يمكن الوصول إلى نماذج Gemini باستخدام مكتبات OpenAI (Python وTypeScript / JavaScript) مع REST API، وذلك من خلال تعديل ثلاثة أسطر من الرمز البرمجي واستخدام مفتاح Gemini API. إذا لم تكن تستخدِم مكتبات OpenAI، ننصحك بالاتصال بواجهة برمجة التطبيقات Gemini API مباشرةً.
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-1.5-flash",
n=1,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": "Explain to me how AI works"
}
]
)
print(response.choices[0].message)
Node.js
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-1.5-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-1.5-flash",
"messages": [
{"role": "user", "content": "Explain to me how AI works"}
]
}'
البث
تتيح Gemini API عرض الردود تدريجيًا.
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-1.5-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)
Node.js
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-1.5-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-1.5-flash",
"messages": [
{"role": "user", "content": "Explain to me how AI works"}
],
"stream": true
}'
استدعاء الدوالّ
تسهِّل عليك ميزة "استدعاء الدالة" الحصول على نتائج البيانات المنظَّمة من نماذج الإنشاء المتاحة في Gemini API.
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-1.5-flash",
messages=messages,
tools=tools,
tool_choice="auto"
)
print(response)
Node.js
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-1.5-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-1.5-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"
}'
فهم الصور
تعتمد نماذج Gemini بشكل أساسي على معالجة الوسائط المتعددة، وتقدّم أفضل أداء في العديد من مهام الرؤية الشائعة.
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-1.5-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])
Node.js
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,${base64Image}`
},
},
],
}
];
try {
const response = await openai.chat.completions.create({
model: "gemini-1.5-flash",
messages: messages,
});
console.log(response.choices[0]);
} catch (error) {
console.error("Error calling Gemini API:", error);
}
}
main();
REST
bash -c '
base64_image=$(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-1.5-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}\" }
}
]
}
]
}"
'
الإخراج المنظَّم
يمكن لنماذج Gemini عرض كائنات JSON بأي بنية تحدّدها.
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-1.5-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)
Node.js
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.beta.chat.completions.parse({
model: "gemini-1.5-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);
عمليات التضمين
تقيس عمليات تضمين النصوص مدى صلة سلاسل النصوص ببعضها، ويمكن إنشاؤها باستخدام واجهة برمجة التطبيقات Gemini API.
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="text-embedding-004"
)
print(response.data[0].embedding)
Node.js
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: "text-embedding-004",
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": "text-embedding-004"
}'
القيود الحالية
لا تزال مكتبات OpenAI متوفّرة في إصدار تجريبي بينما نعمل على توسيع نطاق توفّر الميزات.
إذا كانت لديك أسئلة حول المَعلمات المتوافقة أو الميزات القادمة، أو إذا واجهت أي مشاكل في البدء باستخدام Gemini، يمكنك الانضمام إلى منتدى المطوّرين.