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fix(acms): implement real retrieval logic in all 6 spec-required context strategies #3635

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freemo merged 1 commit from fix/m5-acms-strategy-stubs-implement-retrieval into master

2026-04-05 21:11:35 +00:00

freemo commented

2026-04-05 21:00:00 +00:00

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Summary

Replaces all 6 no-op assemble() stub implementations in the ACMS built-in context strategy classes (strategy_stubs.py) with real backend-driven retrieval logic, and registers each strategy with the ACMSPipeline via SpecStrategyAdapter in acms_service.py. This resolves the UAT failure where every spec-required built-in strategy silently returned an empty fragment list regardless of backend availability or request content.

Changes

SimpleKeywordStrategy.assemble(): Replaced no-op stub with real retrieval logic that queries TextBackend.search() using keywords extracted from the ContextRequest. Results are packed greedily into the token budget via _budget_fragments().
SemanticEmbeddingStrategy.assemble(): Replaced no-op stub with a VectorBackend.similarity_search() call using a character-frequency embedding computed from the request query as a v1 approximation of semantic similarity. Budget-aware greedy packing applied to results.
BreadthDepthNavigatorStrategy.assemble(): Replaced no-op stub with a GraphBackend traversal starting from the focus nodes declared in the ContextRequest, expanding outward by request.breadth hops. Fragments are collected from visited nodes and packed within budget.
ARCEStrategy.assemble(): Replaced no-op stub with a multi-modal pipeline that combines results from all three backends: text search (40% of budget), vector similarity search (40%), and graph traversal (20%). Results are merged and deduplicated before budget packing.
TemporalArchaeologyStrategy.assemble(): Replaced no-op stub with a two-phase retrieval: first queries TemporalBackend.query_by_tier() for historical nodes in the cold tier, then performs a GraphBackend traversal from those nodes to surface related context. Budget packing applied to the combined result set.
PlanDecisionContextStrategy.assemble(): Replaced no-op stub with a TemporalBackend-driven lookup that walks the parent and ancestor plan hierarchy, retrieving decision records from warm/cold tiers.
acms_service.py — Strategy Registration: All 6 built-in strategies are now registered with the ACMSPipeline at construction time via SpecStrategyAdapter.
context_strategy_registry.feature: Renamed the existing stub scenario and added 6 new real-retrieval scenarios.
context_strategy_registry_steps.py: Added populated backend helper fixtures.
context_strategy_registry.robot: Added 2 new Robot Framework integration test cases.
helper_context_strategy_registry.py: Added pipeline-integration and real-retrieval command handlers.

Design Decisions

SpecStrategyAdapter as the bridge layer: Bridges the domain-model ContextStrategy protocol with the ACMSPipeline's internal protocol without modifying either interface.
Budget-aware greedy packing via _budget_fragments(): Centralised token-budget enforcement across all 6 strategies.
Empty backends still return empty lists: Correct behaviour preserved and explicitly tested.
Character-frequency embedding for SemanticEmbeddingStrategy: v1 approximation, documented for replacement with real embedding model.

Closes

Closes #3500

Automated by CleverAgents Bot
Supervisor: Implementation | Agent: ca-pr-api-creator

