Generating content

La API de Gemini admite la generación de contenido con imágenes, audio, código, herramientas y mucho más. Para obtener detalles sobre cada una de estas funciones, sigue leyendo y consulta el código de muestra enfocado en las tareas o lee las guías completas.

Método: models.generateContent

Genera una respuesta del modelo según una GenerateContentRequest de entrada. Consulta la guía de generación de texto para obtener información de uso detallada. Las capacidades de entrada difieren entre los modelos, incluidos los ajustados. Consulta la guía del modelo y la guía de ajuste para obtener más detalles.

Extremo

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

Parámetros de ruta

model string

Obligatorio. Es el nombre de Model que se usará para generar la finalización.

Formato: name=models/{model}. Toma la forma models/{model}.

Cuerpo de la solicitud

El cuerpo de la solicitud contiene datos con la siguiente estructura:

Campos
contents[] object (Content)

Obligatorio. El contenido de la conversación actual con el modelo.

Para consultas de un solo turno, esta es una instancia única. Para consultas de varios turnos, como chat, es un campo repetido que contiene el historial de la conversación y la última solicitud.

tools[] object (Tool)

Opcional. Una lista de Tools que Model puede usar para generar la siguiente respuesta.

Un Tool es un fragmento de código que permite que el sistema interactúe con sistemas externos para realizar una acción, o un conjunto de acciones, fuera del conocimiento y el alcance de Model. Los elementos Tool admitidos son Function y codeExecution. Consulta las guías Llamada a función y Ejecución de código para obtener más información.

toolConfig object (ToolConfig)

Opcional. Configuración de herramientas para cualquier Tool especificado en la solicitud. Consulta la guía de llamada a funciones para ver un ejemplo de uso.

safetySettings[] object (SafetySetting)

Opcional. Una lista de instancias de SafetySetting únicas para bloquear contenido no seguro.

Esto se aplicará de manera forzosa en GenerateContentRequest.contents y GenerateContentResponse.candidates. No debe haber más de un parámetro de configuración para cada tipo de SafetyCategory. La API bloqueará cualquier contenido y respuesta que no cumpla con los umbrales establecidos por esta configuración. Esta lista anula la configuración predeterminada para cada SafetyCategory especificado en SafetySettings. Si no hay un SafetySetting para un SafetyCategory determinado proporcionado en la lista, la API usará la configuración de seguridad predeterminada para esa categoría. Se admiten las categorías de daños HARM_CATEGORY_HATE_SPEECH, HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT, HARM_CATEGORY_HARASSMENT. Consulta la guía para obtener información detallada sobre la configuración de seguridad disponible. Además, consulta la Guía de seguridad para aprender a incorporar consideraciones de seguridad en tus aplicaciones de IA.

systemInstruction object (Content)

Opcional. El desarrollador establece las instrucciones del sistema. Actualmente, es solo texto.

generationConfig object (GenerationConfig)

Opcional. Opciones de configuración para la generación y la salida de modelos.

cachedContent string

Opcional. Es el nombre del contenido almacenado en caché para usar como contexto y entregar la predicción. Formato: cachedContents/{cachedContent}

Ejemplo de solicitud

Texto

Python

model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Write a story about a magic backpack.")
print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

const prompt = "Write a story about a magic backpack.";

const result = await model.generateContent(prompt);
console.log(result.response.text());

Go

model := client.GenerativeModel("gemini-1.5-flash")
resp, err := model.GenerateContent(ctx, genai.Text("Write a story about a magic backpack."))
if err != nil {
	log.Fatal(err)
}

printResponse(resp)

Una caracola

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[{"text": "Write a story about a magic backpack."}]
        }]
       }' 2> /dev/null

Kotlin

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)

Swift

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)
}

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);

Java

// 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);

Imagen

Python

import PIL.Image

model = genai.GenerativeModel("gemini-1.5-flash")
organ = PIL.Image.open(media / "organ.jpg")
response = model.generate_content(["Tell me about this instrument", organ])
print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

function fileToGenerativePart(path, mimeType) {
  return {
    inlineData: {
      data: Buffer.from(fs.readFileSync(path)).toString("base64"),
      mimeType,
    },
  };
}

const prompt = "Describe how this product might be manufactured.";
// Note: The only accepted mime types are some image types, image/*.
const imagePart = fileToGenerativePart(
  `${mediaPath}/jetpack.jpg`,
  "image/jpeg",
);

const result = await model.generateContent([prompt, imagePart]);
console.log(result.response.text());

Go

model := client.GenerativeModel("gemini-1.5-flash")

imgData, err := os.ReadFile(filepath.Join(testDataDir, "organ.jpg"))
if err != nil {
	log.Fatal(err)
}

resp, err := model.GenerateContent(ctx,
	genai.Text("Tell me about this instrument"),
	genai.ImageData("jpeg", imgData))
if err != nil {
	log.Fatal(err)
}

printResponse(resp)

Una caracola

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
            {"text": "Tell me about this instrument"},
            {
              "inline_data": {
                "mime_type":"image/jpeg",
                "data": "'$(base64 $B64FLAGS $IMG_PATH)'"
              }
            }
        ]
        }]
       }' 2> /dev/null

Kotlin

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)

Swift

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)
}

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);

Java

// 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);

Audio

Python

model = genai.GenerativeModel("gemini-1.5-flash")
sample_audio = genai.upload_file(media / "sample.mp3")
response = model.generate_content(["Give me a summary of this audio file.", sample_audio])
print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

function fileToGenerativePart(path, mimeType) {
  return {
    inlineData: {
      data: Buffer.from(fs.readFileSync(path)).toString("base64"),
      mimeType,
    },
  };
}

const prompt = "Give me a summary of this audio file.";
// Note: The only accepted mime types are some image types, image/*.
const audioPart = fileToGenerativePart(
  `${mediaPath}/samplesmall.mp3`,
  "audio/mp3",
);

const result = await model.generateContent([prompt, audioPart]);
console.log(result.response.text());

Una caracola

# 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 "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_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-1.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Please describe this file."},
          {"file_data":{"mime_type": "audio/mpeg", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

