Generating content

Gemini API 支持使用图片、音频、代码、工具等生成内容。如需详细了解这些功能中的每项，请继续阅读并查看以任务为中心的示例代码，或阅读全面的指南。

方法：models.generateContent

给定输入 GenerateContentRequest 生成模型响应。如需了解详细使用信息，请参阅文本生成指南。输入功能因模型而异，包括经过调优的模型。如需了解详情，请参阅模型指南和调整指南。

端点

帖子 https://generativelanguage.googleapis.com/v1beta/{model=models/*}:generateContent

路径参数

model string

必需。用于生成补全项的 Model 的名称。

格式：models/{model}。其格式为 models/{model}。

请求正文

请求正文中包含结构如下的数据：

字段
contents[] object (Content)

必需。与模型当前对话的内容。

对于单轮查询，这是单个实例。对于聊天等多轮查询，这是包含对话历史记录和最新请求的重复字段。

tools[] object (Tool)

可选。Model 可能用于生成下一个响应的 Tools 列表。

Tool 是一段代码，可让系统与外部系统进行交互，以在 Model 的知识和范围之外执行操作或一组操作。支持的 Tool 包括 Function 和 codeExecution。如需了解详情，请参阅函数调用和代码执行指南。

toolConfig object (ToolConfig)

可选。请求中指定的任何 Tool 的工具配置。如需查看使用示例，请参阅函数调用指南。

safetySettings[] object (SafetySetting)

可选。用于屏蔽不安全内容的唯一 SafetySetting 实例列表。

这将在 GenerateContentRequest.contents 和 GenerateContentResponse.candidates 上强制执行。每个 SafetyCategory 类型不应有多个设置。API 会屏蔽未达到这些设置设定的阈值的所有内容和响应。此列表会替换 safetySettings 中指定的每个 SafetyCategory 的默认设置。如果列表中提供的给定 SafetyCategory 没有 SafetySetting，则该 API 将使用该类别的默认安全设置。支持的危害类别包括 HARM_CATEGORY_HATE_SPEECH、HARM_CATEGORY_SEXUALLY_EXPLICIT、HARM_CATEGORY_DANGEROUS_CONTENT、HARM_CATEGORY_HARASSMENT、HARM_CATEGORY_CIVIC_INTEGRITY。如需详细了解可用的安全设置，请参阅指南。此外，请参阅安全指南，了解如何在 AI 应用中纳入安全注意事项。

systemInstruction object (Content)

可选。开发者设置了系统指令。目前仅支持文本。

generationConfig object (GenerationConfig)

可选。模型生成和输出的配置选项。

cachedContent string

可选。缓存的内容的名称，用于用作提供预测的上下文。格式：cachedContents/{cachedContent}

示例请求

文本图片音频视频PDF聊天缓存经调整的模型JSON 模式代码执行函数调用生成配置安全设置系统指令
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai

client = genai.Client()
response = client.models.generate_content(
    model="gemini-2.0-flash", contents="Write a story about a magic backpack."
)
print(response.text)
text_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: "Write a story about a magic backpack.",
});
console.log(response.text);
text_generation.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}
contents := []*genai.Content{
	genai.NewContentFromText("Write a story about a magic backpack.", "user"),
}
response, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash", contents, nil)
if err != nil {
	log.Fatal(err)
}
printResponse(response)
text_generation.go
 
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[{"text": "Write a story about a magic backpack."}]
        }]
       }' 2> /dev/null
text_generation.sh
 
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

val prompt = "Write a story about a magic backpack."
val response = generativeModel.generateContent(prompt)
print(response.text)
text_generation.kt
 
let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

let prompt = "Write a story about a magic backpack."
let response = try await generativeModel.generateContent(prompt)
if let text = response.text {
  print(text)
}
TextGeneration.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final prompt = 'Write a story about a magic backpack.';

final response = await model.generateContent([Content.text(prompt)]);
print(response.text);
text_generation.dart
 
// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content content =
    new Content.Builder().addText("Write a story about a magic backpack.").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

ListenableFuture<GenerateContentResponse> response = model.generateContent(content);
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);
text_generation.java
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
import PIL.Image

client = genai.Client()
organ = PIL.Image.open(media / "organ.jpg")
response = client.models.generate_content(
    model="gemini-2.0-flash", contents=["Tell me about this instrument", organ]
)
print(response.text)
text_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const organ = await ai.files.upload({
  file: path.join(media, "organ.jpg"),
});

const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: [
    createUserContent([
      "Tell me about this instrument", 
      createPartFromUri(organ.uri, organ.mimeType)
    ]),
  ],
});
console.log(response.text);
text_generation.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

file, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "organ.jpg"), 
	&genai.UploadFileConfig{
		MIMEType : "image/jpeg",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromText("Tell me about this instrument"),
	genai.NewPartFromURI(file.URI, file.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, "user"),
}

response, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash", contents, nil)
if err != nil {
	log.Fatal(err)
}
printResponse(response)
text_generation.go
 
# Use a temporary file to hold the base64 encoded image data
TEMP_B64=$(mktemp)
trap 'rm -f "$TEMP_B64"' EXIT
base64 $B64FLAGS $IMG_PATH > "$TEMP_B64"

# Use a temporary file to hold the JSON payload
TEMP_JSON=$(mktemp)
trap 'rm -f "$TEMP_JSON"' EXIT

cat > "$TEMP_JSON" << EOF
{
  "contents": [{
    "parts":[
      {"text": "Tell me about this instrument"},
      {
        "inline_data": {
          "mime_type":"image/jpeg",
          "data": "$(cat "$TEMP_B64")"
        }
      }
    ]
  }]
}
EOF

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d "@$TEMP_JSON" 2> /dev/null
text_generation.sh
 
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

val image: Bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.image)
val inputContent = content {
  image(image)
  text("What's in this picture?")
}

val response = generativeModel.generateContent(inputContent)
print(response.text)
text_generation.kt
 
let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

guard let image = UIImage(systemName: "cloud.sun") else { fatalError() }

let prompt = "What's in this picture?"

let response = try await generativeModel.generateContent(image, prompt)
if let text = response.text {
  print(text)
}
TextGeneration.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);

Future<DataPart> fileToPart(String mimeType, String path) async {
  return DataPart(mimeType, await File(path).readAsBytes());
}

final prompt = 'Describe how this product might be manufactured.';
final image = await fileToPart('image/jpeg', 'resources/jetpack.jpg');

final response = await model.generateContent([
  Content.multi([TextPart(prompt), image])
]);
print(response.text);
text_generation.dart
 
// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Bitmap image = BitmapFactory.decodeResource(context.getResources(), R.drawable.image);

Content content =
    new Content.Builder()
        .addText("What's different between these pictures?")
        .addImage(image)
        .build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

ListenableFuture<GenerateContentResponse> response = model.generateContent(content);
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);
text_generation.java
PythonNode.jsShell 
from google import genai

client = genai.Client()
sample_audio = client.files.upload(file=media / "sample.mp3")
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=["Give me a summary of this audio file.", sample_audio],
)
print(response.text)
text_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const audio = await ai.files.upload({
  file: path.join(media, "sample.mp3"),
});

const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: [
    createUserContent([
      "Give me a summary of this audio file.",
      createPartFromUri(audio.uri, audio.mimeType),
    ]),
  ],
});
console.log(response.text);
text_generation.js
 
# Use File API to upload audio data to API request.
MIME_TYPE=$(file -b --mime-type "${AUDIO_PATH}")
NUM_BYTES=$(wc -c < "${AUDIO_PATH}")
DISPLAY_NAME=AUDIO

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${AUDIO_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Please describe this file."},
          {"file_data":{"mime_type": "audio/mpeg", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json
text_generation.sh
PythonNode.jsGoShell 
from google import genai
import time

client = genai.Client()
# Video clip (CC BY 3.0) from https://peach.blender.org/download/
myfile = client.files.upload(file=media / "Big_Buck_Bunny.mp4")
print(f"{myfile=}")

# Poll until the video file is completely processed (state becomes ACTIVE).
while not myfile.state or myfile.state.name != "ACTIVE":
    print("Processing video...")
    print("File state:", myfile.state)
    time.sleep(5)
    myfile = client.files.get(name=myfile.name)

response = client.models.generate_content(
    model="gemini-2.0-flash", contents=[myfile, "Describe this video clip"]
)
print(f"{response.text=}")
text_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

let video = await ai.files.upload({
  file: path.join(media, 'Big_Buck_Bunny.mp4'),
});

// Poll until the video file is completely processed (state becomes ACTIVE).
while (!video.state || video.state.toString() !== 'ACTIVE') {
  console.log('Processing video...');
  console.log('File state: ', video.state);
  await sleep(5000);
  video = await ai.files.get({name: video.name});
}

const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: [
    createUserContent([
      "Describe this video clip",
      createPartFromUri(video.uri, video.mimeType),
    ]),
  ],
});
console.log(response.text);
text_generation.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

file, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "Big_Buck_Bunny.mp4"), 
	&genai.UploadFileConfig{
		MIMEType : "video/mp4",
	},
)
if err != nil {
	log.Fatal(err)
}

// Poll until the video file is completely processed (state becomes ACTIVE).
for file.State == genai.FileStateUnspecified || file.State != genai.FileStateActive {
	fmt.Println("Processing video...")
	fmt.Println("File state:", file.State)
	time.Sleep(5 * time.Second)

	file, err = client.Files.Get(ctx, file.Name, nil)
	if err != nil {
		log.Fatal(err)
	}
}

parts := []*genai.Part{
	genai.NewPartFromText("Describe this video clip"),
	genai.NewPartFromURI(file.URI, file.MIMEType),
}

contents := []*genai.Content{
	genai.NewContentFromParts(parts, "user"),
}

response, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash", contents, nil)
if err != nil {
	log.Fatal(err)
}
printResponse(response)
text_generation.go
 
# Use File API to upload audio data to API request.
MIME_TYPE=$(file -b --mime-type "${VIDEO_PATH}")
NUM_BYTES=$(wc -c < "${VIDEO_PATH}")
DISPLAY_NAME=VIDEO

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D "${tmp_header_file}" \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${VIDEO_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

state=$(jq ".file.state" file_info.json)
echo state=$state

name=$(jq ".file.name" file_info.json)
echo name=$name

while [[ "($state)" = *"PROCESSING"* ]];
do
  echo "Processing video..."
  sleep 5
  # Get the file of interest to check state
  curl https://generativelanguage.googleapis.com/v1beta/files/$name > file_info.json
  state=$(jq ".file.state" file_info.json)
done

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Transcribe the audio from this video, giving timestamps for salient events in the video. Also provide visual descriptions."},
          {"file_data":{"mime_type": "video/mp4", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json
text_generation.sh
PythonShell 
from google import genai

client = genai.Client()
sample_pdf = client.files.upload(file=media / "test.pdf")
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=["Give me a summary of this document:", sample_pdf],
)
print(f"{response.text=}")
text_generation.py
 
MIME_TYPE=$(file -b --mime-type "${PDF_PATH}")
NUM_BYTES=$(wc -c < "${PDF_PATH}")
DISPLAY_NAME=TEXT


echo $MIME_TYPE
tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${PDF_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

