Gemini Guides

from google import genai
from google.genai import types

def generate():
    client = genai.Client(
        api_key="sk-***", # 🔑 Replace it by your AiHubMix Key
        http_options={"base_url": "https://aihubmix.com/gemini"},
    )

    model = "gemini-2.0-flash"
    contents = [
        types.Content(
            role="user",
            parts=[
                types.Part.from_text(text="""How do I know I'm not wasting time?"""),
            ],
        ),
    ]

    print(client.models.generate_content(
        model=model,
        contents=contents,
    ))

if __name__ == "__main__":
    generate()

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",
)

completion = client.chat.completions.create(
    model="gemini-2.5-flash-preview-04-17-nothink",
    messages=[
        {
            "role": "user",
            "content": "Explain the Occam's Razor concept and provide everyday examples of it"
        }
    ]
)

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

from google import genai
from google.genai import types

file_path = "yourpath/file.jpeg"
with open(file_path, "rb") as f:
    file_bytes = f.read()

client = genai.Client(
    api_key="sk-***", # Replace with the key you generated in AiHubMix
    http_options={"base_url": "https://aihubmix.com/gemini"}
)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents=types.Content(
        parts=[
            types.Part(
                inline_data=types.Blob(
                    data=file_bytes,
                    mime_type="image/jpeg"
                )
            ),
            types.Part(
                text="Describe the image."
            )
        ]
    ),
    config=types.GenerateContentConfig(
        system_instruction="You are a helpful assistant that can describe images.",
        max_output_tokens=768,
        temperature=0.1,
        thinking_config=types.ThinkingConfig(
            thinking_budget=0, include_thoughts=False
        ),
        media_resolution=types.MediaResolution.MEDIA_RESOLUTION_MEDIUM # 256 tokens
    )
)

print(response.text)

from openai import OpenAI

# Define the function declaration for the model
schedule_meeting_function = {
    "name": "schedule_meeting",
    "description": "Schedules a meeting with specified attendees at a given time and date.",
    "parameters": {
        "type": "object",
        "properties": {
            "attendees": {
                "type": "array",
                "items": {"type": "string"},
                "description": "List of people attending the meeting.",
            },
            "date": {
                "type": "string",
                "description": "Date of the meeting (e.g., '2024-07-29')",
            },
            "time": {
                "type": "string",
                "description": "Time of the meeting (e.g., '15:00')",
            },
            "topic": {
                "type": "string",
                "description": "The subject or topic of the meeting.",
            },
        },
        "required": ["attendees", "date", "time", "topic"],
    },
}

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

# Send request with function declarations using OpenAI compatible format
response = client.chat.completions.create(
    model="gemini-2.0-flash",
    messages=[
        {"role": "user", "content": "Schedule a meeting with Bob and Alice for 03/14/2025 at 10:00 AM about the Q3 planning."}
    ],
    tools=[{"type": "function", "function": schedule_meeting_function}],
    tool_choice="auto" ## 📍 Added Aihubmix compatibility, more stable request method
)

# Check for a function call
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    function_call = tool_call.function
    print(f"Function to call: {function_call.name}")
    print(f"Arguments: {function_call.arguments}")
    print(response.usage)
    #  In a real app, you would call your function here:
    #  result = schedule_meeting(**json.loads(function_call.arguments))
else:
    print("No function call found in the response.")
    print(response.choices[0].message.content)

from google import genai
from google.genai import types
import time

def generate():
    client = genai.Client(
        api_key="sk-***", # Replace this with your key from AiHubMix
        http_options={"base_url": "https://aihubmix.com/gemini"},
    )

    model = "gemini-2.5-pro-preview-03-25"
    contents = [
        types.Content(
            role="user",
            parts=[
                types.Part.from_text(text="""How is the "Rule of 72" derived in the financial world?"""),
            ],
        ),
    ]
    generate_content_config = types.GenerateContentConfig(
        response_mime_type="text/plain",
    )

    final_usage_metadata = None
    
    for chunk in client.models.generate_content_stream(
        model=model,
        contents=contents,
        config=generate_content_config,
    ):
        print(chunk.text, end="")
        if chunk.usage_metadata:
            final_usage_metadata = chunk.usage_metadata
    
    # Once all chunks are processed, print the full token usage
    if final_usage_metadata:
        print(f"\nUsage: {final_usage_metadata}")

if __name__ == "__main__":
    generate()