chore(deps): bump json-repair from 0.25.2 to 0.60.1

[dependabot]

Bumps json-repair from 0.25.2 to 0.60.1.

Release notes

Sourced from json-repair's releases.

Release 0.60.1

Added

• A potential infinite loop in schema resolution could cause an application using the library to crash if the schema was self referential

Support this project

json_repair is maintained as a side project and stays free for everyone.

If it saves you debugging time, helps you handle LLM-generated JSON, or is part of your production workflow, please consider:

• starring the repository to help more developers find it
• sharing it with teams working with unreliable JSON output
• sponsoring the project here: https://github.com/sponsors/mangiucugna

Sponsorship helps justify the time spent fixing edge cases, improving performance, and keeping the library reliable.

Release 0.60.0

Added

• Support for repairing string values that contain low smart quote spans like „... when the closing quote is emitted as ASCII " instead of Unicode ”. Issue #198

Support this project

json_repair is maintained as a side project and stays free for everyone.

If it saves you debugging time, helps you handle LLM-generated JSON, or is part of your production workflow, please consider:

• starring the repository to help more developers find it
• sharing it with teams working with unreliable JSON output
• sponsoring the project here: https://github.com/sponsors/mangiucugna

Sponsorship helps justify the time spent fixing edge cases, improving performance, and keeping the library reliable.

Release 0.59.10

Fixed

• Fixed a parser-path repair bug where balanced brace groups inside string values, such as LaTeX fragments like \frac{x^2}{m}, could be mistaken for object structure when a later inline JSON-like snippet appeared in the same string.
• Improved preservation of malformed-but-string-intended explanations containing inline literals such as {"blank_1": "5"} without splitting the surrounding object.

Support this project

json_repair is maintained as a side project and stays free for everyone.

If it saves you debugging time, helps you handle LLM-generated JSON, or is part of your production workflow, please consider:

• starring the repository to help more developers find it
• sharing it with teams working with unreliable JSON output
• sponsoring the project here: https://github.com/sponsors/mangiucugna

Sponsorship helps justify the time spent fixing edge cases, improving performance, and keeping the library reliable.

Release 0.59.9

Fixed

-Fix #196, a regression introduced to fix #170 caused issue with top-level object duplication.

... (truncated)

Commits

• 63992e9 Fix circular schema ref handling
• 7cd8391 Fix #198 support low smart quote spans in strings
• 46be950 update ANGENTS guidance
• 0015c74 Fix README example for #197
• d7f2a3b Fix #195 keep balanced braces in strings
• 4206302 Fix top-level comma-separated object repair
• 9c9db65 Fix #195 preserve LaTeX escapes after prose commas
• dbec25a Bump version to 0.59.7
• 397fbe0 Fix #195 preserve escaped brace groups
• b8cd2e2 Fix #194 line comments with brackets
• Additional commits viewable in compare view

[github-actions]

Benchmark delta (vs main)

Soft regression feedback only — this comment never blocks the PR.
Latency budget: ⚠️ when head > base × 1.3. Accuracy budget: ⚠️ when head < base - 1pp.

Routing summary (single backend × catalog sizes)

size	recall@k (head Δ vs base)	MRR (head Δ vs base)	p99 (ms)
50	✅ 0.5649 (+0.0000)	✅ 0.4978 (+0.0000)	✅ 0.456 (base 0.650)
83	✅ 0.3825 (+0.0000)	✅ 0.3242 (+0.0000)	✅ 0.747 (base 1.205)
1000	✅ 0.1475 (+0.0000)	✅ 0.1456 (+0.0000)	✅ 38.634 (base 47.855)

Per-backend × per-size matrix

backend	size	recall@k (Δ)	MRR (Δ)	p99 (ms)
bm25	100	✅ 0.3825 (+0.0000)	✅ 0.3399 (+0.0000)	✅ 6.093 (base 8.399)
bm25	500	✅ 0.2250 (+0.0000)	✅ 0.2165 (+0.0000)	✅ 29.105 (base 41.865)
bm25	1000	✅ 0.1575 (+0.0000)	✅ 0.1525 (+0.0000)	✅ 86.235 (base 112.387)
embedding_hashing	100	✅ 0.5175 (+0.0000)	✅ 0.4360 (+0.0000)	✅ 7.623 (base 7.218)
embedding_hashing	500	✅ 0.2700 (+0.0000)	✅ 0.2674 (+0.0000)	✅ 41.690 (base 43.171)
embedding_hashing	1000	✅ 0.2000 (+0.0000)	✅ 0.1931 (+0.0000)	✅ 99.776 (base 102.979)
embedding_st	100	skipped (skipped: missing sentence-transformers)	—	—
embedding_st	500	skipped (skipped: missing sentence-transformers)	—	—
embedding_st	1000	skipped (skipped: missing sentence-transformers)	—	—
fuzzy	100	skipped (skipped: missing rapidfuzz)	—	—
fuzzy	500	skipped (skipped: missing rapidfuzz)	—	—
fuzzy	1000	skipped (skipped: missing rapidfuzz)	—	—
tfidf	100	✅ 0.3825 (+0.0000)	✅ 0.3220 (+0.0000)	✅ 1.052 (base 1.193)
tfidf	500	✅ 0.2325 (+0.0000)	✅ 0.2314 (+0.0000)	✅ 9.516 (base 10.803)
tfidf	1000	✅ 0.1475 (+0.0000)	✅ 0.1456 (+0.0000)	✅ 42.341 (base 42.986)

Context pipeline (per scenario)

scenario	tokens	dropped	dedup
large_catalog	1480 (base 1480, Δ+0)	0 (base 0, Δ+0)	0 (base 0, Δ+0)
long_conversation	2500 (base 2500, Δ+0)	0 (base 0, Δ+0)	0 (base 0, Δ+0)
mixed_payload	488 (base 488, Δ+0)	0 (base 0, Δ+0)	0 (base 0, Δ+0)
short_conversation	487 (base 487, Δ+0)	0 (base 0, Δ+0)	0 (base 0, Δ+0)
stress_conversation	6590 (base 6590, Δ+0)	11 (base 11, Δ+0)	4 (base 4, Δ+0)
tiny_payload	256 (base 256, Δ+0)	0 (base 0, Δ+0)	0 (base 0, Δ+0)

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Numbers come from make benchmark / make benchmark-matrix.
Latency is hardware-dependent — treat the markers as a rough guide.
See benchmarks/scorecard.md for the full picture.