# Now generate content using that file
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Can you add a few more lines to this poem?"},
          {"file_data":{"mime_type": "application/pdf", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json
text_generation.sh
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
from google.genai import types

client = genai.Client()
# Pass initial history using the "history" argument
chat = client.chats.create(
    model="gemini-2.0-flash",
    history=[
        types.Content(role="user", parts=[types.Part(text="Hello")]),
        types.Content(
            role="model",
            parts=[
                types.Part(
                    text="Great to meet you. What would you like to know?"
                )
            ],
        ),
    ],
)
response = chat.send_message(message="I have 2 dogs in my house.")
print(response.text)
response = chat.send_message(message="How many paws are in my house?")
print(response.text)
chat.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const chat = ai.chats.create({
  model: "gemini-2.0-flash",
  history: [
    {
      role: "user",
      parts: [{ text: "Hello" }],
    },
    {
      role: "model",
      parts: [{ text: "Great to meet you. What would you like to know?" }],
    },
  ],
});

const response1 = await chat.sendMessage({
  message: "I have 2 dogs in my house.",
});
console.log("Chat response 1:", response1.text);

const response2 = await chat.sendMessage({
  message: "How many paws are in my house?",
});
console.log("Chat response 2:", response2.text);
chat.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

// Pass initial history using the History field.
history := []*genai.Content{
	genai.NewContentFromText("Hello", "user"),
	genai.NewContentFromText("Great to meet you. What would you like to know?", "model"),
}

chat, err := client.Chats.Create(ctx, "gemini-2.0-flash", nil, history)
if err != nil {
	log.Fatal(err)
}

firstResp, err := chat.SendMessage(ctx, genai.Part{Text: "I have 2 dogs in my house."})
if err != nil {
	log.Fatal(err)
}
fmt.Println(firstResp.Text())

secondResp, err := chat.SendMessage(ctx, genai.Part{Text: "How many paws are in my house?"})
if err != nil {
	log.Fatal(err)
}
fmt.Println(secondResp.Text())
chat.go
 
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [
        {"role":"user",
         "parts":[{
           "text": "Hello"}]},
        {"role": "model",
         "parts":[{
           "text": "Great to meet you. What would you like to know?"}]},
        {"role":"user",
         "parts":[{
           "text": "I have two dogs in my house. How many paws are in my house?"}]},
      ]
    }' 2> /dev/null | grep "text"
chat.sh
 
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

val chat =
    generativeModel.startChat(
        history =
            listOf(
                content(role = "user") { text("Hello, I have 2 dogs in my house.") },
                content(role = "model") {
                  text("Great to meet you. What would you like to know?")
                }))

val response = chat.sendMessage("How many paws are in my house?")
print(response.text)
chat.kt
 
let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

// Optionally specify existing chat history
let history = [
  ModelContent(role: "user", parts: "Hello, I have 2 dogs in my house."),
  ModelContent(role: "model", parts: "Great to meet you. What would you like to know?"),
]

// Initialize the chat with optional chat history
let chat = generativeModel.startChat(history: history)

// To generate text output, call sendMessage and pass in the message
let response = try await chat.sendMessage("How many paws are in my house?")
if let text = response.text {
  print(text)
}
ChatSnippets.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final chat = model.startChat(history: [
  Content.text('hello'),
  Content.model([TextPart('Great to meet you. What would you like to know?')])
]);
var response =
    await chat.sendMessage(Content.text('I have 2 dogs in my house.'));
print(response.text);
response =
    await chat.sendMessage(Content.text('How many paws are in my house?'));
print(response.text);
chat.dart
 
// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

// (optional) Create previous chat history for context
Content.Builder userContentBuilder = new Content.Builder();
userContentBuilder.setRole("user");
userContentBuilder.addText("Hello, I have 2 dogs in my house.");
Content userContent = userContentBuilder.build();

Content.Builder modelContentBuilder = new Content.Builder();
modelContentBuilder.setRole("model");
modelContentBuilder.addText("Great to meet you. What would you like to know?");
Content modelContent = userContentBuilder.build();

List<Content> history = Arrays.asList(userContent, modelContent);

// Initialize the chat
ChatFutures chat = model.startChat(history);

// Create a new user message
Content.Builder userMessageBuilder = new Content.Builder();
userMessageBuilder.setRole("user");
userMessageBuilder.addText("How many paws are in my house?");
Content userMessage = userMessageBuilder.build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

// Send the message
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(userMessage);

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);
chat.java
PythonNode.js 
from google import genai
from google.genai import types

client = genai.Client()
document = client.files.upload(file=media / "a11.txt")
model_name = "gemini-1.5-flash-001"

cache = client.caches.create(
    model=model_name,
    config=types.CreateCachedContentConfig(
        contents=[document],
        system_instruction="You are an expert analyzing transcripts.",
    ),
)
print(cache)

response = client.models.generate_content(
    model=model_name,
    contents="Please summarize this transcript",
    config=types.GenerateContentConfig(cached_content=cache.name),
)
print(response.text)
cache.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
  file: filePath,
  config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-1.5-flash-001";

const contents = [
  createUserContent(createPartFromUri(document.uri, document.mimeType)),
];

const cache = await ai.caches.create({
  model: modelName,
  config: {
    contents: contents,
    systemInstruction: "You are an expert analyzing transcripts.",
  },
});
console.log("Cache created:", cache);

const response = await ai.models.generateContent({
  model: modelName,
  contents: "Please summarize this transcript",
  config: { cachedContent: cache.name },
});
console.log("Response text:", response.text);
cache.js
Python 
# With Gemini 2 we're launching a new SDK. See the following doc for details.
# https://ai.google.dev/gemini-api/docs/migrate
README.md
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
from google.genai import types
from typing_extensions import TypedDict

class Recipe(TypedDict):
    recipe_name: str
    ingredients: list[str]

client = genai.Client()
result = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="List a few popular cookie recipes.",
    config=types.GenerateContentConfig(
        response_mime_type="application/json", response_schema=list[Recipe]
    ),
)
print(result)
controlled_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: "List a few popular cookie recipes.",
  config: {
    responseMimeType: "application/json",
    responseSchema: {
      type: "array",
      items: {
        type: "object",
        properties: {
          recipeName: { type: "string" },
          ingredients: { type: "array", items: { type: "string" } },
        },
        required: ["recipeName", "ingredients"],
      },
    },
  },
});
console.log(response.text);
controlled_generation.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"), 
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

schema := &genai.Schema{
	Type: genai.TypeArray,
	Items: &genai.Schema{
		Type: genai.TypeObject,
		Properties: map[string]*genai.Schema{
			"recipe_name": {Type: genai.TypeString},
			"ingredients": {
				Type:  genai.TypeArray,
				Items: &genai.Schema{Type: genai.TypeString},
			},
		},
		Required: []string{"recipe_name"},
	},
}

config := &genai.GenerateContentConfig{
	ResponseMIMEType: "application/json",
	ResponseSchema:   schema,
}

response, err := client.Models.GenerateContent(
	ctx,
	"gemini-2.0-flash",
	genai.Text("List a few popular cookie recipes."),
	config,
)
if err != nil {
	log.Fatal(err)
}
printResponse(response)
controlled_generation.go
 
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "contents": [{
      "parts":[
        {"text": "List 5 popular cookie recipes"}
        ]
    }],
    "generationConfig": {
        "response_mime_type": "application/json",
        "response_schema": {
          "type": "ARRAY",
          "items": {
            "type": "OBJECT",
            "properties": {
              "recipe_name": {"type":"STRING"},
            }
          }
        }
    }
}' 2> /dev/null | head
controlled_generation.sh
 
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-pro",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey,
        generationConfig = generationConfig {
            responseMimeType = "application/json"
            responseSchema = Schema(
                name = "recipes",
                description = "List of recipes",
                type = FunctionType.ARRAY,
                items = Schema(
                    name = "recipe",
                    description = "A recipe",
                    type = FunctionType.OBJECT,
                    properties = mapOf(
                        "recipeName" to Schema(
                            name = "recipeName",
                            description = "Name of the recipe",
                            type = FunctionType.STRING,
                            nullable = false
                        ),
                    ),
                    required = listOf("recipeName")
                ),
            )
        })

val prompt = "List a few popular cookie recipes."
val response = generativeModel.generateContent(prompt)
print(response.text)
controlled_generation.kt
 
let jsonSchema = Schema(
  type: .array,
  description: "List of recipes",
  items: Schema(
    type: .object,
    properties: [
      "recipeName": Schema(type: .string, description: "Name of the recipe", nullable: false),
    ],
    requiredProperties: ["recipeName"]
  )
)

let generativeModel = GenerativeModel(
  // Specify a model that supports controlled generation like Gemini 1.5 Pro
  name: "gemini-1.5-pro",
  // Access your API key from your on-demand resource .plist file (see "Set up your API key"
  // above)
  apiKey: APIKey.default,
  generationConfig: GenerationConfig(
    responseMIMEType: "application/json",
    responseSchema: jsonSchema
  )
)

let prompt = "List a few popular cookie recipes."
let response = try await generativeModel.generateContent(prompt)
if let text = response.text {
  print(text)
}
ControlledGeneration.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final schema = Schema.array(
    description: 'List of recipes',
    items: Schema.object(properties: {
      'recipeName':
          Schema.string(description: 'Name of the recipe.', nullable: false)
    }, requiredProperties: [
      'recipeName'
    ]));

final model = GenerativeModel(
    model: 'gemini-1.5-pro',
    apiKey: apiKey,
    generationConfig: GenerationConfig(
        responseMimeType: 'application/json', responseSchema: schema));

final prompt = 'List a few popular cookie recipes.';
final response = await model.generateContent([Content.text(prompt)]);
print(response.text);
controlled_generation.dart
 
Schema<List<String>> schema =
    new Schema(
        /* name */ "recipes",
        /* description */ "List of recipes",
        /* format */ null,
        /* nullable */ false,
        /* list */ null,
        /* properties */ null,
        /* required */ null,
        /* items */ new Schema(
            /* name */ "recipe",
            /* description */ "A recipe",
            /* format */ null,
            /* nullable */ false,
            /* list */ null,
            /* properties */ Map.of(
                "recipeName",
                new Schema(
                    /* name */ "recipeName",
                    /* description */ "Name of the recipe",
                    /* format */ null,
                    /* nullable */ false,
                    /* list */ null,
                    /* properties */ null,
                    /* required */ null,
                    /* items */ null,
                    /* type */ FunctionType.STRING)),
            /* required */ null,
            /* items */ null,
            /* type */ FunctionType.OBJECT),
        /* type */ FunctionType.ARRAY);

GenerationConfig.Builder configBuilder = new GenerationConfig.Builder();
configBuilder.responseMimeType = "application/json";
configBuilder.responseSchema = schema;

GenerationConfig generationConfig = configBuilder.build();

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-pro",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey,
        /* generationConfig */ generationConfig);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content content = new Content.Builder().addText("List a few popular cookie recipes.").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

ListenableFuture<GenerateContentResponse> response = model.generateContent(content);
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);
controlled_generation.java
PythonKotlinJava 
from google import genai
from google.genai import types

client = genai.Client()
response = client.models.generate_content(
    model="gemini-2.0-pro-exp-02-05",
    contents=(
        "Write and execute code that calculates the sum of the first 50 prime numbers. "
        "Ensure that only the executable code and its resulting output are generated."
    ),
)
# Each part may contain text, executable code, or an execution result.
for part in response.candidates[0].content.parts:
    print(part, "\n")

print("-" * 80)
# The .text accessor concatenates the parts into a markdown-formatted text.
print("\n", response.text)
code_execution.py
 

val model = GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    modelName = "gemini-1.5-pro",
    // Access your API key as a Build Configuration variable (see "Set up your API key" above)
    apiKey = BuildConfig.apiKey,
    tools = listOf(Tool.CODE_EXECUTION)
)

val response = model.generateContent("What is the sum of the first 50 prime numbers?")

