La API de Gemini proporciona acceso alojado a Gemma como una API de programación que puedes usar en el desarrollo o la creación de prototipos de aplicaciones. Esta API es una alternativa conveniente a la configuración de tu propia instancia local de Gemma y del servicio web para controlar las tareas de IA generativa.
Modelos compatibles
La API de Gemini admite los siguientes modelos de Gemma 4:
gemma-4-31b-itgemma-4-26b-a4b-it
En el siguiente ejemplo, se muestra cómo usar Gemma con la API de Gemini:
Python
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemma-4-26b-a4b-it",
contents="Roses are red...",
)
print(response.text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI();
const response = await ai.models.generateContent({
model: "gemma-4-26b-a4b-it",
contents: "Roses are red...",
});
console.log(response.text);
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [{
"parts":[{"text": "Roses are red..."}]
}]
}'
Puedes acceder a la API de Gemini en muchas plataformas, como servicios para dispositivos móviles, web y en la nube, y con varios lenguajes de programación. Para obtener más información sobre los paquetes del SDK de la API de Gemini, consulta la página de descargas del SDK de la API de Gemini. Para obtener una introducción general a la API de Gemini, consulta la guía de inicio rápido de la API de Gemini.
Pensando
Gemma 4 utiliza un "proceso de pensamiento" interno que optimiza su razonamiento de varios pasos, lo que ofrece un rendimiento superior en dominios lógicamente exigentes, como la codificación algorítmica y las demostraciones matemáticas avanzadas.
Si bien Gemma 4 admite estrictamente activar o desactivar esta función, puedes habilitarla
en la API configurando el nivel de pensamiento en "high".
En el siguiente ejemplo, se muestra cómo activar el proceso de pensamiento:
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemma-4-26b-a4b-it",
contents="What is the water formula?",
config=types.GenerateContentConfig(
thinking_config=types.ThinkingConfig(thinking_level="high")
),
)
print(response.text)
JavaScript
import { GoogleGenAI, ThinkingLevel } from "@google/genai";
const ai = new GoogleGenAI();
const response = await ai.models.generateContent({
model: "gemma-4-26b-a4b-it",
contents: "What is the water formula?",
config: {
thinkingConfig: {
thinkingLevel: ThinkingLevel.HIGH,
},
},
});
console.log(response.text);
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [{
"parts":[{"text": "What is the water formula?"}]
}],
"generationConfig": {
"thinkingConfig": {
"thinkingLevel": "high"
}
}
}'
Obtén más información sobre el pensamiento:
- Pensamiento de la API de Gemini (introducción general)
- Pensamiento de Gemma (capacidades específicas de Gemma)
Comprensión de imágenes
Los modelos de Gemma 4 pueden procesar imágenes, lo que permite muchos casos de uso de desarrolladores de vanguardia que históricamente habrían requerido modelos específicos del dominio.
En el siguiente ejemplo, se muestra cómo usar las entradas de imágenes de Gemma con la API de Gemini:
Python
from google import genai
client = genai.Client()
my_file = client.files.upload(file="path/to/sample.jpg")
response = client.models.generate_content(
model="gemma-4-26b-a4b-it",
contents=[my_file, "Caption this image."],
)
print(response.text)
JavaScript
import {
GoogleGenAI,
createUserContent,
createPartFromUri,
} from "@google/genai";
const ai = new GoogleGenAI();
const myfile = await ai.files.upload({
file: "path/to/sample.jpg",
config: { mimeType: "image/jpeg" },
});
const response = await ai.models.generateContent({
model: "gemma-4-26b-a4b-it",
contents: createUserContent([
createPartFromUri(myfile.uri, myfile.mimeType),
"Caption this image.",
]),
});
console.log(response.text);
```
REST
IMAGE_PATH="cats-and-dogs.jpg"
MIME_TYPE=$(file -b --mime-type "${IMAGE_PATH}")
NUM_BYTES=$(wc -c < "${IMAGE_PATH}")
DISPLAY_NAME=IMAGE
tmp_header_file=upload-header.tmp
# Initial resumable request defining metadata.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
-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: ${NUM_BYTES}" \
-H "X-Goog-Upload-Offset: 0" \
-H "X-Goog-Upload-Command: upload, finalize" \
--data-binary "@${IMAGE_PATH}" 2> /dev/null > file_info.json
file_uri=$(jq -r ".file.uri" file_info.json)
echo file_uri=$file_uri
# Now generate content using that file
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [{
"parts":[
{"file_data":{"mime_type": "'"${MIME_TYPE}"'", "file_uri": "'"${file_uri}"'"}},
{"text": "Caption this image."}]
}]
}' 2> /dev/null > response.json
cat response.json
echo
jq -r ".candidates[].content.parts[].text" response.json
Obtén más información sobre la comprensión de imágenes:
- Comprensión de imágenes de la API de Gemini (introducción general)
- Comprensión de imágenes de Gemma (capacidades específicas de Gemma)
Instrucciones del sistema
Puedes pasar una instrucción del sistema para establecer el comportamiento del modelo:
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemma-4-26b-a4b-it",
config=types.GenerateContentConfig(
system_instruction="You are a wise Kyoto tea master. Speak calmly and poetically, using nature metaphors. Keep answers under 3 sentences."
