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semantica/benchmarks/context_memory/test_agentic.py
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Python

from unittest.mock import MagicMock, patch
import pytest
from semantica.context.agent_context import AgentContext
from semantica.context.context_retriever import RetrievedContext
# Fixtures
@pytest.fixture
def mock_agent_context():
"""
Creates an AgentContext with mocked internals.
"""
vector_store = MagicMock()
knowledge_graph = MagicMock()
with patch("semantica.context.agent_context.AgentMemory") as MockMemory, patch(
"semantica.context.agent_context.ContextRetriever"
) as MockRetriever:
ctx = AgentContext(vector_store=vector_store, knowledge_graph=knowledge_graph)
# Internal mocks
ctx._memory = MockMemory.return_value
ctx._retriever = MockRetriever.return_value
return ctx
# Benchmarks
def test_router_overhead(benchmark, mock_agent_context):
"""
Benchmarks the logic that decides between Vector vs Graph retrieval.
"""
mock_agent_context._retriever.retrieve.return_value = []
def op():
return mock_agent_context.retrieve("test query", use_graph=None)
benchmark.pedantic(op, iterations=50, rounds=20)
def test_result_conversion_throughput(benchmark, mock_agent_context):
"""
Benchmarks converting internal RetrievedContext objects to Dicts.
"""
fake_results = [
RetrievedContext(
content=f"Result {i}",
score=0.9,
source="graph:node_1",
metadata={"type": "fact"},
related_entities=[{"id": "e1", "name": "Entity"}],
related_relationships=[{"source": "e1", "target": "e2"}],
)
for i in range(100)
]
mock_agent_context._retriever.retrieve.return_value = fake_results
def op():
return mock_agent_context.retrieve("test", use_graph=True)
benchmark.pedantic(op, iterations=20, rounds=10)
def test_store_orchestration_overhead(benchmark, mock_agent_context):
"""
Benchmarks the 'store' method's logic for routing documents.
"""
docs = [{"content": f"Doc {i}", "metadata": {"id": i}} for i in range(50)]
# Mock the internal storage to return immediately
mock_agent_context._memory.store.return_value = "mem_id"
mock_agent_context._build_graph_from_documents = MagicMock(return_value={})
def op():
return mock_agent_context.store(docs, extract_entities=False)
benchmark.pedantic(op, iterations=10, rounds=10)