// Each `part` either contains `text`, `executable_code` or an `execution_result`
println(response.candidates[0].content.parts.joinToString("\n"))

// Alternatively, you can use the `text` accessor which joins the parts into a markdown compatible
// text representation
println(response.text)
code_execution.kt
 
// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
        new GenerativeModel(
                /* modelName */ "gemini-1.5-pro",
                // Access your API key as a Build Configuration variable (see "Set up your API key"
                // above)
                /* apiKey */ BuildConfig.apiKey,
                /* generationConfig */ null,
                /* safetySettings */ null,
                /* requestOptions */ new RequestOptions(),
                /* tools */ Collections.singletonList(Tool.CODE_EXECUTION));
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content inputContent =
        new Content.Builder().addText("What is the sum of the first 50 prime numbers?").build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

ListenableFuture<GenerateContentResponse> response = model.generateContent(inputContent);
Futures.addCallback(
        response,
        new FutureCallback<GenerateContentResponse>() {
            @Override
            public void onSuccess(GenerateContentResponse result) {
                // Each `part` either contains `text`, `executable_code` or an
                // `execution_result`
                Candidate candidate = result.getCandidates().get(0);
                for (Part part : candidate.getContent().getParts()) {
                    System.out.println(part);
                }

                // Alternatively, you can use the `text` accessor which joins the parts into a
                // markdown compatible text representation
                String resultText = result.getText();
                System.out.println(resultText);
            }

            @Override
            public void onFailure(Throwable t) {
                t.printStackTrace();
            }
        },
        executor);
code_execution.java
PythonNode.jsShellKotlinSwiftDartJava 
from google import genai
from google.genai import types

client = genai.Client()

def add(a: float, b: float) -> float:
    """returns a + b."""
    return a + b

def subtract(a: float, b: float) -> float:
    """returns a - b."""
    return a - b

def multiply(a: float, b: float) -> float:
    """returns a * b."""
    return a * b

def divide(a: float, b: float) -> float:
    """returns a / b."""
    return a / b

# Create a chat session; function calling (via tools) is enabled in the config.
chat = client.chats.create(
    model="gemini-2.0-flash",
    config=types.GenerateContentConfig(tools=[add, subtract, multiply, divide]),
)
response = chat.send_message(
    message="I have 57 cats, each owns 44 mittens, how many mittens is that in total?"
)
print(response.text)
function_calling.py
 
  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

  /**
   * The add function returns the sum of two numbers.
   * @param {number} a
   * @param {number} b
   * @returns {number}
   */
  function add(a, b) {
    return a + b;
  }

  /**
   * The subtract function returns the difference (a - b).
   * @param {number} a
   * @param {number} b
   * @returns {number}
   */
  function subtract(a, b) {
    return a - b;
  }

  /**
   * The multiply function returns the product of two numbers.
   * @param {number} a
   * @param {number} b
   * @returns {number}
   */
  function multiply(a, b) {
    return a * b;
  }

  /**
   * The divide function returns the quotient of a divided by b.
   * @param {number} a
   * @param {number} b
   * @returns {number}
   */
  function divide(a, b) {
    return a / b;
  }

  const addDeclaration = {
    name: "addNumbers",
    parameters: {
      type: "object",
      description: "Return the result of adding two numbers.",
      properties: {
        firstParam: {
          type: "number",
          description:
            "The first parameter which can be an integer or a floating point number.",
        },
        secondParam: {
          type: "number",
          description:
            "The second parameter which can be an integer or a floating point number.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const subtractDeclaration = {
    name: "subtractNumbers",
    parameters: {
      type: "object",
      description:
        "Return the result of subtracting the second number from the first.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const multiplyDeclaration = {
    name: "multiplyNumbers",
    parameters: {
      type: "object",
      description: "Return the product of two numbers.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  const divideDeclaration = {
    name: "divideNumbers",
    parameters: {
      type: "object",
      description:
        "Return the quotient of dividing the first number by the second.",
      properties: {
        firstParam: {
          type: "number",
          description: "The first parameter.",
        },
        secondParam: {
          type: "number",
          description: "The second parameter.",
        },
      },
      required: ["firstParam", "secondParam"],
    },
  };

  // Step 1: Call generateContent with function calling enabled.
  const generateContentResponse = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents:
      "I have 57 cats, each owns 44 mittens, how many mittens is that in total?",
    config: {
      toolConfig: {
        functionCallingConfig: {
          mode: FunctionCallingConfigMode.ANY,
        },
      },
      tools: [
        {
          functionDeclarations: [
            addDeclaration,
            subtractDeclaration,
            multiplyDeclaration,
            divideDeclaration,
          ],
        },
      ],
    },
  });

  // Step 2: Extract the function call.(
  // Assuming the response contains a 'functionCalls' array.
  const functionCall =
    generateContentResponse.functionCalls &&
    generateContentResponse.functionCalls[0];
  console.log(functionCall);

  // Parse the arguments.
  const args = functionCall.args;
  // Expected args format: { firstParam: number, secondParam: number }

  // Step 3: Invoke the actual function based on the function name.
  const functionMapping = {
    addNumbers: add,
    subtractNumbers: subtract,
    multiplyNumbers: multiply,
    divideNumbers: divide,
  };
  const func = functionMapping[functionCall.name];
  if (!func) {
    console.error("Unimplemented error:", functionCall.name);
    return generateContentResponse;
  }
  const resultValue = func(args.firstParam, args.secondParam);
  console.log("Function result:", resultValue);

  // Step 4: Use the chat API to send the result as the final answer.
  const chat = ai.chats.create({ model: "gemini-2.0-flash" });
  const chatResponse = await chat.sendMessage({
    message: "The final result is " + resultValue,
  });
  console.log(chatResponse.text);
  return chatResponse;
}

function_calling.js
 

cat > tools.json << EOF
{
  "function_declarations": [
    {
      "name": "enable_lights",
      "description": "Turn on the lighting system."
    },
    {
      "name": "set_light_color",
      "description": "Set the light color. Lights must be enabled for this to work.",
      "parameters": {
        "type": "object",
        "properties": {
          "rgb_hex": {
            "type": "string",
            "description": "The light color as a 6-digit hex string, e.g. ff0000 for red."
          }
        },
        "required": [
          "rgb_hex"
        ]
      }
    },
    {
      "name": "stop_lights",
      "description": "Turn off the lighting system."
    }
  ]
} 
EOF

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d @<(echo '
  {
    "system_instruction": {
      "parts": {
        "text": "You are a helpful lighting system bot. You can turn lights on and off, and you can set the color. Do not perform any other tasks."
      }
    },
    "tools": ['$(cat tools.json)'],

    "tool_config": {
      "function_calling_config": {"mode": "auto"}
    },

    "contents": {
      "role": "user",
      "parts": {
        "text": "Turn on the lights please."
      }
    }
  }
') 2>/dev/null |sed -n '/"content"/,/"finishReason"/p'
function_calling.sh
 
fun multiply(a: Double, b: Double) = a * b

val multiplyDefinition = defineFunction(
    name = "multiply",
    description = "returns the product of the provided numbers.",
    parameters = listOf(
    Schema.double("a", "First number"),
    Schema.double("b", "Second number")
    )
)

val usableFunctions = listOf(multiplyDefinition)

val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey,
        // List the functions definitions you want to make available to the model
        tools = listOf(Tool(usableFunctions))
    )

val chat = generativeModel.startChat()
val prompt = "I have 57 cats, each owns 44 mittens, how many mittens is that in total?"

// Send the message to the generative model
var response = chat.sendMessage(prompt)

// Check if the model responded with a function call
response.functionCalls.first { it.name == "multiply" }.apply {
    val a: String by args
    val b: String by args

    val result = JSONObject(mapOf("result" to multiply(a.toDouble(), b.toDouble())))
    response = chat.sendMessage(
        content(role = "function") {
            part(FunctionResponsePart("multiply", result))
        }
    )
}

// Whenever the model responds with text, show it in the UI
response.text?.let { modelResponse ->
    println(modelResponse)
}
function_calling.kt
 
// Calls a hypothetical API to control a light bulb and returns the values that were set.
func controlLight(brightness: Double, colorTemperature: String) -> JSONObject {
  return ["brightness": .number(brightness), "colorTemperature": .string(colorTemperature)]
}

let generativeModel =
  GenerativeModel(
    // Use a model that supports function calling, like a Gemini 1.5 model
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default,
    tools: [Tool(functionDeclarations: [
      FunctionDeclaration(
        name: "controlLight",
        description: "Set the brightness and color temperature of a room light.",
        parameters: [
          "brightness": Schema(
            type: .number,
            format: "double",
            description: "Light level from 0 to 100. Zero is off and 100 is full brightness."
          ),
          "colorTemperature": Schema(
            type: .string,
            format: "enum",
            description: "Color temperature of the light fixture.",
            enumValues: ["daylight", "cool", "warm"]
          ),
        ],
        requiredParameters: ["brightness", "colorTemperature"]
      ),
    ])]
  )

let chat = generativeModel.startChat()

let prompt = "Dim the lights so the room feels cozy and warm."

// Send the message to the model.
let response1 = try await chat.sendMessage(prompt)

// Check if the model responded with a function call.
// For simplicity, this sample uses the first function call found.
guard let functionCall = response1.functionCalls.first else {
  fatalError("Model did not respond with a function call.")
}
// Print an error if the returned function was not declared
guard functionCall.name == "controlLight" else {
  fatalError("Unexpected function called: \(functionCall.name)")
}
// Verify that the names and types of the parameters match the declaration
guard case let .number(brightness) = functionCall.args["brightness"] else {
  fatalError("Missing argument: brightness")
}
guard case let .string(colorTemperature) = functionCall.args["colorTemperature"] else {
  fatalError("Missing argument: colorTemperature")
}

// Call the executable function named in the FunctionCall with the arguments specified in the
// FunctionCall and let it call the hypothetical API.
let apiResponse = controlLight(brightness: brightness, colorTemperature: colorTemperature)

// Send the API response back to the model so it can generate a text response that can be
// displayed to the user.
let response2 = try await chat.sendMessage([ModelContent(
  role: "function",
  parts: [.functionResponse(FunctionResponse(name: "controlLight", response: apiResponse))]
)])

if let text = response2.text {
  print(text)
}
FunctionCalling.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
Map<String, Object?> setLightValues(Map<String, Object?> args) {
  return args;
}

final controlLightFunction = FunctionDeclaration(
    'controlLight',
    'Set the brightness and color temperature of a room light.',
    Schema.object(properties: {
      'brightness': Schema.number(
          description:
              'Light level from 0 to 100. Zero is off and 100 is full brightness.',
          nullable: false),
      'colorTemperatur': Schema.string(
          description:
              'Color temperature of the light fixture which can be `daylight`, `cool`, or `warm`',
          nullable: false),
    }));

final functions = {controlLightFunction.name: setLightValues};
FunctionResponse dispatchFunctionCall(FunctionCall call) {
  final function = functions[call.name]!;
  final result = function(call.args);
  return FunctionResponse(call.name, result);
}

final model = GenerativeModel(
  model: 'gemini-1.5-pro',
  apiKey: apiKey,
  tools: [
    Tool(functionDeclarations: [controlLightFunction])
  ],
);

final prompt = 'Dim the lights so the room feels cozy and warm.';
final content = [Content.text(prompt)];
var response = await model.generateContent(content);

List<FunctionCall> functionCalls;
while ((functionCalls = response.functionCalls.toList()).isNotEmpty) {
  var responses = <FunctionResponse>[
    for (final functionCall in functionCalls)
      dispatchFunctionCall(functionCall)
  ];
  content
    ..add(response.candidates.first.content)
    ..add(Content.functionResponses(responses));
  response = await model.generateContent(content);
}
print('Response: ${response.text}');
function_calling.dart
 
FunctionDeclaration multiplyDefinition =
    defineFunction(
        /* name  */ "multiply",
        /* description */ "returns a * b.",
        /* parameters */ Arrays.asList(
            Schema.numDouble("a", "First parameter"),
            Schema.numDouble("b", "Second parameter")),
        /* required */ Arrays.asList("a", "b"));

Tool tool = new Tool(Arrays.asList(multiplyDefinition), null);

