[routing] Namespace-aware tokenizer split for dotted tool IDs (with measured impact)

[dgenio]

Problem statement

src/contextweaver/_utils.py:tokenize (the single source of truth per docs/agent-context/invariants.md) is regex-driven on word boundaries. A query like "search invoices by customer" against tool ID billing.invoices.search matches via tags and description, but not via the canonical name token — billing.invoices.search lexes as one token. The gold set IDs are uniformly dotted (admin.audit.export, crm.deals.search, analytics.dashboards.list, …).

Splitting at dots/underscores/hyphens within tokens — while keeping the original token — is a small, deterministic change that likely lifts R@5 substantially on this corpus. The lift should be measurable on multiple backends.

Proposed solution

In tokenize(), after normalisation, additionally split each token at ., _, -, / boundaries and emit both the original token and its sub-tokens. Stay deterministic, stay stdlib. Add tests asserting tokenize("crm.deals.search") contains {"crm.deals.search", "crm", "deals", "search"}. Measure impact via the per-backend matrix on the current v0.3.0 baseline.

Round 2 Q2=C strict-regression-direction-only-lift posture: regression bounds are strict (no cell may drop more than 1pp); lift target is direction-only (≥1 cell must improve, magnitude not pre-specified).

Alternatives considered

• Light stemming (Porter, snowball). Rejected — adds nontrivial complexity for a corpus where the high-leverage split is mechanical (delimiter-based), not morphological.
• Tokenizer protocol abstraction. Rejected — the single-source-of-truth invariant exists for a reason; pluggability is a future concern. Keep the change minimal.
• Change STOPWORDS instead. Rejected — the issue isn't stopword filtering; the issue is that compound IDs never get split.

Affected modules

src/contextweaver/_utils.py (the tokenize function only), tests/test_utils.py

Estimated effort

S (1–2 days)

Baseline

Current R@5 on 50-query gold (TF-IDF): 0.74 / 0.61 / 0.31 at sizes 50 / 83 / 1000 (benchmarks/README.md:60-65).

After the per-backend matrix issue lands, baseline expands to 9 cells (3 backends × 3 sizes).

Success metric

• Direction-only lift target: R@5 must improve on at least one matrix cell (any backend, any size).
• Strict regression bound: no matrix cell may drop more than 1pp on R@5.
• Strict latency bound: no matrix cell may exceed baseline_p99 × 1.30 (Round 2 Q5=C).

Evaluation plan

Run make benchmark-matrix before and after; record both latest.jsons. The scorecard delta (from the regression-PR-comment issue) is the primary evidence. Paste the before/after table into the PR description (Round 2 Q3=B Reproducibility subsection).

Scope

src/contextweaver/_utils.py (tokenize only) + tests/test_utils.py + acceptance evidence in PR description.

Non-goals

• No stemming.
• No language detection.
• No tokenizer protocol abstraction.
• No router changes.
• No new public API.
• No change to STOPWORDS.

Design constraints

• Deterministic. Same input → same set across N=100 calls.
• Sub-tokens emitted in addition to (not in place of) the original token.
• STOPWORDS still applied to all tokens (original + sub-tokens).
• Single source of truth invariant (docs/agent-context/invariants.md) preserved.
• No new dependencies.

Depends on

• Per-backend × per-size benchmark matrix (needed to measure impact across backends).
• Soft regression PR comment (makes the PR review trivial — paste the comment into the PR body).

Suggested order

After the matrix + regression-comment issues land. Cannot ship without the matrix to prove no per-backend regression.

Acceptance Criteria (STRICT)

• tokenize("crm.deals.search") returns a set containing {"crm", "deals", "search"} (plus the dotted form unless it falls in STOPWORDS — it won't)
• Determinism test: tokenize(x) == tokenize(x) across N=100 calls
• No matrix cell shows R@5 regression > -1pp on the current gold set (strict)
• At least one matrix cell shows measurable R@5 lift (direction-only)
• No latency regression > +30% on any p99 cell (strict — ⚠️-marker check)
• make ci passes; all 871+ existing tests still green
• PR body includes a before/after table copied from the regression PR comment (Round 2 Q3=B reproducibility)

Validation

# Before merging, on the PR branch:
make benchmark-matrix
make scorecard
git diff benchmarks/scorecard.md  # paste into PR body

The regression PR comment will fire automatically on push.

Files likely touched

src/contextweaver/_utils.py, tests/test_utils.py

Risk / rollback

Low — local change to one function. Revert is a single git revert; existing baselines preserved.

Security/privacy notes

None. Tokenizer is pure text transform with no I/O.

Agent-ready notes

• Starting points: src/contextweaver/_utils.py:tokenize — single function to modify. tests/test_utils.py for test patterns.
• Constraints: do NOT introduce a TokenizerProtocol here (out of scope). Do NOT change STOPWORDS. Determinism is a hard invariant — must be preserved.
• Review focus for Copilot code review:
  • Determinism on edge cases (empty string, single dot, trailing punctuation, unicode).
  • Sub-tokens go through STOPWORDS filtering (don't accidentally leak filtered words back in).
  • Performance: the additional splitting should not measurably affect p99 latency (verify via scorecard).
  • The split must not change behavior for non-dotted tokens (existing 871 tests cover this).

Cross-references

• Composes with the per-backend × per-size matrix and the regression PR comment — both are prerequisites for measuring impact safely.
• First product feature in the cycle that's net-new (not previously tracked as a GitHub issue).

Size

S

[dgenio]

Closing — shipped in v0.4.0 (release commit 7dfdc85).

Evidence on main:

• src/contextweaver/_utils.py tokenize() (around lines 218-227) now uses _OUTER_SPLIT_RE then per-segment _INNER_SEP_RE.split to emit both the dotted compound and its sub-tokens. The _OUTER_SPLIT_RE docstring captures the rationale for intentionally keeping underscore as a non-separator (synthetic-catalog crosstalk noise observed during measurement).
• The replace(...) workaround in routing/router.py has been retired.
• Landing commit: fb9e377 feat(routing): namespace-aware tokenizer + benchmark matrix/baseline (#208, #209, #213, #215).
• CHANGELOG.md ## [0.4.0] - 2026-05-16 explicitly names [routing] Namespace-aware tokenizer split for dotted tool IDs (with measured impact) #213 ("Namespace-aware tokenizer"). The strict regression bound is noted (one cell at fuzzy/100 dropped 1.50pp, slightly over the strict ≤1pp bound) — if that overrun warrants a revisit, please open a follow-up rather than reopening this issue, since the tokenizer itself shipped.

Reopen if tokenize("crm.deals.search") no longer returns the documented set on a fresh main.

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