[Bug] `calculate_term_weights` divides by zero when input is all stop words

Quick one. In sdk/nexent/core/nlp/tokenizer.py:59:

tf_weights = {term: weight / total_weight for term, weight in term_stats.items()}

total_weight is only incremented inside the if word not in analyse.default_tfidf.stop_words and word.strip() branch (lines 46-56). If every token is a stop word (e.g., a query consisting only of punctuation, whitespace, or common particles like "的 是 在"), total_weight == 0.0 and term_stats is empty.

In that case the dict comprehension is empty (no division), so today the function silently returns {} from the meaningful-terms guard at line 98 — but the moment any non-stop token slips in, the division will execute, and any future change that pre-populates term_stats without updating total_weight will raise ZeroDivisionError. The dependency between the two accumulators is fragile and undocumented.

Suggested fix: guard explicitly.

if total_weight == 0.0:
    return {}
tf_weights = {term: weight / total_weight for term, weight in term_stats.items()}

This makes the contract obvious and prevents the latent footgun.

Start in sdk/nexent/core/nlp/tokenizer.py at calculate_term_weights around line 59, and inspect how total_weight and term_stats are built. Exercise the function with all-stop-word input and with a non-stop token; done means zero-weight input returns {} without ZeroDivisionError and normal weighting still works.