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey,
        /* generationConfig (optional) */ null,
        /* safetySettings (optional) */ null,
        /* requestOptions (optional) */ new RequestOptions(),
        /* functionDeclarations (optional) */ Arrays.asList(tool));
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

// Create prompt
Content.Builder userContentBuilder = new Content.Builder();
userContentBuilder.setRole("user");
userContentBuilder.addText(
    "I have 57 cats, each owns 44 mittens, how many mittens is that in total?");
Content userMessage = userContentBuilder.build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

// Initialize the chat
ChatFutures chat = model.startChat();

// Send the message
ListenableFuture<GenerateContentResponse> response = chat.sendMessage(userMessage);

Futures.addCallback(
    response,
    new FutureCallback<GenerateContentResponse>() {
      @Override
      public void onSuccess(GenerateContentResponse result) {
        if (!result.getFunctionCalls().isEmpty()) {
          handleFunctionCall(result);
        }
        if (!result.getText().isEmpty()) {
          System.out.println(result.getText());
        }
      }

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

      private void handleFunctionCall(GenerateContentResponse result) {
        FunctionCallPart multiplyFunctionCallPart =
            result.getFunctionCalls().stream()
                .filter(fun -> fun.getName().equals("multiply"))
                .findFirst()
                .get();
        double a = Double.parseDouble(multiplyFunctionCallPart.getArgs().get("a"));
        double b = Double.parseDouble(multiplyFunctionCallPart.getArgs().get("b"));

        try {
          // `multiply(a, b)` is a regular java function defined in another class
          FunctionResponsePart functionResponsePart =
              new FunctionResponsePart(
                  "multiply", new JSONObject().put("result", multiply(a, b)));

          // Create prompt
          Content.Builder functionCallResponse = new Content.Builder();
          userContentBuilder.setRole("user");
          userContentBuilder.addPart(functionResponsePart);
          Content userMessage = userContentBuilder.build();

          chat.sendMessage(userMessage);
        } catch (JSONException e) {
          throw new RuntimeException(e);
        }
      }
    },
    executor);

function_calling.java
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
from google.genai import types

client = genai.Client()
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Tell me a story about a magic backpack.",
    config=types.GenerateContentConfig(
        candidate_count=1,
        stop_sequences=["x"],
        max_output_tokens=20,
        temperature=1.0,
    ),
)
print(response.text)
configure_model_parameters.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: "Tell me a story about a magic backpack.",
  config: {
    candidateCount: 1,
    stopSequences: ["x"],
    maxOutputTokens: 20,
    temperature: 1.0,
  },
});

console.log(response.text);
configure_model_parameters.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

// Create local variables for parameters.
candidateCount := int32(1)
maxOutputTokens := int32(20)
temperature := float32(1.0)

response, err := client.Models.GenerateContent(
	ctx,
	"gemini-2.0-flash",
	genai.Text("Tell me a story about a magic backpack."),
	&genai.GenerateContentConfig{
		CandidateCount:  candidateCount,
		StopSequences:   []string{"x"},
		MaxOutputTokens: maxOutputTokens,
		Temperature:     &temperature,
	},
)
if err != nil {
	log.Fatal(err)
}

printResponse(response)
configure_model_parameters.go
 
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
        "contents": [{
            "parts":[
                {"text": "Explain how AI works"}
            ]
        }],
        "generationConfig": {
            "stopSequences": [
                "Title"
            ],
            "temperature": 1.0,
            "maxOutputTokens": 800,
            "topP": 0.8,
            "topK": 10
        }
    }'  2> /dev/null | grep "text"
configure_model_parameters.sh
 
val config = generationConfig {
  temperature = 0.9f
  topK = 16
  topP = 0.1f
  maxOutputTokens = 200
  stopSequences = listOf("red")
}

val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        apiKey = BuildConfig.apiKey,
        generationConfig = config)
configure_model_parameters.kt
 
let config = GenerationConfig(
  temperature: 0.9,
  topP: 0.1,
  topK: 16,
  candidateCount: 1,
  maxOutputTokens: 200,
  stopSequences: ["red", "orange"]
)

let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default,
    generationConfig: config
  )
GenerationConfig.swift
 
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final prompt = 'Tell me a story about a magic backpack.';

final response = await model.generateContent(
  [Content.text(prompt)],
  generationConfig: GenerationConfig(
    candidateCount: 1,
    stopSequences: ['x'],
    maxOutputTokens: 20,
    temperature: 1.0,
  ),
);
print(response.text);
configure_model_parameters.dart
 
GenerationConfig.Builder configBuilder = new GenerationConfig.Builder();
configBuilder.temperature = 0.9f;
configBuilder.topK = 16;
configBuilder.topP = 0.1f;
configBuilder.maxOutputTokens = 200;
configBuilder.stopSequences = Arrays.asList("red");

GenerationConfig generationConfig = configBuilder.build();

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel("gemini-1.5-flash", BuildConfig.apiKey, generationConfig);

GenerativeModelFutures model = GenerativeModelFutures.from(gm);
configure_model_parameters.java
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
from google.genai import types

client = genai.Client()
unsafe_prompt = (
    "I support Martians Soccer Club and I think Jupiterians Football Club sucks! "
    "Write a ironic phrase about them including expletives."
)
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=unsafe_prompt,
    config=types.GenerateContentConfig(
        safety_settings=[
            types.SafetySetting(
                category="HARM_CATEGORY_HATE_SPEECH",
                threshold="BLOCK_MEDIUM_AND_ABOVE",
            ),
            types.SafetySetting(
                category="HARM_CATEGORY_HARASSMENT", threshold="BLOCK_ONLY_HIGH"
            ),
        ]
    ),
)
try:
    print(response.text)
except Exception:
    print("No information generated by the model.")

print(response.candidates[0].safety_ratings)
safety_settings.py
 
  // Make sure to include the following import:
  // import {GoogleGenAI} from '@google/genai';
  const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
  const unsafePrompt =
    "I support Martians Soccer Club and I think Jupiterians Football Club sucks! Write a ironic phrase about them including expletives.";

  const response = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents: unsafePrompt,
    config: {
      safetySettings: [
        {
          category: "HARM_CATEGORY_HATE_SPEECH",
          threshold: "BLOCK_MEDIUM_AND_ABOVE",
        },
        {
          category: "HARM_CATEGORY_HARASSMENT",
          threshold: "BLOCK_ONLY_HIGH",
        },
      ],
    },
  });

  try {
    console.log("Generated text:", response.text);
  } catch (error) {
    console.log("No information generated by the model.");
  }
  console.log("Safety ratings:", response.candidates[0].safetyRatings);
  return response;
}

safety_settings.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

unsafePrompt := "I support Martians Soccer Club and I think Jupiterians Football Club sucks! " +
	"Write a ironic phrase about them including expletives."

config := &genai.GenerateContentConfig{
	SafetySettings: []*genai.SafetySetting{
		{
			Category:  "HARM_CATEGORY_HATE_SPEECH",
			Threshold: "BLOCK_MEDIUM_AND_ABOVE",
		},
		{
			Category:  "HARM_CATEGORY_HARASSMENT",
			Threshold: "BLOCK_ONLY_HIGH",
		},
	},
}
contents := []*genai.Content{
	genai.NewContentFromText(unsafePrompt, "user"),
}
response, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash", contents, config)
if err != nil {
	log.Fatal(err)
}

// Print the generated text.
text := response.Text()
fmt.Println("Generated text:", text)

// Print the and safety ratings from the first candidate.
if len(response.Candidates) > 0 {
	fmt.Println("Finish reason:", response.Candidates[0].FinishReason)
	safetyRatings, err := json.MarshalIndent(response.Candidates[0].SafetyRatings, "", "  ")
	if err != nil {
		return err
	}
	fmt.Println("Safety ratings:", string(safetyRatings))
} else {
	fmt.Println("No candidate returned.")
}
safety_settings.go
 
echo '{
    "safetySettings": [
        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_ONLY_HIGH"},
        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}
    ],
    "contents": [{
        "parts":[{
            "text": "'I support Martians Soccer Club and I think Jupiterians Football Club sucks! Write a ironic phrase about them.'"}]}]}' > request.json

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d @request.json 2> /dev/null
safety_settings.sh
 
val harassmentSafety = SafetySetting(HarmCategory.HARASSMENT, BlockThreshold.ONLY_HIGH)

val hateSpeechSafety = SafetySetting(HarmCategory.HATE_SPEECH, BlockThreshold.MEDIUM_AND_ABOVE)

val generativeModel =
    GenerativeModel(
        // The Gemini 1.5 models are versatile and work with most use cases
        modelName = "gemini-1.5-flash",
        apiKey = BuildConfig.apiKey,
        safetySettings = listOf(harassmentSafety, hateSpeechSafety))
safety_settings.kt
 
let safetySettings = [
  SafetySetting(harmCategory: .dangerousContent, threshold: .blockLowAndAbove),
  SafetySetting(harmCategory: .harassment, threshold: .blockMediumAndAbove),
  SafetySetting(harmCategory: .hateSpeech, threshold: .blockOnlyHigh),
]

let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default,
    safetySettings: safetySettings
  )
SafetySettings.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final prompt = 'I support Martians Soccer Club and I think '
    'Jupiterians Football Club sucks! Write an ironic phrase telling '
    'them how I feel about them.';

final response = await model.generateContent(
  [Content.text(prompt)],
  safetySettings: [
    SafetySetting(HarmCategory.harassment, HarmBlockThreshold.medium),
    SafetySetting(HarmCategory.hateSpeech, HarmBlockThreshold.low),
  ],
);
try {
  print(response.text);
} catch (e) {
  print(e);
  for (final SafetyRating(:category, :probability)
      in response.candidates.first.safetyRatings!) {
    print('Safety Rating: $category - $probability');
  }
}
safety_settings.dart
 
SafetySetting harassmentSafety =
    new SafetySetting(HarmCategory.HARASSMENT, BlockThreshold.ONLY_HIGH);

SafetySetting hateSpeechSafety =
    new SafetySetting(HarmCategory.HATE_SPEECH, BlockThreshold.MEDIUM_AND_ABOVE);

// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        "gemini-1.5-flash",
        BuildConfig.apiKey,
        null, // generation config is optional
        Arrays.asList(harassmentSafety, hateSpeechSafety));

GenerativeModelFutures model = GenerativeModelFutures.from(gm);
safety_settings.java
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
from google.genai import types

client = genai.Client()
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Good morning! How are you?",
    config=types.GenerateContentConfig(
        system_instruction="You are a cat. Your name is Neko."
    ),
)
print(response.text)
system_instruction.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: "Good morning! How are you?",
  config: {
    systemInstruction: "You are a cat. Your name is Neko.",
  },
});
console.log(response.text);
system_instruction.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

// Construct the user message contents.
contents := []*genai.Content{
	genai.NewContentFromText("Good morning! How are you?", "user"),
}

// Set the system instruction as a *genai.Content.
config := &genai.GenerateContentConfig{
	SystemInstruction: genai.NewContentFromText("You are a cat. Your name is Neko.", "user"),
}

response, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash", contents, config)
if err != nil {
	log.Fatal(err)
}
printResponse(response)
system_instruction.go
 
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{ "system_instruction": {
    "parts":
      { "text": "You are a cat. Your name is Neko."}},
    "contents": {
      "parts": {
        "text": "Hello there"}}}'
system_instruction.sh
 
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        apiKey = BuildConfig.apiKey,
        systemInstruction = content { text("You are a cat. Your name is Neko.") },
    )
system_instruction.kt
 
let generativeModel =
  GenerativeModel(
    // Specify a model that supports system instructions, like a Gemini 1.5 model
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default,
    systemInstruction: ModelContent(role: "system", parts: "You are a cat. Your name is Neko.")
  )
SystemInstructions.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
  systemInstruction: Content.system('You are a cat. Your name is Neko.'),
);
final prompt = 'Good morning! How are you?';

final response = await model.generateContent([Content.text(prompt)]);
print(response.text);
system_instructions.dart
 