Video

Python

import time

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

# Videos need to be processed before you can use them.
while myfile.state.name == "PROCESSING":
    print("processing video...")
    time.sleep(5)
    myfile = genai.get_file(myfile.name)

model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content([myfile, "Describe this video clip"])
print(f"{response.text=}")

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
// import { GoogleAIFileManager, FileState } from "@google/generative-ai/server";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

const fileManager = new GoogleAIFileManager(process.env.API_KEY);

const uploadResult = await fileManager.uploadFile(
  `${mediaPath}/Big_Buck_Bunny.mp4`,
  { mimeType: "video/mp4" },
);

let file = await fileManager.getFile(uploadResult.file.name);
while (file.state === FileState.PROCESSING) {
  process.stdout.write(".");
  // Sleep for 10 seconds
  await new Promise((resolve) => setTimeout(resolve, 10_000));
  // Fetch the file from the API again
  file = await fileManager.getFile(uploadResult.file.name);
}

if (file.state === FileState.FAILED) {
  throw new Error("Video processing failed.");
}

const prompt = "Describe this video clip";
const videoPart = {
  fileData: {
    fileUri: uploadResult.file.uri,
    mimeType: uploadResult.file.mimeType,
  },
};

const result = await model.generateContent([prompt, videoPart]);
console.log(result.response.text());

Go

model := client.GenerativeModel("gemini-1.5-flash")

file, err := client.UploadFileFromPath(ctx, filepath.Join(testDataDir, "earth.mp4"), nil)
if err != nil {
	log.Fatal(err)
}
defer client.DeleteFile(ctx, file.Name)

// Videos need to be processed before you can use them.
for file.State == genai.FileStateProcessing {
	log.Printf("processing %s", file.Name)
	time.Sleep(5 * time.Second)
	var err error
	if file, err = client.GetFile(ctx, file.Name); err != nil {
		log.Fatal(err)
	}
}
if file.State != genai.FileStateActive {
	log.Fatalf("uploaded file has state %s, not active", file.State)
}

resp, err := model.GenerateContent(ctx,
	genai.Text("Describe this video clip"),
	genai.FileData{URI: file.URI})
if err != nil {
	log.Fatal(err)
}

printResponse(resp)

Una caracola

# 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 "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_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

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-1.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Please describe this file."},
          {"file_data":{"mime_type": "video/mp4", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

PDF

Python

model = genai.GenerativeModel("gemini-1.5-flash")
sample_pdf = genai.upload_file(media / "test.pdf")
response = model.generate_content(["Give me a summary of this document:", sample_pdf])
print(f"{response.text=}")

Una caracola

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 "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_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-1.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Can you add a few more lines to this poem?"},
          {"file_data":{"mime_type": "application/pdf", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

Chat

Python

model = genai.GenerativeModel("gemini-1.5-flash")
chat = model.start_chat(
    history=[
        {"role": "user", "parts": "Hello"},
        {"role": "model", "parts": "Great to meet you. What would you like to know?"},
    ]
)
response = chat.send_message("I have 2 dogs in my house.")
print(response.text)
response = chat.send_message("How many paws are in my house?")
print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
const chat = model.startChat({
  history: [
    {
      role: "user",
      parts: [{ text: "Hello" }],
    },
    {
      role: "model",
      parts: [{ text: "Great to meet you. What would you like to know?" }],
    },
  ],
});
let result = await chat.sendMessage("I have 2 dogs in my house.");
console.log(result.response.text());
result = await chat.sendMessage("How many paws are in my house?");
console.log(result.response.text());

Go

model := client.GenerativeModel("gemini-1.5-flash")
cs := model.StartChat()

cs.History = []*genai.Content{
	{
		Parts: []genai.Part{
			genai.Text("Hello, I have 2 dogs in my house."),
		},
		Role: "user",
	},
	{
		Parts: []genai.Part{
			genai.Text("Great to meet you. What would you like to know?"),
		},
		Role: "model",
	},
}

res, err := cs.SendMessage(ctx, genai.Text("How many paws are in my house?"))
if err != nil {
	log.Fatal(err)
}
printResponse(res)

Una caracola

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GOOGLE_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"

Kotlin

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)

Swift

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)
}

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);

Java

// 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);

Caché

Python

document = genai.upload_file(path=media / "a11.txt")
model_name = "gemini-1.5-flash-001"
cache = genai.caching.CachedContent.create(
    model=model_name,
    system_instruction="You are an expert analyzing transcripts.",
    contents=[document],
)
print(cache)

model = genai.GenerativeModel.from_cached_content(cache)
response = model.generate_content("Please summarize this transcript")
print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleAICacheManager, GoogleAIFileManager } from "@google/generative-ai/server";
// import { GoogleGenerativeAI } from "@google/generative-ai";
const cacheManager = new GoogleAICacheManager(process.env.API_KEY);
const fileManager = new GoogleAIFileManager(process.env.API_KEY);

const uploadResult = await fileManager.uploadFile(`${mediaPath}/a11.txt`, {
  mimeType: "text/plain",
});

const cacheResult = await cacheManager.create({
  model: "models/gemini-1.5-flash-001",
  contents: [
    {
      role: "user",
      parts: [
        {
          fileData: {
            fileUri: uploadResult.file.uri,
            mimeType: uploadResult.file.mimeType,
          },
        },
      ],
    },
  ],
});

console.log(cacheResult);

const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModelFromCachedContent(cacheResult);
const result = await model.generateContent(
  "Please summarize this transcript.",
);
console.log(result.response.text());

Modelo ajustado

Python

model = genai.GenerativeModel(model_name="tunedModels/my-increment-model")
result = model.generate_content("III")
print(result.text)  # "IV"

