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Documentation Index

Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt

Use this file to discover all available pages before exploring further.

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1. Uso

Na tarefa de completion FIM (Fill In the Middle), os usuários inserem o prefixo e o sufixo do conteúdo que desejam manter, e o modelo gera a parte que falta com base nesses prompts. Esse método de completion é comum em aplicações como auto-completion de código e geração de conteúdo no meio de um texto.

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2. Formato de Dados

Na interface chat/completions, basta definir o model id como o id do modelo na Galeria de Modelos, disponível no canto superior direito do card do módulo. 
{ 
    "model": "model id",
    "messages": "prompt",
    "params": "params",
    "extra_body": {"prefix":"prefix content", "suffix":"optional suffix content"}
}
Na interface completions 
{
    "model": "model info",
    "prompt": "prompt",
    "suffix": "prompt"
}

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3. Exemplo

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3.1 Usando FIM Completion Baseado na Interface chat.completions da OpenAI:

from openai import OpenAI

client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # Replace with the key you generated in AiHubMix
    base_url="https://aihubmix.com/v1"
)

messages = [
    {"role": "user", "content": "Please write a sum function code"},
]

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages,
    extra_body={
            "prefix": f"""
def sum_numbers(numbers):
    # If the list is empty, return 0
    if not numbers:
        return 0
""",
            "suffix": f"""
# Run Test
numbers = [1, 2, 3, 4, 5]
result = sum_numbers(numbers)
print("Sum of numbers:", result)
"""
    },
    stream=True,
    max_tokens=4096
)

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end='')

​
3.2 Completion Usando FIM Baseado na API Completions da OpenAI:


client = OpenAI(
    api_key="Aihubmix APIKEY", 
    base_url="https://aihubmix.com/v1"
)

response = client.completions.create(
    model="deepseek-ai/DeepSeek-V2.5",
    prompt=f"""
def quick_sort(arr):
   # Basic situation: if the array length is less than or equal to 1, return the array
    if len(arr) <= 1:
        return arr
    else:
""",
    suffix=f"""
# Test quick_sort function
arr = [3, 6, 8, 10, 1, 2, 1]
sorted_arr = quick_sort(arr)
print("Sorted array:", sorted_arr)
""",
    stream=True,
    max_tokens=4096
)

for chunk in response:
    print(chunk.choices[0].text, end='')
Última atualização: 2026-06-01