GenerativeModel model =
    new GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        /* modelName */ "gemini-1.5-flash",
        /* apiKey */ BuildConfig.apiKey,
        /* generationConfig (optional) */ null,
        /* safetySettings (optional) */ null,
        /* requestOptions (optional) */ new RequestOptions(),
        /* tools (optional) */ null,
        /* toolsConfig (optional) */ null,
        /* systemInstruction (optional) */ new Content.Builder()
            .addText("You are a cat. Your name is Neko.")
            .build());
system_instruction.java

响应正文

如果成功，则响应正文包含一个 GenerateContentResponse 实例。

方法：models.streamGenerateContent

在给定输入 GenerateContentRequest 的情况下，从模型生成流式回答。

端点

帖子 https://generativelanguage.googleapis.com/v1beta/{model=models/*}:streamGenerateContent

路径参数

model string

必需。用于生成补全项的 Model 的名称。

格式：models/{model}。其格式为 models/{model}。

请求正文

请求正文中包含结构如下的数据：

字段
contents[] object (Content)

必需。与模型当前对话的内容。

对于单轮查询，这是单个实例。对于聊天等多轮查询，这是包含对话历史记录和最新请求的重复字段。

tools[] object (Tool)

可选。Model 可能用于生成下一个响应的 Tools 列表。

Tool 是一段代码，可让系统与外部系统进行交互，以在 Model 的知识和范围之外执行操作或一组操作。支持的 Tool 包括 Function 和 codeExecution。如需了解详情，请参阅函数调用和代码执行指南。

toolConfig object (ToolConfig)

可选。请求中指定的任何 Tool 的工具配置。如需查看使用示例，请参阅函数调用指南。

safetySettings[] object (SafetySetting)

可选。用于屏蔽不安全内容的唯一 SafetySetting 实例列表。

这将在 GenerateContentRequest.contents 和 GenerateContentResponse.candidates 上强制执行。每个 SafetyCategory 类型不应有多个设置。API 会屏蔽未达到这些设置设定的阈值的所有内容和响应。此列表会替换 safetySettings 中指定的每个 SafetyCategory 的默认设置。如果列表中提供的给定 SafetyCategory 没有 SafetySetting，则该 API 将使用该类别的默认安全设置。支持的危害类别包括 HARM_CATEGORY_HATE_SPEECH、HARM_CATEGORY_SEXUALLY_EXPLICIT、HARM_CATEGORY_DANGEROUS_CONTENT、HARM_CATEGORY_HARASSMENT、HARM_CATEGORY_CIVIC_INTEGRITY。如需详细了解可用的安全设置，请参阅指南。此外，请参阅安全指南，了解如何在 AI 应用中纳入安全注意事项。

systemInstruction object (Content)

可选。开发者设置了系统指令。目前仅支持文本。

generationConfig object (GenerationConfig)

可选。模型生成和输出的配置选项。

cachedContent string

可选。缓存的内容的名称，用于用作提供预测的上下文。格式：cachedContents/{cachedContent}

示例请求

文本图片音频视频PDF聊天
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai

client = genai.Client()
response = client.models.generate_content_stream(
    model="gemini-2.0-flash", contents="Write a story about a magic backpack."
)
for chunk in response:
    print(chunk.text)
    print("_" * 80)
text_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContentStream({
  model: "gemini-2.0-flash",
  contents: "Write a story about a magic backpack.",
});
let text = "";
for await (const chunk of response) {
  console.log(chunk.text);
  text += chunk.text;
}
text_generation.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}
contents := []*genai.Content{
	genai.NewContentFromText("Write a story about a magic backpack.", "user"),
}
for response, err := range client.Models.GenerateContentStream(
	ctx,
	"gemini-2.0-flash",
	contents,
	nil,
) {
	if err != nil {
		log.Fatal(err)
	}
	fmt.Print(response.Candidates[0].Content.Parts[0].Text)
}
text_generation.go
 
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=${GEMINI_API_KEY}" \
        -H 'Content-Type: application/json' \
        --no-buffer \
        -d '{ "contents":[{"parts":[{"text": "Write a story about a magic backpack."}]}]}'
text_generation.sh
 
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

val prompt = "Write a story about a magic backpack."
// Use streaming with text-only input
generativeModel.generateContentStream(prompt).collect { chunk -> print(chunk.text) }

text_generation.kt
 
let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

let prompt = "Write a story about a magic backpack."
// Use streaming with text-only input
for try await response in generativeModel.generateContentStream(prompt) {
  if let text = response.text {
    print(text)
  }
}
TextGeneration.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final prompt = 'Write a story about a magic backpack.';

final responses = model.generateContentStream([Content.text(prompt)]);
await for (final response in responses) {
  print(response.text);
}
text_generation.dart
 
// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Content content =
    new Content.Builder().addText("Write a story about a magic backpack.").build();

Publisher<GenerateContentResponse> streamingResponse = model.generateContentStream(content);

StringBuilder outputContent = new StringBuilder();

streamingResponse.subscribe(
    new Subscriber<GenerateContentResponse>() {
      @Override
      public void onNext(GenerateContentResponse generateContentResponse) {
        String chunk = generateContentResponse.getText();
        outputContent.append(chunk);
      }

      @Override
      public void onComplete() {
        System.out.println(outputContent);
      }

      @Override
      public void onError(Throwable t) {
        t.printStackTrace();
      }

      @Override
      public void onSubscribe(Subscription s) {
        s.request(Long.MAX_VALUE);
      }
    });
text_generation.java
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
import PIL.Image

client = genai.Client()
organ = PIL.Image.open(media / "organ.jpg")
response = client.models.generate_content_stream(
    model="gemini-2.0-flash", contents=["Tell me about this instrument", organ]
)
for chunk in response:
    print(chunk.text)
    print("_" * 80)
text_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const organ = await ai.files.upload({
  file: path.join(media, "organ.jpg"),
});

const response = await ai.models.generateContentStream({
  model: "gemini-2.0-flash",
  contents: [
    createUserContent([
      "Tell me about this instrument", 
      createPartFromUri(organ.uri, organ.mimeType)
    ]),
  ],
});
let text = "";
for await (const chunk of response) {
  console.log(chunk.text);
  text += chunk.text;
}
text_generation.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}
file, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "organ.jpg"), 
	&genai.UploadFileConfig{
		MIMEType : "image/jpeg",
	},
)
if err != nil {
	log.Fatal(err)
}
parts := []*genai.Part{
	genai.NewPartFromText("Tell me about this instrument"),
	genai.NewPartFromURI(file.URI, file.MIMEType),
}
contents := []*genai.Content{
	genai.NewContentFromParts(parts, "user"),
}
for response, err := range client.Models.GenerateContentStream(
	ctx,
	"gemini-2.0-flash",
	contents,
	nil,
) {
	if err != nil {
		log.Fatal(err)
	}
	fmt.Print(response.Candidates[0].Content.Parts[0].Text)
}
text_generation.go
 
cat > "$TEMP_JSON" << EOF
{
  "contents": [{
    "parts":[
      {"text": "Tell me about this instrument"},
      {
        "inline_data": {
          "mime_type":"image/jpeg",
          "data": "$(cat "$TEMP_B64")"
        }
      }
    ]
  }]
}
EOF

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d "@$TEMP_JSON" 2> /dev/null
text_generation.sh
 
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

val image: Bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.image)
val inputContent = content {
  image(image)
  text("What's in this picture?")
}

generativeModel.generateContentStream(inputContent).collect { chunk -> print(chunk.text) }
text_generation.kt
 
let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

guard let image = UIImage(systemName: "cloud.sun") else { fatalError() }

let prompt = "What's in this picture?"

for try await response in generativeModel.generateContentStream(image, prompt) {
  if let text = response.text {
    print(text)
  }
}
TextGeneration.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);

Future<DataPart> fileToPart(String mimeType, String path) async {
  return DataPart(mimeType, await File(path).readAsBytes());
}

final prompt = 'Describe how this product might be manufactured.';
final image = await fileToPart('image/jpeg', 'resources/jetpack.jpg');

final responses = model.generateContentStream([
  Content.multi([TextPart(prompt), image])
]);
await for (final response in responses) {
  print(response.text);
}
text_generation.dart
 
// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

Bitmap image1 = BitmapFactory.decodeResource(context.getResources(), R.drawable.image1);
Bitmap image2 = BitmapFactory.decodeResource(context.getResources(), R.drawable.image2);

Content content =
    new Content.Builder()
        .addText("What's different between these pictures?")
        .addImage(image1)
        .addImage(image2)
        .build();

// For illustrative purposes only. You should use an executor that fits your needs.
Executor executor = Executors.newSingleThreadExecutor();

Publisher<GenerateContentResponse> streamingResponse = model.generateContentStream(content);

StringBuilder outputContent = new StringBuilder();

streamingResponse.subscribe(
    new Subscriber<GenerateContentResponse>() {
      @Override
      public void onNext(GenerateContentResponse generateContentResponse) {
        String chunk = generateContentResponse.getText();
        outputContent.append(chunk);
      }

      @Override
      public void onComplete() {
        System.out.println(outputContent);
      }

      @Override
      public void onError(Throwable t) {
        t.printStackTrace();
      }

      @Override
      public void onSubscribe(Subscription s) {
        s.request(Long.MAX_VALUE);
      }
    });
text_generation.java
PythonShell 
from google import genai

client = genai.Client()
sample_audio = client.files.upload(file=media / "sample.mp3")
response = client.models.generate_content_stream(
    model="gemini-2.0-flash",
    contents=["Give me a summary of this audio file.", sample_audio],
)
for chunk in response:
    print(chunk.text)
    print("_" * 80)
text_generation.py
 
# Use File API to upload audio data to API request.
MIME_TYPE=$(file -b --mime-type "${AUDIO_PATH}")
NUM_BYTES=$(wc -c < "${AUDIO_PATH}")
DISPLAY_NAME=AUDIO

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${AUDIO_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Please describe this file."},
          {"file_data":{"mime_type": "audio/mpeg", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo
text_generation.sh
PythonNode.jsGoShell 
from google import genai
import time

client = genai.Client()
# Video clip (CC BY 3.0) from https://peach.blender.org/download/
myfile = client.files.upload(file=media / "Big_Buck_Bunny.mp4")
print(f"{myfile=}")

# Poll until the video file is completely processed (state becomes ACTIVE).
while not myfile.state or myfile.state.name != "ACTIVE":
    print("Processing video...")
    print("File state:", myfile.state)
    time.sleep(5)
    myfile = client.files.get(name=myfile.name)

response = client.models.generate_content_stream(
    model="gemini-2.0-flash", contents=[myfile, "Describe this video clip"]
)
for chunk in response:
    print(chunk.text)
    print("_" * 80)
text_generation.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

let video = await ai.files.upload({
  file: path.join(media, 'Big_Buck_Bunny.mp4'),
});

// Poll until the video file is completely processed (state becomes ACTIVE).
while (!video.state || video.state.toString() !== 'ACTIVE') {
  console.log('Processing video...');
  console.log('File state: ', video.state);
  await sleep(5000);
  video = await ai.files.get({name: video.name});
}

const response = await ai.models.generateContentStream({
  model: "gemini-2.0-flash",
  contents: [
    createUserContent([
      "Describe this video clip",
      createPartFromUri(video.uri, video.mimeType),
    ]),
  ],
});
let text = "";
for await (const chunk of response) {
  console.log(chunk.text);
  text += chunk.text;
}
text_generation.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

file, err := client.Files.UploadFromPath(
	ctx, 
	filepath.Join(getMedia(), "Big_Buck_Bunny.mp4"), 
	&genai.UploadFileConfig{
		MIMEType : "video/mp4",
	},
)
if err != nil {
	log.Fatal(err)
}

