npm install @google/genai@1.34.0

npm install @google/generative-ai  # DO NOT USE!

export GEMINI_API_KEY="..."

GEMINI_API_KEY=...

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

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

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Explain quantum computing in simple terms'
});

console.log(response.text);

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [{ parts: [{ text: 'Explain quantum computing in simple terms' }] }]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

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

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

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'Write a haiku about artificial intelligence'
});

console.log(response.text);

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        {
          parts: [
            { text: 'Write a haiku about artificial intelligence' }
          ]
        }
      ]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

{
  text: string,                  // Convenience accessor for text content
  candidates: [
    {
      content: {
        parts: [
          { text: string }       // Generated text
        ],
        role: string             // "model"
      },
      finishReason: string,      // "STOP" | "MAX_TOKENS" | "SAFETY" | "OTHER"
      index: number
    }
  ],
  usageMetadata: {
    promptTokenCount: number,
    candidatesTokenCount: number,
    totalTokenCount: number
  }
}

const response = await ai.models.generateContentStream({
  model: 'gemini-2.5-flash',
  contents: 'Write a 200-word story about time travel'
});

for await (const chunk of response) {
  process.stdout.write(chunk.text);
}

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:streamGenerateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [{ parts: [{ text: 'Write a 200-word story about time travel' }] }]
    }),
  }
);

const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  buffer += decoder.decode(value, { stream: true });
  const lines = buffer.split('\n');
  buffer = lines.pop() || '';

  for (const line of lines) {
    if (line.trim() === '' || line.startsWith('data: [DONE]')) continue;
    if (!line.startsWith('data: ')) continue;

    try {
      const data = JSON.parse(line.slice(6));
      const text = data.candidates[0]?.content?.parts[0]?.text;
      if (text) {
        process.stdout.write(text);
      }
    } catch (e) {
      // Skip invalid JSON
    }
  }
}

import { GoogleGenAI } from '@google/genai';
import fs from 'fs';

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

// From file
const imageData = fs.readFileSync('/path/to/image.jpg');
const base64Image = imageData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'What is in this image?' },
        {
          inlineData: {
            data: base64Image,
            mimeType: 'image/jpeg'
          }
        }
      ]
    }
  ]
});

console.log(response.text);

const imageData = fs.readFileSync('/path/to/image.jpg');
const base64Image = imageData.toString('base64');

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        {
          parts: [
            { text: 'What is in this image?' },
            {
              inlineData: {
                data: base64Image,
                mimeType: 'image/jpeg'
              }
            }
          ]
        }
      ]
    }),
  }
);

const data = await response.json();
console.log(data.candidates[0].content.parts[0].text);

// Video must be < 2 minutes for inline data
const videoData = fs.readFileSync('/path/to/video.mp4');
const base64Video = videoData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Describe what happens in this video' },
        {
          inlineData: {
            data: base64Video,
            mimeType: 'video/mp4'
          }
        }
      ]
    }
  ]
});

console.log(response.text);

const audioData = fs.readFileSync('/path/to/audio.mp3');
const base64Audio = audioData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Transcribe and summarize this audio' },
        {
          inlineData: {
            data: base64Audio,
            mimeType: 'audio/mp3'
          }
        }
      ]
    }
  ]
});

console.log(response.text);

const pdfData = fs.readFileSync('/path/to/document.pdf');
const base64Pdf = pdfData.toString('base64');

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Summarize the key points in this PDF' },
        {
          inlineData: {
            data: base64Pdf,
            mimeType: 'application/pdf'
          }
        }
      ]
    }
  ]
});

console.log(response.text);

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: [
    {
      parts: [
        { text: 'Compare these two images and describe the differences:' },
        { inlineData: { data: base64Image1, mimeType: 'image/jpeg' } },
        { inlineData: { data: base64Image2, mimeType: 'image/jpeg' } }
      ]
    }
  ]
});

