使用服务器提示模板进行多轮对话


本指南适用于任何依赖于多轮对话的功能，包括：

简要概览

对于多轮互动，Firebase AI Logic SDK 会管理对话的状态。使用服务器提示模板时，这也适用。

多轮对话和服务器提示模板的基本工作流程与单轮请求的基本工作流程基本相同，但存在一些重要区别：

  1. 使用 Firebase 控制台中的引导式界面创建模板。

    对于多轮互动，您必须在模板的内容中添加 {{history}} 标记，该标记用于告知模板在何处注入由客户端 SDK 管理的对话轮次。

  2. 使用 Firebase 控制台中的测试体验，在真实请求中测试模板。

    对于多轮对话互动，控制台测试体验只能帮助测试初始轮次。您可以使用该模板和实际应用来测试后续对话轮次（历史记录）的处理方式。

  3. 使用 templateGenerativeModel 从应用代码访问模板。

    对于多轮互动，您必须使用 startChat 和 sendMessage（就像您在不使用服务器提示模板时进行多轮互动一样）。

请注意，对于函数调用，还有一些其他差异，本页后面的相应部分会对此进行说明。


多轮对话（聊天）

如果您尚未查看，请在不使用服务器提示模板时，查看构建多轮对话（聊天）的一般指南。

服务器提示模板的基本格式

对于 Firebase AI Logic，Firebase 控制台提供了一个引导式界面，供您指定模板的前言和内容。

服务器提示模板使用基于 Dotprompt 的语法和格式。 如需了解详情，请参阅模板格式、语法和示例。

以下示例模板展示了在构建多轮对话（聊天）时模板最重要的组件。请注意，模板的内容中添加了 {{history}} 标记，该标记用于告知模板在何处注入由客户端 SDK 管理的对话轮次。

---
model: 'gemini-3-flash-preview'
---

{{role "system"}}
You help customers with their invoices, including answering questions or providing their invoices to them.
If an invoice is requested, it must be a clearly structured invoice document that uses a tabular or clearly delineated list format for line items.

{{history}}

在代码中使用模板

点击您的 Gemini API 提供商，以查看此页面上特定于提供商的内容和代码。

以下示例客户端代码展示了如何在代码中使用模板。请注意，在构建多轮交互时，templateGenerativeModel 会与 startChat 和 sendMessage 一起使用。

Swift

For Swift, using server prompt templates with multi-turn interactions is not yet supported. Check back soon!

Kotlin 


// ...

// Initialize the Gemini Developer API backend service
// Create a `TemplateGenerativeModel` instance.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .templateGenerativeModel()

// Start a chat session with a template.
val chatSession = model.startChat(
  // Specify your template ID
  templateId= "my-chat-template-v1-0-0",
  inputs = emptyMap()
)

// Send messages.
// The template's system instructions and model config apply to every turn automatically.
// The SDK automatically manages the state of the conversation.
val response = chatSession.sendMessage(
  content("user") { text("I need a copy of my invoice.") }
)

val text = response.text
println(text)

Java 

For Java, using server prompt templates with multi-turn interactions is not yet supported. Check back soon!

Web 


// ...

// Initialize the Gemini Developer API backend service.
const ai = getAI(app, { backend: new GoogleAIBackend() });

// Create a `TemplateGenerativeModel` instance.
const model = getTemplateGenerativeModel(ai);

// Start a chat session with a template.
const chatSession = model.startChat({
  // Specify your template ID.
  templateId: 'my-chat-template-v1-0-0',
});

// Send messages.
// The template's system instructions and model config apply to every turn automatically.
// The SDK automatically manages the state of the conversation.
const result = await chatSession.sendMessage("I need a copy of my invoice.");

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

Dart 


// ...

// Initialize the Gemini Developer API backend service.
// Create a `TemplateGenerativeModel` instance.
final model = FirebaseAI.googleAI().templateGenerativeModel();

// Start a chat session with a template.
final chatSession = model.startChat(
  // Specify your template ID.
  templateId: 'my-chat-template-v1-0-0',
);

// Send messages.
// The template's system instructions and model config apply to every turn automatically.
// The SDK automatically manages the state of the conversation.
final response = await chatSession.sendMessage(
  Content.text('I need a copy of my invoice.'),
);

final text = response.text;
print(text);

Unity 


// ...

