คู่มือสำหรับนักพัฒนาซอฟต์แวร์ Gemini 3

from google import genai

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-preview",
    contents="Find the race condition in this multi-threaded C++ snippet: [code here]",
)

print(response.text)

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

async function run() {
  const response = await ai.models.generateContent({
    model: "gemini-3-pro-preview",
    contents: "Find the race condition in this multi-threaded C++ snippet: [code here]",
  });

  console.log(response.text);
}

run();

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [{
      "parts": [{"text": "Find the race condition in this multi-threaded C++ snippet: [code here]"}]
    }]
  }'

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-preview",
    contents="How does AI work?",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="low")
    ),
)

print(response.text)

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const response = await ai.models.generateContent({
    model: "gemini-3-pro-preview",
    contents: "How does AI work?",
    config: {
      thinkingConfig: {
        thinkingLevel: "low",
      }
    },
  });

console.log(response.text);

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [{
      "parts": [{"text": "How does AI work?"}]
    }],
    "generationConfig": {
      "thinkingConfig": {
        "thinkingLevel": "low"
      }
    }
  }'

from google import genai
from google.genai import types
import base64

# The media_resolution parameter is currently only available in the v1alpha API version.
client = genai.Client(http_options={'api_version': 'v1alpha'})

response = client.models.generate_content(
    model="gemini-3-pro-preview",
    contents=[
        types.Content(
            parts=[
                types.Part(text="What is in this image?"),
                types.Part(
                    inline_data=types.Blob(
                        mime_type="image/jpeg",
                        data=base64.b64decode("..."),
                    ),
                    media_resolution={"level": "media_resolution_high"}
                )
            ]
        )
    ]
)

print(response.text)

import { GoogleGenAI } from "@google/genai";

// The media_resolution parameter is currently only available in the v1alpha API version.
const ai = new GoogleGenAI({ apiVersion: "v1alpha" });

async function run() {
  const response = await ai.models.generateContent({
    model: "gemini-3-pro-preview",
    contents: [
      {
        parts: [
          { text: "What is in this image?" },
          {
            inlineData: {
              mimeType: "image/jpeg",
              data: "...",
            },
            mediaResolution: {
              level: "media_resolution_high"
            }
          }
        ]
      }
    ]
  });

  console.log(response.text);
}

run();

curl "https://generativelanguage.googleapis.com/v1alpha/models/gemini-3-pro-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [{
      "parts": [
        { "text": "What is in this image?" },
        {
          "inlineData": {
            "mimeType": "image/jpeg",
            "data": "..."
          },
          "mediaResolution": {
            "level": "media_resolution_high"
          }
        }
      ]
    }]
  }'

from google import genai
from google.genai import types
from pydantic import BaseModel, Field
from typing import List

class MatchResult(BaseModel):
    winner: str = Field(description="The name of the winner.")
    final_match_score: str = Field(description="The final match score.")
    scorers: List[str] = Field(description="The name of the scorer.")

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-preview",
    contents="Search for all details for the latest Euro.",
    config={
        "tools": [
            {"google_search": {}},
            {"url_context": {}}
        ],
        "response_mime_type": "application/json",
        "response_json_schema": MatchResult.model_json_schema(),
    },  
)

result = MatchResult.model_validate_json(response.text)
print(result)

import { GoogleGenAI } from "@google/genai";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const ai = new GoogleGenAI({});

const matchSchema = z.object({
  winner: z.string().describe("The name of the winner."),
  final_match_score: z.string().describe("The final score."),
  scorers: z.array(z.string()).describe("The name of the scorer.")
});

async function run() {
  const response = await ai.models.generateContent({
    model: "gemini-3-pro-preview",
    contents: "Search for all details for the latest Euro.",
    config: {
      tools: [
        { googleSearch: {} },
        { urlContext: {} }
      ],
      responseMimeType: "application/json",
      responseJsonSchema: zodToJsonSchema(matchSchema),
    },
  });

  const match = matchSchema.parse(JSON.parse(response.text));
  console.log(match);
}

run();

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [{
      "parts": [{"text": "Search for all details for the latest Euro."}]
    }],
    "tools": [
      {"googleSearch": {}},
      {"urlContext": {}}
    ],
    "generationConfig": {
        "responseMimeType": "application/json",
        "responseJsonSchema": {
            "type": "object",
            "properties": {
                "winner": {"type": "string", "description": "The name of the winner."},
                "final_match_score": {"type": "string", "description": "The final score."},
                "scorers": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "The name of the scorer."
                }
            },
            "required": ["winner", "final_match_score", "scorers"]
        }
    }
  }'

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Generate an infographic of the current weather in Tokyo.",
    config=types.GenerateContentConfig(
        tools=[{"google_search": {}}],
        image_config=types.ImageConfig(
            aspect_ratio="16:9",
            image_size="4K"
        )
    )
)

image_parts = [part for part in response.parts if part.inline_data]

if image_parts:
    image = image_parts[0].as_image()
    image.save('weather_tokyo.png')
    image.show()

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({});

async function run() {
  const response = await ai.models.generateContent({
    model: "gemini-3-pro-image-preview",
    contents: "Generate a visualization of the current weather in Tokyo.",
    config: {
      tools: [{ googleSearch: {} }],
      imageConfig: {
        aspectRatio: "16:9",
        imageSize: "4K"
      }
    }
  });

  for (const part of response.candidates[0].content.parts) {
    if (part.inlineData) {
      const imageData = part.inlineData.data;
      const buffer = Buffer.from(imageData, "base64");
      fs.writeFileSync("weather_tokyo.png", buffer);
    }
  }
}

run();

