trustgraph/tests/integration/test_agent_structured_query_integration.py

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"""
Integration tests for React Agent with Structured Query Tool
These tests verify the end-to-end functionality of the React agent
using the structured-query tool to query structured data with natural language.
Following the TEST_STRATEGY.md approach for integration testing.
"""
import pytest
import json
from unittest.mock import AsyncMock, MagicMock
from trustgraph.schema import (
AgentRequest, AgentResponse,
StructuredQueryRequest, StructuredQueryResponse,
Error
)
from trustgraph.agent.react.service import Processor
@pytest.mark.integration
class TestAgentStructuredQueryIntegration:
"""Integration tests for React agent with structured query tool"""
@pytest.fixture
def agent_processor(self):
"""Create agent processor with structured query tool configured"""
proc = Processor(
taskgroup=MagicMock(),
pulsar_client=AsyncMock(),
max_iterations=3
)
Add unified explainability support and librarian storage for (#693) Add unified explainability support and librarian storage for all retrieval engines Implements consistent explainability/provenance tracking across GraphRAG, DocumentRAG, and Agent retrieval engines. All large content (answers, thoughts, observations) is now stored in librarian rather than as inline literals in the knowledge graph. Explainability API: - New explainability.py module with entity classes (Question, Exploration, Focus, Synthesis, Analysis, Conclusion) and ExplainabilityClient - Quiescence-based eventual consistency handling for trace fetching - Content fetching from librarian with retry logic CLI updates: - tg-invoke-graph-rag -x/--explainable flag returns explain_id - tg-invoke-document-rag -x/--explainable flag returns explain_id - tg-invoke-agent -x/--explainable flag returns explain_id - tg-list-explain-traces uses new explainability API - tg-show-explain-trace handles all three trace types Agent provenance: - Records session, iterations (think/act/observe), and conclusion - Stores thoughts and observations in librarian with document references - New predicates: tg:thoughtDocument, tg:observationDocument DocumentRAG provenance: - Records question, exploration (chunk retrieval), and synthesis - Stores answers in librarian with document references Schema changes: - AgentResponse: added explain_id, explain_graph fields - RetrievalResponse: added explain_id, explain_graph fields - agent_iteration_triples: supports thought_document_id, observation_document_id Update tests.
2026-03-12 21:40:09 +00:00
# Mock the client method for structured query
proc.client = MagicMock()
Add unified explainability support and librarian storage for (#693) Add unified explainability support and librarian storage for all retrieval engines Implements consistent explainability/provenance tracking across GraphRAG, DocumentRAG, and Agent retrieval engines. All large content (answers, thoughts, observations) is now stored in librarian rather than as inline literals in the knowledge graph. Explainability API: - New explainability.py module with entity classes (Question, Exploration, Focus, Synthesis, Analysis, Conclusion) and ExplainabilityClient - Quiescence-based eventual consistency handling for trace fetching - Content fetching from librarian with retry logic CLI updates: - tg-invoke-graph-rag -x/--explainable flag returns explain_id - tg-invoke-document-rag -x/--explainable flag returns explain_id - tg-invoke-agent -x/--explainable flag returns explain_id - tg-list-explain-traces uses new explainability API - tg-show-explain-trace handles all three trace types Agent provenance: - Records session, iterations (think/act/observe), and conclusion - Stores thoughts and observations in librarian with document references - New predicates: tg:thoughtDocument, tg:observationDocument DocumentRAG provenance: - Records question, exploration (chunk retrieval), and synthesis - Stores answers in librarian with document references Schema changes: - AgentResponse: added explain_id, explain_graph fields - RetrievalResponse: added explain_id, explain_graph fields - agent_iteration_triples: supports thought_document_id, observation_document_id Update tests.