// Initialize the Gemini Developer API backend service.
var firebaseAI = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `TemplateGenerativeModel` instance.
var model = firebaseAI.GetTemplateGenerativeModel();

// Start a chat session with a template.
var chatSession = model.StartChat(
    // Specify your template ID.
    "my-chat-template-v1-0-0"
);

// Send messages.
// The template's system instructions and model config apply to every turn automatically.
// The SDK automatically manages the state of the conversation.
try
{
   var response = await chatSession.SendMessageAsync(ModelContent.Text("I need a copy of my invoice."));
   Debug.Log($"Response Text: {response.Text}");
}
catch (Exception e) {
  Debug.LogError($"An error occurred: {e.Message}");
}


函数调用

如果您尚未这样做，请查看不使用服务器提示模板时的函数调用常规指南。本指南介绍了如何使用服务器提示模板，并假定您已大致了解函数调用的工作原理。

服务器提示模板的基本格式

对于 Firebase AI Logic，Firebase 控制台提供了一个引导式界面，供您指定模板的前言和内容。

服务器提示模板使用基于 Dotprompt 的语法和格式。 如需了解详情，请参阅模板格式、语法和示例。

以下示例模板展示了使用函数调用时模板的最重要组成部分。请注意以下几点：

  • 在模板的前言中，通过在 tools 对象中提供函数声明，列出模型可访问的函数。

    • 为模型可访问的每个函数定义 name （必需）和 description （可选）。

    • 为模型可访问的每个函数定义架构。

      以下示例模板假定您要在模板中定义函数架构。不过，您可以在客户端代码中提供函数的架构。在客户端代码中定义的架构 会替换模板中定义的任何架构。在本页面的后面部分，您可以找到在客户端代码中定义架构的模板和客户端代码示例。

  • 在模板的内容中，添加 {{history}} 标记，该标记用于告知模板在何处注入由客户端 SDK 管理的对话轮次。

在模板中定义了函数架构的模板示例

---
model: gemini-3-flash-preview
tools:
  - name: fetchWeather
    description: Get the weather conditions for a specific city on a specific date.
    input:
      schema:
        location(object, The name of the city and its state for which to get the weather. Only cities in the USA are supported.):
          city: string, The city of the location.
          state: string, The state of the location.
        date: string, The date for which to get the weather. Date must be in the format YYYY-MM-DD.

---

What was the weather like in Boston, Massachusetts on 10/17 in year 2024?

{{history}}

在代码中使用模板

点击您的 Gemini API 提供商，以查看此页面上特定于提供商的内容和代码。

以下示例客户端代码展示了如何在代码中使用模板。 请注意以下几点：

  • 使用多轮交互时，请将 templateGenerativeModel 与 startChat 和 sendMessage 搭配使用。

  • 在客户端代码中初始化模型期间，请勿列出模型有权访问的函数。相反，函数必须列在模板的 frontmatter 的 tools 对象中（见上文）。

  • 以下示例客户端代码假定您正在模板中定义函数架构。如果您决定在客户端代码中定义架构，则该架构会替换模板定义的架构。在本页面的后面部分，您可以查看在客户端代码中定义架构的示例模板和客户端代码。

  • 检查模型是否在满足请求的过程中返回函数调用。如果需要，应用需要执行本地逻辑，然后将结果发送回模型。

在模板中定义了函数架构的客户端代码示例

Swift

For Swift, using server prompt templates with multi-turn interactions is not yet supported. Check back soon!

Kotlin 


// ...

// Initialize the Gemini Developer API backend service.
// Create a `TemplateGenerativeModel` instance.
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
    .templateGenerativeModel()

// Start a chat session with a template that has functions listed as tools.
val chatSession = model.startChat(
    // Specify your template ID
    templateId = "my-function-calling-template-v1-0-0",
    inputs = emptyMap()
)

// Send a message that might trigger a function call.
val response = chatSession.sendMessage(
    content("user") { text(userMessage) }
)

// When the model responds with one or more function calls, invoke the function(s).
// Note that this is the same as when *not* using server prompt templates.
val functionCalls = response.functionCalls
val fetchWeatherCall = functionCalls.find { it.name == "fetchWeather" }

// Forward the structured input data from the model to the hypothetical external API.
val functionResponse = fetchWeatherCall?.let {
  // Alternatively, if your `Location` class is marked as @Serializable, you can use
  // val location = Json.decodeFromJsonElement(it.args["location"]!!)
  val location = Location(
      it.args["location"]!!.jsonObject["city"]!!.jsonPrimitive.content,
      it.args["location"]!!.jsonObject["state"]!!.jsonPrimitive.content
  )
  val date = it.args["date"]!!.jsonPrimitive.content
  fetchWeather(location, date)
}

Java 

For Java, using server prompt templates with multi-turn interactions is not yet supported. Check back soon!

