Working tree-of-thought for agent selection
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@ -109,27 +109,27 @@ public class IoASystem {
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Arrays.asList("webSearch", "getNewsUpdates", "scheduleAppointment")),
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new Task("task6", "Assist in planning a multi-city European vacation for a family of four",
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Arrays.asList("travel", "family planning"),
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Arrays.asList("bookTravel", "calculateDistance", "getWeather", "findRestaurants")),
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Arrays.asList("bookTravel", "calculateDistance", "getWeather", "findRestaurants"))
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new Task("task7", "Organize an international tech conference with virtual and in-person components",
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Arrays.asList("event planning", "tech expertise", "marketing", "travel coordination", "content creation"),
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Arrays.asList("scheduleAppointment", "webSearch", "bookTravel", "getWeather", "findRestaurants", "getNewsUpdates")),
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// new Task("task7", "Organize an international tech conference with virtual and in-person components",
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// Arrays.asList("event planning", "tech expertise", "marketing", "travel coordination", "content creation"),
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// Arrays.asList("scheduleAppointment", "webSearch", "bookTravel", "getWeather", "findRestaurants", "getNewsUpdates")),
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new Task("task8", "Develop and launch a multi-lingual mobile app for sustainable tourism",
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Arrays.asList("software development", "travel", "language expertise", "environmental science", "user experience design"),
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Arrays.asList("webSearch", "translate", "getWeather", "findRestaurants", "getNewsUpdates", "compareProductPrices")),
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// new Task("task8", "Develop and launch a multi-lingual mobile app for sustainable tourism",
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// Arrays.asList("software development", "travel", "language expertise", "environmental science", "user experience design"),
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// Arrays.asList("webSearch", "translate", "getWeather", "findRestaurants", "getNewsUpdates", "compareProductPrices")),
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new Task("task9", "Create a comprehensive health and wellness program for a large corporation, including mental health support",
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Arrays.asList("health", "nutrition", "psychology", "corporate wellness", "data analysis"),
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Arrays.asList("findFitnessClasses", "getRecipe", "setReminder", "getWeather", "scheduleAppointment", "getFinancialAdvice")),
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// new Task("task9", "Create a comprehensive health and wellness program for a large corporation, including mental health support",
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// Arrays.asList("health", "nutrition", "psychology", "corporate wellness", "data analysis"),
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// Arrays.asList("findFitnessClasses", "getRecipe", "setReminder", "getWeather", "scheduleAppointment", "getFinancialAdvice")),
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new Task("task10", "Plan and execute a global product launch campaign for a revolutionary eco-friendly technology",
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Arrays.asList("marketing", "environmental science", "international business", "public relations", "social media"),
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Arrays.asList("webSearch", "getNewsUpdates", "scheduleAppointment", "translate", "compareProductPrices", "bookTravel")),
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// new Task("task10", "Plan and execute a global product launch campaign for a revolutionary eco-friendly technology",
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// Arrays.asList("marketing", "environmental science", "international business", "public relations", "social media"),
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// Arrays.asList("webSearch", "getNewsUpdates", "scheduleAppointment", "translate", "compareProductPrices", "bookTravel")),
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new Task("task11", "Design and implement a smart city initiative focusing on transportation, energy, and public safety",
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Arrays.asList("urban planning", "environmental science", "data analysis", "public policy", "technology integration"),
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Arrays.asList("webSearch", "getWeather", "calculateDistance", "getNewsUpdates", "getFinancialAdvice", "findHomeServices"))
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// new Task("task11", "Design and implement a smart city initiative focusing on transportation, energy, and public safety",
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// Arrays.asList("urban planning", "environmental science", "data analysis", "public policy", "technology integration"),
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// Arrays.asList("webSearch", "getWeather", "calculateDistance", "getNewsUpdates", "getFinancialAdvice", "findHomeServices"))
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);
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@ -16,4 +16,8 @@ public class AgentInfo {
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this.capabilities = capabilities;
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this.tools = tools;
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}
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public List<String> getCapabilities() {
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return this.capabilities;
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}
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}
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@ -3,9 +3,7 @@ package com.ioa.agent;
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import com.ioa.tool.ToolRegistry;
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import org.springframework.stereotype.Component;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.*;
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import java.util.stream.Collectors;
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@Component
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@ -32,12 +30,28 @@ public class AgentRegistry {
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}
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public List<AgentInfo> searchAgents(List<String> capabilities) {
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return searchAgents(capabilities, 1.0); // Default to exact match
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}
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public List<AgentInfo> searchAgents(List<String> capabilities, double matchThreshold) {
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return agents.values().stream()
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.filter(agent -> agent.getCapabilities().containsAll(capabilities))
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.filter(agent -> calculateMatchScore(agent.getCapabilities(), capabilities) >= matchThreshold)
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.sorted(Comparator.comparingDouble((AgentInfo agent) -> calculateMatchScore(agent.getCapabilities(), capabilities)).reversed())
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.collect(Collectors.toList());
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}
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public List<AgentInfo> searchAgentsPartial(List<String> capabilities) {
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return searchAgents(capabilities, 0.0); // Return all agents with any matching capability
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}
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private double calculateMatchScore(List<String> agentCapabilities, List<String> requiredCapabilities) {
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long matchingCapabilities = requiredCapabilities.stream()
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.filter(agentCapabilities::contains)
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.count();
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return (double) matchingCapabilities / requiredCapabilities.size();
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}
