Fourteen modules import `json` without using it. Ruff flags every one with F401, and a word-boundary search for `json` in each file matches only the import line itself, including inside strings, comments and annotations.
Two exclusions, both deliberate. Migration files are left alone: the import is equally dead there, but those files are frozen history and not worth the churn. `models/chats.py` has the same dead import and is handled in its own change, so it is skipped here to avoid two changes touching the same line.
No behaviour change.
build_matcher compiled a caller-supplied pattern with Python's backtracking re and ran it over every line of every reachable file, with no timeout, no thread offload and no length caps. is_regex_pattern promotes any pattern containing a metacharacter, and a bare pipe counts, so no explicit regex flag is needed to reach the compiler. The search loop is synchronous inside an async handler, and UVICORN_WORKERS defaults to 1, so the cost lands on every other user of the instance. MAX_GREP_RESULTS bounds how many matches are reported, not how much work is done.
Backtracking cost is exponential in the length of the text being matched, so capping the pattern or the line does not bound it: the subject in the measurements below is 30 characters. `(x|x)*y` against a line of 30 x took 80 seconds, `(a+)+$` against 32 a took 169 seconds, and the same subject with a literal pattern took 0.6 microseconds.
Matching now runs on the regex module, which accepts a per-search timeout that re has no equivalent for. The timeout is the actual bound: regex resolves many classic catastrophic patterns instantly, but not all of them, and `(a|aa)+$` and `(?:a|a)*$` still need it. The budget covers a whole tool call rather than a single search, because a pipeline builds one matcher per segment and a per-search budget would multiply by segment count, and because a per-line timeout would allow timeout multiplied by line count. It is carried in a context variable so one command shares it without threading a parameter through every handler, and it is charged only for time spent inside search(), so database round-trips and other coroutines cannot consume it. Exhausting it raises, and both entry points already render that as an error for the model to read.
Note for anyone tracking search behaviour: re and the regex module define \w, \W and \b differently on non-ASCII text. re follows str.isalnum(), the regex module follows UTS#18, so \w no longer matches superscripts and fractions such as the ones in Nd-adjacent categories, and now does match combining marks. POSIX classes like [[:alpha:]] are interpreted rather than read as a literal set, and \p{...} compiles instead of erroring. Results on ASCII content are unchanged.
regex was already installed as a transitive dependency of nltk, tiktoken and transformers. It is now declared directly, pinned in pyproject.toml and requirements.txt to the version the lockfile already resolves.
is_regex_pattern only recognized the BRE-escaped form \| and not a bare |,
so a pattern like "Jornak|Silverlake|Orissa" was treated as one literal
string (including the pipe characters) and silently returned no matches.
This contradicted the tool docstring, which explicitly advertises
"error|warn" as an auto-detected regex example, and misled models into
concluding the searched terms were absent from the file.
Checking for a bare | also covers the escaped form, since \| contains |,
and normalize_regex already converts escaped pipes before compilation.
Literal patterns without regex metacharacters are unaffected.
Fixes#26781