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

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

// Define function declarations
const getCurrentWeather = {
  name: 'get_current_weather',
  description: 'Get the current weather for a location',
  parametersJsonSchema: {
    type: 'object',
    properties: {
      location: {
        type: 'string',
        description: 'City name, e.g. San Francisco'
      },
      unit: {
        type: 'string',
        enum: ['celsius', 'fahrenheit']
      }
    },
    required: ['location']
  }
};

// Make request with tools
const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What\'s the weather in Tokyo?',
  config: {
    tools: [
      { functionDeclarations: [getCurrentWeather] }
    ]
  }
});

// Check if model wants to call a function
const functionCall = response.candidates[0].content.parts[0].functionCall;

if (functionCall) {
  console.log('Function to call:', functionCall.name);
  console.log('Arguments:', functionCall.args);

  // Execute the function (your implementation)
  const weatherData = await fetchWeather(functionCall.args.location);

  // Send function result back to model
  const finalResponse = await ai.models.generateContent({
    model: 'gemini-2.5-flash',
    contents: [
      'What\'s the weather in Tokyo?',
      response.candidates[0].content, // Original assistant response with function call
      {
        parts: [
          {
            functionResponse: {
              name: functionCall.name,
              response: weatherData
            }
          }
        ]
      }
    ],
    config: {
      tools: [
        { functionDeclarations: [getCurrentWeather] }
      ]
    }
  });

  console.log(finalResponse.text);
}

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      contents: [
        { parts: [{ text: 'What\'s the weather in Tokyo?' }] }
      ],
      tools: [
        {
          functionDeclarations: [
            {
              name: 'get_current_weather',
              description: 'Get the current weather for a location',
              parameters: {
                type: 'object',
                properties: {
                  location: {
                    type: 'string',
                    description: 'City name'
                  }
                },
                required: ['location']
              }
            }
          ]
        }
      ]
    }),
  }
);

const data = await response.json();
const functionCall = data.candidates[0]?.content?.parts[0]?.functionCall;

if (functionCall) {
  // Execute function and send result back (same flow as SDK)
}

const tools = [
  {
    functionDeclarations: [
      {
        name: 'get_weather',
        description: 'Get weather for a location',
        parametersJsonSchema: {
          type: 'object',
          properties: {
            location: { type: 'string' }
          },
          required: ['location']
        }
      },
      {
        name: 'get_population',
        description: 'Get population of a city',
        parametersJsonSchema: {
          type: 'object',
          properties: {
            city: { type: 'string' }
          },
          required: ['city']
        }
      }
    ]
  }
];

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What is the weather and population of Tokyo?',
  config: { tools }
});

// Model may return MULTIPLE function calls in parallel
const functionCalls = response.candidates[0].content.parts.filter(
  part => part.functionCall
);

console.log(`Model wants to call ${functionCalls.length} functions in parallel`);

import { FunctionCallingConfigMode } from '@google/genai';

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'What\'s the weather?',
  config: {
    tools: [{ functionDeclarations: [getCurrentWeather] }],
    toolConfig: {
      functionCallingConfig: {
        mode: FunctionCallingConfigMode.ANY, // Force function call
        // mode: FunctionCallingConfigMode.AUTO, // Model decides (default)
        // mode: FunctionCallingConfigMode.NONE, // Never call functions
        allowedFunctionNames: ['get_current_weather'] // Optional: restrict to specific functions
      }
    }
  }
});

const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  systemInstruction: 'You are a helpful AI assistant that always responds in the style of a pirate. Use nautical terminology and end sentences with "arrr".',
  contents: 'Explain what a database is'
});

console.log(response.text);
// Output: "Ahoy there! A database be like a treasure chest..."

const response = await fetch(
  `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent`,
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'x-goog-api-key': env.GEMINI_API_KEY,
    },
    body: JSON.stringify({
      systemInstruction: {
        parts: [
          { text: 'You are a helpful AI assistant that always responds in the style of a pirate.' }
        ]
      },
      contents: [
        { parts: [{ text: 'Explain what a database is' }] }
      ]
    }),
  }
);