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Slides of the presentation

Slide deck

Quarkus A2A Demo

A2A demo by Emmanuel Bernard

emmanuelbernard/quarkus-a2a-deepdive)

glaforge

import com.google.adk.agents.BaseAgent;
import com.google.adk.agents.LlmAgent;
import com.google.adk.tools.BaseTool;
import com.google.adk.tools.mcp.McpToolset;
import com.google.adk.tools.mcp.SseServerParameters;
import com.google.adk.tools.mcp.StreamableHttpServerParameters;
import com.google.adk.web.AdkWebServer;

import java.util.List;

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## Google: Market Research Report (Q2 2025)

Executive Summary:

This report provides an overview of Google, its recent performance,
and current market position. Google, a subsidiary of Alphabet Inc.,
continues to be a dominant force in the technology industry, driven
by its core search engine, cloud services, and advancements in
artificial intelligence. Recent financial results for Q2 2025
exceeded expectations, demonstrating strong growth across key

glaforge

var companyProfiler = LlmAgent.builder()
    .name("company-profiler")
    .description(
        "Provides a general overview of a company.")
    .instruction("""
        Your role is to provide a brief overview of the
        given company.
        Include its mission, headquarters, and current CEO.
        Use the Google Search Tool to find this information.
        """)

glaforge

package agents;

import static java.nio.charset.StandardCharsets.UTF_8;
import java.util.Scanner;

import com.google.adk.agents.BaseAgent;
import com.google.adk.agents.LlmAgent;
import com.google.adk.events.Event;
import com.google.adk.runner.InMemoryRunner;
import com.google.adk.sessions.Session;

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import dev.langchain4j.agent.tool.ToolExecutionRequest;
import dev.langchain4j.mcp.McpToolProvider;
import dev.langchain4j.mcp.client.DefaultMcpClient;
import dev.langchain4j.mcp.client.McpClient;
import dev.langchain4j.mcp.client.transport.McpTransport;
import dev.langchain4j.mcp.client.transport.http.HttpMcpTransport;
import dev.langchain4j.model.vertexai.VertexAiGeminiChatModel;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.service.tool.ToolProvider;

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public class SentenceWindowRetrieval {
  public static void main(String[] args) throws IOException {

        Document capitalDocument = Document.from("...");

        VertexAiEmbeddingModel embeddingModel = VertexAiEmbeddingModel.builder()
            .project(System.getenv("GCP_PROJECT_ID"))
            .endpoint(System.getenv("GCP_VERTEXAI_ENDPOINT"))
            .location(System.getenv("GCP_LOCATION"))
            .publisher("google")

glaforge

Okay, here's a breakdown of how to create a new LangChain4j embedding store module for Google Cloud Firestore, along with the key steps and considerations, mirroring the structure of existing modules like langchain4j-milvus.

Project Structure

Your project structure should follow the established pattern. I'll create a simplified version based on the most relevant parts from the provided file listing. The full structure would be much larger (like the main langchain4j project), but this captures the essentials:

langchain4j/
└── langchain4j-embedding-store-google-firestore/  (or similar name)
    ├── pom.xml                                     (Your module's Maven build file)

glaforge

Okay, let's outline the steps to create a new language model module for LangChain4j and propose its inclusion. Based on the provided file structure, you'll be focusing on creating a new module similar to the existing ones (e.g., langchain4j-open-ai, langchain4j-ollama, etc.). Here's a breakdown of the process, referencing the structure you've provided:

Key Steps and Considerations

1. Understand the Abstractions and SPI: LangChain4j, like its Python counterpart, is built around core abstractions. You need to understand these to implement your integration correctly. The core abstractions you must implement are:

   • ChatLanguageModel / StreamingChatLanguageModel: For conversational models (like ChatGPT, Gemini). Implement ChatLanguageModel for synchronous responses, and StreamingChatLanguageModel if the model supports streaming responses token by token.

• LanguageModel / StreamingLanguageModel: For models with a simpler text-in, text-out interface (less com

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import com.google.gson.Gson;
import dev.langchain4j.data.message.*;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.model.vertexai.SchemaHelper;
import dev.langchain4j.model.vertexai.VertexAiGeminiChatModel;

import java.io.IOException;
import java.nio.file.FileVisitOption;
import java.nio.file.Files;
import java.nio.file.Path;