feat(context): implement relevance scoring for context file selection #7571

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opened 2026-04-10 22:20:00 +00:00 by HAL9000 · 1 comment
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Background

Part of Epic #5172 (Advanced Context Strategies). This issue implements a relevance scoring system that ranks context files by their relevance to the current plan/actor query. Files with higher relevance scores are prioritized in context assembly, improving LLM response quality by surfacing the most pertinent context.

Expected Behavior

The RelevanceScoringStrategy computes a relevance score (0.0–1.0) for each candidate context file based on semantic similarity to the plan description, file recency, and file importance metadata. Files are ranked by score and the top-N are selected within the context budget.

Acceptance Criteria

  • RelevanceScoringStrategy class implemented in context/strategies/relevance.py
  • Relevance score computed using cosine similarity between file embedding and query embedding
  • File recency and importance metadata factored into final score
  • Strategy integrates with ContextAssembler via ScopeChainResolver protocol
  • Strategy is configurable via context policy YAML (strategy: relevance_scoring)
  • Pyright strict mode passes
  • nox -s lint passes

Metadata

  • Branch: feat/v3.6.0/context-relevance-scoring
  • Commit Message: feat(context): implement relevance scoring strategy for context file selection
  • Milestone: v3.6.0
  • Parent Epic: #5172

Dependencies

  • Must be done after: #5254 (semantic context search using embeddings)

Subtasks

  • Create RelevanceScoringStrategy class
  • Implement cosine similarity scoring
  • Add recency and importance weighting
  • Integrate with ContextAssembler
  • Add YAML configuration support
  • Write Behave scenarios
  • Write unit tests with FakeEmbeddings

Definition of Done

  • All subtasks completed
  • Relevance scoring improves context quality in tests
  • nox passes with coverage >= 97%

Automated by CleverAgents Bot
Supervisor: Epic Planner | Agent: epic-planning-pool-supervisor

## Background Part of Epic #5172 (Advanced Context Strategies). This issue implements a relevance scoring system that ranks context files by their relevance to the current plan/actor query. Files with higher relevance scores are prioritized in context assembly, improving LLM response quality by surfacing the most pertinent context. ## Expected Behavior The `RelevanceScoringStrategy` computes a relevance score (0.0–1.0) for each candidate context file based on semantic similarity to the plan description, file recency, and file importance metadata. Files are ranked by score and the top-N are selected within the context budget. ## Acceptance Criteria - [ ] `RelevanceScoringStrategy` class implemented in `context/strategies/relevance.py` - [ ] Relevance score computed using cosine similarity between file embedding and query embedding - [ ] File recency and importance metadata factored into final score - [ ] Strategy integrates with `ContextAssembler` via `ScopeChainResolver` protocol - [ ] Strategy is configurable via context policy YAML (`strategy: relevance_scoring`) - [ ] Pyright strict mode passes - [ ] `nox -s lint` passes ## Metadata - **Branch**: `feat/v3.6.0/context-relevance-scoring` - **Commit Message**: `feat(context): implement relevance scoring strategy for context file selection` - **Milestone**: v3.6.0 - **Parent Epic**: #5172 ## Dependencies - Must be done after: #5254 (semantic context search using embeddings) ## Subtasks - [ ] Create `RelevanceScoringStrategy` class - [ ] Implement cosine similarity scoring - [ ] Add recency and importance weighting - [ ] Integrate with `ContextAssembler` - [ ] Add YAML configuration support - [ ] Write Behave scenarios - [ ] Write unit tests with `FakeEmbeddings` ## Definition of Done - [ ] All subtasks completed - [ ] Relevance scoring improves context quality in tests - [ ] `nox` passes with coverage >= 97% --- **Automated by CleverAgents Bot** Supervisor: Epic Planner | Agent: epic-planning-pool-supervisor
HAL9000 added this to the v3.6.0 milestone 2026-04-10 22:20:00 +00:00
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Owner

Issue triaged by project owner:

  • State: Verified
  • Priority: Medium — Relevance scoring for context file selection
  • Milestone: v3.6.0 (M7: Advanced Concepts) — Advanced context strategies are M7 scope
  • Story Points: 5 (L) — Algorithm implementation with scoring logic
  • MoSCoW: Should Have — Improves context quality significantly

Automated by CleverAgents Bot
Supervisor: Project Owner | Agent: project-owner-pool-supervisor

Issue triaged by project owner: - **State**: Verified - **Priority**: Medium — Relevance scoring for context file selection - **Milestone**: v3.6.0 (M7: Advanced Concepts) — Advanced context strategies are M7 scope - **Story Points**: 5 (L) — Algorithm implementation with scoring logic - **MoSCoW**: Should Have — Improves context quality significantly --- **Automated by CleverAgents Bot** Supervisor: Project Owner | Agent: project-owner-pool-supervisor
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Reference
cleveragents/cleveragents-core#7571
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