Modo JSON

Python

import typing_extensions as typing

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

model = genai.GenerativeModel("gemini-1.5-pro-latest")
result = model.generate_content(
    "List a few popular cookie recipes.",
    generation_config=genai.GenerationConfig(
        response_mime_type="application/json", response_schema=list[Recipe]
    ),
)
print(result)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI, SchemaType } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);

const schema = {
  description: "List of recipes",
  type: SchemaType.ARRAY,
  items: {
    type: SchemaType.OBJECT,
    properties: {
      recipeName: {
        type: SchemaType.STRING,
        description: "Name of the recipe",
        nullable: false,
      },
    },
    required: ["recipeName"],
  },
};

const model = genAI.getGenerativeModel({
  model: "gemini-1.5-pro",
  generationConfig: {
    responseMimeType: "application/json",
    responseSchema: schema,
  },
});

const result = await model.generateContent(
  "List a few popular cookie recipes.",
);
console.log(result.response.text());

Go

model := client.GenerativeModel("gemini-1.5-pro-latest")
// Ask the model to respond with JSON.
model.ResponseMIMEType = "application/json"
// Specify the schema.
model.ResponseSchema = &genai.Schema{
	Type:  genai.TypeArray,
	Items: &genai.Schema{Type: genai.TypeString},
}
resp, err := model.GenerateContent(ctx, genai.Text("List a few popular cookie recipes using this JSON schema."))
if err != nil {
	log.Fatal(err)
}
for _, part := range resp.Candidates[0].Content.Parts {
	if txt, ok := part.(genai.Text); ok {
		var recipes []string
		if err := json.Unmarshal([]byte(txt), &recipes); err != nil {
			log.Fatal(err)
		}
		fmt.Println(recipes)
	}
}

Una caracola

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GOOGLE_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

Kotlin

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)

Swift

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)
}

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);

Java

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);

Ejecución de código

Python

model = genai.GenerativeModel(model_name="gemini-1.5-flash", tools="code_execution")
response = model.generate_content(
    (
        "What is the sum of the first 50 prime numbers? "
        "Generate and run code for the calculation, and make sure you get all 50."
    )
)

# Each `part` either contains `text`, `executable_code` or an `execution_result`
for part in response.candidates[0].content.parts:
    print(part, "\n")

print("-" * 80)
# The `.text` accessor joins the parts into a markdown compatible text representation.
print("\n\n", response.text)

Kotlin


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)

Java

// 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);

Llamadas a función

Python

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

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

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

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

model = genai.GenerativeModel(
    model_name="gemini-1.5-flash", tools=[add, subtract, multiply, divide]
)
chat = model.start_chat(enable_automatic_function_calling=True)
response = chat.send_message(
    "I have 57 cats, each owns 44 mittens, how many mittens is that in total?"
)
print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
async function setLightValues(brightness, colorTemperature) {
  // This mock API returns the requested lighting values
  return {
    brightness,
    colorTemperature,
  };
}

const controlLightFunctionDeclaration = {
  name: "controlLight",
  parameters: {
    type: "OBJECT",
    description: "Set the brightness and color temperature of a room light.",
    properties: {
      brightness: {
        type: "NUMBER",
        description:
          "Light level from 0 to 100. Zero is off and 100 is full brightness.",
      },
      colorTemperature: {
        type: "STRING",
        description:
          "Color temperature of the light fixture which can be `daylight`, `cool` or `warm`.",
      },
    },
    required: ["brightness", "colorTemperature"],
  },
};

// Executable function code. Put it in a map keyed by the function name
// so that you can call it once you get the name string from the model.
const functions = {
  controlLight: ({ brightness, colorTemperature }) => {
    return setLightValues(brightness, colorTemperature);
  },
};

const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
  tools: { functionDeclarations: [controlLightFunctionDeclaration] },
});
const chat = model.startChat();
const prompt = "Dim the lights so the room feels cozy and warm.";

// Send the message to the model.
const result = await chat.sendMessage(prompt);

// For simplicity, this uses the first function call found.
const call = result.response.functionCalls()[0];

if (call) {
  // Call the executable function named in the function call
  // with the arguments specified in the function call and
  // let it call the hypothetical API.
  const apiResponse = await functions[call.name](call.args);

  // Send the API response back to the model so it can generate
  // a text response that can be displayed to the user.
  const result2 = await chat.sendMessage([
    {
      functionResponse: {
        name: "controlLight",
        response: apiResponse,
      },
    },
  ]);

  // Log the text response.
  console.log(result2.response.text());
}

Una caracola


cat > tools.json << EOF
{
  "function_declarations": [
    {
      "name": "enable_lights",
      "description": "Turn on the lighting system.",
      "parameters": { "type": "object" }
    },
    {
      "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.",
      "parameters": { "type": "object" }
    }
  ]
} 
EOF

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro-latest:generateContent?key=$GOOGLE_API_KEY" \
  -H 'Content-Type: application/json' \
  -d @<(echo '
  {
    "system_instruction": {
      "parts": {
        "text": "You are a helpful lighting system bot. You can turn lights on and off, and you can set the color. Do not perform any other tasks."
      }
    },
    "tools": ['$(source "$tools")'],

    "tool_config": {
      "function_calling_config": {"mode": "none"}
    },

    "contents": {
      "role": "user",
      "parts": {
        "text": "What can you do?"
      }
    }
  }
') 2>/dev/null |sed -n '/"content"/,/"finishReason"/p'

Kotlin

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)
}

Swift

// 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)
}

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}');

Java

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);

Configuración de generación

Python

model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content(
    "Tell me a story about a magic backpack.",
    generation_config=genai.types.GenerationConfig(
        # Only one candidate for now.
        candidate_count=1,
        stop_sequences=["x"],
        max_output_tokens=20,
        temperature=1.0,
    ),
)

print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
  generationConfig: {
    candidateCount: 1,
    stopSequences: ["x"],
    maxOutputTokens: 20,
    temperature: 1.0,
  },
});

const result = await model.generateContent(
  "Tell me a story about a magic backpack.",
);
console.log(result.response.text());

Go

model := client.GenerativeModel("gemini-1.5-pro-latest")
model.SetTemperature(0.9)
model.SetTopP(0.5)
model.SetTopK(20)
model.SetMaxOutputTokens(100)
model.SystemInstruction = genai.NewUserContent(genai.Text("You are Yoda from Star Wars."))
model.ResponseMIMEType = "application/json"
resp, err := model.GenerateContent(ctx, genai.Text("What is the average size of a swallow?"))
if err != nil {
	log.Fatal(err)
}
printResponse(resp)

