Dynamiczne aktualizowanie aplikacji Firebase AI Logic za pomocą Zdalnej konfiguracji Firebase

// Set default values for Remote Config parameters.
remoteConfig.defaultConfig = {
  model_name: 'gemini-3.8-flash',
  system_instructions:
    'You are a helpful assistant who knows everything there is to know about Firebase!',
  prompt: 'I am a developer who wants to know more about Firebase!',
  agent_platform_location: 'global',
};

// Set default values for Remote Config parameters.
remoteConfig.setDefaults(const {
  "model_name": "gemini-3.8-flash",
  "system_instructions": "You are a helpful assistant who knows everything there is to know about Firebase!",
  "prompt": "I am a developer who wants to know more about Firebase!",
  "agent_platform_location": "global"
});

// Set default values for Remote Config parameters.
await remoteConfig.SetDefaultsAsync(
  new System.Collections.Generic.Dictionary<string, object>() {
    { "model_name", "gemini-3.8-flash" },
    { "system_instructions", "You are a helpful assistant who knows everything there is to know about Firebase!" },
    { "prompt", "I am a developer who wants to know more about Firebase!" },
    { "agent_platform_location", "global" }
  }
);

// Fetch and activate Remote Config values
remoteConfig.fetchAndActivate { status, error in
  if let error = error {
    print("Error fetching Remote Config: \(error.localizedDescription)")
  }
}

// Fetch and activate Remote Config values
remoteConfig.fetchAndActivate()
      .addOnCompleteListener(this) { task ->
          if (task.isSuccessful) {
              val updated = task.result
              Log.d(TAG, "Remote Config values fetched and activated: $updated")
          } else {
              Log.e(TAG, "Error fetching Remote Config", task.exception)
          }
      }

  // Fetch and activate Remote Config values
  mFirebaseRemoteConfig.fetchAndActivate()
    .addOnCompleteListener(this, new OnCompleteListener<Boolean>() {
        @Override
        public void onComplete(@NonNull Task<Boolean> task) {
            if (task.isSuccessful()) {
                boolean updated = task.getResult();
                Log.d(TAG, "Config params updated: " + updated);
            } else {
                Log.e(TAG, "Error fetching Remote Config", task.exception)
            }
          }
    });

// Fetch and activate Remote Config.
remoteConfig.fetchAndActivate();

// Assign Remote Config values.
String? _modelName = remoteConfig.getString("model_name");
String? _systemInstructions = remoteConfig.getString("system_instructions");
String? _prompt = remoteConfig.getString("prompt");
String? _agentPlatformLocation = remoteConfig.getString("agent_platform_location");

// Fetch and activate Remote Config values.
await remoteConfig.FetchAndActivateAsync();

// Add real-time Remote Config
remoteConfig.addOnConfigUpdateListener { configUpdate, error in
  guard let configUpdate = configUpdate, error == nil else {
    print("Error listening for config updates: \(error?.localizedDescription ?? "No error available")")
    return
  }

  print("Updated keys: \(configUpdate.updatedKeys)")
  remoteConfig.activate { changed, error in
    guard error == nil else {
      print("Error activating config: \(error?.localizedDescription ?? "No error available")")
      return
    }
    print("Activated config successfully")
  }
}

      // Add a real-time Remote Config listener
      remoteConfig.addOnConfigUpdateListener(object : ConfigUpdateListener {
          override fun onUpdate(configUpdate : ConfigUpdate) {
              Log.d(ContentValues.TAG, "Updated keys: " + configUpdate.updatedKeys);
              remoteConfig.activate().addOnCompleteListener {
                  // Optionally, add an action to perform on update here.
              }
          }

          override fun onError(error : FirebaseRemoteConfigException) {
              Log.w(ContentValues.TAG, "Config update error with code: " + error.code, error)
          }
      }

  // Add a real-time Remote Config listener
  remoteConfig.addOnConfigUpdateListener(new ConfigUpdateListener() {
      @Override
      public void onUpdate(ConfigUpdate configUpdate) {
          Log.d(ContentValues.TAG, "Updated keys: " + configUpdate.getUpdatedKeys());
                remoteConfig.activate().addOnCompleteListener(new OnCompleteListener<Boolean>() {
                  @Override
                  public void onComplete(@NonNull Task<Boolean> task) {
                      // Optionally, add an action to perform on update here.
                  }
              });
          }

      @Override
      public void onError(FirebaseRemoteConfigException error) {
          Log.w(ContentValues.TAG, "Config update error with code: " + error.getCode(), error);
      }
  });

 // Add a real-time Remote Config listener
 onConfigUpdate(remoteConfig, {
   next: (configUpdate) => {
      console.log("Updated keys:", configUpdate.getUpdatedKeys());
      if (configUpdate.getUpdatedKeys().has("welcome_message")) {
         activate(remoteConfig).then(() => {
         showWelcomeMessage();
         });
      }
   },
   error: (error) => {
      console.log("Config update error:", error);
   },
   complete: () => {
      console.log("Listening stopped.");
   }
});

