color: rgba(203, 232, 237, 0.71);
--chat-bubble-right-color: rgb(157, 238, 244);
--chat-bubble-font-color: #333;
--chat-bubble-font-right-color: #333;
--chat-bubble-delay: 0.2s;
}
.middle {
margin-top: 2em;
overflow: hidden;
width: 50%;
margin-left: auto;
margin-right: auto;
display: flex;
}
</style>
</head>
<body>
<demo-title></demo-title>
<div class="middle">
<demo-chat>
<chat-bot></chat-bot>
</demo-chat>
</div>
</body>
</html>

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quarkus:
langchain4j:
openai:
api-key: #PUT_YOUR_TOKEN_HERE
base-url: https://inference-llama33-70b-maas.apps.ai.kvant.cloud/v1/
timeout: 60s
#https://docs.langchain4j.dev/tutorials/model-parameters/
chat-model:
model-name: inference-llama33-70b
temperature: 1.0
#max-tokens: 1000
#frequency-penalty: 2
frequency-penalty: 0
log-requests: true
log-responses: true
embedding-model:
log-responses: true
log-requests: true
#embedding-model:
# provider: dev.langchain4j.model.embedding.onnx.bgesmallenq.BgeSmallEnQuantizedEmbeddingModel
pgvector:
dimension: 1024
#This configuration takes precedence over the embedding-model.provider one.
l4j:
custom-embedding-model:
#This is 1024 dimension model. pgvector must be configured accordingly
model-name: inference-multilingual-e5l
base-url: https://inference-multilingual-e5l-maas.apps.ai.kvant.cloud/v1
api-key: #PUT_YOUR_TOKEN_HERE
log-requests: false
log-responses: false
#Not supported by current implementation
#dimensions: ${quarkus.langchain4j.pgvector.dimension}
rag:
location: src/main/resources/rag

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