Web 


// ...

// Initialize the Gemini Developer API backend service.
const ai = getAI(app, { backend: new GoogleAIBackend() });

// Create a `TemplateGenerativeModel` instance.
const model = getTemplateGenerativeModel(ai);

// Start a chat session with a template that has functions listed as tools.
const chatSession = model.startChat({
  // Specify your template ID
  templateId: 'my-function-calling-template-v1-0-0',
});

// Send a message that might trigger a function call.
const result = await chatSession.sendMessage(userMessage);

// When the model responds with one or more function calls, invoke the function(s).
// Note that this is the same as when *not* using server prompt templates.
const functionCalls = result.response.functionCalls();
let functionCall;
let functionResult;

if (functionCalls.length > 0) {
  for (const call of functionCalls) {
    if (call.name === "fetchWeather") {
      // Forward the structured input data prepared by the model
      // to the hypothetical external API.
      functionResult = await fetchWeather(call.args);
      functionCall = call;
    }
  }
}

Dart 


// ...

// Initialize the Gemini Developer API backend service.
// Create a `TemplateGenerativeModel` instance.
final model = FirebaseAI.googleAI().templateGenerativeModel()

// Start a chat session with a template that has functions listed as tools.
var chatSession = model.startChat(
    // Specify your template ID
    'my-function-calling-template-v1-0-0',
);

// Send a message that might trigger a function call.
var response = await chatSession.sendMessage(
    Content.text(userMessage),
);

// Check if the model wants to call a function.
// Note that this is the same as when *not* using server prompt templates.
final functionCalls = response?.functionCalls.toList();
// When the model responds with one or more function calls, invoke the function(s).
if (functionCalls != null && functionCalls.isNotEmpty) {
  for (final functionCall in functionCalls) {
    if (functionCall.name == 'fetchWeather') {
      Map<String, dynamic> location =
          functionCall.args['location']! as Map<String, dynamic>;
      var date = functionCall.args['date']! as String;
      var city = location['city'] as String;
      var state = location['state'] as String;
      final functionResult =
          await fetchWeather(Location(city, state), date);
      // Send the response to the model so that it can use the result to
      // generate text for the user.
      response = await chatSession.sendMessage(
        Content.functionResponse(functionCall.name, functionResult),
      );
    }
  }
}

Unity 


// ...

// Initialize the Gemini Developer API backend service.
var firebaseAI = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `TemplateGenerativeModel` instance.
var model = firebaseAI.GetTemplateGenerativeModel();

// Start a chat session with a template that has functions listed as tools.
var chatSession = model.StartChat(
    // Specify your template ID
    "my-function-calling-template-v1-0-0"
);

try
{
   // Send a message that might trigger a function call.
   var response = await chatSession.SendMessageAsync(ModelContent.Text(userMessage));
   var functionResponses = new List();

  // When the model responds with one or more function calls, invoke the function(s).
  // Note that this is the same as when *not* using server prompt templates.
  foreach (var functionCall in response.FunctionCalls) {
    if (functionCall.Name == "fetchWeather") {
      // TODO(developer): Handle invalid arguments.
      var location = functionCall.Args["location"] as Dictionary<string, object>;
      var city = location["city"] as string;
      var state = location["state"] as string;
      var date = functionCall.Args["date"] as string;

      functionResponses.Add(ModelContent.FunctionResponse(
        name: functionCall.Name,
        // Forward the structured input data prepared by the model
        // to the hypothetical external API.
        response: FetchWeather(city: city, state: state, date: date)
      ));
    }
    // TODO(developer): Handle other potential function calls, if any.
  }

  // Send the function responses back to the model.
  var functionResponseResult = await chatSession.SendMessageAsync(functionResponses);
}
catch (Exception e) {
  Debug.LogError($"An error occurred: {e.Message}");
}