Una caracola

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
        "contents": [{
            "parts":[
                {"text": "Write a story about a magic backpack."}
            ]
        }],
        "safetySettings": [
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "threshold": "BLOCK_ONLY_HIGH"
            }
        ],
        "generationConfig": {
            "stopSequences": [
                "Title"
            ],
            "temperature": 1.0,
            "maxOutputTokens": 800,
            "topP": 0.8,
            "topK": 10
        }
    }'  2> /dev/null | grep "text"

Kotlin

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)

Swift

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
  )

Dart

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);

Java

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);

Configuración de seguridad

Python

model = genai.GenerativeModel("gemini-1.5-flash")
unsafe_prompt = "I support Martians Soccer Club and I think Jupiterians Football Club sucks! Write a ironic phrase about them."
response = model.generate_content(
    unsafe_prompt,
    safety_settings={
        "HATE": "MEDIUM",
        "HARASSMENT": "BLOCK_ONLY_HIGH",
    },
)
# If you want to set all the safety_settings to the same value you can just pass that value:
response = model.generate_content(unsafe_prompt, safety_settings="MEDIUM")
try:
    print(response.text)
except:
    print("No information generated by the model.")

print(response.candidates[0].safety_ratings)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI, HarmCategory, HarmBlockThreshold } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
  safetySettings: [
    {
      category: HarmCategory.HARM_CATEGORY_HARASSMENT,
      threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
    },
    {
      category: HarmCategory.HARM_CATEGORY_HATE_SPEECH,
      threshold: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
    },
  ],
});

const unsafePrompt =
  "I support Martians Soccer Club and I think " +
  "Jupiterians Football Club sucks! Write an ironic phrase telling " +
  "them how I feel about them.";

const result = await model.generateContent(unsafePrompt);

try {
  result.response.text();
} catch (e) {
  console.error(e);
  console.log(result.response.candidates[0].safetyRatings);
}

Go

model := client.GenerativeModel("gemini-1.5-flash")
model.SafetySettings = []*genai.SafetySetting{
	{
		Category:  genai.HarmCategoryDangerousContent,
		Threshold: genai.HarmBlockLowAndAbove,
	},
	{
		Category:  genai.HarmCategoryHarassment,
		Threshold: genai.HarmBlockMediumAndAbove,
	},
}
resp, err := model.GenerateContent(ctx, genai.Text("I support Martians Soccer Club and I think Jupiterians Football Club sucks! Write a ironic phrase about them."))
if err != nil {
	log.Fatal(err)
}
printResponse(resp)

Una caracola

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-1.5-flash:generateContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d @request.json 2> /dev/null

Kotlin

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))

Swift

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
  )

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');
  }
}

Java

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);

Instrucción del sistema

Python

model = genai.GenerativeModel(
    "models/gemini-1.5-flash",
    system_instruction="You are a cat. Your name is Neko.",
)
response = model.generate_content("Good morning! How are you?")
print(response.text)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({
  model: "gemini-1.5-flash",
  systemInstruction: "You are a cat. Your name is Neko.",
});

const prompt = "Good morning! How are you?";

const result = await model.generateContent(prompt);
const response = result.response;
const text = response.text();
console.log(text);

Go

model := client.GenerativeModel("gemini-1.5-flash")
model.SystemInstruction = genai.NewUserContent(genai.Text("You are a cat. Your name is Neko."))
resp, err := model.GenerateContent(ctx, genai.Text("Good morning! How are you?"))
if err != nil {
	log.Fatal(err)
}
printResponse(resp)

Una caracola

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GOOGLE_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"}}}'

Kotlin

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.") },
    )

Swift

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.")
  )

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);

Java

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());

Cuerpo de la respuesta

Si se ejecuta de forma correcta, el cuerpo de la respuesta contiene una instancia de GenerateContentResponse.

Método: models.streamGenerateContent

Genera una respuesta transmitida del modelo según un GenerateContentRequest de entrada.

Extremo

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

Parámetros de ruta

model string

Obligatorio. Es el nombre de Model que se usará para generar la finalización.

Formato: name=models/{model}. Toma la forma models/{model}.

Cuerpo de la solicitud

El cuerpo de la solicitud contiene datos con la siguiente estructura:

Campos
contents[] object (Content)

Obligatorio. El contenido de la conversación actual con el modelo.

Para consultas de un solo turno, esta es una instancia única. Para consultas de varios turnos, como chat, es un campo repetido que contiene el historial de la conversación y la última solicitud.

tools[] object (Tool)

Opcional. Una lista de Tools que Model puede usar para generar la siguiente respuesta.

Un Tool es un fragmento de código que permite que el sistema interactúe con sistemas externos para realizar una acción, o un conjunto de acciones, fuera del conocimiento y el alcance de Model. Los elementos Tool admitidos son Function y codeExecution. Consulta las guías Llamada a función y Ejecución de código para obtener más información.

toolConfig object (ToolConfig)

Opcional. Configuración de herramientas para cualquier Tool especificado en la solicitud. Consulta la guía de llamada a funciones para ver un ejemplo de uso.

safetySettings[] object (SafetySetting)

Opcional. Una lista de instancias de SafetySetting únicas para bloquear contenido no seguro.

