Les modèles Gemini sont accessibles à l'aide des bibliothèques OpenAI (Python et TypeScript/JavaScript) ainsi que de l'API REST, en mettant à jour trois lignes de code et en utilisant votre clé API Gemini. Si vous n'utilisez pas encore les bibliothèques OpenAI, nous vous recommandons d'appeler directement l'API Gemini.
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"}
]
}'
Streaming
L'API Gemini est compatible avec les réponses en streaming.
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
}'
Appel de fonction
L'appel de fonction vous permet d'obtenir plus facilement des sorties de données structurées à partir de modèles génératifs. Il est compatible avec l'API Gemini.
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"
}'
Compréhension des images
Les modèles Gemini sont natifs et multimodaux, et offrent des performances de pointe dans de nombreuses tâches de vision courantes.
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}\" }
}
]
}
]
}"
'
Embeddings
Les embeddings de texte mesurent la relation entre des chaînes de texte et peuvent être générés à l'aide de l'API Gemini.
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"
}'
Limites actuelles
La prise en charge des bibliothèques OpenAI est toujours en version bêta pendant que nous étoffons les fonctionnalités disponibles. Les fonctionnalités suivantes sont limitées:
Si vous avez des questions sur les paramètres compatibles, les fonctionnalités à venir ou si vous rencontrez des difficultés pour commencer à utiliser Gemini, rejoignez notre forum pour les développeurs.