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b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc 280d562 b0ef2dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | from unittest.mock import MagicMock, patch
import numpy as np
def test_search_interventions_filters_by_score():
"""
Ensures the search function correctly filters out results
that are below the minimum similarity score threshold.
"""
from src.fot_recommender.rag_pipeline import search_interventions
# 1. Arrange: Create mock objects and sample data
mock_model = MagicMock()
mock_index = MagicMock()
# Fake knowledge base
sample_kb = [{"id": 1, "content": "high score"}, {"id": 2, "content": "low score"}]
# Configure the mock FAISS index to return specific scores and indices
# Let's say it finds two results, one with a high score (0.9) and one low (0.3)
mock_index.search.return_value = (
np.array([[0.9, 0.3]]), # scores
np.array([[0, 1]]), # indices
)
# 2. Act: Run the search with a minimum score of 0.5
results = search_interventions(
query="test query",
model=mock_model,
index=mock_index,
knowledge_base=sample_kb,
k=2,
min_similarity_score=0.5,
)
# 3. Assert: Check that only the high-scoring result was returned
assert len(results) == 1
assert results[0][0]["content"] == "high score" # Check the chunk content
assert results[0][1] == 0.9 # Check the score
def test_generate_recommendation_summary_builds_correct_prompt():
"""
Ensures that the context from retrieved chunks and the student narrative
are correctly formatted into the final prompt sent to the LLM.
"""
from src.fot_recommender.rag_pipeline import generate_recommendation_summary
# 1. Arrange: Create sample inputs
sample_chunks = [
(
{
"title": "Tip 1",
"original_content": "Do this.",
"source_document": "doc_A",
},
0.9,
),
]
student_narrative = "Student is struggling."
# 2. Act & Assert: Use a patch to intercept the API call
# This temporarily replaces genai.GenerativeModel with our mock
with patch(
"src.fot_recommender.rag_pipeline.genai.GenerativeModel"
) as mock_gen_model:
# Create a mock instance that the function will use
mock_model_instance = MagicMock()
mock_gen_model.return_value = mock_model_instance
generate_recommendation_summary(
retrieved_chunks=sample_chunks,
student_narrative=student_narrative,
api_key="fake_key",
persona="teacher",
)
# 3. Assert: Check what our function tried to do
# Was the API call made once?
mock_model_instance.generate_content.assert_called_once()
# Get the actual prompt that was passed to the LLM
actual_prompt = mock_model_instance.generate_content.call_args[0][0]
# Check if our key pieces of information are in the prompt
assert "Student is struggling." in actual_prompt
assert "--- Intervention Chunk 1 ---" in actual_prompt
assert "Title: Tip 1" in actual_prompt
assert "Content: Do this." in actual_prompt
assert "(Source Document: doc_A)" in actual_prompt
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