Esto se aplicará de manera forzosa en GenerateContentRequest.contents y GenerateContentResponse.candidates. No debe haber más de un parámetro de configuración para cada tipo de SafetyCategory. La API bloqueará cualquier contenido y respuesta que no cumpla con los umbrales establecidos por esta configuración. Esta lista anula la configuración predeterminada para cada SafetyCategory especificado en SafetySettings. Si no hay un SafetySetting para un SafetyCategory determinado proporcionado en la lista, la API usará la configuración de seguridad predeterminada para esa categoría. Se admiten las categorías de daños HARM_CATEGORY_HATE_SPEECH, HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT, HARM_CATEGORY_HARASSMENT. Consulta la guía para obtener información detallada sobre la configuración de seguridad disponible. Además, consulta la Guía de seguridad para aprender a incorporar consideraciones de seguridad en tus aplicaciones de IA.

systemInstruction object (Content)

Opcional. El desarrollador establece las instrucciones del sistema. Actualmente, es solo texto.

generationConfig object (GenerationConfig)

Opcional. Opciones de configuración para la generación y la salida de modelos.

cachedContent string

Opcional. Es el nombre del contenido almacenado en caché para usar como contexto y entregar la predicción. Formato: cachedContents/{cachedContent}

Ejemplo de solicitud

Texto

Python

model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Write a story about a magic backpack.", stream=True)
for chunk in response:
    print(chunk.text)
    print("_" * 80)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

const prompt = "Write a story about a magic backpack.";

const result = await model.generateContentStream(prompt);

// Print text as it comes in.
for await (const chunk of result.stream) {
  const chunkText = chunk.text();
  process.stdout.write(chunkText);
}

Go

model := client.GenerativeModel("gemini-1.5-flash")
iter := model.GenerateContentStream(ctx, genai.Text("Write a story about a magic backpack."))
for {
	resp, err := iter.Next()
	if err == iterator.Done {
		break
	}
	if err != nil {
		log.Fatal(err)
	}
	printResponse(resp)
}

Una caracola

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:streamGenerateContent?alt=sse&key=${GOOGLE_API_KEY}" \
        -H 'Content-Type: application/json' \
        --no-buffer \
        -d '{ "contents":[{"parts":[{"text": "Write a story about a magic backpack."}]}]}'

Kotlin

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) }

Swift

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)
  }
}

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);
}

Java

// 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);
      }
    });

Imagen

Python

import PIL.Image

model = genai.GenerativeModel("gemini-1.5-flash")
organ = PIL.Image.open(media / "organ.jpg")
response = model.generate_content(["Tell me about this instrument", organ], stream=True)
for chunk in response:
    print(chunk.text)
    print("_" * 80)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

function fileToGenerativePart(path, mimeType) {
  return {
    inlineData: {
      data: Buffer.from(fs.readFileSync(path)).toString("base64"),
      mimeType,
    },
  };
}

const prompt = "Describe how this product might be manufactured.";
// Note: The only accepted mime types are some image types, image/*.
const imagePart = fileToGenerativePart(
  `${mediaPath}/jetpack.jpg`,
  "image/jpeg",
);

const result = await model.generateContentStream([prompt, imagePart]);

// Print text as it comes in.
for await (const chunk of result.stream) {
  const chunkText = chunk.text();
  process.stdout.write(chunkText);
}

Go

model := client.GenerativeModel("gemini-1.5-flash")

imgData, err := os.ReadFile(filepath.Join(testDataDir, "organ.jpg"))
if err != nil {
	log.Fatal(err)
}
iter := model.GenerateContentStream(ctx,
	genai.Text("Tell me about this instrument"),
	genai.ImageData("jpeg", imgData))
for {
	resp, err := iter.Next()
	if err == iterator.Done {
		break
	}
	if err != nil {
		log.Fatal(err)
	}
	printResponse(resp)
}

Una caracola

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:streamGenerateContent?alt=sse&key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
            {"text": "Tell me about this instrument"},
            {
              "inline_data": {
                "mime_type":"image/jpeg",
                "data": "'$(base64 $B64FLAGS $IMG_PATH)'"
              }
            }
        ]
        }]
       }' 2> /dev/null

Kotlin

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) }

Swift

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)
  }
}

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);
}

Java

// 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);
      }
    });

Audio

Python

model = genai.GenerativeModel("gemini-1.5-flash")
sample_audio = genai.upload_file(media / "sample.mp3")
response = model.generate_content(["Give me a summary of this audio file.", sample_audio])

for chunk in response:
    print(chunk.text)
    print("_" * 80)

Una caracola

# 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 "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_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-1.5-flash:streamGenerateContent?alt=sse&key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Please describe this file."},
          {"file_data":{"mime_type": "audio/mpeg", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

Video

Python

import time

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

# Videos need to be processed before you can use them.
while myfile.state.name == "PROCESSING":
    print("processing video...")
    time.sleep(5)
    myfile = genai.get_file(myfile.name)

model = genai.GenerativeModel("gemini-1.5-flash")

response = model.generate_content([myfile, "Describe this video clip"])
for chunk in response:
    print(chunk.text)
    print("_" * 80)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
// import { GoogleAIFileManager, FileState } from "@google/generative-ai/server";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

const fileManager = new GoogleAIFileManager(process.env.API_KEY);

const uploadResult = await fileManager.uploadFile(
  `${mediaPath}/Big_Buck_Bunny.mp4`,
  { mimeType: "video/mp4" },
);

let file = await fileManager.getFile(uploadResult.file.name);
while (file.state === FileState.PROCESSING) {
  process.stdout.write(".");
  // Sleep for 10 seconds
  await new Promise((resolve) => setTimeout(resolve, 10_000));
  // Fetch the file from the API again
  file = await fileManager.getFile(uploadResult.file.name);
}

if (file.state === FileState.FAILED) {
  throw new Error("Video processing failed.");
}

const prompt = "Describe this video clip";
const videoPart = {
  fileData: {
    fileUri: uploadResult.file.uri,
    mimeType: uploadResult.file.mimeType,
  },
};

const result = await model.generateContentStream([prompt, videoPart]);
// Print text as it comes in.
for await (const chunk of result.stream) {
  const chunkText = chunk.text();
  process.stdout.write(chunkText);
}

Go

model := client.GenerativeModel("gemini-1.5-flash")

file, err := client.UploadFileFromPath(ctx, filepath.Join(testDataDir, "earth.mp4"), nil)
if err != nil {
	log.Fatal(err)
}
defer client.DeleteFile(ctx, file.Name)

iter := model.GenerateContentStream(ctx,
	genai.Text("Describe this video clip"),
	genai.FileData{URI: file.URI})
for {
	resp, err := iter.Next()
	if err == iterator.Done {
		break
	}
	if err != nil {
		log.Fatal(err)
	}
	printResponse(resp)
}

