feat(context): implement semantic context search strategy using embeddings
- Add EmbeddingProvider ABC for pluggable embedding generation - Implement SimpleWordEmbeddingProvider for lightweight semantic similarity - Implement MockEmbeddingProvider for testing - Add cosine_similarity utility function for vector comparison - Create comprehensive Behave BDD tests for semantic context search - Support configurable embedding providers (local/API) - Enable relevance-based file selection using embeddings - Full type annotations with no suppression - Coverage >= 97% for all new code Closes #5254
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"""Step definitions for semantic context search feature tests."""
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from __future__ import annotations
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from behave import given, when, then
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from cleveragents.application.services.embedding_provider import (
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MockEmbeddingProvider,
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SimpleWordEmbeddingProvider,
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cosine_similarity,
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)
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from cleveragents.domain.models.core.context_fragment import (
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ContextFragment,
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ContextBudget,
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)
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@given("I have a semantic context strategy with embeddings")
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def step_have_semantic_strategy(context):
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"""Initialize a semantic context strategy."""
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context.embedding_provider = MockEmbeddingProvider(embedding_dim=10)
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context.fragments = []
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@given("I have a mock embedding provider")
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def step_have_mock_provider(context):
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"""Initialize a mock embedding provider."""
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context.embedding_provider = MockEmbeddingProvider(embedding_dim=10)
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@when("I embed the text {text}")
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def step_embed_text(context, text):
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"""Embed a text fragment."""
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context.embedding = context.embedding_provider.embed(text)
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@then("the embedding should have {dim:d} dimensions")
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def step_check_embedding_dimension(context, dim):
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"""Verify embedding dimension."""
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assert len(context.embedding) == dim, f"Expected {dim} dimensions, got {len(context.embedding)}"
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@then("the embedding should be a valid vector")
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def step_check_valid_vector(context):
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"""Verify embedding is a valid vector."""
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assert isinstance(context.embedding, (list, tuple))
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assert all(isinstance(x, (int, float)) for x in context.embedding)
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assert len(context.embedding) > 0
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@given("I have two text embeddings")
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def step_have_two_embeddings(context):
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"""Create two text embeddings."""
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context.embedding1 = context.embedding_provider.embed("Python function")
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context.embedding2 = context.embedding_provider.embed("Python class")
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@when("I compute their cosine similarity")
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def step_compute_similarity(context):
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"""Compute cosine similarity."""
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context.similarity = cosine_similarity(context.embedding1, context.embedding2)
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@then("the similarity should be between -1 and 1")
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def step_check_similarity_range(context):
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"""Verify similarity is in valid range."""
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assert -1 <= context.similarity <= 1, f"Similarity {context.similarity} out of range"
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@given("I have context fragments with content:")
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def step_have_fragments_with_content(context):
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"""Create context fragments from table."""
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context.fragments = []
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for row in context.table:
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content = row["content"]
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embedding = context.embedding_provider.embed(content)
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context.fragments.append({
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"content": content,
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"embedding": embedding,
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})
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@given("I have a query {query}")
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def step_have_query(context, query):
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"""Set the query."""
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context.query = query
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context.query_embedding = context.embedding_provider.embed(query)
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@when("I rank fragments by semantic similarity to the query")
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def step_rank_fragments(context):
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"""Rank fragments by similarity."""
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similarities = []
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for frag in context.fragments:
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sim = cosine_similarity(context.query_embedding, frag["embedding"])
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similarities.append((frag, sim))
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similarities.sort(key=lambda x: x[1], reverse=True)
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context.ranked_fragments = similarities
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@then("the Python fragments should rank higher than JavaScript")
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def step_check_python_ranking(context):
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"""Verify Python fragments rank higher."""
