Same chat loop on Ollama without cloud keys

Use this as an operator-facing rebuild of the ideas in “Part 5 — Run the Same Loop on Ollama (no API key, OpenAI dialect)”: clear stages, ordered code slots, and recovery notes that survive a handoff. Overview works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion.

from openai import OpenAI

# Part 2's SDK, different address
client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama",  # required, unused
)

resp = client.chat.completions.create(
    model="llama3.2",
    messages=[
        {
            "role": "system",
            "content": "You are a concise "
                       "assistant.",
        },
        {
            "role": "user",
            "content": "Where are you running "
                       "right now?",
        },
    ],
    temperature=0,
)
print(resp.choices[0].message.content)

msgs = [{
    "role": "system",
    "content": "You are a concise assistant.",
}]

def ask(text: str) -> str:
    msgs.append(
        {"role": "user", "content": text}
    )
    resp = client.chat.completions.create(
        model="llama3.2",
        messages=msgs,  # full history
        temperature=0,
    )
    reply = resp.choices[0].message.content
    msgs.append({
        "role": "assistant",
        "content": reply,
    })
    return reply

print(ask("Define a context window."))
print(ask("Now for a five-year-old."))
print(ask("Which answer was shorter?"))

# one-time: install from ollama.com, then
ollama pull llama3.2

pip install -r requirements.txt
python examples/part05_ollama.py

Operational checklist

When working through Operational checklist, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest.

Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline.

Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.

Pin dependency versions and record the image digest that ran the demo. Reproducibility beats tribal knowledge.

Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion.

Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.

Before promoting the stack, freeze versions, capture a golden transcript for the critical path, and confirm rollback steps. Shared environments need rate limits, tenancy checks, and a clear owner for secret rotation. Prefer boring reliability over clever one-off demos.

Batch note for 25763587d271: keep provider keys out of the repo, set a per-session token ceiling, and store transcripts next to the eval fixtures so later model swaps stay comparable.

For hardening note 0, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline.

Hardening detail 0/681: measure wall time, error class, and token spend for this note, then decide whether to keep the change based on a fixed question set rather than anecdote.

When working through hardening note 1, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments.

Hardening detail 1/681: measure wall time, error class, and token spend for this note, then decide whether to keep the change based on a fixed question set rather than anecdote.

hardening note 2 works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.

Hardening detail 2/681: measure wall time, error class, and token spend for this note, then decide whether to keep the change based on a fixed question set rather than anecdote.

For hardening note 3, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion.

Hardening detail 3/681: measure wall time, error class, and token spend for this note, then decide whether to keep the change based on a fixed question set rather than anecdote.