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{
"cells": [
{
"cell_type": "markdown",
"id": "1d983d23",
"metadata": {},
"source": [
"# This is a tutorial on how to use IOAI's provided LLM proxy"
]
},
{
"cell_type": "markdown",
"id": "f5033564",
"metadata": {},
"source": [
"## Step 1: initialize your OpenAI client\n",
"\n",
"Please use your provided api key here."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "699ae2b2",
"metadata": {},
"outputs": [],
"source": [
"from openai import AsyncClient\n",
"\n",
"BASE_URL = \"https://ioai-llm-proxy.up.railway.app/prox/v1\"\n",
"API_KEY = \"<YOUR_IOAI_API_KEY>\"\n",
"\n",
"openai_client = AsyncClient(\n",
" base_url=BASE_URL,\n",
" api_key=API_KEY,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8f2b8e77",
"metadata": {},
"source": [
"## Step 2: Generate output\n",
"\n",
"Note that we have an allowlist on model ids:\n",
"\n",
"- `openai/gpt-4.1`\n",
"- `openai/gpt-4.1-mini`\n",
"- `openai/gpt-4.1-nano`\n",
"- `openai/gpt-4o`\n",
"- `openai/gpt-4o-mini`\n",
"- `gpt-4o-mini`\n",
"- `gpt-4o`\n",
"- `gpt-4.1`\n",
"- `gpt-4.1-mini`\n",
"- `gpt-4.1-nano`\n",
"- `google/gemini-2.5-pro`\n",
"- `google/gemini-2.5-flash`\n",
"- `moonshotai/kimi-k2`\n",
"- `qwen/qwen3-235b-a22b-07-25`\n",
"- `anthropic/claude-sonnet-4`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "96895459",
"metadata": {},
"outputs": [],
"source": [
"response = await openai_client.chat.completions.create(\n",
" model=\"openai/gpt-4o-mini\",\n",
" messages=[{\"role\": \"user\", \"content\": \"Hello, world!\"}],\n",
")\n",
"\n",
"print(response)\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"id": "6c0b2e3e",
"metadata": {},
"source": [
"## Advanced: Structured Outputs\n",
"\n",
"You can use structured outputs to generate objects that fit a specific structure."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f67373b",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import BaseModel\n",
"from rich import print as rprint\n",
"\n",
"class CalendarItem(BaseModel):\n",
" title: str\n",
" month: int\n",
" date: int\n",
" year: int\n",
" description: str\n",
"\n",
"result = await openai_client.beta.chat.completions.parse(\n",
" model=\"openai/gpt-4o-mini\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant that generates calendar items.\"},\n",
" {\"role\": \"user\", \"content\": \"IOAI opening ceremony at Aug. 1, 2025\"}\n",
" ],\n",
" response_format=CalendarItem,\n",
")\n",
"\n",
"print(result)\n",
"rprint(result.choices[0].message.parsed)"
]
},
{
"cell_type": "markdown",
"id": "d1d6dafa",
"metadata": {},
"source": [
"## Batch requests with retry\n",
"\n",
"To generate large amounts of LLM completions efficiently and rhobustly, we can leverage:\n",
"\n",
"- Concurrent requests capped by an async Semaphore -- so that we can make multiple requests at the same time without overwhelming our bandwidth\n",
"- Exponential backoff based retry for each request -- so that we gracefully retry when unexpected network / provider errors happen\n",
"\n",
"The below is an example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f591a91f",
"metadata": {},
"outputs": [],
"source": [
"class RankingExtraction(BaseModel):\n",
" ranking: int\n",
" contest: str\n",
"\n",
"texts = [f\"We ranked {i}th in the IOAI 2025\" for i in range(1, 101)]\n",
"len(texts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80f7dafa",
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"from tenacity import retry, stop_after_attempt, wait_exponential\n",
"from asyncio import Semaphore\n",
"from tqdm.notebook import tqdm\n",
"from tqdm.asyncio import tqdm as tqdm_asyncio\n",
"\n",
"@retry(\n",
" stop=stop_after_attempt(3),\n",
" wait=wait_exponential(multiplier=1, min=4, max=10),\n",
")\n",
"async def process_one(txt: str) -> RankingExtraction:\n",
" result = await openai_client.beta.chat.completions.parse(\n",
" model=\"openai/gpt-4o-mini\",\n",
" messages=[{\"role\": \"user\", \"content\": txt}],\n",
" response_format=RankingExtraction,\n",
" )\n",
" return result.choices[0].message.parsed"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13380165",
"metadata": {},
"outputs": [],
"source": [
"print(texts[0])\n",
"print(await process_one(texts[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "be960117",
"metadata": {},
"outputs": [],
"source": [
"async def process_all(texts: List[str]):\n",
" semaphore = Semaphore(50) # we limit to making 50 requests concurrently\n",
" async def _process_with_sema(txt: str):\n",
" async with semaphore:\n",
" return await process_one(txt)\n",
" return await tqdm_asyncio.gather(\n",
" *[_process_with_sema(txt) for txt in texts],\n",
" desc=\"Processing\",\n",
" total=len(texts),\n",
" )\n",
"\n",
"results = await process_all(texts)\n",
"\n",
"results[:10]"
]
},
{
"cell_type": "markdown",
"id": "80bee95e",
"metadata": {},
"source": [
"# Check your credits\n",
"\n",
"You get $10 of credits, so use it economically! You can run the following cell to check the amount of credits you have used."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81d2e662",
"metadata": {},
"outputs": [],
"source": [
"from httpx import get\n",
"\n",
"result = get(f\"https://ioai-llm-proxy.up.railway.app/credits/{API_KEY}\")\n",
"\n",
"resp = result.json()\n",
"\n",
"print(f\"\"\"\n",
"Credits limit: ${resp['limit']}\n",
"Used: ${resp['usage']}\n",
"Credits remaining: ${resp['limit'] - resp['usage']}\n",
"\"\"\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
"nbformat_minor": 5
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