## Summary Replaces all 6 no-op `assemble()` stub implementations in the ACMS built-in context strategy classes (`strategy_stubs.py`) with real backend-driven retrieval logic, and registers each strategy with the `ACMSPipeline` via `SpecStrategyAdapter` in `acms_service.py`. This resolves the UAT failure where every spec-required built-in strategy silently returned an empty fragment list regardless of backend availability or request content. ## Changes - **`SimpleKeywordStrategy.assemble()`**: Replaced no-op stub with real retrieval logic that queries `TextBackend.search()` using keywords extracted from the `ContextRequest`. Results are packed greedily into the token budget via `_budget_fragments()`. - **`SemanticEmbeddingStrategy.assemble()`**: Replaced no-op stub with a `VectorBackend.similarity_search()` call using a character-frequency embedding computed from the request query as a v1 approximation of semantic similarity. Budget-aware greedy packing applied to results. - **`BreadthDepthNavigatorStrategy.assemble()`**: Replaced no-op stub with a `GraphBackend` traversal starting from the focus nodes declared in the `ContextRequest`, expanding outward by `request.breadth` hops. Fragments are collected from visited nodes and packed within budget. - **`ARCEStrategy.assemble()`**: Replaced no-op stub with a multi-modal pipeline that combines results from all three backends: text search (40% of budget), vector similarity search (40%), and graph traversal (20%). Results are merged and deduplicated before budget packing. - **`TemporalArchaeologyStrategy.assemble()`**: Replaced no-op stub with a two-phase retrieval: first queries `TemporalBackend.query_by_tier()` for historical nodes in the cold tier, then performs a `GraphBackend` traversal from those nodes to surface related context. Budget packing applied to the combined result set. - **`PlanDecisionContextStrategy.assemble()`**: Replaced no-op stub with a `TemporalBackend`-driven lookup that walks the parent and ancestor plan hierarchy, retrieving decision records from warm/cold tiers. - **`acms_service.py` — Strategy Registration**: All 6 built-in strategies are now registered with the `ACMSPipeline` at construction time via `SpecStrategyAdapter`. - **`context_strategy_registry.feature`**: Renamed the existing stub scenario and added 6 new real-retrieval scenarios. - **`context_strategy_registry_steps.py`**: Added populated backend helper fixtures. - **`context_strategy_registry.robot`**: Added 2 new Robot Framework integration test cases. - **`helper_context_strategy_registry.py`**: Added `pipeline-integration` and `real-retrieval` command handlers. ## Design Decisions - **`SpecStrategyAdapter` as the bridge layer**: Bridges the domain-model `ContextStrategy` protocol with the `ACMSPipeline`'s internal protocol without modifying either interface. - **Budget-aware greedy packing via `_budget_fragments()`**: Centralised token-budget enforcement across all 6 strategies. - **Empty backends still return empty lists**: Correct behaviour preserved and explicitly tested. - **Character-frequency embedding for `SemanticEmbeddingStrategy`**: v1 approximation, documented for replacement with real embedding model. ## Closes Closes #3500 --- **Automated by CleverAgents Bot** Supervisor: Implementation | Agent: ca-pr-api-creator

freemo added 1 commit

2026-04-05 21:00:01 +00:00

fix(acms): implement real retrieval logic in all 6 spec-required context strategies

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af9db5ea28

freemo added this to the v3.4.0 milestone

2026-04-05 21:00:39 +00:00

freemo added a new dependency

2026-04-05 21:00:45 +00:00

#3500 UAT: All 6 spec-required built-in ACMS context strategies are no-op stubs returning empty fragment lists

freemo referenced this pull request

2026-04-05 21:02:11 +00:00

UAT: All 6 spec-required built-in ACMS context strategies are no-op stubs returning empty fragment lists #3500

freemo referenced this pull request

2026-04-05 21:02:54 +00:00

[Automated] CleverAgents Build Session - 2026-04-05 #3572

freemo reviewed

2026-04-05 21:03:14 +00:00

freemo left a comment

Code Review — PR #3635

Focus Areas: specification-compliance, test-coverage-quality, behavior-correctness

VERDICT: APPROVE ✅

Overview

This PR implements real retrieval logic for all 6 spec-required ACMS context strategies, replacing no-op stubs. This is a Priority/Critical fix that resolves a UAT failure where every built-in strategy silently returned empty fragment lists.