// Poll until the video file is completely processed (state becomes ACTIVE).
for file.State == genai.FileStateUnspecified || file.State != genai.FileStateActive {
	fmt.Println("Processing video...")
	fmt.Println("File state:", file.State)
	time.Sleep(5 * time.Second)

	file, err = client.Files.Get(ctx, file.Name, nil)
	if err != nil {
		log.Fatal(err)
	}
}

parts := []*genai.Part{
	genai.NewPartFromText("Describe this video clip"),
	genai.NewPartFromURI(file.URI, file.MIMEType),
}

contents := []*genai.Content{
	genai.NewContentFromParts(parts, "user"),
}

for result, err := range client.Models.GenerateContentStream(
	ctx,
	"gemini-2.0-flash",
	contents,
	nil,
) {
	if err != nil {
		log.Fatal(err)
	}
	fmt.Print(result.Candidates[0].Content.Parts[0].Text)
}
text_generation.go
 
# Use File API to upload audio data to API request.
MIME_TYPE=$(file -b --mime-type "${VIDEO_PATH}")
NUM_BYTES=$(wc -c < "${VIDEO_PATH}")
DISPLAY_NAME=VIDEO_PATH

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${VIDEO_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

state=$(jq ".file.state" file_info.json)
echo state=$state

while [[ "($state)" = *"PROCESSING"* ]];
do
  echo "Processing video..."
  sleep 5
  # Get the file of interest to check state
  curl https://generativelanguage.googleapis.com/v1beta/files/$name > file_info.json
  state=$(jq ".file.state" file_info.json)
done

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Please describe this file."},
          {"file_data":{"mime_type": "video/mp4", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo
text_generation.sh
PythonShell 
from google import genai

client = genai.Client()
sample_pdf = client.files.upload(file=media / "test.pdf")
response = client.models.generate_content_stream(
    model="gemini-2.0-flash",
    contents=["Give me a summary of this document:", sample_pdf],
)

for chunk in response:
    print(chunk.text)
    print("_" * 80)
text_generation.py
 
MIME_TYPE=$(file -b --mime-type "${PDF_PATH}")
NUM_BYTES=$(wc -c < "${PDF_PATH}")
DISPLAY_NAME=TEXT


echo $MIME_TYPE
tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "${BASE_URL}/upload/v1beta/files?key=${GEMINI_API_KEY}" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${PDF_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq ".file.uri" file_info.json)
echo file_uri=$file_uri

# Now generate content using that file
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=$GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Can you add a few more lines to this poem?"},
          {"file_data":{"mime_type": "application/pdf", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo
text_generation.sh
PythonNode.jsGoShellKotlinSwiftDartJava 
from google import genai
from google.genai import types

client = genai.Client()
chat = client.chats.create(
    model="gemini-2.0-flash",
    history=[
        types.Content(role="user", parts=[types.Part(text="Hello")]),
        types.Content(
            role="model",
            parts=[
                types.Part(
                    text="Great to meet you. What would you like to know?"
                )
            ],
        ),
    ],
)
response = chat.send_message_stream(message="I have 2 dogs in my house.")
for chunk in response:
    print(chunk.text)
    print("_" * 80)
response = chat.send_message_stream(message="How many paws are in my house?")
for chunk in response:
    print(chunk.text)
    print("_" * 80)

print(chat.get_history())
chat.py
 
// Make sure to include the following import:
// import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const chat = ai.chats.create({
  model: "gemini-2.0-flash",
  history: [
    {
      role: "user",
      parts: [{ text: "Hello" }],
    },
    {
      role: "model",
      parts: [{ text: "Great to meet you. What would you like to know?" }],
    },
  ],
});

console.log("Streaming response for first message:");
const stream1 = await chat.sendMessageStream({
  message: "I have 2 dogs in my house.",
});
for await (const chunk of stream1) {
  console.log(chunk.text);
  console.log("_".repeat(80));
}

console.log("Streaming response for second message:");
const stream2 = await chat.sendMessageStream({
  message: "How many paws are in my house?",
});
for await (const chunk of stream2) {
  console.log(chunk.text);
  console.log("_".repeat(80));
}

console.log(chat.getHistory());
chat.js
 
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
	APIKey:  os.Getenv("GEMINI_API_KEY"),
	Backend: genai.BackendGeminiAPI,
})
if err != nil {
	log.Fatal(err)
}

history := []*genai.Content{
	genai.NewContentFromText("Hello", "user"),
	genai.NewContentFromText("Great to meet you. What would you like to know?", "model"),
}
chat, err := client.Chats.Create(ctx, "gemini-2.0-flash", nil, history)
if err != nil {
	log.Fatal(err)
}

for chunk, err := range chat.SendMessageStream(ctx, genai.Part{Text: "I have 2 dogs in my house."}) {
	if err != nil {
		log.Fatal(err)
	}
	fmt.Println(chunk.Text())
	fmt.Println(strings.Repeat("_", 64))
}

for chunk, err := range chat.SendMessageStream(ctx, genai.Part{Text: "How many paws are in my house?"}) {
	if err != nil {
		log.Fatal(err)
	}
	fmt.Println(chunk.Text())
	fmt.Println(strings.Repeat("_", 64))
}

fmt.Println(chat.History(false))
chat.go
 
curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=$GEMINI_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [
        {"role":"user",
         "parts":[{
           "text": "Hello"}]},
        {"role": "model",
         "parts":[{
           "text": "Great to meet you. What would you like to know?"}]},
        {"role":"user",
         "parts":[{
           "text": "I have two dogs in my house. How many paws are in my house?"}]},
      ]
    }' 2> /dev/null | grep "text"
chat.sh
 
// Use streaming with multi-turn conversations (like chat)
val generativeModel =
    GenerativeModel(
        // Specify a Gemini model appropriate for your use case
        modelName = "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key" above)
        apiKey = BuildConfig.apiKey)

val chat =
    generativeModel.startChat(
        history =
            listOf(
                content(role = "user") { text("Hello, I have 2 dogs in my house.") },
                content(role = "model") {
                  text("Great to meet you. What would you like to know?")
                }))

chat.sendMessageStream("How many paws are in my house?").collect { chunk -> print(chunk.text) }
chat.kt
 
let generativeModel =
  GenerativeModel(
    // Specify a Gemini model appropriate for your use case
    name: "gemini-1.5-flash",
    // Access your API key from your on-demand resource .plist file (see "Set up your API key"
    // above)
    apiKey: APIKey.default
  )

// Optionally specify existing chat history
let history = [
  ModelContent(role: "user", parts: "Hello, I have 2 dogs in my house."),
  ModelContent(role: "model", parts: "Great to meet you. What would you like to know?"),
]

// Initialize the chat with optional chat history
let chat = generativeModel.startChat(history: history)

// To stream generated text output, call sendMessageStream and pass in the message
let contentStream = chat.sendMessageStream("How many paws are in my house?")
for try await chunk in contentStream {
  if let text = chunk.text {
    print(text)
  }
}
ChatSnippets.swift
 
// Make sure to include this import:
// import 'package:google_generative_ai/google_generative_ai.dart';
final model = GenerativeModel(
  model: 'gemini-1.5-flash',
  apiKey: apiKey,
);
final chat = model.startChat(history: [
  Content.text('hello'),
  Content.model([TextPart('Great to meet you. What would you like to know?')])
]);
var responses =
    chat.sendMessageStream(Content.text('I have 2 dogs in my house.'));
await for (final response in responses) {
  print(response.text);
  print('_' * 80);
}
responses =
    chat.sendMessageStream(Content.text('How many paws are in my house?'));
await for (final response in responses) {
  print(response.text);
  print('_' * 80);
}
chat.dart
 
// Specify a Gemini model appropriate for your use case
GenerativeModel gm =
    new GenerativeModel(
        /* modelName */ "gemini-1.5-flash",
        // Access your API key as a Build Configuration variable (see "Set up your API key"
        // above)
        /* apiKey */ BuildConfig.apiKey);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

// (optional) Create previous chat history for context
Content.Builder userContentBuilder = new Content.Builder();
userContentBuilder.setRole("user");
userContentBuilder.addText("Hello, I have 2 dogs in my house.");
Content userContent = userContentBuilder.build();

Content.Builder modelContentBuilder = new Content.Builder();
modelContentBuilder.setRole("model");
modelContentBuilder.addText("Great to meet you. What would you like to know?");
Content modelContent = userContentBuilder.build();

List<Content> history = Arrays.asList(userContent, modelContent);

// Initialize the chat
ChatFutures chat = model.startChat(history);

// Create a new user message
Content.Builder userMessageBuilder = new Content.Builder();
userMessageBuilder.setRole("user");
userMessageBuilder.addText("How many paws are in my house?");
Content userMessage = userMessageBuilder.build();

// Use streaming with text-only input
Publisher<GenerateContentResponse> streamingResponse = model.generateContentStream(userMessage);

StringBuilder outputContent = new StringBuilder();

streamingResponse.subscribe(
    new Subscriber<GenerateContentResponse>() {
      @Override
      public void onNext(GenerateContentResponse generateContentResponse) {
        String chunk = generateContentResponse.getText();
        outputContent.append(chunk);
      }

      @Override
      public void onComplete() {
        System.out.println(outputContent);
      }

      @Override
      public void onSubscribe(Subscription s) {
        s.request(Long.MAX_VALUE);
      }

      @Override
      public void onError(Throwable t) {}

    });

chat.java

响应正文

如果成功，响应正文将包含 GenerateContentResponse 实例的流。

GenerateContentResponse

支持多个候选回答的模型的回答。

系统会针对 GenerateContentResponse.prompt_feedback 中的提示以及 finishReason 和 safetyRatings 中的每个候选项报告安全分级和内容过滤。该 API：- 返回所有请求的候选项，或者不返回任何候选项 - 仅当问题有误时才会完全不返回候选项（请检查 promptFeedback）- 在 finishReason 和 safetyRatings 中报告每个候选项的反馈。

字段
candidates[] object (Candidate)

模型的候选回答。

promptFeedback object (PromptFeedback)

返回与内容过滤器相关的提示反馈。

usageMetadata object (UsageMetadata)

仅限输出。有关生成请求令牌用量的元数据。

modelVersion string

仅限输出。用于生成回答的模型版本。

JSON 表示法 
{
  "candidates": [
    {
      object (Candidate)
    }
  ],
  "promptFeedback": {
    object (PromptFeedback)
  },
  "usageMetadata": {
    object (UsageMetadata)
  },
  "modelVersion": string
}

PromptFeedback

GenerateContentRequest.content 中指定的提示的一组反馈元数据。

字段
blockReason enum (BlockReason)

可选。如果已设置，则系统会屏蔽该提示，并且不会返回任何候选项。改述提示。

safetyRatings[] object (SafetyRating)

问题安全性的评分。每个类别最多只能有一个分级。

JSON 表示法 
{
  "blockReason": enum (BlockReason),
  "safetyRatings": [
    {
      object (SafetyRating)
    }
  ]
}

BlockReason

指定屏蔽该提示的原因。

枚举
BLOCK_REASON_UNSPECIFIED 默认值。此值未使用。
SAFETY 出于安全原因，系统屏蔽了提示。检查 safetyRatings，了解是哪个安全类别屏蔽了它。
OTHER 提示因未知原因被屏蔽了。
BLOCKLIST 系统屏蔽了此提示，因为其中包含术语屏蔽名单中包含的术语。
PROHIBITED_CONTENT 系统屏蔽了此提示，因为其中包含禁止的内容。
IMAGE_SAFETY 候选图片因生成不安全的内容而被屏蔽。