2026-03-12 21:40:09 +00:00
# Mock librarian to avoid hanging on save operations
proc.save_answer_content = AsyncMock(return_value=None)
return proc
@pytest.fixture
def structured_query_tool_config(self):
"""Configuration for structured-query tool"""
import json
return {
"tool": {
"structured-query": json.dumps({
"name": "structured-query",
"description": "Query structured data using natural language",
"type": "structured-query"
})
}
}
@pytest.mark.asyncio
async def test_agent_structured_query_basic_integration(self, agent_processor, structured_query_tool_config):
"""Test basic agent integration with structured query tool"""
# Arrange - Load tool configuration
await agent_processor.on_tools_config(structured_query_tool_config, "v1")
# Create agent request
request = AgentRequest(
question="I need to find all customers from New York. Use the structured query tool to get this information.",
state="",
group=None,
history=[],
user="test_user"
)
msg = MagicMock()
msg.value.return_value = request
msg.properties.return_value = {"id": "agent-test-001"}
consumer = MagicMock()
# Mock response producer for the flow
response_producer = AsyncMock()
# Mock structured query response
structured_query_response = {
"data": json.dumps({
"customers": [
{"id": "1", "name": "John Doe", "email": "john@example.com", "state": "New York"},
{"id": "2", "name": "Jane Smith", "email": "jane@example.com", "state": "New York"}
]
}),
"errors": [],
"error": None
}
# Mock the structured query client
mock_structured_client = AsyncMock()
mock_structured_client.structured_query.return_value = structured_query_response
# Mock the prompt client that agent calls for reasoning
mock_prompt_client = AsyncMock()
mock_prompt_client.agent_react.return_value = """Thought: I need to find customers from New York using structured query
Action: structured-query
Args: {
"question": "Find all customers from New York"
}"""
# Set up flow context routing
def flow_context(service_name):
if service_name == "structured-query-request":
return mock_structured_client
elif service_name == "prompt-request":
return mock_prompt_client
elif service_name == "response":
return response_producer
else:
return AsyncMock()
# Mock flow parameter in agent_processor.on_request
flow = MagicMock()
flow.side_effect = flow_context
# Act
await agent_processor.on_request(msg, consumer, flow)
# Assert
# Verify structured query was called
mock_structured_client.structured_query.assert_called_once()
call_args = mock_structured_client.structured_query.call_args
# Check keyword arguments
question_arg = call_args.kwargs.get("question") or call_args[1].get("question")
assert "customers" in question_arg.lower()
assert "new york" in question_arg.lower()
# Verify responses were sent (agent sends multiple responses for thought/observation)
assert response_producer.send.call_count >= 1
# Check all the responses that were sent
all_calls = response_producer.send.call_args_list
responses = [call[0][0] for call in all_calls]
# Verify at least one response is of correct type and has no error
assert any(isinstance(resp, AgentResponse) and resp.error is None for resp in responses)
@pytest.mark.asyncio
async def test_agent_structured_query_error_handling(self, agent_processor, structured_query_tool_config):
"""Test agent handling of structured query errors"""
# Arrange
await agent_processor.on_tools_config(structured_query_tool_config, "v1")
request = AgentRequest(
question="Find data from a table that doesn't exist using structured query.",
state="",
group=None,
history=[],
user="test_user"
)
msg = MagicMock()
msg.value.return_value = request
msg.properties.return_value = {"id": "agent-error-test"}
consumer = MagicMock()
# Mock response producer for the flow
response_producer = AsyncMock()
# Mock structured query error response
structured_query_error_response = {
"data": None,
"errors": ["Table 'nonexistent' not found in schema"],
"error": {"type": "structured-query-error", "message": "Schema not found"}
}
mock_structured_client = AsyncMock()
mock_structured_client.structured_query.return_value = structured_query_error_response
# Mock the prompt client that agent calls for reasoning
mock_prompt_client = AsyncMock()
mock_prompt_client.agent_react.return_value = """Thought: I need to query for a table that might not exist
Action: structured-query
Args: {
"question": "Find data from a table that doesn't exist"
}"""
# Set up flow context routing
def flow_context(service_name):
