Feature: Semantic context search using embeddings
  As a context assembly system
  I want to use semantic embeddings to find relevant files
  So that I can select the most semantically relevant context fragments

  Background:
    Given I have a semantic context strategy with embeddings
    And I have a mock embedding provider

  Scenario: Embed text fragments
    When I embed the text "Python function definition"
    Then the embedding should have 10 dimensions
    And the embedding should be a valid vector

  Scenario: Compute cosine similarity between embeddings
    Given I have two text embeddings
    When I compute their cosine similarity
    Then the similarity should be between -1 and 1

  Scenario: Rank fragments by semantic similarity
    Given I have context fragments with content:
      | content                    |
      | Python function definition |
      | JavaScript class syntax    |
      | Python class implementation|
    And I have a query "Python code structure"
    When I rank fragments by semantic similarity to the query
    Then the Python fragments should rank higher than JavaScript

  Scenario: Filter fragments by minimum similarity threshold
    Given I have context fragments with content:
      | content                    |
      | Python function definition |
      | Unrelated database schema  |
      | Python class implementation|
    And I have a query "Python code"
    And I have a minimum similarity threshold of 0.3
    When I filter fragments by similarity threshold
    Then only semantically similar fragments should be included

  Scenario: Semantic strategy selects relevant files
    Given I have a semantic context strategy
    And I have context fragments:
      | uko_node | content                    | token_count |
      | file1.py | Python function definition | 10          |
      | file2.js | JavaScript class syntax    | 10          |
      | file3.py | Python class implementation| 10          |
    And I have a context budget of 30 tokens
    When I assemble semantic context with query "Python code structure"
    Then the selected fragments should include Python files
    And the selected fragments should be ranked by relevance

  Scenario: Embedding provider configuration
    Given I have an embedding provider configuration
    When I create a semantic strategy with the configuration
    Then the strategy should use the configured embedding provider
    And the strategy should report semantic search capability