Gemini モデルには、OpenAI ライブラリ（Python と TypeScript / Javascript）と REST API を使用してアクセスできます。これには、3 行のコードを更新し、Gemini API キーを使用します。OpenAI ライブラリを使用していない場合は、Gemini API を直接呼び出すことをおすすめします。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) response = client.chat.completions.create( model="gemini-2.5-flash", messages=[ {"role": "system", "content": "You are a helpful assistant."}, { "role": "user", "content": "Explain to me how AI works" } ] ) print(response.choices[0].message) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/" }); const response = await openai.chat.completions.create({ model: "gemini-2.0-flash", messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "Explain to me how AI works", }, ], }); console.log(response.choices[0].message); REST
curl "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d '{ "model": "gemini-2.0-flash", "messages": [ {"role": "user", "content": "Explain to me how AI works"} ] }' 変更点3 行だけです。
api_key="GEMINI_API_KEY": 「GEMINI_API_KEY」を実際の Gemini API キーに置き換えます。このキーは Google AI Studio で取得できます。base_url="https://generativelanguage.googleapis.com/v1beta/openai/": これにより、OpenAI ライブラリはデフォルトの URL ではなく Gemini API エンドポイントにリクエストを送信します。model="gemini-2.0-flash": 互換性のある Gemini モデルを選択する
思考
Gemini 2.5 モデルは、複雑な問題を思考するようトレーニングされているため、推論が大幅に改善されています。Gemini API には、モデルの思考量をきめ細かく制御できる「思考予算」パラメータが用意されています。
Gemini API とは異なり、OpenAI API には 3 つの思考制御レベル（"low"、"medium"、"high"）があり、それぞれ 1,024、8,192、24,576 のトークンに対応しています。
思考を無効にする場合は、reasoning_effort を "none" に設定します（2.5 Pro モデルでは推論をオフにできないことに注意してください）。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) response = client.chat.completions.create( model="gemini-2.5-flash", reasoning_effort="low", messages=[ {"role": "system", "content": "You are a helpful assistant."}, { "role": "user", "content": "Explain to me how AI works" } ] ) print(response.choices[0].message) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/" }); const response = await openai.chat.completions.create({ model: "gemini-2.5-flash", reasoning_effort: "low", messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "Explain to me how AI works", }, ], }); console.log(response.choices[0].message); REST
curl "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d '{ "model": "gemini-2.5-flash", "reasoning_effort": "low", "messages": [ {"role": "user", "content": "Explain to me how AI works"} ] }' Gemini 思考モデルは思考の要約も生成し、正確な思考予算を使用できます。extra_body フィールドを使用して、これらのフィールドをリクエストに含めることができます。
reasoning_effort と thinking_budget は機能が重複しているため、同時に使用することはできません。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) response = client.chat.completions.create( model="gemini-2.5-flash", messages=[{"role": "user", "content": "Explain to me how AI works"}], extra_body={ 'extra_body': { "google": { "thinking_config": { "thinking_budget": 800, "include_thoughts": True } } } } ) print(response.choices[0].message) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/" }); const response = await openai.chat.completions.create({ model: "gemini-2.5-flash", messages: [{role: "user", content: "Explain to me how AI works",}], extra_body: { "google": { "thinking_config": { "thinking_budget": 800, "include_thoughts": true } } } }); console.log(response.choices[0].message); REST
curl "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d '{ "model": "gemini-2.5-flash", "messages": [{"role": "user", "content": "Explain to me how AI works"}], "extra_body": { "google": { "thinking_config": { "include_thoughts": true } } } }' ストリーミング
Gemini API はストリーミング レスポンスをサポートしています。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) response = client.chat.completions.create( model="gemini-2.0-flash", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"} ], stream=True ) for chunk in response: print(chunk.choices[0].delta) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/" }); async function main() { const completion = await openai.chat.completions.create({ model: "gemini-2.0-flash", messages: [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"} ], stream: true, }); for await (const chunk of completion) { console.log(chunk.choices[0].delta.content); } } main(); REST
curl "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d '{ "model": "gemini-2.0-flash", "messages": [ {"role": "user", "content": "Explain to me how AI works"} ], "stream": true }' 関数呼び出し