函数调用 - 在客户端代码中定义架构

请务必查看上文中关于函数调用如何与服务器提示模板搭配使用的部分（尤其是 {{history}} 标记在模板内容中的使用）。如果您想在客户端代码（而不是在模板中）中定义函数架构，请参阅本部分提供的示例模板和客户端代码。

请注意以下有关在客户端代码中定义函数架构的信息：

  • 如果您在客户端代码中定义了函数的架构（如下例所示），客户端架构 将替换该函数的任何模板定义的架构。

  • 如需在客户端代码中定义函数架构，请先编写函数声明，然后在 startChat 中提供该声明，而不是在模型初始化期间（如果您不使用服务器提示模板，则是在模型初始化期间提供声明）。

  • 即使函数声明指定了 name，模板也必须列出您希望模型能够访问的函数。模板中的 name 必须与客户端代码中的 name 相匹配。

在客户端代码中定义了函数架构的模板示例

---
model: gemini-3-flash-preview
tools:
  - name: fetchWeather
    description: Get the weather conditions for a specific city on a specific date.
---

What was the weather like in Boston, Massachusetts on 10/17 in year 2024, formatted in CELSIUS?

{{history}}

在客户端代码中定义了函数架构的客户端代码示例
（如需了解此示例中省略的详细信息，请参阅上方的客户端代码示例）

Swift

For Swift, using server prompt templates with multi-turn interactions is not yet supported. Check back soon!

Kotlin 

// ...

// Initialize your desired Gemini API backend service.
// Create a `TemplateGenerativeModel` instance.
...

// Define the schema for any functions listed in your template.
val fetchWeatherTool = functionDeclarations(
    functionDeclarations = listOf(
        FunctionDeclaration(
            name = "fetchWeather",
            description = "Returns the weather for a given location at a given time",
            parameters = mapOf(
                "location" to Schema.obj(
                    description = "The name of the city and its state for which to get the weather. Only cities in the USA are supported.",
                    properties = mapOf(
                        "city" to Schema.string(
                            description = "The city of the location."
                        ),
                        "state" to Schema.string(
                            description = "The state of the location."
                        ),
                        "zipCode" to Schema.string(
                            description = "Optional zip code of the location.",
                            nullable = true
                        )
                    ),
                    optionalProperties = listOf("zipCode")
                ),
                "date" to Schema.string(
                    description = "The date for which to get the weather. Date must be in the format: YYYY-MM-DD."
                ),
                "unit" to Schema.enumeration(
                    description = "The temperature unit.",
                    values = listOf("CELSIUS", "FAHRENHEIT"),
                    nullable = true
                )

            ),
            optionalParameters = listOf("unit"),
        )
    )
)

// Start a chat session with a template that has functions listed as tools.
var chatSessionWithSchemaOverride = model.startChat(
    // Specify your template ID.
    templateId = "my-function-calling-template-with-no-function-schema-v1-0-0",
    // In `startChat`, provide the schema for any functions listed in your template.
    // This client-side schema will override any schema defined in the template.
    tools = listOf(fetchWeatherTool)
)

// Send a message that might trigger a function call.
...

// When the model responds with one or more function calls, invoke the function(s).
// Note that this is the same as when *not* using server prompt templates.
...

// Forward the structured input data from the model to the hypothetical external API.
...

Java 

For Java, using server prompt templates with multi-turn interactions is not yet supported. Check back soon!

Web 

// ...

// Initialize your desired Gemini API backend service.
...

// Create a `TemplateGenerativeModel` instance.
...

// Start a chat session with a template that has functions listed as tools.
const chatSessionWithSchemaOverride = model.startChat({
  // Specify your template ID.
  templateId: 'my-function-calling-template-with-no-function-schema-v1-0-0',
  // In `startChat`, provide the schema for any functions listed in your template.
  // This client-side schema will override any schema defined in the template.
  tools: [
    {
      functionDeclarations: [
        {
          name: "fetchWeather",
          parameters: {
            type: Type.OBJECT,
            properties: {
              location: {
                type: Type.OBJECT,
                description: "The name of the city and its state for which to get the weather. Only cities in the USA are supported.",
                properties: {
                  city: {
                    type: Type.STRING,
                    description: "The city of the location."
                  },
                  state: {
                    type: Type.STRING,
                    description: "The state of the location."
                  },
                  zipCode: {
                    type: Type.INTEGER,
                    description: "Optional zip code of the location.",
                    nullable: true
                  },
                },
                required: ["city", "state"],
              },
              date: {
                type: Type.STRING,
                description: "The date for which to get the weather. Date must be in the format: YYYY-MM-DD.",
              },
              unit: {
                type: Type.STRING,
                description: "The temperature unit.",
                enum: ["CELSIUS", "FAHRENHEIT"],
                nullable: true,
              },
            },
            required: ["location", "date"],
          },
        },
      ],
    }
  ],
});