// Add a real-time Remote Config listener
remoteConfig.onConfigUpdated.listen((event) async {
  await remoteConfig.activate();
});

// Add a real-time Remote Config listener to automatically update whenever
// a new template is published.
// Note: the parameters can be anonymous as they are unused.

remoteConfig.OnConfigUpdateListener += (_, _) => {
  remoteConfig.ActivateAsync();
};

// Initialize the Agent Platform Gemini API backend service (formerly Vertex AI).
// The location for where to access the model will be sourced from Remote Config
let agentPlatformLocation = remoteConfig.configValue(forKey: "agent_platform_location").stringValue
let ai = FirebaseAI.firebaseAI(backend: .agentPlatform(location: agentPlatformLocation))

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
let modelName = remoteConfig.configValue(forKey: "model_name").stringValue
let systemInstructions = remoteConfig.configValue(forKey: "system_instructions").stringValue

let model = ai.generativeModel(
  modelName: modelName,
  systemInstruction: ModelContent(role: "system", parts: systemInstructions)
)

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
let userPrompt = remoteConfig.configValue(forKey: "prompt").stringValue

// To generate text output, call `generateContent` with the text input
let response = try await model.generateContent(userPrompt)
if let text = response.text {
  print(text)
}

// Initialize the Agent Platform Gemini API backend service (formerly Vertex AI).
// The location for where to access the model will be sourced from Remote Config
val ai = Firebase.ai(backend = GenerativeBackend.agentPlatform(location = remoteConfig.getString("agent_platform_location")))

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
val model = ai.generativeModel(
  modelName = remoteConfig.getString("model_name"),
  systemInstruction = content { text(remoteConfig.getString("system_instructions")) }
)

// To generate text output, call `generateContent` with the text input
// The text in the prompt will be sourced from Remote Config
val response = model.generateContent(remoteConfig.getString("prompt"))
print(response.text)

// Initialize the Agent Platform Gemini API backend service (formerly Vertex AI).
// The location for where to access the model will be sourced from Remote Config
FirebaseAI ai = FirebaseAI.getInstance(
        GenerativeBackend.agentPlatform(remoteConfig.getString("agent_platform_location")));

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
GenerativeModel gm = ai.generativeModel(
        /* modelName */ remoteConfig.getString("model_name"),
        /* generationConfig (optional) */ null,
        /* safetySettings (optional) */ null,
        /* tools (optional) */ null,
        /* toolsConfig (optional) */ null,
        /* systemInstruction (optional) */ new Content.Builder()
                .addText(remoteConfig.getString("system_instructions")).build(),
        /* requestOptions (optional) */ new RequestOptions()
);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
Content userPrompt = new Content.Builder().addText(
        remoteConfig.getString("prompt")).build();

// To generate text output, call `generateContent` with the text input
ListenableFuture<GenerateContentResponse> response = model.generateContent(userPrompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        String resultText = result.getText();
        System.out.println(resultText);
    }

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Agent Platform Gemini API backend service (formerly Vertex AI).
// The location for where to access the model will be sourced from Remote Config
const ai = getAI(firebaseApp, { backend: new AgentPlatformBackend(agentPlatformLocation) });

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
const model = getGenerativeModel(ai, {
  model: modelName,
  systemInstruction: systemInstruction
});

// Wrap in an async function so you can use await
async function run() {
  // Provide a prompt that contains text
  // The text in the prompt will be sourced from Remote Config
  const userPrompt = prompt;

  // To generate text output, call `generateContent` with the text input
  const result = await model.generateContent(userPrompt);

  const response = result.response;
  const text = response.text();
  console.log(text);
}

// Initialize the Agent Platform Gemini API backend service (formerly Vertex AI).
// The location for where to access the model will be sourced from Remote Config
final ai = await FirebaseAI.agentPlatform(location: _agentPlatformLocation);

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
final model =
      ai.generativeModel(
        model: _modelName,
        systemInstruction: Content.system(_systemInstructions),
      );

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
final _userPrompt = [Content.text(_prompt)];

// To generate text output, call `generateContent` with the text input
final response = await model.generateContent(_userPrompt);
print(response.text);

// Initialize the Agent Platform Gemini API backend service (formerly Vertex AI).
// The location for where to access the model will be sourced from Remote Config
var agentPlatformLocation = remoteConfig.GetValue("agent_platform_location").StringValue;
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.AgentPlatform(agentPlatformLocation));

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
var modelName = remoteConfig.GetValue("model_name").StringValue;
var systemInstructions = remoteConfig.GetValue("system_instructions").StringValue;

var model = ai.GetGenerativeModel(
  modelName: modelName,
  systemInstruction: ModelContent.Text(systemInstructions)
);

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
var userPrompt = remoteConfig.GetValue("prompt").StringValue;

// To generate text output, call `GenerateContentAsync` with the text input
var response = await model.GenerateContentAsync(userPrompt);
UnityEngine.Debug.Log(response.Text ?? "No text in response.");