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public List<AgentInfo> getAllAgents() {
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return List.copyOf(agents.values());
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return new ArrayList<>(agents.values());
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}
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}
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@ -4,76 +4,62 @@ import com.ioa.agent.AgentInfo;
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import com.ioa.agent.AgentRegistry;
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import com.ioa.model.BedrockLanguageModel;
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import com.ioa.task.Task;
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import com.ioa.util.TreeOfThought;
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import org.springframework.stereotype.Component;
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import java.util.Arrays;
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import java.util.Collections;
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import java.util.List;
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import java.util.stream.Collectors;
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@Component
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public class TeamFormation {
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private AgentRegistry agentRegistry;
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private BedrockLanguageModel model;
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private TreeOfThought treeOfThought;
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public TeamFormation(AgentRegistry agentRegistry, BedrockLanguageModel model) {
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this.agentRegistry = agentRegistry;
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this.model = model;
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this.treeOfThought = new TreeOfThought(model);
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}
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public List<AgentInfo> formTeam(Task task) {
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List<String> requiredCapabilities = task.getRequiredCapabilities();
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List<String> requiredTools = task.getRequiredTools();
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List<AgentInfo> potentialAgents = agentRegistry.searchAgents(requiredCapabilities);
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List<AgentInfo> potentialAgents = agentRegistry.searchAgentsPartial(requiredCapabilities);
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System.out.println("DEBUG: Potential agents: " + potentialAgents);
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String initialPrompt = "Task: " + task.getDescription() + "\n" +
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"Required capabilities: " + String.join(", ", requiredCapabilities) + "\n" +
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"Required tools: " + String.join(", ", requiredTools) + "\n" +
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"Available agents:\n" + formatAgentDetails(potentialAgents) + "\n" +
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"Instructions: Analyze the task requirements and the available agents. " +
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"Form the best team by selecting agents whose combined capabilities and tools meet the task requirements. " +
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"Consider the following steps:\n" +
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"1. Identify which capabilities and tools are crucial for the task.\n" +
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"2. Match these requirements with the available agents.\n" +
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"3. Consider how agents can complement each other's skills and tools.\n" +
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"4. Aim to cover all required capabilities and tools with the smallest effective team.\n" +
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"5. If a perfect match isn't possible, prioritize the most important requirements.\n" +
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"Provide your reasoning for each step, then conclude with a final team selection in this format: 'Selected Team: agent1, agent2, ...'";
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String teamFormationTask = "Form the best team for this task: " + task.getDescription() +
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"\nRequired capabilities: " + requiredCapabilities +
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"\nRequired tools: " + requiredTools +
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"\nAvailable agents and their tools: " + formatAgentTools(potentialAgents) +
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"\nAnalyze the task, evaluate agents, and propose a team composition. " +
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"Conclude with a final team selection in the format: 'Final Team Selection: agent1, agent2, ...'";
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System.out.println("DEBUG: Sending initial prompt to language model: " + initialPrompt);
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String reasoning = model.generate(initialPrompt, null);
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System.out.println("DEBUG: Language model reasoning:\n" + reasoning);
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String reasoning = treeOfThought.reason(teamFormationTask, 3, 2);
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System.out.println("DEBUG: Tree of Thought reasoning:\n" + reasoning);
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return parseTeamComposition(reasoning, potentialAgents);
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}
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private String formatAgentDetails(List<AgentInfo> agents) {
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StringBuilder sb = new StringBuilder();
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for (AgentInfo agent : agents) {
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sb.append(agent.getId()).append(":\n")
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.append(" Capabilities: ").append(String.join(", ", agent.getCapabilities())).append("\n")
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.append(" Tools: ").append(String.join(", ", agent.getTools())).append("\n\n");
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}
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return sb.toString();
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private String formatAgentTools(List<AgentInfo> agents) {
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return agents.stream()
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.map(agent -> agent.getId() + " (capabilities: " + agent.getCapabilities() + ", tools: " + agent.getTools() + ")")
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.collect(Collectors.joining(", "));
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}
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private List<AgentInfo> parseTeamComposition(String reasoning, List<AgentInfo> potentialAgents) {
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// Extract the team selection from the reasoning
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String[] lines = reasoning.split("\n");
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String selectedTeamLine = "";
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for (String line : lines) {
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if (line.startsWith("Selected Team:")) {
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selectedTeamLine = line.substring("Selected Team:".length()).trim();
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if (line.startsWith("Final Team Selection:")) {
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selectedTeamLine = line.substring("Final Team Selection:".length()).trim();
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break;
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}
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}
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if (selectedTeamLine.isEmpty()) {
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System.out.println("DEBUG: No team selection found in the response.");
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return Collections.emptyList();
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return List.of();
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}
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List<String> selectedIds = Arrays.asList(selectedTeamLine.split(",\\s*"));
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