),
contents="What is the purpose of the tea ceremony?"
)
print(response.text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI();
const response = await ai.models.generateContent({
model: "gemma-4-26b-a4b-it",
contents: "What is the purpose of the tea ceremony?",
config: {
systemInstruction: "You are a wise Kyoto tea master. Speak calmly and poetically, using nature metaphors. Keep answers under 3 sentences."
}
});
console.log(response.text);
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [{
"parts":[{"text": "What is the purpose of the tea ceremony?"}]
}],
"systemInstruction": {
"parts": [{"text": "You are a wise Kyoto tea master. Speak calmly and poetically, using nature metaphors. Keep answers under 3 sentences."}]
}
}'
Conversaciones de varios turnos
El SDK proporciona una interfaz de chat que realiza un seguimiento automático del historial de conversaciones:
Python
from google import genai
client = genai.Client()
chat = client.chats.create(model="gemma-4-26b-a4b-it")
response = chat.send_message("What are the three most famous castles in Japan?")
print(response.text)
response = chat.send_message("Which one should I visit in spring for cherry blossoms?")
print(response.text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI();
const chat = ai.chats.create({ model: "gemma-4-26b-a4b-it" });
let response = await chat.sendMessage({ message: "What are the three most famous castles in Japan?" });
console.log(response.text);
response = await chat.sendMessage({ message: "Which one should I visit in spring for cherry blossoms?" });
console.log(response.text);
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [
{
"role": "user",
"parts": [{ "text": "What are the three most famous castles in Japan?" }]
},
{
"role": "model",
"parts": [{ "text": "Himeji Castle, Matsumoto Castle, and Kumamoto Castle are often considered the top three." }]
},
{
"role": "user",
"parts": [{ "text": "Which one should I visit in spring for cherry blossoms?" }]
}
]
}'
Llamadas a funciones
Define herramientas como declaraciones de funciones. El modelo decide cuándo llamarlas:
Python
from google import genai
from google.genai import types
# Define the function declaration
get_weather = {
"name": "get_weather",
"description": "Get current weather for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, e.g. 'San Francisco, CA'",
},
},
"required": ["location"],
},
}
client = genai.Client()
tools = types.Tool(function_declarations=[get_weather])
config = types.GenerateContentConfig(tools=[tools])
response = client.models.generate_content(
model="gemma-4-26b-a4b-it",
contents="Should I bring an umbrella to Kyoto today?",
config=config,
)
# The model returns a function call instead of text
if response.function_calls:
for fc in response.function_calls:
print(f"Function to call: {fc.name}")
print(f"ID: {fc.id}")
print(f"Arguments: {fc.args}")
else:
print("No function call found in the response.")
print(response.text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI();
const get_weather = {
name: "get_weather",
description: "Get current weather for a given location.",
parameters: {
type: "object",
properties: {
location: {
type: "string",
description: "City and state, e.g. 'San Francisco, CA'",
},
},
required: ["location"],
},
};
const response = await ai.models.generateContent({
model: "gemma-4-26b-a4b-it",
contents: "Should I bring an umbrella to Kyoto today?",
config: {
tools: [{ functionDeclarations: [get_weather] }]
}
});
if (response.functionCalls) {
for (const fc of response.functionCalls) {
console.log(`Function to call: ${fc.name}`);
console.log(`Arguments: ${JSON.stringify(fc.args)}`);
}
} else {
console.log("No function call found in the response.");
console.log(response.text);
}
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [{
"parts":[{"text": "Should I bring an umbrella to Kyoto today?"}]
}],
"tools": [{
"functionDeclarations": [{
"name": "get_weather",
"description": "Get current weather for a given location.",
"parameters": {
"type": "OBJECT",
"properties": {
"location": {
"type": "STRING",
"description": "City and state, e.g. 'San Francisco, CA'"
}
},
"required": ["location"]
}
}]
}]
}'
Búsqueda de Google
Fundamenta las respuestas de Gemma 4 en datos web en tiempo real con la Búsqueda de Google:
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemma-4-26b-a4b-it",
contents="What are the dates for cherry blossom season in Tokyo this year?",
config=types.GenerateContentConfig(
tools=[{"google_search":{}}]
),
)
print(response.text)
# Access grounding metadata for citations
for chunk in response.candidates[0].grounding_metadata.grounding_chunks:
print(f"Source: {chunk.web.title} — {chunk.web.uri}")
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI();
const response = await ai.models.generateContent({
model: "gemma-4-26b-a4b-it",
contents: "What are the dates for cherry blossom season in Tokyo this year?",
config: {
tools: [{ googleSearch: {} }]
}
});
console.log(response.text);
if (response.candidates?.[0]?.groundingMetadata?.groundingChunks) {
for (const chunk of response.candidates[0].groundingMetadata.groundingChunks) {
if (chunk.web) {
console.log(`Source: ${chunk.web.title} — ${chunk.web.uri}`);
}
}
}
REST
curl "https://generativelanguage.googleapis.com/v1beta/models/gemma-4-26b-a4b-it:generateContent" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
"contents": [{
"parts":[{"text": "What are the dates for cherry blossom season in Tokyo this year?"}]
}],
"tools": [{"googleSearch": {}}]
}'