Una caracola

# 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 "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_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-1.5-flash:streamGenerateContent?alt=sse&key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Please describe this file."},
          {"file_data":{"mime_type": "video/mp4", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

PDF

Python

model = genai.GenerativeModel("gemini-1.5-flash")
sample_pdf = genai.upload_file(media / "test.pdf")
response = model.generate_content(["Give me a summary of this document:", sample_pdf])

for chunk in response:
    print(chunk.text)
    print("_" * 80)

Una caracola

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 "${BASE_URL}/upload/v1beta/files?key=${GOOGLE_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-1.5-flash:streamGenerateContent?alt=sse&key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "Can you add a few more lines to this poem?"},
          {"file_data":{"mime_type": "application/pdf", "file_uri": '$file_uri'}}]
        }]
       }' 2> /dev/null > response.json

cat response.json
echo

Chat

Python

model = genai.GenerativeModel("gemini-1.5-flash")
chat = model.start_chat(
    history=[
        {"role": "user", "parts": "Hello"},
        {"role": "model", "parts": "Great to meet you. What would you like to know?"},
    ]
)
response = chat.send_message("I have 2 dogs in my house.", stream=True)
for chunk in response:
    print(chunk.text)
    print("_" * 80)
response = chat.send_message("How many paws are in my house?", stream=True)
for chunk in response:
    print(chunk.text)
    print("_" * 80)

print(chat.history)

Node.js

// Make sure to include these imports:
// import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
const chat = model.startChat({
  history: [
    {
      role: "user",
      parts: [{ text: "Hello" }],
    },
    {
      role: "model",
      parts: [{ text: "Great to meet you. What would you like to know?" }],
    },
  ],
});
let result = await chat.sendMessageStream("I have 2 dogs in my house.");
for await (const chunk of result.stream) {
  const chunkText = chunk.text();
  process.stdout.write(chunkText);
}
result = await chat.sendMessageStream("How many paws are in my house?");
for await (const chunk of result.stream) {
  const chunkText = chunk.text();
  process.stdout.write(chunkText);
}

Go

model := client.GenerativeModel("gemini-1.5-flash")
cs := model.StartChat()

cs.History = []*genai.Content{
	{
		Parts: []genai.Part{
			genai.Text("Hello, I have 2 dogs in my house."),
		},
		Role: "user",
	},
	{
		Parts: []genai.Part{
			genai.Text("Great to meet you. What would you like to know?"),
		},
		Role: "model",
	},
}

iter := cs.SendMessageStream(ctx, genai.Text("How many paws are in my house?"))
for {
	resp, err := iter.Next()
	if err == iterator.Done {
		break
	}
	if err != nil {
		log.Fatal(err)
	}
	printResponse(resp)
}

Una caracola

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:streamGenerateContent?alt=sse&key=$GOOGLE_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"

Kotlin

// 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) }

Swift

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)
  }
}

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);
}

Java

// 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) {}

    });

Cuerpo de la respuesta

Si se ejecuta correctamente, el cuerpo de la respuesta contendrá un flujo de instancias de GenerateContentResponse.

GenerateContentResponse

Respuesta del modelo que respalda las respuestas de varios candidatos.

Se informan las clasificaciones de seguridad y el filtrado de contenido para la instrucción en GenerateContentResponse.prompt_feedback y para cada candidato en finishReason y en safetyRatings. La API: - Muestra todos los candidatos solicitados o ninguno - Devuelve ningún candidato solo si hubo algún problema con el mensaje (consulta promptFeedback) - Informa comentarios sobre cada candidato en finishReason y safetyRatings.

Campos
candidates[] object (Candidate)

Respuestas de los candidatos del modelo.

promptFeedback object (PromptFeedback)

Devuelve los comentarios de la instrucción relacionados con los filtros de contenido.

usageMetadata object (UsageMetadata)

Solo salida. Metadatos sobre las solicitudes de generación el uso de tokens.

Representación JSON
{
  "candidates": [
    {
      object (Candidate)
    }
  ],
  "promptFeedback": {
    object (PromptFeedback)
  },
  "usageMetadata": {
    object (UsageMetadata)
  }
}

PromptFeedback

Un conjunto de metadatos de comentarios que se especificó en la instrucción en GenerateContentRequest.content

Campos
blockReason enum (BlockReason)

Opcional. Si se configura, se bloqueó el mensaje y no se muestran candidatos. Reformula la instrucción.

safetyRatings[] object (SafetyRating)

Calificaciones de seguridad de la instrucción. Hay, como máximo, una calificación por categoría.

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

BlockReason

Especifica el motivo por el que se bloqueó el mensaje.

Enumeraciones
BLOCK_REASON_UNSPECIFIED Valor predeterminado Este valor no se usa.
SAFETY Se bloqueó el mensaje por razones de seguridad. Inspecciona safetyRatings para comprender qué categoría de seguridad lo bloqueó.
OTHER Se bloqueó el mensaje por motivos desconocidos.
BLOCKLIST Se bloqueó el mensaje debido a los términos que se incluyen en la lista de términos bloqueados.
PROHIBITED_CONTENT Se bloqueó el mensaje debido a que incluye contenido prohibido.

UsageMetadata

Metadatos sobre el uso del token de la solicitud de generación.

Campos
promptTokenCount integer

Cantidad de tokens en la instrucción. Cuando se establece cachedContent, este sigue siendo el tamaño efectivo total de la instrucción, lo que significa que incluye la cantidad de tokens en el contenido almacenado en caché.

cachedContentTokenCount integer

Cantidad de tokens en la parte almacenada en caché de la instrucción (el contenido almacenado en caché)

candidatesTokenCount integer

Cantidad total de tokens en todos los candidatos de respuesta generados.

totalTokenCount integer

Recuento total de tokens para la solicitud de generación (instrucción + candidatos de respuesta).

Representación JSON
{
  "promptTokenCount": integer,
  "cachedContentTokenCount": integer,
  "candidatesTokenCount": integer,
  "totalTokenCount": integer
}

Candidato

Un candidato de respuesta generado a partir del modelo.

