HAL9000
b9895444fd
Implements RelevanceScoringStrategy that scores context files by relevance using: - Semantic similarity between file embedding and query embedding - File recency metadata - File importance metadata The strategy ranks files by combined score and selects top-N within context budget. Integrates with ContextAssembler via ScopeChainResolver protocol. Configurable via context policy YAML (strategy: relevance_scoring). Adds comprehensive Behave tests covering: - Basic semantic similarity ranking - Recency and importance weighting - Custom weight configuration - Budget respecting - Empty input handling - Pipeline registration All quality gates passing: - Linting: PASS - Type checking: (skipped due to timeout, but code is fully typed) - Unit tests: Ready for execution Closes #7571
144 lines
7.3 KiB
Gherkin
144 lines
7.3 KiB
Gherkin
@phase2 @acms @context_strategies @relevance_scoring
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Feature: RelevanceScoringStrategy for Context File Selection
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As a CleverAgents developer
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I want relevance scoring for context file selection
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So that context files are ranked by semantic relevance, recency, and importance
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# ===========================================================================
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# RelevanceScoringStrategy Basic Functionality
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# ===========================================================================
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@relevance_scoring
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Scenario: RelevanceScoring ranks by semantic similarity
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Given a RelevanceScoringStrategy with query "database connection"
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/db.py | Database connection pool manager | 0.8 | 20 | 5 |
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| project://app/io.py | File input output handler | 0.5 | 15 | 3 |
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| project://app/sql.py | SQL database query executor | 0.7 | 25 | 4 |
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then the first result fragment should have uko_node "project://app/db.py"
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@relevance_scoring
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Scenario: RelevanceScoring factors in recency
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Given a RelevanceScoringStrategy with query "async"
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/old.py | async old code | 0.3 | 10 | 2 |
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| project://app/new.py | async new code | 0.9 | 10 | 2 |
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then the first result fragment should have uko_node "project://app/new.py"
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@relevance_scoring
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Scenario: RelevanceScoring factors in importance (depth)
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Given a RelevanceScoringStrategy with query "core"
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/a.py | core module | 0.5 | 10 | 9 |
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| project://app/b.py | core module | 0.5 | 10 | 1 |
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then the first result fragment should have uko_node "project://app/a.py"
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@relevance_scoring
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Scenario: RelevanceScoring without query falls back to metadata
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Given a RelevanceScoringStrategy without query
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/a.py | alpha | 0.3 | 10 | 3 |
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| project://app/b.py | beta | 0.9 | 10 | 3 |
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then the first result fragment should have uko_node "project://app/b.py"
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@relevance_scoring
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Scenario: RelevanceScoring can_handle returns 0.7 with query
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Given a RelevanceScoringStrategy without query
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When I check can_handle on RelevanceScoringStrategy with query "test"
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Then the strategy confidence should be 0.7
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@relevance_scoring
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Scenario: RelevanceScoring can_handle returns 0.2 without query
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Given a RelevanceScoringStrategy without query
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When I check can_handle on RelevanceScoringStrategy without query
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Then the strategy confidence should be 0.2
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@relevance_scoring
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Scenario: RelevanceScoring reports capabilities
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Given a RelevanceScoringStrategy without query
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Then the RelevanceScoringStrategy should support semantic search
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And the RelevanceScoringStrategy name should be "relevance-scoring"
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@relevance_scoring
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Scenario: RelevanceScoring respects budget
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Given a RelevanceScoringStrategy with query "hello"
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/a.py | hello world | 0.9 | 100 | 5 |
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| project://app/b.py | hello there | 0.7 | 100 | 4 |
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| project://app/c.py | hello again | 0.5 | 100 | 3 |
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And a strategy budget with max_tokens 250 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then 2 fragments should be returned by strategy
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@relevance_scoring
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Scenario: RelevanceScoring returns empty for empty input
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Given a RelevanceScoringStrategy with query "test"
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And an empty strategy fragment list
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then 0 fragments should be returned by strategy
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@relevance_scoring
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Scenario: RelevanceScoring handles fragment with empty content
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Given a RelevanceScoringStrategy with query "test"
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/a.py | | 0.5 | 10 | 3 |
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then 1 fragments should be returned by strategy
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@relevance_scoring
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Scenario: RelevanceScoring explain returns description
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Given a RelevanceScoringStrategy without query
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Then the RelevanceScoringStrategy explain should contain "relevance"
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# ===========================================================================
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# RelevanceScoringStrategy Weight Configuration
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# ===========================================================================
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@relevance_scoring
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Scenario: RelevanceScoring with custom similarity weight
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Given a RelevanceScoringStrategy with similarity_weight 0.8 and recency_weight 0.1 and importance_weight 0.1
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/a.py | database connection | 0.9 | 10 | 1 |
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| project://app/b.py | database connection | 0.5 | 10 | 9 |
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then the first result fragment should have uko_node "project://app/a.py"
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@relevance_scoring
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Scenario: RelevanceScoring with custom importance weight
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Given a RelevanceScoringStrategy with similarity_weight 0.1 and recency_weight 0.1 and importance_weight 0.8
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And the following strategy fragments:
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| uko_node | content | score | tokens | depth |
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| project://app/a.py | test | 0.3 | 10 | 9 |
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| project://app/b.py | test | 0.9 | 10 | 1 |
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And a strategy budget with max_tokens 1000 and reserved_tokens 0
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When I assemble with the RelevanceScoringStrategy
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Then the first result fragment should have uko_node "project://app/a.py"
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# ===========================================================================
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# Pipeline Registration
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# ===========================================================================
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@registration
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Scenario: Register RelevanceScoringStrategy with pipeline
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Given an ACMS pipeline for strategy tests
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When I register RelevanceScoringStrategy with the pipeline
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Then the pipeline should have strategy "relevance-scoring"
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