79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
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"""The one-and-only AI primitive used by every demo in this folder.
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Every script in scripts/ imports `ask` from this module. The whole point of
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the example is that *every* "smart function" you'll see is the same call —
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just a different prompt going in, and a string coming back out. The model
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doesn't know whether it's doing arithmetic, sentiment analysis, or reading
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text out of a photo. It is, in every case, predicting what comes next.
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The model runs locally inside the `ollama` container started by the
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docker-compose file alongside this script. We hit its HTTP API; nothing
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about your input ever leaves the machine.
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"""
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from __future__ import annotations
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import base64
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import os
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from pathlib import Path
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import httpx
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OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://localhost:11434")
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OLLAMA_MODEL = os.environ.get("OLLAMA_MODEL", "qwen3.5:9b")
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# Reasonable timeout for a CPU-only first run; vision calls can be slow.
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_TIMEOUT = httpx.Timeout(connect=10.0, read=300.0, write=60.0, pool=10.0)
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def ask(prompt: str, image: str | Path | None = None, *, temperature: float = 0.0) -> str:
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"""Ask the local model to continue some text.
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Args:
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prompt: The text the model sees. Whatever you write here is the
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"body" of your AI function — the same way regular Python functions
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have a body of code, an AI function has a body of prompt.
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image: Optional path to a local image. When provided, the model
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sees the image alongside the prompt (this needs a vision-capable
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model — `qwen2.5vl` is the default).
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temperature: 0 makes outputs roughly deterministic, which is what we
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want for demos. Crank it up for more variety, down for fewer surprises.
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Returns:
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The model's continuation as a plain string, with surrounding whitespace
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stripped.
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"""
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payload: dict = {
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"model": OLLAMA_MODEL,
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"prompt": prompt,
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"stream": False,
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"options": {"temperature": temperature},
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# Some Qwen builds support a "thinking" mode where the model writes
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# out an internal monologue before answering. For this workshop we
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# want clean, direct completions, so we ask for that explicitly.
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"think": False,
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}
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if image is not None:
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image_path = Path(image)
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if not image_path.exists():
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raise FileNotFoundError(f"Image not found: {image_path}")
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payload["images"] = [base64.b64encode(image_path.read_bytes()).decode("ascii")]
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with httpx.Client(timeout=_TIMEOUT) as client:
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try:
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r = client.post(f"{OLLAMA_URL}/api/generate", json=payload)
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except httpx.ConnectError as e:
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raise RuntimeError(
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f"Could not reach Ollama at {OLLAMA_URL}. "
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"Is the docker-compose stack running? Try `docker compose up -d`."
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) from e
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r.raise_for_status()
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return r.json()["response"].strip()
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if __name__ == "__main__":
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# Sanity check — confirms the model is reachable and answering.
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print(f"Asking {OLLAMA_MODEL} at {OLLAMA_URL} to say hello...")
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print(ask("Say hello in five words or fewer."))
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