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [{
      "parts": [{"text": "Generate a visualization of the current weather in Tokyo."}]
    }],
    "tools": [{"googleSearch": {}}],
    "generationConfig": {
        "imageConfig": {
          "aspectRatio": "16:9",
          "imageSize": "4K"
      }
    }
  }'

from google import genai
from google.genai import types

import requests

client = genai.Client()

# This is a manual, two turn multimodal function calling workflow:

# 1. Define the function tool
get_image_declaration = types.FunctionDeclaration(
  name="get_image",
  description="Retrieves the image file reference for a specific order item.",
  parameters={
      "type": "object",
      "properties": {
          "item_name": {
              "type": "string",
              "description": "The name or description of the item ordered (e.g., 'instrument')."
          }
      },
      "required": ["item_name"],
  },
)
tool_config = types.Tool(function_declarations=[get_image_declaration])

# 2. Send a message that triggers the tool
prompt = "Show me the instrument I ordered last month."
response_1 = client.models.generate_content(
  model="gemini-3-flash-preview",
  contents=[prompt],
  config=types.GenerateContentConfig(
      tools=[tool_config],
  )
)

# 3. Handle the function call
function_call = response_1.function_calls[0]
requested_item = function_call.args["item_name"]
print(f"Model wants to call: {function_call.name}")

# Execute your tool (e.g., call an API)
# (This is a mock response for the example)
print(f"Calling external tool for: {requested_item}")

function_response_data = {
  "image_ref": {"$ref": "instrument.jpg"},
}
image_path = "https://goo.gle/instrument-img"
image_bytes = requests.get(image_path).content
function_response_multimodal_data = types.FunctionResponsePart(
  inline_data=types.FunctionResponseBlob(
    mime_type="image/jpeg",
    display_name="instrument.jpg",
    data=image_bytes,
  )
)

# 4. Send the tool's result back
# Append this turn's messages to history for a final response.
history = [
  types.Content(role="user", parts=[types.Part(text=prompt)]),
  response_1.candidates[0].content,
  types.Content(
    role="tool",
    parts=[
        types.Part.from_function_response(
          name=function_call.name,
          response=function_response_data,
          parts=[function_response_multimodal_data]
        )
    ],
  )
]

response_2 = client.models.generate_content(
  model="gemini-3-flash-preview",
  contents=history,
  config=types.GenerateContentConfig(
      tools=[tool_config],
      thinking_config=types.ThinkingConfig(include_thoughts=True)
  ),
)

print(f"\nFinal model response: {response_2.text}")

import { GoogleGenAI, Type } from '@google/genai';

const client = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

// This is a manual, two turn multimodal function calling workflow:
// 1. Define the function tool
const getImageDeclaration = {
  name: 'get_image',
  description: 'Retrieves the image file reference for a specific order item.',
  parameters: {
    type: Type.OBJECT,
    properties: {
      item_name: {
        type: Type.STRING,
        description: "The name or description of the item ordered (e.g., 'instrument').",
      },
    },
    required: ['item_name'],
  },
};

const toolConfig = {
  functionDeclarations: [getImageDeclaration],
};

// 2. Send a message that triggers the tool
const prompt = 'Show me the instrument I ordered last month.';
const response1 = await client.models.generateContent({
  model: 'gemini-3-flash-preview',
  contents: prompt,
  config: {
    tools: [toolConfig],
  },
});

// 3. Handle the function call
const functionCall = response1.functionCalls[0];
const requestedItem = functionCall.args.item_name;
console.log(`Model wants to call: ${functionCall.name}`);

// Execute your tool (e.g., call an API)
// (This is a mock response for the example)
console.log(`Calling external tool for: ${requestedItem}`);

const functionResponseData = {
  image_ref: { $ref: 'instrument.jpg' },
};

const imageUrl = "https://goo.gle/instrument-img";
const response = await fetch(imageUrl);
const imageArrayBuffer = await response.arrayBuffer();
const base64ImageData = Buffer.from(imageArrayBuffer).toString('base64');

const functionResponseMultimodalData = {
  inlineData: {
    mimeType: 'image/jpeg',
    displayName: 'instrument.jpg',
    data: base64ImageData,
  },
};

// 4. Send the tool's result back
// Append this turn's messages to history for a final response.
const history = [
  { role: 'user', parts: [{ text: prompt }] },
  response1.candidates[0].content,
  {
    role: 'tool',
    parts: [
      {
        functionResponse: {
          name: functionCall.name,
          response: functionResponseData,
          parts: [functionResponseMultimodalData],
        },
      },
    ],
  },
];

const response2 = await client.models.generateContent({
  model: 'gemini-3-flash-preview',
  contents: history,
  config: {
    tools: [toolConfig],
    thinkingConfig: { includeThoughts: true },
  },
});

console.log(`\nFinal model response: ${response2.text}`);

IMG_URL="https://goo.gle/instrument-img"

MIME_TYPE=$(curl -sIL "$IMG_URL" | grep -i '^content-type:' | awk -F ': ' '{print $2}' | sed 's/\r$//' | head -n 1)
if [[ -z "$MIME_TYPE" || ! "$MIME_TYPE" == image/* ]]; then
  MIME_TYPE="image/jpeg"
fi

# Check for macOS
if [[ "$(uname)" == "Darwin" ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -b 0)
elif [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64)
else
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -w0)
fi

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      ...,
      {
        "role": "user",
        "parts": [
        {
            "functionResponse": {
              "name": "get_image",
              "response": {
                "image_ref": {
                  "$ref": "instrument.jpg"
                }
              },
              "parts": [
                {
                  "inlineData": {
                    "displayName": "instrument.jpg",
                    "mimeType":"'"$MIME_TYPE"'",
                    "data": "'"$IMAGE_B64"'"
                  }
                }
              ]
            }
          }
        ]
      }
    ]
  }'