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
freemo
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