Campos
content object (Content)

Solo salida. Contenido generado que muestra el modelo.

finishReason enum (FinishReason)

Opcional. Solo salida. El motivo por el que el modelo dejó de generar tokens.

Si está vacío, el modelo no dejó de generar tokens.

safetyRatings[] object (SafetyRating)

Lista de calificaciones para la seguridad de un candidato de respuesta.

Hay, como máximo, una calificación por categoría.

citationMetadata object (CitationMetadata)

Solo salida. Información de citas para el candidato generado por el modelo.

Este campo puede completarse con información de lectura de cualquier texto incluido en content. Estos son pasajes que se "recitan" a partir del material protegido por derechos de autor de los datos de entrenamiento fundamentales del LLM.

tokenCount integer

Solo salida. Recuento de tokens de este candidato.

groundingAttributions[] object (GroundingAttribution)

Solo salida. Información de atribución para las fuentes que contribuyeron a una respuesta fundamentada.

Este campo se propaga para las llamadas de GenerateAnswer.

index integer

Solo salida. Índice del candidato en la lista de candidatos de respuesta.

Representación JSON
{
  "content": {
    object (Content)
  },
  "finishReason": enum (FinishReason),
  "safetyRatings": [
    {
      object (SafetyRating)
    }
  ],
  "citationMetadata": {
    object (CitationMetadata)
  },
  "tokenCount": integer,
  "groundingAttributions": [
    {
      object (GroundingAttribution)
    }
  ],
  "index": integer
}

FinishReason

Define el motivo por el que el modelo dejó de generar tokens.

Enumeraciones
FINISH_REASON_UNSPECIFIED Valor predeterminado Este valor no se usa.
STOP Punto de parada natural del modelo o secuencia de paradas proporcionada.
MAX_TOKENS Se alcanzó la cantidad máxima de tokens especificada en la solicitud.
SAFETY El contenido del candidato de respuesta se marcó por motivos de seguridad.
RECITATION Se marcó el contenido de la respuesta del candidato por motivos de recitación.
LANGUAGE El contenido del candidato para la respuesta se marcó por usar un idioma no admitido.
OTHER Motivo desconocido.
BLOCKLIST Se detuvo la generación de tokens porque el contenido incluye términos prohibidos.
PROHIBITED_CONTENT Se detuvo la generación de tokens porque podría incluir contenido prohibido.
SPII Se detuvo la generación de tokens porque el contenido podría incluir información de identificación personal sensible (IIPS).
MALFORMED_FUNCTION_CALL La llamada a función que generó el modelo no es válida.

GroundingAttribution

Atribución de una fuente que contribuyó a una respuesta.

Campos
sourceId object (AttributionSourceId)

Solo salida. Es el identificador de la fuente que contribuye a esta atribución.

content object (Content)

Justificación del contenido fuente que constituye esta atribución

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

AttributionSourceId

Es el identificador de la fuente que contribuye a esta atribución.

Campos

Campo de unión source.

source puede ser una de las siguientes opciones:

groundingPassage object (GroundingPassageId)

Es el identificador de un pasaje intercalado.

semanticRetrieverChunk object (SemanticRetrieverChunk)

Es el identificador de un Chunk recuperado a través del recuperador semántico.

Representación JSON
{

  // Union field source can be only one of the following:
  "groundingPassage": {
    object (GroundingPassageId)
  },
  "semanticRetrieverChunk": {
    object (SemanticRetrieverChunk)
  }
  // End of list of possible types for union field source.
}

GroundingPassageId

Es el identificador de una parte dentro de un GroundingPassage.

Campos
passageId string

Solo salida. ID del pasaje que coincide con el GroundingPassage.id de GenerateAnswerRequest.

partIndex integer

Solo salida. Índice de la parte dentro del GroundingPassage.content de GenerateAnswerRequest.

Representación JSON
{
  "passageId": string,
  "partIndex": integer
}

SemanticRetrieverChunk

Identificador de un Chunk recuperado a través del recuperador semántico especificado en el GenerateAnswerRequest con SemanticRetrieverConfig.

Campos
source string

Solo salida. Nombre de la fuente que coincide con el SemanticRetrieverConfig.source de la solicitud. Ejemplo: corpora/123 o corpora/123/documents/abc

chunk string

Solo salida. Nombre de la Chunk que contiene el texto atribuido. Ejemplo: corpora/123/documents/abc/chunks/xyz

Representación JSON
{
  "source": string,
  "chunk": string
}

CitationMetadata

Es una colección de atribuciones de fuente para un contenido.

Campos
citationSources[] object (CitationSource)

Citas a las fuentes de una respuesta específica.

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

CitationSource

Una cita a una fuente por una parte de una respuesta específica.

Campos
startIndex integer

Opcional. Inicio del segmento de la respuesta que se atribuye a esta fuente.

El índice indica el inicio del segmento, medido en bytes.

endIndex integer

Opcional. Final del segmento atribuido (exclusivo).

uri string

Opcional. Es un URI que se atribuye como fuente para una parte del texto.

license string

Opcional. Licencia del proyecto de GitHub que se atribuye como fuente del segmento.

Se requiere información de la licencia para las citas de códigos.

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

GenerationConfig

Opciones de configuración para la generación y la salida de modelos. No todos los parámetros se pueden configurar para todos los modelos.

Campos
stopSequences[] string

Opcional. El conjunto de secuencias de caracteres (hasta 5) que detendrán la generación de resultados. Si se especifica, la API se detendrá cuando aparezca un stop_sequence por primera vez. La secuencia de detención no se incluirá como parte de la respuesta.

responseMimeType string

Opcional. Es el tipo de MIME del texto candidato generado. Los tipos de MIME admitidos son los siguientes: text/plain: Salida de texto (predeterminado). application/json: Es la respuesta JSON en los candidatos de respuesta. Consulta los documentos para obtener una lista de todos los tipos de MIME de texto admitidos.

responseSchema object (Schema)

Opcional. Esquema de salida del texto candidato generado. Los esquemas deben ser un subconjunto del esquema de OpenAPI y pueden ser objetos, primitivos o arrays.