関数呼び出しを使用すると、生成モデルから構造化データ出力を簡単に取得できます。これは Gemini API でサポートされています。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) tools = [ { "type": "function", "function": { "name": "get_weather", "description": "Get the weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. Chicago, IL", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ] messages = [{"role": "user", "content": "What's the weather like in Chicago today?"}] response = client.chat.completions.create( model="gemini-2.0-flash", messages=messages, tools=tools, tool_choice="auto" ) print(response) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/" }); async function main() { const messages = [{"role": "user", "content": "What's the weather like in Chicago today?"}]; const tools = [ { "type": "function", "function": { "name": "get_weather", "description": "Get the weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. Chicago, IL", }, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, } } ]; const response = await openai.chat.completions.create({ model: "gemini-2.0-flash", messages: messages, tools: tools, tool_choice: "auto", }); console.log(response); } main(); REST
curl "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d '{ "model": "gemini-2.0-flash", "messages": [ { "role": "user", "content": "What'\''s the weather like in Chicago today?" } ], "tools": [ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. Chicago, IL" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], "tool_choice": "auto" }' 画像理解
Gemini モデルはネイティブにマルチモーダルであり、多くの一般的なビジョン タスクでクラス最高のパフォーマンスを提供します。
Python
import base64 from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) # Function to encode the image def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') # Getting the base64 string base64_image = encode_image("Path/to/agi/image.jpeg") response = client.chat.completions.create( model="gemini-2.0-flash", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image?", }, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{base64_image}" }, }, ], } ], ) print(response.choices[0]) JavaScript
import OpenAI from "openai"; import fs from 'fs/promises'; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/" }); async function encodeImage(imagePath) { try { const imageBuffer = await fs.readFile(imagePath); return imageBuffer.toString('base64'); } catch (error) { console.error("Error encoding image:", error); return null; } } async function main() { const imagePath = "Path/to/agi/image.jpeg"; const base64Image = await encodeImage(imagePath); const messages = [ { "role": "user", "content": [ { "type": "text", "text": "What is in this image?", }, { "type": "image_url", "image_url": { "url": `data:image/jpeg;base64,${base64Image}` }, }, ], } ]; try { const response = await openai.chat.completions.create({ model: "gemini-2.0-flash", messages: messages, }); console.log(response.choices[0]); } catch (error) { console.error("Error calling Gemini API:", error); } } main(); REST
bash -c ' base64_image=$(base64 -i "Path/to/agi/image.jpeg"); curl "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d "{ \"model\": \"gemini-2.0-flash\", \"messages\": [ { \"role\": \"user\", \"content\": [ { \"type\": \"text\", \"text\": \"What is in this image?\" }, { \"type\": \"image_url\", \"image_url\": { \"url\": \"data:image/jpeg;base64,${base64_image}\" } } ] } ] }" ' 画像を生成する
画像を生成する:
Python
import base64 from openai import OpenAI from PIL import Image from io import BytesIO client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/", ) response = client.images.generate( model="imagen-3.0-generate-002", prompt="a portrait of a sheepadoodle wearing a cape", response_format='b64_json', n=1, ) for image_data in response.data: image = Image.open(BytesIO(base64.b64decode(image_data.b64_json))) image.show() JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/", }); async function main() { const image = await openai.images.generate( { model: "imagen-3.0-generate-002", prompt: "a portrait of a sheepadoodle wearing a cape", response_format: "b64_json", n: 1, } ); console.log(image.data); } main(); REST
curl "https://generativelanguage.googleapis.com/v1beta/openai/images/generations" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d '{ "model": "imagen-3.0-generate-002", "prompt": "a portrait of a sheepadoodle wearing a cape", "response_format": "b64_json", "n": 1, }' 音声の理解
音声入力を分析する:
Python
import base64 from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) with open("/path/to/your/audio/file.wav", "rb") as audio_file: base64_audio = base64.b64encode(audio_file.read()).decode('utf-8') response = client.chat.completions.create( model="gemini-2.0-flash", messages=[ { "role": "user", "content": [ { "type": "text", "text": "Transcribe this audio", }, { "type": "input_audio", "input_audio": { "data": base64_audio, "format": "wav" } } ], } ], ) print(response.choices[0].message.content) JavaScript
import fs from "fs"; import OpenAI from "openai"; const client = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/", }); const audioFile = fs.readFileSync("/path/to/your/audio/file.wav"); const base64Audio = Buffer.from(audioFile).toString("base64"); async function main() { const response = await client.chat.completions.create({ model: "gemini-2.0-flash", messages: [ { role: "user", content: [ { type: "text", text: "Transcribe this audio", }, { type: "input_audio", input_audio: { data: base64Audio, format: "wav", }, }, ], }, ], }); console.log(response.choices[0].message.content); } main(); REST
