Использование APIMart с Gemini CLI

npm install -g @google/gemini-cli

gemini --version

$env:GEMINI_API_KEY = "sk-xxxxxxxxxxxx"
$env:GEMINI_BASE_URL = "https://api.apimart.ai/v1"

export GEMINI_API_KEY="sk-xxxxxxxxxxxx"
export GEMINI_BASE_URL="https://api.apimart.ai/v1"

[System.Environment]::SetEnvironmentVariable('GEMINI_API_KEY', 'sk-xxxxxxxxxxxx', 'User')
[System.Environment]::SetEnvironmentVariable('GEMINI_BASE_URL', 'https://api.apimart.ai/v1', 'User')

$env:GEMINI_API_KEY = [System.Environment]::GetEnvironmentVariable('GEMINI_API_KEY', 'User')
$env:GEMINI_BASE_URL = [System.Environment]::GetEnvironmentVariable('GEMINI_BASE_URL', 'User')

# Bash
nano ~/.bashrc

# Zsh (по умолчанию в macOS)
nano ~/.zshrc

# APIMart Gemini CLI
export GEMINI_API_KEY="sk-xxxxxxxxxxxx"
export GEMINI_BASE_URL="https://api.apimart.ai/v1"

source ~/.bashrc   # Bash
source ~/.zshrc    # Zsh

# .env
GEMINI_API_KEY=sk-xxxxxxxxxxxx
GEMINI_BASE_URL=https://api.apimart.ai/v1

export $(cat .env | xargs) && gemini chat

Get-Content .env | ForEach-Object {
    $name, $value = $_.split('=')
    Set-Content env:\$name $value
}
gemini chat

echo $GEMINI_API_KEY
echo $GEMINI_BASE_URL

echo $env:GEMINI_API_KEY
echo $env:GEMINI_BASE_URL

gemini chat

gemini "Give a short overview of the history of artificial intelligence"

gemini chat --model gpt-4o

gemini "Write a Python quicksort implementation" --model claude-sonnet-4-5-20250929

gemini --input prompt.txt

cat prompt.txt | gemini

gemini "Generate a React component" --output component.jsx

import openai

# APIMart
openai.api_key = "sk-xxxxxxxxxxxx"  # Your APIMart API key
openai.api_base = "https://api.apimart.ai/v1"

response = openai.ChatCompletion.create(
    model="gemini-2.0-flash-exp",
    messages=[
        {"role": "user", "content": "Hi—please introduce yourself"}
    ]
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-xxxxxxxxxxxx",
  baseURL: "https://api.apimart.ai/v1",
});

async function main() {
  const completion = await client.chat.completions.create({
    model: "gemini-2.0-flash-exp",
    messages: [{ role: "user", content: "Hi—please introduce yourself" }],
  });

  console.log(completion.choices[0].message.content);
}

main();

curl https://api.apimart.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxxxx" \
  -d '{
    "model": "gemini-2.0-flash-exp",
    "messages": [
      {"role": "user", "content": "Hi—please introduce yourself"}
    ]
  }'

response = openai.ChatCompletion.create(
    model="gemini-2.0-flash-exp",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg"
                    }
                }
            ]
        }
    ]
)

stream = openai.ChatCompletion.create(
    model="gemini-2.0-flash-exp",
    messages=[{"role": "user", "content": "Write a short poem about spring"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end='')

response = openai.ChatCompletion.create(
    model="gemini-2.0-flash-exp",
    messages=[{"role": "user", "content": "Your question"}],
    temperature=0.7,        # randomness (0–2)
    max_tokens=2000,        # max output length
    top_p=0.9,              # nucleus sampling
    presence_penalty=0,     # topic diversity
    frequency_penalty=0     # repetition penalty
)

import openai

openai.api_key = "sk-xxxxxxxxxxxx"
openai.api_base = "https://api.apimart.ai/v1"

try:
    response = openai.ChatCompletion.create(
        model="gemini-2.0-flash-exp",
        messages=[{"role": "user", "content": "test"}],
        max_tokens=10
    )
    print("✅ API configuration OK")
    print(f"Reply: {response.choices[0].message.content}")
except Exception as e:
    print(f"❌ API configuration failed: {e}")

import openai
import time

def call_with_retry(max_retries=3):
    for i in range(max_retries):
        try:
            response = openai.ChatCompletion.create(
                model="gemini-2.0-flash-exp",
                messages=[{"role": "user", "content": "Your question"}]
            )
            return response
        except openai.error.RateLimitError:
            if i < max_retries - 1:
                time.sleep(2 ** i)
                continue
            raise
        except Exception as e:
            print(f"Error: {e}")
            raise

response = call_with_retry()

def choose_model(complexity):
    if complexity == "simple":
        return "gpt-4o-mini"
    elif complexity == "medium":
        return "gemini-2.0-flash-exp"
    return "gpt-5"

model = choose_model("simple")
response = openai.ChatCompletion.create(
    model=model,
    messages=[{"role": "user", "content": "Your question"}],
    max_tokens=500
)

response = openai.ChatCompletion.create(
    model="gemini-2.0-flash-exp",
    messages=[
        {
            "role": "system",
            "content": "You are an expert Python assistant who writes clear, efficient code."
        },
        {
            "role": "user",
            "content": "Implement quicksort for me"
        }
    ]
)