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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 commits from fix/m5-acms-strategy-stubs-implement-retrieval into master 2026-04-05 21:11:35 +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

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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
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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freemo added this to the v3.4.0 milestone 2026-04-05 21:00:39 +00:00
freemo reviewed 2026-04-05 21:03:14 +00:00
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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 removed this from the v3.4.0 milestone 2026-04-06 21:04:25 +00:00
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aditya (Aditya Chhabra) aleenaumair (Aleena Umair) brent.edwards (Brent Edwards) CoreRasurae (Luis Mendes) drew (Drew Morris) eugen.thaci (Eugen Thaci) freemo (Jeffrey Phillips Freeman) HAL9000 (HAL 9000) HAL9001 (HAL9001) hamza.khyari (Hamza Khyari) hurui200320 (Rui Hu) justin.morris khird (Kyle Hird) org.cleveragents
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#3500 UAT: All 6 spec-required built-in ACMS context strategies are no-op stubs returning empty fragment lists
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Reference: cleveragents/cleveragents-core#3635
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