راهنمای استفاده از کلید‌های API پلتفرم AvalAI

pip install -U openai
pip install -U langchain
pip install -U langchain_openai

from langchain_openai import ChatOpenAI  # pip install -U langchain_openai

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello world!"},
]
# استفاده از مدل gpt-4o-mini
model_name = "gpt-4o-mini"


llm = ChatOpenAI(
    model=model_name, base_url="https://api.avalai.ir/v1", api_key="aa-***********"
)

llm.invoke(messages)

# استفاده از مدل‌ Claude 3.5 Sonnet
# model_name="anthropic.claude-3-5-sonnet-20240620-v1:0"

llm = ChatOpenAI(
    model=model_name, base_url="https://api.avalai.ir/v1", api_key="aa-***********"
)

llm.invoke(messages)

from langchain_openai import ChatOpenAI # pip install -U langchain_openai
import base64
import os

base_url = "https://api.avalai.ir/v1"
api_key = "AVALAI_API_KEY"

model_name = "gpt-4o"

# استفاده از مدل gemini-2.0-flash-exp
# model_name = "gemini-2.0-flash-exp"


# استفاده از مدل anthropic.claude-3-5-sonnet-20240620-v1:0
# model_name = "anthropic.claude-3-5-sonnet-20240620-v1:0"

llm = ChatOpenAI(
    base_url=base_url,
    model=model_name,
    api_key=api_key,
)

IMAGE_PATH = "sample.png"

# Open the image file and encode it as a base64 string
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

base64_image = encode_image(IMAGE_PATH)
image_ext = os.path.splitext(IMAGE_PATH)[1][1:]

messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant.",
    },
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Describe the image.",
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/{image_ext};base64,{base64_image}",
                    "detail": "auto",
                },
            },
        ],
    },
]

ai_message = llm.invoke(messages)
print(ai_message.content)

from openai import OpenAI # pip install -U openai

client = OpenAI(
base_url="https://api.avalai.ir/v1",
api_key="AVALAI_API_KEY"
)

speech_file_path = "./speech.mp3"

response = client.audio.speech.create(
    model="tts-1",
    voice="alloy",
    input="Today is a wonderful day to build something people love!",
)

response.stream_to_file(speech_file_path) 

from openai import OpenAI # pip install -U openai

client = OpenAI(
    base_url="https://api.avalai.ir/v1",
    api_key="AVALAI_API_KEY"
)

audio_file = open("/path/to/file/speech.mp3", "rb")
transcription = client.audio.transcriptions.create(
    model="whisper-1", 
    file=audio_file, 
    response_format="text"
)
print(transcription.text)

from openai import OpenAI

client = OpenAI(base_url="https://api.avalai.ir/v1", api_key="AVALAI_API_KEY")

response = client.images.generate(
    model="dall-e-3",
    prompt="a white siamese cat",
    size="1024x1024",
    quality="standard",
    n=1,
)

image_url = response.data[0].url

# Install-Package Betalgo.Ranul.OpenAI

public static async IAsyncEnumerable InvokeStreamAsync(string model = "gpt-3.5-turbo")
    {
        static string AvalAIApiKey = "Your api key";
        static string BaseUrl = "https://api.avalai.ir/v1";
        var openAiService = new OpenAIService(new OpenAiOptions()
        {
            ApiKey = AvalAIApiKey,
            BaseDomain = BaseUrl
        });

        var completionResult = openAiService.ChatCompletion.CreateCompletionAsStream(new ChatCompletionCreateRequest
        {
            Messages = new List
            {
                ChatMessage.FromSystem("You are a helpful assistant."),
                ChatMessage.FromUser("How to implement avalai base url in betalgo .NET openai package?"),
                ChatMessage.FromAssistant("You can read about it in https://avalai.ir/blog/how-to-use-avalai-api-keys ."),
            },
            Model = model,
            MaxTokens = 50//optional
        });
        await foreach (var completion in completionResult)
        {
            if (completion.Successful)
            {
                yield return completion.Choices.FirstOrDefault()?.Message.Content;
            }
            else
            {
                
                if (completion.Error == null)
                {
                    throw new Exception("Unknown Error");
                }
                Debug.WriteLine($"{completion.Error.Code}: {completion.Error.Message}");
            }
        }

    }

import OpenAI from "openai";

const baseURL = "https://api.avalai.ir/v1";

const openai = new OpenAI({
    apiKey: process.env.AVALAI_API_KEY,
    baseURL: baseURL
});

const chatCompletion = await openai.chat.completions.create({
    messages: [{ role: "user", content: "Say hi" }],
    model: "gpt-3.5-turbo",
});

// برای استفاده از دیگر ارائه دهندگان کافیست نام مدل را تغییر دهید
// composer require openai-php/client
// composer require guzzlehttp/guzzle

$yourApiKey = getenv('AVALAI_API_KEY');

$client = OpenAI::factory()
->withApiKey($yourApiKey)
->withBaseUri('api.avalai.ir/v1') 
->withStreamHandler(fn (RequestInterface $request): ResponseInterface => $client->send($request, [
'stream' => true
]))
->make();

$result = $client->chat()->create([
    'model' => 'gpt-4o-mini',
    'messages' => [
        ['role' => 'user', 'content' => 'Hello!'],
    ],
]);

echo $result->choices[0]->message->content;