bash -c ' base64_audio=$(base64 -i "/path/to/your/audio/file.wav"); curl "https://generativelanguage.googleapis.com/v1beta/openai/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d "{ \"model\": \"gemini-2.0-flash\", \"messages\": [ { \"role\": \"user\", \"content\": [ { \"type\": \"text\", \"text\": \"Transcribe this audio file.\" }, { \"type\": \"input_audio\", \"input_audio\": { \"data\": \"${base64_audio}\", \"format\": \"wav\" } } ] } ] }" ' 構造化出力
Gemini モデルは、定義した構造で JSON オブジェクトを出力できます。
Python
from pydantic import BaseModel from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) class CalendarEvent(BaseModel): name: str date: str participants: list[str] completion = client.beta.chat.completions.parse( model="gemini-2.0-flash", messages=[ {"role": "system", "content": "Extract the event information."}, {"role": "user", "content": "John and Susan are going to an AI conference on Friday."}, ], response_format=CalendarEvent, ) print(completion.choices[0].message.parsed) JavaScript
import OpenAI from "openai"; import { zodResponseFormat } from "openai/helpers/zod"; import { z } from "zod"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai" }); const CalendarEvent = z.object({ name: z.string(), date: z.string(), participants: z.array(z.string()), }); const completion = await openai.chat.completions.parse({ model: "gemini-2.0-flash", messages: [ { role: "system", content: "Extract the event information." }, { role: "user", content: "John and Susan are going to an AI conference on Friday" }, ], response_format: zodResponseFormat(CalendarEvent, "event"), }); const event = completion.choices[0].message.parsed; console.log(event); エンベディング
テキスト エンベディングは、テキスト文字列の関連性を測定し、Gemini API を使用して生成できます。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) response = client.embeddings.create( input="Your text string goes here", model="gemini-embedding-001" ) print(response.data[0].embedding) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/" }); async function main() { const embedding = await openai.embeddings.create({ model: "gemini-embedding-001", input: "Your text string goes here", }); console.log(embedding); } main(); REST
curl "https://generativelanguage.googleapis.com/v1beta/openai/embeddings" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer GEMINI_API_KEY" \ -d '{ "input": "Your text string goes here", "model": "gemini-embedding-001" }' extra_body
Gemini でサポートされている機能の中には、OpenAI モデルでは利用できないものの、extra_body フィールドを使用して有効にできるものがあります。
extra_body 機能
safety_settings | Gemini の SafetySetting に対応します。 |
cached_content | Gemini の GenerateContentRequest.cached_content に対応します。 |
thinking_config | Gemini の ThinkingConfig に対応します。 |
cached_content
extra_body を使用して cached_content を設定する例を次に示します。
Python
from openai import OpenAI client = OpenAI( api_key=MY_API_KEY, base_url="https://generativelanguage.googleapis.com/v1beta/" ) stream = client.chat.completions.create( model="gemini-2.5-pro", n=1, messages=[ { "role": "user", "content": "Summarize the video" } ], stream=True, stream_options={'include_usage': True}, extra_body={ 'extra_body': { 'google': { 'cached_content': "cachedContents/0000aaaa1111bbbb2222cccc3333dddd4444eeee" } } } ) for chunk in stream: print(chunk) print(chunk.usage.to_dict()) モデルの一覧表示
利用可能な Gemini モデルのリストを取得します。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) models = client.models.list() for model in models: print(model.id) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/", }); async function main() { const list = await openai.models.list(); for await (const model of list) { console.log(model); } } main(); REST
curl https://generativelanguage.googleapis.com/v1beta/openai/models \ -H "Authorization: Bearer GEMINI_API_KEY" モデルを取得する
Gemini モデルを取得します。
Python
from openai import OpenAI client = OpenAI( api_key="GEMINI_API_KEY", base_url="https://generativelanguage.googleapis.com/v1beta/openai/" ) model = client.models.retrieve("gemini-2.0-flash") print(model.id) JavaScript
import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "GEMINI_API_KEY", baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/", }); async function main() { const model = await openai.models.retrieve("gemini-2.0-flash"); console.log(model.id); } main(); REST
curl https://generativelanguage.googleapis.com/v1beta/openai/models/gemini-2.0-flash \ -H "Authorization: Bearer GEMINI_API_KEY" 現在の制限事項
機能サポートの拡大に伴い、OpenAI ライブラリのサポートはまだベータ版です。
サポートされているパラメータ、今後の機能についてご不明な点がある場合や、Gemini の使用を開始する際に問題が発生した場合は、デベロッパー フォーラムにご参加ください。
次のステップ
OpenAI 互換性 Colab を試して、より詳細な例を確認してください。