2 Commits

Author	SHA1	Message	Date
aditya	137d040c4d	feat(acms): implement DepthReductionCompressor for skeleton compression ... Add a production skeleton compressor that re-renders inherited fragments to overview depths via the UKO detail-level map chain, fits the result within the configured skeleton budget, and wires the pipeline default to the new compressor. Address prior review feedback by extracting the render visitors into a dedicated module, restoring projected metadata to native runtime types, constraining builtin component resolution with an allowlist, and keeping child-context inheritance compatible with CRP context fragments for the Robot integration path. Reproduced the Forgejo lint job in a clean python:3.13-slim container with the CI commands All checks passed! and 1740 files already formatted; both passed, so the earlier lint failure appears to have been transient runner behavior rather than a source-level defect. ISSUES CLOSED: #919	2026-04-01 06:16:41 +00:00
freemo	4221582368	feat(acms): implement pipeline Phase 3 components ... Implemented the remaining ACMS pipeline components and advanced context strategies: Pipeline Phase 3: - FragmentOrdererProtocol + RelevanceCoherenceOrderer: orders fragments by relevance while maintaining narrative coherence via UKO node prefix grouping. Groups related fragments together, sorts groups by max relevance, and within groups orders by relevance desc / depth asc. - PreambleGeneratorProtocol + ProvenancePreambleGenerator: generates provenance preamble with strategy contributions (fragment counts and token percentages), confidence indicators (avg/min/max), tier and depth distribution, UKO node coverage, and coverage gap detection. Advanced Strategies: - ArceStrategy (quality 0.95): adaptive recursive context expansion with iterative multi-backend refinement and configurable iteration limit (default 5) to prevent unbounded refinement. Uses composite scoring (relevance + depth + diversity) with contextual boosting for fragments related to the current top-ranked anchor set. - TemporalArchaeologyStrategy (quality 0.5): historical context retrieval from graph+cold backends. Prioritises cold-tier fragments using a temporal scoring model (tier bonus + relevance + depth). - PlanDecisionContextStrategy (quality 0.7): decision history retrieval from warm/cold backends. Prioritises warm then cold tier fragments for correction and retry scenarios. All strategies registered in strategy registry with correct quality scores and backend requirements. All components implement their respective Protocol interfaces and can be injected into the ContextAssemblyPipeline via constructor dependency injection. Tests: - 33 BDD scenarios in features/acms_pipeline_phase3.feature - Robot Framework integration tests in robot/acms_pipeline_phase3.robot - ASV performance benchmarks in benchmarks/acms_pipeline_phase3_bench.py ISSUES CLOSED: #545	2026-03-07 14:53:18 +00:00