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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
import pytest
import pandas as pd
from unittest.mock import Mock, patch, MagicMock
from typing import Dict, Any
from azure.ai.evaluation._evaluate._evaluate_aoai import (
_generate_data_source_config,
_get_data_source,
_begin_eval_run,
WRAPPER_KEY,
)
@pytest.mark.unittest
class TestAOAINestedDataIntegration:
"""Test suite for AOAI evaluation integration with nested data structures."""
def test_aoai_eval_run_with_flat_data(self):
"""Test _begin_eval_run with flat data structure."""
# Setup test data
input_df = pd.DataFrame(
[
{"query": "What is AI?", "response": "AI is...", "ground_truth": "AI"},
{"query": "What is ML?", "response": "ML is...", "ground_truth": "ML"},
]
)
column_mapping = {
"query": "${data.query}",
"response": "${data.response}",
"ground_truth": "${data.ground_truth}",
}
# Mock the client
mock_client = Mock()
mock_run = Mock()
mock_run.id = "test-run-123"
mock_client.evals.runs.create.return_value = mock_run
# Call the function
run_id = _begin_eval_run(
client=mock_client,
eval_group_id="test-group-456",
run_name="test-run",
input_data_df=input_df,
column_mapping=column_mapping,
)
# Verify the client was called
assert run_id == "test-run-123"
mock_client.evals.runs.create.assert_called_once()
# Get the call arguments
call_kwargs = mock_client.evals.runs.create.call_args[1]
# Verify eval_id
assert call_kwargs["eval_id"] == "test-group-456"
assert call_kwargs["name"] == "test-run"
# Verify data_source structure
data_source = call_kwargs["data_source"]
assert data_source["type"] == "jsonl"
assert "source" in data_source
assert data_source["source"]["type"] == "file_content"
# Verify content
content = data_source["source"]["content"]
assert len(content) == 2
# Each item should be wrapped
for item in content:
assert WRAPPER_KEY in item
assert "query" in item[WRAPPER_KEY]
assert "response" in item[WRAPPER_KEY]
assert "ground_truth" in item[WRAPPER_KEY]
def test_aoai_eval_run_with_nested_data(self):
"""Test _begin_eval_run with nested data structure."""
# Setup nested test data
input_df = pd.DataFrame(
[
{
"item.query": "Security question",
"item.context.company.policy.security.passwords.rotation_days": "90",
"item.context.company.policy.security.network.vpn.required": "true",
"item.response": "Password rotation is 90 days.",
"item.ground_truth": "90",
}
]
)
column_mapping = {
"query": "${data.item.query}",
"rotation_days": "${data.item.context.company.policy.security.passwords.rotation_days}",
"vpn_required": "${data.item.context.company.policy.security.network.vpn.required}",
"response": "${data.item.response}",
"ground_truth": "${data.item.ground_truth}",
}
# Mock the client
mock_client = Mock()
mock_run = Mock()
mock_run.id = "nested-run-789"
mock_client.evals.runs.create.return_value = mock_run
# Call the function
run_id = _begin_eval_run(
client=mock_client,
eval_group_id="nested-group-101",
run_name="nested-test-run",
input_data_df=input_df,
column_mapping=column_mapping,
)
# Verify
assert run_id == "nested-run-789"
mock_client.evals.runs.create.assert_called_once()
# Get the data source
call_kwargs = mock_client.evals.runs.create.call_args[1]
data_source = call_kwargs["data_source"]
content = data_source["source"]["content"]
# Verify nested structure was built
assert len(content) == 1
item_root = content[0][WRAPPER_KEY]
# Check nested paths exist
assert "query" in item_root
assert "context" in item_root
assert "company" in item_root["context"]
assert "policy" in item_root["context"]["company"]
assert "security" in item_root["context"]["company"]["policy"]
assert "passwords" in item_root["context"]["company"]["policy"]["security"]
assert "rotation_days" in item_root["context"]["company"]["policy"]["security"]["passwords"]
assert item_root["context"]["company"]["policy"]["security"]["passwords"]["rotation_days"] == "90"
def test_data_source_config_matches_data_source_for_nested(self):
"""Test that schema config and data source align for nested structures."""
