Add semantic memory retrieval test

[Bryan-Roe]

Summary

• add unit test to verify semantic memory retrieval

Testing

• python 01-core-implementations/python/tests/unit/memory/test_semantic_memory_retrieval.py (fails: ModuleNotFoundError: No module named 'numpy')

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https://chatgpt.com/codex/tasks/task_e_6886a79400d083228045f0d4e0ce5e79

Summary by Sourcery

Tests:

• Introduce test_semantic_memory_retrieval.py with FakeEmbeddingGenerator and create_memory helper to test SemanticTextMemory.search accuracy for various queries.

[sourcery-ai]

Reviewer's Guide

Introduce a new pytest unit test for SemanticTextMemory that uses a FakeEmbeddingGenerator and VolatileMemoryStore to verify accurate retrieval based on simple synthetic embeddings.

File-Level Changes

Change	Details	Files
Add semantic memory retrieval unit test	Implement FakeEmbeddingGenerator to produce deterministic embeddings from text Add create_memory helper to initialize SemanticTextMemory with volatile storage Define async pytest test saving entries and asserting search results for 'hiking', 'tour', and 'Iceland'	01-core-implementations/python/tests/unit/memory/test_semantic_memory_retrieval.py

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[Copilot]

Pull Request Overview

This PR adds a unit test to verify semantic memory retrieval functionality. The test validates that the semantic memory system can accurately retrieve stored information based on search queries.

Key Changes

• Creates a new test file for semantic memory retrieval functionality
• Implements a fake embedding generator for testing purposes
• Adds test cases to verify accurate retrieval of stored text information

Comments suppressed due to low confidence (1)

01-core-implementations/python/tests/unit/memory/test_semantic_memory_retrieval.py:28

• [nitpick] The test only verifies exact matches but doesn't test semantic similarity or ranking when multiple results are returned. Consider adding test cases for partial matches and verifying the ranking order of search results.

async def test_retrieval_accuracy():

[sourcery-ai]

Hey @Bryan-Roe - I've reviewed your changes - here's some feedback:

• Add numpy to the project’s test or dev dependencies so that import numpy works when running tests.
• Consider parameterizing the query/text assertions in test_retrieval_accuracy to reduce repetition and improve readability.
• Add a test case with limit > 1 (and multiple expected results) to verify the search result ordering beyond single-item lookups.

Prompt for AI Agents

Please address the comments from this code review:
## Overall Comments
- Add numpy to the project’s test or dev dependencies so that `import numpy` works when running tests.
- Consider parameterizing the query/text assertions in `test_retrieval_accuracy` to reduce repetition and improve readability.
- Add a test case with limit > 1 (and multiple expected results) to verify the search result ordering beyond single-item lookups.

## Individual Comments

### Comment 1
<location> `01-core-implementations/python/tests/unit/memory/test_semantic_memory_retrieval.py:2` </location>
<code_context>
+import asyncio
+import numpy as np
+import pytest
+
</code_context>

<issue_to_address>
Test requires numpy but does not skip or handle missing dependency.

Add a check or use pytest.skipif to skip the test if numpy is not installed, ensuring the test suite runs smoothly without numpy.

Suggested implementation:

```python
import asyncio
import pytest

try:
    import numpy as np
    HAS_NUMPY = True
except ImportError:
    HAS_NUMPY = False

from semantic_kernel.memory.volatile_memory_store import VolatileMemoryStore

```

```python
@pytest.mark.skipif(not HAS_NUMPY, reason="numpy is not installed")
class FakeEmbeddingGenerator(EmbeddingGeneratorBase):

```
</issue_to_address>

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