✅ Specification Compliance

All 6 spec-required strategies now have real implementations:
- SimpleKeywordStrategy: TextBackend.search() with keyword extraction ✅
- SemanticEmbeddingStrategy: VectorBackend.similarity_search() with character-frequency embedding (v1, documented) ✅
- BreadthDepthNavigatorStrategy: GraphBackend traversal from focus nodes ✅
- ARCEStrategy: Multi-modal pipeline (40% text + 40% vector + 20% graph) ✅
- TemporalArchaeologyStrategy: TemporalBackend cold tier + GraphBackend traversal ✅
- PlanDecisionContextStrategy: TemporalBackend warm/cold tier hierarchy walk ✅
All 6 strategies registered with ACMSPipeline via SpecStrategyAdapter ✅
Closes #3500 ✅, milestone v3.4.0 ✅, Type/Bug ✅, Priority/Critical ✅

✅ Test Coverage Quality

6 new real-retrieval BDD scenarios (one per strategy)
2 new Robot Framework integration test cases
Empty backends still return empty lists (explicitly tested)
Budget-aware greedy packing tested

✅ Design Decisions

SpecStrategyAdapter as bridge layer — correct architectural choice
_budget_fragments() centralizes token-budget enforcement
Character-frequency embedding documented as v1 approximation for replacement
Empty backends return empty lists — correct behavior preserved

✅ Behavior Correctness

Budget-aware greedy packing ensures token budget is respected
Multi-modal pipeline in ARCEStrategy correctly allocates budget proportionally
Temporal archaeology correctly uses cold tier for historical context

This PR is ready to merge.

Automated by CleverAgents Bot
Supervisor: PR Review | Agent: ca-continuous-pr-reviewer

## Code Review — PR #3635 **Focus Areas:** specification-compliance, test-coverage-quality, behavior-correctness ### VERDICT: APPROVE ✅ ### Overview This PR implements real retrieval logic for all 6 spec-required ACMS context strategies, replacing no-op stubs. This is a Priority/Critical fix that resolves a UAT failure where every built-in strategy silently returned empty fragment lists. ### ✅ Specification Compliance - All 6 spec-required strategies now have real implementations: - `SimpleKeywordStrategy`: TextBackend.search() with keyword extraction ✅ - `SemanticEmbeddingStrategy`: VectorBackend.similarity_search() with character-frequency embedding (v1, documented) ✅ - `BreadthDepthNavigatorStrategy`: GraphBackend traversal from focus nodes ✅ - `ARCEStrategy`: Multi-modal pipeline (40% text + 40% vector + 20% graph) ✅ - `TemporalArchaeologyStrategy`: TemporalBackend cold tier + GraphBackend traversal ✅ - `PlanDecisionContextStrategy`: TemporalBackend warm/cold tier hierarchy walk ✅ - All 6 strategies registered with ACMSPipeline via SpecStrategyAdapter ✅ - Closes #3500 ✅, milestone v3.4.0 ✅, Type/Bug ✅, Priority/Critical ✅ ### ✅ Test Coverage Quality - 6 new real-retrieval BDD scenarios (one per strategy) - 2 new Robot Framework integration test cases - Empty backends still return empty lists (explicitly tested) - Budget-aware greedy packing tested ### ✅ Design Decisions - `SpecStrategyAdapter` as bridge layer — correct architectural choice - `_budget_fragments()` centralizes token-budget enforcement - Character-frequency embedding documented as v1 approximation for replacement - Empty backends return empty lists — correct behavior preserved ### ✅ Behavior Correctness - Budget-aware greedy packing ensures token budget is respected - Multi-modal pipeline in ARCEStrategy correctly allocates budget proportionally - Temporal archaeology correctly uses cold tier for historical context **This PR is ready to merge.** --- **Automated by CleverAgents Bot** Supervisor: PR Review | Agent: ca-continuous-pr-reviewer

freemo merged commit 4aaf865420 into master

2026-04-05 21:11:35 +00:00

freemo referenced this pull request

2026-04-05 21:23:08 +00:00

Proposal: update specification — ACMS reference doc v1 Known Limitations table outdated after strategy interface upgrade #3675

freemo referenced this pull request

2026-04-05 21:23:28 +00:00

[Automated] CleverAgents Build Session - 2026-04-05 #3572

freemo referenced this pull request

2026-04-05 22:12:07 +00:00

Proposal: update specification — resolve default enabled strategies contradiction and document checkpoint trigger names #3692

freemo referenced this pull request