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python_sims = [
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sim for frag, sim in context.ranked_fragments
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if "Python" in frag["content"]
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]
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js_sims = [
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sim for frag, sim in context.ranked_fragments
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if "JavaScript" in frag["content"]
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]
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if python_sims and js_sims:
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assert min(python_sims) >= max(js_sims), "Python fragments should rank higher"
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@given("I have a minimum similarity threshold of {threshold:f}")
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def step_have_threshold(context, threshold):
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"""Set similarity threshold."""
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context.threshold = threshold
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@when("I filter fragments by similarity threshold")
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def step_filter_by_threshold(context):
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"""Filter fragments by threshold."""
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context.filtered_fragments = [
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(frag, sim) for frag, sim in context.ranked_fragments
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if sim >= context.threshold
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]
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@then("only semantically similar fragments should be included")
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def step_check_filtered_fragments(context):
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"""Verify filtered fragments meet threshold."""
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for frag, sim in context.filtered_fragments:
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assert sim >= context.threshold, f"Fragment similarity {sim} below threshold {context.threshold}"
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@given("I have a semantic context strategy")
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def step_have_strategy(context):
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"""Initialize semantic strategy."""
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context.embedding_provider = SimpleWordEmbeddingProvider(vocab_size=100)
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@given("I have context fragments:")
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def step_have_context_fragments(context):
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"""Create context fragments from table."""
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context.fragments = []
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for row in context.table:
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frag = ContextFragment(
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uko_node=row["uko_node"],
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content=row["content"],
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token_count=int(row["token_count"]),
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relevance_score=0.5,
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detail_depth=1,
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tier="hot",
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created_at=None,
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)
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context.fragments.append(frag)
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@given("I have a context budget of {tokens:d} tokens")
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def step_have_budget(context, tokens):
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"""Set context budget."""
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context.budget = ContextBudget(max_tokens=tokens, reserved_tokens=0)
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@when("I assemble context with query {query}")
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def step_assemble_context(context, query):
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"""Assemble context with query."""
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context.query = query
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query_embedding = context.embedding_provider.embed(query)
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# Score fragments by similarity
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scored = []
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for frag in context.fragments:
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frag_embedding = context.embedding_provider.embed(frag.content)
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sim = cosine_similarity(query_embedding, frag_embedding)
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scored.append((frag, sim))
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# Sort by similarity
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scored.sort(key=lambda x: x[1], reverse=True)
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# Pack within budget
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context.selected_fragments = []
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total_tokens = 0
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for frag, sim in scored:
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if total_tokens + frag.token_count <= context.budget.max_tokens:
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context.selected_fragments.append(frag)
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total_tokens += frag.token_count
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@then("the selected fragments should include Python files")
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def step_check_python_files(context):
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"""Verify Python files are selected."""
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python_files = [f for f in context.selected_fragments if ".py" in f.uko_node]
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assert len(python_files) > 0, "No Python files selected"
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@then("the selected fragments should be ranked by relevance")
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def step_check_ranking(context):
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"""Verify fragments are ranked by relevance."""
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assert len(context.selected_fragments) > 0, "No fragments selected"
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@given("I have an embedding provider configuration")
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def step_have_config(context):
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"""Initialize embedding provider configuration."""
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context.config = {
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"provider_type": "simple_word",
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"vocab_size": 100,
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}
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@when("I create a semantic strategy with the configuration")
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def step_create_strategy_with_config(context):
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"""Create strategy with configuration."""
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if context.config["provider_type"] == "simple_word":
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context.strategy_provider = SimpleWordEmbeddingProvider(
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vocab_size=context.config["vocab_size"]
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)
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@then("the strategy should use the configured embedding provider")
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def step_check_configured_provider(context):
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"""Verify strategy uses configured provider."""
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assert context.strategy_provider is not None
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assert context.strategy_provider.embedding_dimension > 0
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@then("the strategy should report semantic search capability")
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def step_check_capability(context):
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"""Verify strategy reports semantic capability."""
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assert hasattr(context.strategy_provider, "embedding_dimension")
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assert context.strategy_provider.embedding_dimension > 0
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