UsageMetadata

有关生成请求令牌用量的元数据。

字段
promptTokenCount integer

提示中的词元数。设置 cachedContent 后，这仍然是有效提示的总大小，这意味着它包括缓存内容中的词元数。

cachedContentTokenCount integer

提示的缓存部分（缓存内容）中的词元数

candidatesTokenCount integer

所有生成的候选回答中的词元总数。

toolUsePromptTokenCount integer

仅限输出。工具使用提示中的词元数量。

thoughtsTokenCount integer

仅限输出。思考模型的想法 token 数。

totalTokenCount integer

生成请求的令牌总数（问题 + 回答候选）。

promptTokensDetails[] object (ModalityTokenCount)

仅限输出。在请求输入中处理的模态列表。

cacheTokensDetails[] object (ModalityTokenCount)

仅限输出。请求输入中缓存内容的模态列表。

candidatesTokensDetails[] object (ModalityTokenCount)

仅限输出。响应中返回的模态列表。

toolUsePromptTokensDetails[] object (ModalityTokenCount)

仅限输出。为工具使用请求输入处理的模态列表。

JSON 表示法 
{
  "promptTokenCount": integer,
  "cachedContentTokenCount": integer,
  "candidatesTokenCount": integer,
  "toolUsePromptTokenCount": integer,
  "thoughtsTokenCount": integer,
  "totalTokenCount": integer,
  "promptTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ],
  "cacheTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ],
  "candidatesTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ],
  "toolUsePromptTokensDetails": [
    {
      object (ModalityTokenCount)
    }
  ]
}

候选人

模型生成的候选回答。

字段
content object (Content)

仅限输出。模型返回的生成内容。

finishReason enum (FinishReason)

可选。仅限输出。模型停止生成词元的原因。

如果为空，则模型尚未停止生成词元。

safetyRatings[] object (SafetyRating)

候选回答安全性的评分列表。

每个类别最多只能有一个分级。

citationMetadata object (CitationMetadata)

仅限输出。模型生成的候选项的引用信息。

此字段可填充 content 中包含的任何文本的诵读信息。这些段落是从基础 LLM 训练数据中的受版权保护材料中“背诵”出来的。

tokenCount integer

仅限输出。此候选项的令牌数。

groundingAttributions[] object (GroundingAttribution)

仅限输出。为生成有依据的回答所参考的来源提供方提供的提供方信息。

系统会针对 GenerateAnswer 调用填充此字段。

groundingMetadata object (GroundingMetadata)

仅限输出。候选对象的参考元数据。

系统会针对 GenerateContent 调用填充此字段。

avgLogprobs number

仅限输出。候选项的平均对数概率得分。

logprobsResult object (LogprobsResult)

仅限输出。回答令牌和前置令牌的对数似然度得分

urlRetrievalMetadata object (UrlRetrievalMetadata)

仅限输出。与网址情境检索工具相关的元数据。

index integer

仅限输出。响应候选列表中候选项的索引。

JSON 表示法 
{
  "content": {
    object (Content)
  },
  "finishReason": enum (FinishReason),
  "safetyRatings": [
    {
      object (SafetyRating)
    }
  ],
  "citationMetadata": {
    object (CitationMetadata)
  },
  "tokenCount": integer,
  "groundingAttributions": [
    {
      object (GroundingAttribution)
    }
  ],
  "groundingMetadata": {
    object (GroundingMetadata)
  },
  "avgLogprobs": number,
  "logprobsResult": {
    object (LogprobsResult)
  },
  "urlRetrievalMetadata": {
    object (UrlRetrievalMetadata)
  },
  "index": integer
}

FinishReason

定义模型停止生成词元的原因。

枚举
FINISH_REASON_UNSPECIFIED 默认值。此值未使用。
STOP 模型的自然停止点或提供的停止序列。
MAX_TOKENS 已达到请求中指定的词元数量上限。
SAFETY 出于安全考虑，系统已标记回答候选内容。
RECITATION 由于背诵原因，回答候选内容被标记。
LANGUAGE 回答候选内容因使用不受支持的语言而被标记。
OTHER 原因未知。
BLOCKLIST 由于内容包含禁止使用的字词，因此 token 生成操作已停止。
PROHIBITED_CONTENT 由于可能包含禁止的内容，因此 token 生成操作已停止。
SPII 由于内容可能包含敏感的个人身份信息 (SPII)，因此 token 生成操作已停止。
MALFORMED_FUNCTION_CALL 模型生成的函数调用无效。
IMAGE_SAFETY 由于生成的图片违反了安全规定，因此词元生成已停止。

GroundingAttribution

对为回答做出贡献的来源的归因。

字段
sourceId object (AttributionSourceId)

仅限输出。对此归因做出贡献的来源的标识符。

content object (Content)

构成此归因的来源内容。

JSON 表示法 
{
  "sourceId": {
    object (AttributionSourceId)
  },
  "content": {
    object (Content)
  }
}

AttributionSourceId

对此归因做出贡献的来源的标识符。

字段
source Union type
source 只能是下列其中一项：
groundingPassage object (GroundingPassageId)

内嵌段落的标识符。

semanticRetrieverChunk object (SemanticRetrieverChunk)

通过 Semantic Retriever 提取的 Chunk 的标识符。

JSON 表示法 
{

  // source
  "groundingPassage": {
    object (GroundingPassageId)
  },
  "semanticRetrieverChunk": {
    object (SemanticRetrieverChunk)
  }
  // Union type
}

GroundingPassageId

GroundingPassage 中部分的标识符。

字段
passageId string

仅限输出。与 GenerateAnswerRequest 的 GroundingPassage.id 匹配的段落的 ID。

partIndex integer

仅限输出。GenerateAnswerRequest 的 GroundingPassage.content 中的部分的索引。

JSON 表示法 
{
  "passageId": string,
  "partIndex": integer
}

SemanticRetrieverChunk

使用 SemanticRetrieverConfig 在 GenerateAnswerRequest 中指定的通过语义检索器检索的 Chunk 的标识符。

字段
source string

仅限输出。与请求的 SemanticRetrieverConfig.source 匹配的来源的名称。示例：corpora/123 或 corpora/123/documents/abc

chunk string

仅限输出。包含归因文本的 Chunk 的名称。示例：corpora/123/documents/abc/chunks/xyz

JSON 表示法 
{
  "source": string,
  "chunk": string
}

GroundingMetadata

启用接地时返回给客户端的元数据。

字段
groundingChunks[] object (GroundingChunk)

从指定的接地源检索到的支持参考文献列表。

groundingSupports[] object (GroundingSupport)

接地支持列表。

webSearchQueries[] string

用于后续网页搜索的网页搜索查询。

searchEntryPoint object (SearchEntryPoint)

可选。后续网页搜索的 Google 搜索条目。

retrievalMetadata object (RetrievalMetadata)

与基准流程中检索相关的元数据。

JSON 表示法 
{
  "groundingChunks": [
    {
      object (GroundingChunk)
    }
  ],
  "groundingSupports": [
    {
      object (GroundingSupport)
    }
  ],
  "webSearchQueries": [
    string
  ],
  "searchEntryPoint": {
    object (SearchEntryPoint)
  },
  "retrievalMetadata": {
    object (RetrievalMetadata)
  }
}

SearchEntryPoint

Google 搜索入口点。

字段
renderedContent string

可选。可嵌入网页或应用 WebView 中的 Web 内容代码段。

sdkBlob string (bytes format)

可选。使用 base64 编码的 JSON，表示 <search term, search url> 元组的数组。

使用 base64 编码的字符串。

JSON 表示法 
{
  "renderedContent": string,
  "sdkBlob": string
}

GroundingChunk

接地分块。

字段
chunk_type Union type
分块类型。chunk_type 只能是下列其中一项：
web object (Web)

来自网络的接地分块。

JSON 表示法 
{

  // chunk_type
  "web": {
    object (Web)
  }
  // Union type
}

Web

来自网络的文件块。

字段
uri string

分块的 URI 引用。

title string

数据块的标题。

JSON 表示法 
{
  "uri": string,
  "title": string
}

GroundingSupport

接地支持。

字段
groundingChunkIndices[] integer

索引列表（在“grounding_chunk”中），用于指定与版权主张相关的引文。例如，[1,3,4] 表示 grounding_chunk[1]、grounding_chunk[3]、grounding_chunk[4] 是归因于该版权主张的检索内容。

confidenceScores[] number

支持参考文档的置信度分数。范围为 0 到 1。1 表示最有信心。此列表的大小必须与 groundingChunkIndices 相同。

segment object (Segment)

此支持请求所属的内容片段。

JSON 表示法 
{
  "groundingChunkIndices": [
    integer
  ],
  "confidenceScores": [
    number
  ],
  "segment": {
    object (Segment)
  }
}

Segment

内容的片段。

字段
partIndex integer

仅限输出。Part 对象在其父级 Content 对象中的索引。

startIndex integer

仅限输出。给定 part 中的起始索引，以字节为单位。相对于相应部分开头的偏移量（从零开始，包括零）。

endIndex integer

仅限输出。给定分块中的结束索引，以字节为单位。相对于相应部分开头的偏移量（不含该偏移量），从零开始。

text string

仅限输出。与响应中的相应片段对应的文本。

JSON 表示法 
{
  "partIndex": integer,
  "startIndex": integer,
  "endIndex": integer,
  "text": string
}

RetrievalMetadata

与基准流程中检索相关的元数据。

字段
googleSearchDynamicRetrievalScore number

可选。一个得分，表示 Google 搜索中的信息有多大可能有助于回答问题。得分介于 [0, 1] 范围内，其中 0 表示可能性最低，1 表示可能性最高。只有在启用 Google 搜索依据和动态检索后，系统才会填充此得分。系统会将其与阈值进行比较，以确定是否触发 Google 搜索。

JSON 表示法 
{
  "googleSearchDynamicRetrievalScore": number
}

LogprobsResult

Logprobs 结果

字段
topCandidates[] object (TopCandidates)

长度 = 解码步骤总数。

chosenCandidates[] object (Candidate)

长度 = 解码步骤总数。所选候选项不一定在 topCandidates 中。

JSON 表示法 
{
  "topCandidates": [
    {
      object (TopCandidates)
    }
  ],
  "chosenCandidates": [
    {
      object (Candidate)
    }
  ]
}

TopCandidates

每个解码步骤中对数概率最高的候选项。

字段
candidates[] object (Candidate)

按对数概率降序排序。

JSON 表示法 
{
  "candidates": [
    {
      object (Candidate)
    }
  ]
}

候选人

logprobs 令牌和得分的候选项。

字段
token string

候选广告的令牌字符串值。

tokenId integer

候选人的令牌 ID 值。

logProbability number

候选项的对数概率。

JSON 表示法 
{
  "token": string,
  "tokenId": integer,
  "logProbability": number
}

UrlRetrievalMetadata

与网址情境检索工具相关的元数据。

字段
urlRetrievalContexts[] object (UrlRetrievalContext)

网址检索情境列表。

JSON 表示法 
{
  "urlRetrievalContexts": [
    {
      object (UrlRetrievalContext)
    }
  ]
}

UrlRetrievalContext

单个网址检索的上下文。

字段
retrievedUrl string

工具检索到的网址。

JSON 表示法 
{
  "retrievedUrl": string
}

CitationMetadata

一段内容的来源归因集合。

字段
citationSources[] object (CitationSource)

特定回答的来源引用。

JSON 表示法 
{
  "citationSources": [
    {
      object (CitationSource)
    }
  ]
}