if service_name == "structured-query-request":
return mock_structured_client
elif service_name == "prompt-request":
return mock_prompt_client
elif service_name == "response":
return response_producer
else:
return AsyncMock()
flow = MagicMock()
flow.side_effect = flow_context
# Act
await agent_processor.on_request(msg, consumer, flow)
# Assert
mock_structured_client.structured_query.assert_called_once()
assert response_producer.send.call_count >= 1
all_calls = response_producer.send.call_args_list
responses = [call[0][0] for call in all_calls]
# Agent should handle the error gracefully
assert any(isinstance(resp, AgentResponse) for resp in responses)
# The tool should have returned an error response that contains error info
call_args = mock_structured_client.structured_query.call_args
question_arg = call_args.kwargs.get("question") or call_args[1].get("question")
assert "table" in question_arg.lower() or "exist" in question_arg.lower()
@pytest.mark.asyncio
async def test_agent_multi_step_structured_query_reasoning(self, agent_processor, structured_query_tool_config):
"""Test agent using structured query in multi-step reasoning"""
# Arrange
await agent_processor.on_tools_config(structured_query_tool_config, "v1")
request = AgentRequest(
question="First find all customers from California, then tell me how many orders they have made.",
state="",
group=None,
history=[],
user="test_user"
)
msg = MagicMock()
msg.value.return_value = request
msg.properties.return_value = {"id": "agent-multi-step-test"}
consumer = MagicMock()
# Mock response producer for the flow
response_producer = AsyncMock()
# Mock structured query response (just one for this test)
customers_response = {
"data": json.dumps({
"customers": [
{"id": "101", "name": "Alice Johnson", "state": "California"},
{"id": "102", "name": "Bob Wilson", "state": "California"}
]
}),
"errors": [],
"error": None
}
mock_structured_client = AsyncMock()
mock_structured_client.structured_query.return_value = customers_response
# Mock the prompt client that agent calls for reasoning
mock_prompt_client = AsyncMock()
mock_prompt_client.agent_react.return_value = """Thought: I need to find customers from California first
Action: structured-query
Args: {
"question": "Find all customers from California"
}"""
# Set up flow context routing
def flow_context(service_name):
if service_name == "structured-query-request":
return mock_structured_client
elif service_name == "prompt-request":
return mock_prompt_client
elif service_name == "response":
return response_producer
else:
return AsyncMock()
flow = MagicMock()
flow.side_effect = flow_context
# Act
await agent_processor.on_request(msg, consumer, flow)
# Assert
# Should have made structured query call
assert mock_structured_client.structured_query.call_count >= 1
assert response_producer.send.call_count >= 1
all_calls = response_producer.send.call_args_list
responses = [call[0][0] for call in all_calls]
assert any(isinstance(resp, AgentResponse) for resp in responses)
# Verify the structured query was called with customer-related question
call_args = mock_structured_client.structured_query.call_args
question_arg = call_args.kwargs.get("question") or call_args[1].get("question")
assert "california" in question_arg.lower()
@pytest.mark.asyncio
async def test_agent_structured_query_with_collection_parameter(self, agent_processor):
"""Test structured query tool with collection parameter"""
# Arrange - Configure tool with collection
import json
tool_config_with_collection = {
"tool": {
"structured-query": json.dumps({
"name": "structured-query",
"description": "Query structured data using natural language",
"type": "structured-query",
"collection": "sales_data"
})
}
}
await agent_processor.on_tools_config(tool_config_with_collection, "v1")
request = AgentRequest(
question="Query the sales data for recent transactions.",
state="",
group=None,
history=[],
user="test_user"
)
msg = MagicMock()
msg.value.return_value = request
msg.properties.return_value = {"id": "agent-collection-test"}
consumer = MagicMock()
# Mock response producer for the flow
response_producer = AsyncMock()
# Mock structured query response
sales_response = {
"data": json.dumps({
"transactions": [
{"id": "tx1", "amount": 299.99, "date": "2024-01-15"},
{"id": "tx2", "amount": 149.50, "date": "2024-01-16"}
]
}),
"errors": [],
"error": None
}
mock_structured_client = AsyncMock()
mock_structured_client.structured_query.return_value = sales_response