// Send a message that might trigger a function call.
...

// When the model responds with one or more function calls, invoke the function(s).
// Note that this is the same as when *not* using server prompt templates.
...

Dart 

// ...

// Initialize your desired Gemini API backend service.
// Create a `TemplateGenerativeModel` instance.
...

// Start a chat session with a template that has functions listed as tools.
final chatSessionWithSchemaOverride = model?.startChat(
      // Specify your template ID.
      'my-function-calling-template-with-no-function-schema-v1-0-0',
      inputs: {},
      // In `startChat`, provide the schema for any functions listed in your template.
      // This client-side schema will override any schema defined in the template.
      tools: [
        TemplateTool.functionDeclarations(
          [
            TemplateFunctionDeclaration(
              'fetchWeather',
              parameters: {
                'location': JSONSchema.object(
                  description:
                      'The name of the city and its state for which to get '
                      'the weather. Only cities in the USA are supported.',
                  properties: {
                    'city': JSONSchema.string(
                      description: 'The city of the location.',
                    ),
                    'state': JSONSchema.string(
                      description: 'The state of the location.',
                    ),
                    'zipCode': JSONSchema.integer(
                      description: 'Optional zip code of the location.',
                      nullable: true,
                    ),
                  },
                  optionalProperties: ['zipCode'],
                ),
                'date': JSONSchema.string(
                  description: 'The date for which to get the weather. '
                      'Date must be in the format: YYYY-MM-DD.',
                ),
                'unit': JSONSchema.enumString(
                  enumValues: ['CELSIUS', 'FAHRENHEIT'],
                  description: 'The temperature unit.',
                  nullable: true,
                ),
              },
              optionalParameters: ['unit'],
            ),
          ],
        ),
      ],
    );

// Send a message that might trigger a function call.
...

// Check if the model wants to call a function.
// Note that this is the same as when *not* using server prompt templates.
...

Unity 

// ...

// Initialize your desired Gemini API backend service.
...

// Create a `TemplateGenerativeModel` instance.
...

// Define the schema for any functions listed in your template.
var fetchWeatherTool = new TemplateTool.FunctionDeclaration(
    name: "fetchWeather",
    parameters: new Dictionary<string, JsonSchema>() {
        { "location", JsonSchema.Object(
            description: "The name of the city and its state for which to get the weather. Only cities in the USA are supported.",
            properties: new Dictionary<string, JsonSchema>() {
                { "city", JsonSchema.String(description: "The city of the location.") },
                { "state", JsonSchema.String(description: "The state of the location.") },
                { "zipCode", JsonSchema.Int(description: "Optional zip code of the location.", nullable: true) }
            },
            optionalProperties: new[] { "zipCode" })
        },
        { "date", JsonSchema.String(description: "The date for which to get the weather. Date must be in the format: YYYY-MM-DD.")},
        { "unit", JsonSchema.Enum(
            values: new[] { "CELSIUS", "FAHRENHEIT" },
            description: "The temperature unit.",
            nullable: true)
        }
    },
    optionalParameters: new[] { "unit" }
);

// Start a chat session with a template that has functions listed as tools.
var chatSessionWithSchemaOverride = model.StartChat(
    // Specify your template ID.
    templateId: "my-function-calling-template-with-no-function-schema-v1-0-0",
    // In `startChat`, provide the schema for any functions listed in your template.
    // This client-side schema will override any schema defined in the template.
    tools: new[] { fetchWeatherTool }
);

try
{
   // Send a message that might trigger a function call.
   ...

  // When the model responds with one or more function calls, invoke the function(s).
  // Note that this is the same as when *not* using server prompt templates.
  ...
}

// ...


接下来怎么做？