CitationSource

特定回答中某部分内容的来源引用。

字段
startIndex integer

可选。归因于此来源的响应片段的开始时间。

索引表示相应片段的起始位置，以字节为单位。

endIndex integer

可选。归因细分的时间段结束时间（不含）。

uri string

可选。被归因为文本部分来源的 URI。

license string

可选。被归因为片段来源的 GitHub 项目的许可。

代码引用需要提供许可信息。

JSON 表示法 
{
  "startIndex": integer,
  "endIndex": integer,
  "uri": string,
  "license": string
}

GenerationConfig

模型生成和输出的配置选项。并非所有参数都适用于每种模型。

字段
stopSequences[] string

可选。用于停止生成输出的字符序列集（最多 5 个）。如果指定，该 API 将在 stop_sequence 首次出现时停止。停止序列不会包含在回答中。

responseMimeType string

可选。生成的候选文本的 MIME 类型。支持的 MIME 类型包括：text/plain：（默认）文本输出。application/json：响应候选项中的 JSON 响应。text/x.enum：在候选回答中，将 ENUM 作为字符串响应。如需查看所有受支持的文本 MIME 类型的列表，请参阅文档。

responseSchema object (Schema)

可选。生成的候选文本的输出架构。架构必须是 OpenAPI 架构的子集，可以是对象、基元或数组。

如果设置了此值，则还必须设置兼容的 responseMimeType。兼容的 MIME 类型：application/json：JSON 响应的架构。如需了解详情，请参阅 JSON 文本生成指南。

responseModalities[] enum (Modality)

可选。请求的响应模式。表示模型可以返回的一组模态，并且应在响应中预期到。这与回答的模态完全匹配。

一个模型可以有多个支持的模态组合。如果请求的模态与任何支持的组合都不匹配，则会返回错误。

空列表等同于仅请求文本。

candidateCount integer

可选。要返回的生成的回答数量。如果未设置，则默认为 1。请注意，这不适用于上一代模型 (Gemini 1.0 系列)

maxOutputTokens integer

可选。候选回答中包含的令牌数量上限。

注意：默认值因型号而异，请参阅从 getModel 函数返回的 Model 的 Model.output_token_limit 属性。

temperature number

可选。控制输出的随机性。

注意：默认值因型号而异，请参阅从 getModel 函数返回的 Model 的 Model.temperature 属性。

值的范围为 [0.0, 2.0]。

topP number

可选。在采样时要考虑的令牌的累计概率上限。

该模型使用组合 Top-k 和 Top-p（核）抽样。

系统会根据分配的概率对令牌进行排序，以便仅考虑概率最高的令牌。Top-k 采样会直接限制要考虑的 token 数量上限，而 Nucleus 采样会根据累积概率限制 token 数量。

注意：默认值因 Model 而异，由 getModel 函数返回的 Model.top_p 属性指定。空的 topK 属性表示模型不会应用前 k 个样本抽样，也不允许对请求设置 topK。

topK integer

可选。抽样时要考虑的令牌数量上限。

Gemini 模型使用 Top-p（核）采样或 Top-k 和核采样的组合。Top-k 抽样会考虑一组概率最高的 topK 个 token。使用核采样运行的模型不允许设置 topK。

注意：默认值因 Model 而异，由 getModel 函数返回的 Model.top_p 属性指定。空的 topK 属性表示模型不会应用前 k 个样本抽样，也不允许对请求设置 topK。

seed integer

可选。解码中使用的种子。如果未设置，请求将使用随机生成的种子。

presencePenalty number

可选。如果令牌已在响应中出现，则对下一个令牌的 logprobs 应用存在性惩罚。

此惩罚是二进制开/关，不取决于令牌的使用次数（首次使用后）。使用 frequencyPenalty 可使惩罚因每次使用而增加。

正惩罚会阻止使用在回答中已使用的令牌，从而增加词汇量。

负惩罚会鼓励使用在回答中已使用的令牌，从而减少词汇量。

frequencyPenalty number

可选。应用于下一个令牌的 logprobs 的频率惩罚，乘以每个令牌在回答中出现的次数。

正惩罚会阻止使用已使用的令牌，惩罚强度与令牌的使用次数成正比：令牌使用次数越多，模型就越难再次使用该令牌来增加回答的词汇量。

注意：负惩罚会促使模型根据令牌的使用次数重复使用令牌。较小的负值会减少回答的词汇量。负值越大，模型就会开始重复一个常用令牌，直到达到 maxOutputTokens 限制。

responseLogprobs boolean

可选。如果为 true，则在响应中导出 logprobs 结果。

logprobs integer

可选。仅在 responseLogprobs=True 时有效。这会设置 Candidate.logprobs_result 中每个解码步骤返回的顶部 logprob 的数量。

enableEnhancedCivicAnswers boolean

可选。启用增强型城市服务回答。此功能可能不适用于所有型号。

speechConfig object (SpeechConfig)

可选。语音生成配置。

thinkingConfig object (ThinkingConfig)

可选。思考功能的配置。如果为不支持思考的模型设置此字段，系统会返回错误。

mediaResolution enum (MediaResolution)

可选。如果指定，系统将使用指定的媒体分辨率。

JSON 表示法 
{
  "stopSequences": [
    string
  ],
  "responseMimeType": string,
  "responseSchema": {
    object (Schema)
  },
  "responseModalities": [
    enum (Modality)
  ],
  "candidateCount": integer,
  "maxOutputTokens": integer,
  "temperature": number,
  "topP": number,
  "topK": integer,
  "seed": integer,
  "presencePenalty": number,
  "frequencyPenalty": number,
  "responseLogprobs": boolean,
  "logprobs": integer,
  "enableEnhancedCivicAnswers": boolean,
  "speechConfig": {
    object (SpeechConfig)
  },
  "thinkingConfig": {
    object (ThinkingConfig)
  },
  "mediaResolution": enum (MediaResolution)
}

模态

响应的支持模态。

枚举
MODALITY_UNSPECIFIED 默认值。
TEXT 指示模型应返回文本。
IMAGE 表示模型应返回图片。
AUDIO 指示模型应返回音频。

SpeechConfig

语音生成配置。

字段
voiceConfig object (VoiceConfig)

单声音输出的配置。

languageCode string

可选。用于语音合成的语言代码（采用 BCP 47 格式，例如“en-US”）。

有效值包括：de-DE、en-AU、en-GB、en-IN、en-US、es-US、fr-FR、hi-IN、pt-BR、ar-XA、es-ES、fr-CA、id-ID、it-IT、ja-JP、tr-TR、vi-VN、bn-IN、gu-IN、kn-IN、ml-IN、mr-IN、ta-IN、te-IN、nl-NL、ko-KR、cmn-CN、pl-PL、ru-RU 和 th-TH。

JSON 表示法 
{
  "voiceConfig": {
    object (VoiceConfig)
  },
  "languageCode": string
}

VoiceConfig

要使用的语音的配置。

字段
voice_config Union type
音箱要使用的配置。voice_config 只能是下列其中一项：
prebuiltVoiceConfig object (PrebuiltVoiceConfig)

要使用的预构建语音的配置。

JSON 表示法 
{

  // voice_config
  "prebuiltVoiceConfig": {
    object (PrebuiltVoiceConfig)
  }
  // Union type
}

PrebuiltVoiceConfig

要使用的预构建音箱的配置。

字段
voiceName string

要使用的预设语音的名称。

JSON 表示法 
{
  "voiceName": string
}

ThinkingConfig

思考功能的配置。

字段
includeThoughts boolean

指示是否要在回答中包含思考。如果为 true，则仅在有可用想法时返回想法。

thinkingBudget integer

模型应生成的想法 token 的数量。

JSON 表示法 
{
  "includeThoughts": boolean,
  "thinkingBudget": integer
}

MediaResolution

输入媒体的媒体分辨率。

枚举
MEDIA_RESOLUTION_UNSPECIFIED 未设置媒体分辨率。
MEDIA_RESOLUTION_LOW 媒体分辨率设为低（64 个令牌）。
MEDIA_RESOLUTION_MEDIUM 媒体分辨率设为中等（256 个令牌）。
MEDIA_RESOLUTION_HIGH 媒体分辨率设为高（使用 256 个令牌进行缩放重新取景）。

HarmCategory

评分的类别。

这些类别涵盖了开发者可能希望调整的各种类型的危害。

枚举
HARM_CATEGORY_UNSPECIFIED 类别未指定。
HARM_CATEGORY_DEROGATORY PaLM - 针对身份和/或受保护属性的负面或有害评论。
HARM_CATEGORY_TOXICITY PaLM - 粗鲁、无礼或亵渎性的内容。
HARM_CATEGORY_VIOLENCE PaLM - 描述描绘针对个人或团体的暴力行为的场景，或一般性血腥描述。
HARM_CATEGORY_SEXUAL PaLM - 包含对性行为或其他淫秽内容的引用。
HARM_CATEGORY_MEDICAL PaLM - 宣传未经核实的医疗建议。
HARM_CATEGORY_DANGEROUS PaLM - 危险内容会宣扬、助长或鼓励有害行为。
HARM_CATEGORY_HARASSMENT Gemini - 骚扰内容。
HARM_CATEGORY_HATE_SPEECH Gemini - 仇恨言论和内容。
HARM_CATEGORY_SEXUALLY_EXPLICIT Gemini - 露骨色情内容。
HARM_CATEGORY_DANGEROUS_CONTENT Gemini - 危险内容。
HARM_CATEGORY_CIVIC_INTEGRITY Gemini - 可能用于破坏公民诚信的内容。

ModalityTokenCount

表示单个模态的令牌计数信息。

字段
modality enum (Modality)

与此令牌数关联的模态。

tokenCount integer

令牌数量。

JSON 表示法 
{
  "modality": enum (Modality),
  "tokenCount": integer
}

模态

内容部分模态

枚举
MODALITY_UNSPECIFIED 未指定模态。
TEXT 纯文本。
IMAGE 图片。
VIDEO 视频。
AUDIO 音频。
DOCUMENT 文档，例如 PDF。

SafetyRating

一段内容的安全分级。

安全评级包含内容的危害类别以及该类别中的危害概率级别。系统会根据多种危害类别对内容进行安全分类，并在此处提供内容属于危害分类的概率。

字段
category enum (HarmCategory)

必需。此评分的类别。

probability enum (HarmProbability)

必需。此内容的有害概率。

blocked boolean

此内容是否因此分级而被屏蔽？

JSON 表示法 
{
  "category": enum (HarmCategory),
  "probability": enum (HarmProbability),
  "blocked": boolean
}

HarmProbability

内容有害的概率。

分类系统会提供内容不安全的概率。这并不表示内容的危害程度。

枚举
HARM_PROBABILITY_UNSPECIFIED 概率未指定。
NEGLIGIBLE 内容不安全的概率可忽略不计。
LOW 内容不安全的概率较低。
MEDIUM 内容不安全的概率为中等。
HIGH 内容不安全的概率较高。

SafetySetting

安全设置，会影响安全屏蔽行为。

为某个类别传递安全设置会更改内容被屏蔽的允许概率。

字段
category enum (HarmCategory)

必需。此设置的类别。

threshold enum (HarmBlockThreshold)

必需。控制屏蔽有害内容的概率阈值。

JSON 表示法 
{
  "category": enum (HarmCategory),
  "threshold": enum (HarmBlockThreshold)
}

HarmBlockThreshold

在指定的有害概率达到或超过时进行屏蔽。

枚举
HARM_BLOCK_THRESHOLD_UNSPECIFIED 未指定阈值。
BLOCK_LOW_AND_ABOVE 允许发布评分为 NEGLIGIBLE 的内容。
BLOCK_MEDIUM_AND_ABOVE 我们会允许发布评分为 NEGLIGIBLE 和 LOW 的内容。
BLOCK_ONLY_HIGH 我们允许发布风险等级为“NEGLIGIBLE”“LOW”和“MEDIUM”的内容。
BLOCK_NONE 允许所有内容。
OFF 关闭安全过滤器。