Four weeks. Every pattern — validation, resilient reads, idempotent writes, retry, checkpoint — now in one capstone function. Before any API call, validate the config. Then fetch emails, idempotently create a task per email, record each outcome in a checkpoint. What does the return dict hold?
The config-validation errors if any, the fetched count, the checkpoint list of [title, ok], and maybe a top-level ok flag so callers can branch easily?
Exactly. A single dict carrying every piece of evidence the caller needs — or every error they need to fix before rerunning. The happy path:
{"ok": True, "emails": 5, "checkpoint": [["Invoice due", True], ...]}
And the failure path is just {"ok": False, "errors": ["label"]} — same shape, different keys?
Right. Fail fast on bad config; the rest of the function never runs. When config passes, every API call is wrapped, every write is idempotent, every title lands in the checkpoint. Here is the full capstone:
def run_full_pipeline(config: dict, max_emails: int) -> dict:
required = ["label"]
errors = [k for k in required if not config.get(k)]
if errors:
return {"ok": False, "errors": errors}
try:
resp = toolset.execute_action(Action.GMAIL_FETCH_EMAILS, {"max_results": max_emails})
except Exception as exc:
print(f"Fetch failed: {exc}")
return {"ok": False, "errors": ["gmail_fetch"]}
emails = resp.get("messages", [])
existing = toolset.execute_action(Action.GOOGLETASKS_LIST_TASKS, {"max_results": 100})
existing_titles = {t.get("title", "") for t in existing.get("items", [])}
checkpoint = []
for m in emails:
title = f"{config['label']}: {m.get('snippet', '')[:40]}"
if title in existing_titles:
checkpoint.append([title, True])
continue
try:
toolset.execute_action(Action.GOOGLETASKS_CREATE_TASK, {"title": title})
checkpoint.append([title, True])
except Exception as exc:
print(f"Create failed for '{title}': {exc}")
checkpoint.append([title, False])
ok_count = sum(1 for _, ok in checkpoint if ok)
print(f"Pipeline: {ok_count}/{len(checkpoint)} succeeded across {len(emails)} emails")
return {"ok": True, "emails": len(emails), "checkpoint": checkpoint}
Why fetch the existing tasks once outside the loop instead of calling list-then-create per email?
One API call instead of N. Loading existing titles into a set lets every title check run in O(1) inside the loop — massively cheaper than a per-email list call. That optimization matters the moment max_emails gets real.
So one function — config validation, resilient fetch, idempotent-via-set create, checkpoint — takes me from 'running arbitrary code' to 'running a real pipeline I can re-invoke safely'?
Every pattern from the whole track, composed into one function you can deploy and re-run. That is the production shape.
TL;DR: One function, five patterns: validate, resilient fetch, batched idempotency, retry-ready checkpoint, single report dict.
[title, ok] entries ready for resume| Case | Keys |
|---|---|
| config fails | ok: False, errors: [...] |
| fetch fails | ok: False, errors: ['gmail_fetch'] |
| success | ok: True, emails: N, checkpoint: [...] |
Four weeks. Every pattern — validation, resilient reads, idempotent writes, retry, checkpoint — now in one capstone function. Before any API call, validate the config. Then fetch emails, idempotently create a task per email, record each outcome in a checkpoint. What does the return dict hold?
The config-validation errors if any, the fetched count, the checkpoint list of [title, ok], and maybe a top-level ok flag so callers can branch easily?
Exactly. A single dict carrying every piece of evidence the caller needs — or every error they need to fix before rerunning. The happy path:
{"ok": True, "emails": 5, "checkpoint": [["Invoice due", True], ...]}And the failure path is just {"ok": False, "errors": ["label"]} — same shape, different keys?
Right. Fail fast on bad config; the rest of the function never runs. When config passes, every API call is wrapped, every write is idempotent, every title lands in the checkpoint. Here is the full capstone:
def run_full_pipeline(config: dict, max_emails: int) -> dict:
required = ["label"]
errors = [k for k in required if not config.get(k)]
if errors:
return {"ok": False, "errors": errors}
try:
resp = toolset.execute_action(Action.GMAIL_FETCH_EMAILS, {"max_results": max_emails})
except Exception as exc:
print(f"Fetch failed: {exc}")
return {"ok": False, "errors": ["gmail_fetch"]}
emails = resp.get("messages", [])
existing = toolset.execute_action(Action.GOOGLETASKS_LIST_TASKS, {"max_results": 100})
existing_titles = {t.get("title", "") for t in existing.get("items", [])}
checkpoint = []
for m in emails:
title = f"{config['label']}: {m.get('snippet', '')[:40]}"
if title in existing_titles:
checkpoint.append([title, True])
continue
try:
toolset.execute_action(Action.GOOGLETASKS_CREATE_TASK, {"title": title})
checkpoint.append([title, True])
except Exception as exc:
print(f"Create failed for '{title}': {exc}")
checkpoint.append([title, False])
ok_count = sum(1 for _, ok in checkpoint if ok)
print(f"Pipeline: {ok_count}/{len(checkpoint)} succeeded across {len(emails)} emails")
return {"ok": True, "emails": len(emails), "checkpoint": checkpoint}Why fetch the existing tasks once outside the loop instead of calling list-then-create per email?
One API call instead of N. Loading existing titles into a set lets every title check run in O(1) inside the loop — massively cheaper than a per-email list call. That optimization matters the moment max_emails gets real.
So one function — config validation, resilient fetch, idempotent-via-set create, checkpoint — takes me from 'running arbitrary code' to 'running a real pipeline I can re-invoke safely'?
Every pattern from the whole track, composed into one function you can deploy and re-run. That is the production shape.
TL;DR: One function, five patterns: validate, resilient fetch, batched idempotency, retry-ready checkpoint, single report dict.
[title, ok] entries ready for resume| Case | Keys |
|---|---|
| config fails | ok: False, errors: [...] |
| fetch fails | ok: False, errors: ['gmail_fetch'] |
| success | ok: True, emails: N, checkpoint: [...] |
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