# Mock the prompt client that agent calls for reasoning
mock_prompt_client = AsyncMock()
mock_prompt_client.agent_react.return_value = """Thought: I need to query the sales data
Action: structured-query
Args: {
"question": "Query the sales data for recent transactions"
}"""
# Set up flow context routing
def flow_context(service_name):
if service_name == "structured-query-request":
return mock_structured_client
elif service_name == "prompt-request":
return mock_prompt_client
elif service_name == "response":
return response_producer
else:
return AsyncMock()
flow = MagicMock()
flow.side_effect = flow_context
# Act
await agent_processor.on_request(msg, consumer, flow)
# Assert
mock_structured_client.structured_query.assert_called_once()
# Verify the tool was configured with collection parameter
# (Collection parameter is passed to tool constructor, not to query method)
assert response_producer.send.call_count >= 1
all_calls = response_producer.send.call_args_list
responses = [call[0][0] for call in all_calls]
assert any(isinstance(resp, AgentResponse) for resp in responses)
# Check the query was about sales/transactions
call_args = mock_structured_client.structured_query.call_args
question_arg = call_args.kwargs.get("question") or call_args[1].get("question")
assert "sales" in question_arg.lower() or "transactions" in question_arg.lower()
@pytest.mark.asyncio
async def test_agent_structured_query_tool_argument_validation(self, agent_processor, structured_query_tool_config):
"""Test that structured query tool arguments are properly validated"""
# Arrange
await agent_processor.on_tools_config(structured_query_tool_config, "v1")
# Check that the tool was registered with correct arguments
tools = agent_processor.agent.tools
assert "structured-query" in tools
structured_tool = tools["structured-query"]
arguments = structured_tool.arguments
# Verify tool has the expected argument structure
assert len(arguments) == 1
question_arg = arguments[0]
assert question_arg.name == "question"
assert question_arg.type == "string"
assert "structured data" in question_arg.description.lower()
@pytest.mark.asyncio
async def test_agent_structured_query_json_formatting(self, agent_processor, structured_query_tool_config):
"""Test that structured query results are properly formatted for agent consumption"""
# Arrange
await agent_processor.on_tools_config(structured_query_tool_config, "v1")
request = AgentRequest(
question="Get customer information and format it nicely.",
state="",
group=None,
history=[],
user="test_user"
)
msg = MagicMock()
msg.value.return_value = request
msg.properties.return_value = {"id": "agent-format-test"}
consumer = MagicMock()
# Mock response producer for the flow
response_producer = AsyncMock()
# Mock structured query response with complex data
complex_response = {
"data": json.dumps({
"customers": [
{
"id": "c1",
"name": "Enterprise Corp",
"contact": {
"email": "contact@enterprise.com",
"phone": "555-0123"
},
"orders": [
{"id": "o1", "total": 5000.00, "items": 15},
{"id": "o2", "total": 3200.50, "items": 8}
]
}
]
}),
"errors": [],
"error": None
}
mock_structured_client = AsyncMock()
mock_structured_client.structured_query.return_value = complex_response
# Mock the prompt client that agent calls for reasoning
mock_prompt_client = AsyncMock()
mock_prompt_client.agent_react.return_value = """Thought: I need to get customer information
Action: structured-query
Args: {
"question": "Get customer information and format it nicely"
}"""
# Set up flow context routing
def flow_context(service_name):
if service_name == "structured-query-request":
return mock_structured_client
elif service_name == "prompt-request":
return mock_prompt_client
elif service_name == "response":
return response_producer
else:
return AsyncMock()
flow = MagicMock()
flow.side_effect = flow_context
# Act
await agent_processor.on_request(msg, consumer, flow)
# Assert
mock_structured_client.structured_query.assert_called_once()
assert response_producer.send.call_count >= 1
# The tool should have properly formatted the JSON for agent consumption
all_calls = response_producer.send.call_args_list
responses = [call[0][0] for call in all_calls]
assert any(isinstance(resp, AgentResponse) for resp in responses)
# Check that the query was about customer information
call_args = mock_structured_client.structured_query.call_args
question_arg = call_args.kwargs.get("question") or call_args[1].get("question")
assert "customer" in question_arg.lower()