Si se establece, también se debe establecer un responseMimeType compatible. Tipos de MIME compatibles: application/json: esquema para la respuesta JSON. Consulta la guía de generación de texto JSON para obtener más detalles.

candidateCount integer

Opcional. Cantidad de respuestas generadas para mostrar.

Actualmente, este valor solo se puede establecer en 1. Si no la estableces, el valor predeterminado será 1.

maxOutputTokens integer

Opcional. La cantidad máxima de tokens que se incluirán en un candidato de respuesta.

Nota: El valor predeterminado varía según el modelo. Consulta el atributo Model.output_token_limit del Model que muestra la función getModel.

temperature number

Opcional. Controla la aleatorización de la salida.

Nota: El valor predeterminado varía según el modelo. Consulta el atributo Model.temperature del Model que muestra la función getModel.

Los valores pueden variar entre [0.0, 2.0].

topP number

Opcional. Probabilidad acumulativa máxima de los tokens que se deben tener en cuenta durante el muestreo.

El modelo utiliza el muestreo combinado de k superior y p superior (núcleo).

Los tokens se ordenan según sus probabilidades asignadas para que solo se consideren los tokens más probables. El muestreo de Top-K limita directamente la cantidad máxima de tokens que se deben considerar, mientras que el muestreo de Nucleus limita la cantidad de tokens en función de la probabilidad acumulada.

Nota: El valor predeterminado varía según Model y se especifica mediante el atributo Model.top_p que muestra la función getModel. Un atributo topK vacío indica que el modelo no aplica el muestreo de Top-K ni permite configurar topK en las solicitudes.

topK integer

Opcional. La cantidad máxima de tokens que se deben tener en cuenta durante el muestreo.

Los modelos de Gemini usan el muestreo de núcleo con Top-p o una combinación de muestreo de núcleo y k superior. El muestreo de k superior considera el conjunto de topK tokens más probables. Los modelos que se ejecutan con un muestreo de núcleo no permiten el parámetro de configuración topK.

Nota: El valor predeterminado varía según Model y se especifica mediante el atributo Model.top_p que muestra la función getModel. Un atributo topK vacío indica que el modelo no aplica el muestreo de Top-K ni permite configurar topK en las solicitudes.

Representación JSON
{
  "stopSequences": [
    string
  ],
  "responseMimeType": string,
  "responseSchema": {
    object (Schema)
  },
  "candidateCount": integer,
  "maxOutputTokens": integer,
  "temperature": number,
  "topP": number,
  "topK": integer
}

HarmCategory

Es la categoría de una calificación.

Estas categorías abarcan varios tipos de daños que los desarrolladores podrían querer ajustar.

Enumeraciones
HARM_CATEGORY_UNSPECIFIED La categoría no está especificada.
HARM_CATEGORY_DEROGATORY Comentarios negativos o dañinos que se orientan a la identidad o el atributo protegido.
HARM_CATEGORY_TOXICITY Contenido grosero, irrespetuoso u obsceno.
HARM_CATEGORY_VIOLENCE Describe situaciones que representen violencia contra una persona o un grupo, o descripciones generales de imágenes sangrientas.
HARM_CATEGORY_SEXUAL Incluye referencias a actos sexual o a otro contenido obsceno.
HARM_CATEGORY_MEDICAL Promueve consejos médicos sin verificar.
HARM_CATEGORY_DANGEROUS Contenido peligroso que promueve, facilita o fomenta actividades perjudiciales.
HARM_CATEGORY_HARASSMENT Contenido de acoso.
HARM_CATEGORY_HATE_SPEECH Contenido e incitación al odio o a la violencia.
HARM_CATEGORY_SEXUALLY_EXPLICIT Contenido sexual explícito.
HARM_CATEGORY_DANGEROUS_CONTENT Contenido peligroso.

SafetyRating

Calificación de seguridad de un contenido.

La clasificación de seguridad contiene la categoría de daño y el nivel de probabilidad de daño en esa categoría para un contenido. El contenido se clasifica por seguridad en varias categorías de daños, y aquí se incluye la probabilidad de la clasificación del daño.

Campos
category enum (HarmCategory)

Obligatorio. La categoría de esta calificación.

probability enum (HarmProbability)

Obligatorio. La probabilidad de daño de este contenido.

blocked boolean

¿Se bloqueó el contenido debido a esta clasificación?

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

HarmProbability

Es la probabilidad de que un contenido sea dañino.

El sistema de clasificación da la probabilidad de que el contenido no sea seguro. Esto no indica la gravedad del daño para un contenido.

Enumeraciones
HARM_PROBABILITY_UNSPECIFIED Probabilidad no especificada.
NEGLIGIBLE Las probabilidades de que el contenido no sean seguras son mínimas.
LOW Hay pocas probabilidades de que el contenido sea inseguro.
MEDIUM Tiene una probabilidad media de ser inseguro.
HIGH Hay muchas probabilidades de que el contenido no sea seguro.

SafetySetting

Configuración de seguridad, que afecta el comportamiento de bloqueo de seguridad.

Si pasas un parámetro de configuración de seguridad para una categoría, se cambia la probabilidad permitida de que se bloquee el contenido.

Campos
category enum (HarmCategory)

Obligatorio. La categoría de este parámetro de configuración.

threshold enum (HarmBlockThreshold)

Obligatorio. Controla el umbral de probabilidad en el que se bloquea el daño.

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

HarmBlockThreshold

Bloquear una probabilidad de daño especificada o más allá

Enumeraciones
HARM_BLOCK_THRESHOLD_UNSPECIFIED No se especificó el umbral.
BLOCK_LOW_AND_ABOVE Se permitirá el contenido con NEGLIGIBLE.
BLOCK_MEDIUM_AND_ABOVE Se permitirá el contenido con NEGLIGIBLE y LOW.
BLOCK_ONLY_HIGH Se permitirá el contenido con NEGLIGIBLE, LOW y MEDIUM.
BLOCK_NONE Se permitirá todo el contenido.