v0.8.0

v0.8.0 (2026-02-23)

This release is published under the Apache-2.0 License.

Bug Fixes

• Install project at build time instead of runtime (4850036)

Add a second uv sync --frozen after copying source to install the project package during the Docker build. Replace uv run entrypoint with direct .venv/bin invocation so nothing is resolved at container start.

• Remove stale QUESTIONS and match functions from accuracy.py (435e9fe)

The local QUESTIONS dict shadowed the import from fixtures.py, limiting standalone accuracy.py to only 2 fixtures instead of all 8. The local match functions also referenced re without importing it.

• Split accuracy into separate JSON/TOON tables, remove raw backup (dbe7119)

Split the combined accuracy matrix into separate JSON (baseline) and TOON (balanced profile) tables for readability. Remove raw_results_pre_profiles.json from repo.

Documentation

• Add CSV/XML to README, note on CSV token behavior (b1305fe)

Update supported formats, CLI examples, benchmark table, and question count. Add note explaining that CSV already being tabular means TOON adds minimal overhead — the value is auto-detection, type inference, and heuristic column elision rather than token reduction.

• Rename JSON to Raw in README benchmark tables (4d4aaa6)

Features

• Add app_performance.csv benchmark fixture (ff03d7d)

30 microservices x 25 columns APM dashboard export designed to exercise TOON heuristics: 4 all-zero cols, 3 all-null cols, 1 constant col elided; latency and errors tuple-grouped (25→13 effective columns). 15 accuracy questions including annotation-reading tests. qwen3:4b scores 93% TOON vs 87% raw at 1.6x speed.

• Add CSV and XML benchmark fixtures with accuracy questions (de3770a)

Add server_metrics.csv (25 servers x 10 columns) and deploy_inventory.xml (20 deployments across 3 environments) as accuracy benchmark fixtures. Update load_sample to handle non-JSON formats via parse_input, add 15 questions each covering direct lookups, filtering, cross-reference, and multi-hop queries.

XML fixture shows 65.6% token reduction from eliminating duplicated tags — exactly the enterprise API use case the condenser targets.

• Add CSV/TSV parser with type inference (a35492c)

Register a new CSV parser after JSON/YAML in the parser registry. Uses csv.Sniffer for dialect detection (comma, tab, pipe, semicolon), requires 2+ columns and at least one data row. Type normalization converts numeric strings to int/float and empty values to None.

• Add format_hint config for per-tool format override (1d8dea4)

ServerConfig gains format_hint and tool_format_hints fields, parsed from FORMAT_HINT / TOOL_FORMAT_HINTS env vars and JSON config. The proxy passes the resolved hint through to parse_input().

• Add XML parser with tree-to-dict conversion (17ca4db)

Uses xml.etree.ElementTree to parse XML into nested dicts/lists that the existing condense pipeline handles natively. Attributes become @attr keys, repeated child elements become lists, and text values get int/float/bool coercion. Registered after CSV (lowest auto-detect priority).

Refactoring

• Extract parser registry into parsers.py (8ba88e5)

Move parse_input() from condenser.py into a new parsers module with an extensible registry (Parser NamedTuple, PARSER_REGISTRY, register_parser). Existing importers continue to work via re-export from condenser.py.

• Rename benchmark format label from json to raw (ad6057f)

The baseline format is the original input (JSON, CSV, XML, etc.), not always JSON. Rename format field, display labels, variable names, and help text throughout accuracy.py and matrix.py. Also add app_performance.csv to matrix DEFAULT_FIXTURES.

• Rename condense_json/toon_encode_json to format-agnostic names (7d24b54)

condense_json() → condense_text(), toon_encode_json() → toon_encode(). Old names remain as deprecated aliases that emit DeprecationWarning. All internal callers updated to use new names.

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Detailed Changes: v0.7.0...v0.8.0