ChatGPT

Agent

Utilities

Examples

Inference

import asyncio

from environs import Env
from core_mixins.decorators.async_ import SyncWrapper

from core_genai.interfaces import IAgent
from core_genai.chatgpt.agent import ChatGPTAgent

env = Env()
env.read_env(".env")

agent: ChatGPTAgent = IAgent.create_agent("ChatGPTAgent", api_key=env("CHATGPT_API_KEY"))

MODEL = "gpt-4.1-nano"
PROMPT = [{"role": "user", "content": "Explain how AI works in a few words"}]


async def run_async() -> None:
    output = await agent.analyze(model=MODEL, prompt=PROMPT)
    print("[async]", agent.get_text(output))
    print("[meta]", agent.get_metadata(output))


def run_sync() -> None:
    with SyncWrapper(agent) as sync_agent:
        output = sync_agent.analyze(model=MODEL, prompt=PROMPT)
        print("[sync]", agent.get_text(output))


if __name__ == "__main__":
    asyncio.run(run_async())
    run_sync()

Batch inference

Pass a list of BatchRequest objects with prompt (required) and custom_id (optional). schedule_job builds the full JSONL record and uploads the file automatically. Poll until the state is in TERMINAL_STATES, then parse results from extract_job_results.

import asyncio

from core_genai.agents import ChatGPTAgent
from core_genai.agents.chatgpt import BatchRequest
from core_genai.interfaces import IAgent
from environs import Env

env = Env()
env.read_env(".env")

MODEL = "gpt-4o-mini"
POLL_INTERVAL = 30

PROMPTS = [
    "Explain what machine learning is in one sentence.",
    "Explain what a neural network is in one sentence.",
    "Explain what reinforcement learning is in one sentence.",
]

agent: ChatGPTAgent = IAgent.create_agent("ChatGPTAgent", api_key=env("CHATGPT_API_KEY"))


def build_requests() -> list[BatchRequest]:
    return [
        BatchRequest(
            custom_id=f"request-{i}",
            prompt=[{"role": "user", "content": prompt}],
        )
        for i, prompt in enumerate(PROMPTS)
    ]


async def poll_until_done(batch_id: str) -> str:
    while True:
        state = await agent.check_job_status(batch_id)
        print(f"  status: {state}")

        if state in ChatGPTAgent.TERMINAL_STATES:
            return state

        await asyncio.sleep(POLL_INTERVAL)


async def main() -> None:
    requests = build_requests()
    print(f"Scheduling batch job with {len(requests)} request(s)...")

    job = await agent.schedule_job(requests=requests, model=MODEL)
    batch_id = job["job_id"]
    print(f"Job scheduled: {batch_id}  (created: {job['created_at']})")

    print("Polling for completion...")
    final_state = await poll_until_done(batch_id)
    print(f"Job finished with state: {final_state}")

    result = await agent.extract_job_results(batch_id)
    if result["error"]:
        print(f"Error: {result['error']}")
        return

    responses = result["results"] or []
    print(f"\n{len(responses)} response(s):\n")
    for i, resp in enumerate(responses, 1):
        body = resp.get("response", {}).get("body", {})
        choices = body.get("choices", [])
        text = choices[0].get("message", {}).get("content", "") if choices else ""
        print(f"[{i}] {PROMPTS[i - 1]!r}")
        print(f"    {text}\n")


async def extract_results(batch_id: str) -> None:
    result = await agent.extract_job_results(batch_id)
    if result["error"]:
        print(f"Error: {result['error']}")
        return

    responses = result["results"] or []
    print(f"{batch_id}: {len(responses)} response(s)\n")
    for i, resp in enumerate(responses, 1):
        body = resp.get("response", {}).get("body", {})
        choices = body.get("choices", [])
        text = choices[0].get("message", {}).get("content", "") if choices else ""
        print(f"[{i}] {text}\n")


async def check_batches(*batch_ids: str) -> None:
    for batch_id in batch_ids:
        state = await agent.check_job_status(batch_id)
        print(f"{batch_id}: {state}")


if __name__ == "__main__":
    asyncio.run(main())

    asyncio.run(check_batches(
        "batch_...",
        "batch_...",
    ))

    asyncio.run(extract_results("batch_..."))