input_df = pd.DataFrame(
[
{
"item.query": "Test query",
"item.context.field1": "value1",
"item.context.field2": "value2",
"item.response": "Test response",
}
]
)
column_mapping = {
"query": "${data.item.query}",
"field1": "${data.item.context.field1}",
"field2": "${data.item.context.field2}",
"response": "${data.item.response}",
}
# Generate both config and data source
config = _generate_data_source_config(input_df, column_mapping)
data_source = _get_data_source(input_df, column_mapping)
# Verify config structure
assert config["type"] == "custom"
schema = config["item_schema"]
assert schema["type"] == "object"
# Verify schema has nested structure (wrapper stripped)
assert "query" in schema["properties"]
assert "context" in schema["properties"]
assert schema["properties"]["context"]["type"] == "object"
# Verify data source structure matches
content = data_source["source"]["content"]
item_root = content[0][WRAPPER_KEY]
# All schema properties should exist in data
assert "query" in item_root
assert "context" in item_root
assert "field1" in item_root["context"]
assert "field2" in item_root["context"]
assert "response" in item_root
def test_data_source_config_matches_data_source_for_flat(self):
"""Test that schema config and data source align for flat structures."""
input_df = pd.DataFrame([{"query": "Test", "response": "Answer", "score": "5"}])
column_mapping = {"query": "${data.query}", "response": "${data.response}", "score": "${data.score}"}
# Generate both config and data source
config = _generate_data_source_config(input_df, column_mapping)
data_source = _get_data_source(input_df, column_mapping)
# Verify flat config structure
assert config["type"] == "custom"
schema = config["item_schema"]
assert schema["type"] == "object"
# Flat mode: properties match mapping keys
assert set(schema["properties"].keys()) == {"query", "response", "score"}
# Verify data source
content = data_source["source"]["content"]
item_root = content[0][WRAPPER_KEY]
# All properties should exist
assert "query" in item_root
assert "response" in item_root
assert "score" in item_root
def test_data_source_with_run_outputs_and_nested_data(self):
"""Test data source generation with both run outputs and nested data."""
input_df = pd.DataFrame(
[
{
"item.query": "Test query",
"item.context.metadata.id": "123",
"__outputs.generated_response": "Generated text",
}
]
)
column_mapping = {
"query": "${data.item.query}",
"metadata_id": "${data.item.context.metadata.id}",
"response": "${run.outputs.generated_response}",
}
# Generate data source
data_source = _get_data_source(input_df, column_mapping)
# Verify structure
content = data_source["source"]["content"]
item_root = content[0][WRAPPER_KEY]
# Nested data paths
assert "query" in item_root
assert "context" in item_root
assert "metadata" in item_root["context"]
assert item_root["context"]["metadata"]["id"] == "123"
# Run outputs (just leaf name)
assert "generated_response" in item_root
assert item_root["generated_response"] == "Generated text"
def test_complex_nested_structure_multiple_branches(self):
"""Test nested structure with multiple branches at same level."""
input_df = pd.DataFrame(
[
{
"item.user.name": "Alice",
"item.user.email": "alice@example.com",
"item.system.version": "1.0",
"item.system.region": "us-east",
"item.query": "Test",
}
]
)
column_mapping = {
"name": "${data.item.user.name}",
"email": "${data.item.user.email}",
"version": "${data.item.system.version}",
"region": "${data.item.system.region}",
"query": "${data.item.query}",
}
# Generate config and data
config = _generate_data_source_config(input_df, column_mapping)
data_source = _get_data_source(input_df, column_mapping)
# Verify schema has both branches
schema = config["item_schema"]
assert "user" in schema["properties"]
assert "system" in schema["properties"]
assert "query" in schema["properties"]
# Verify data has both branches
item_root = data_source["source"]["content"][0][WRAPPER_KEY]
assert "user" in item_root
assert "system" in item_root
assert item_root["user"]["name"] == "Alice"
assert item_root["user"]["email"] == "alice@example.com"
assert item_root["system"]["version"] == "1.0"
assert item_root["system"]["region"] == "us-east"
|
