virtual_board_member/tests/test_week5_agentic_rag.py

"""
Week 5: Agentic RAG & Multi-Agent Orchestration Tests
Tests for state-of-the-art autonomous agent-based retrieval and reasoning system.
"""

import pytest
import asyncio
from unittest.mock import AsyncMock, MagicMock, patch
from typing import Dict, Any, List

from app.services.agentic_rag_service import (
    AgenticRAGService,
    Agent,
    ResearchAgent,
    AnalysisAgent,
    SynthesisAgent,
    AgentType,
    ReasoningType,
    AgentTask,
    ReasoningStep
)
from app.services.vector_service import VectorService
from app.services.llm_service import llm_service
from app.core.cache import cache_service


class TestAgenticRAGFoundation:
    """Test the foundational agentic RAG system components."""
    
    @pytest.fixture
    async def agentic_rag_service(self):
        """Create a test instance of AgenticRAGService."""
        with patch('app.services.agentic_rag_service.VectorService') as mock_vector_service:
            mock_vector_service.return_value = AsyncMock()
            service = AgenticRAGService()
            yield service
    
    @pytest.fixture
    def sample_task(self):
        """Create a sample agent task for testing."""
        return AgentTask(
            id="test-task-1",
            agent_type=AgentType.RESEARCH,
            description="Test research task",
            input_data={"query": "What are our Q4 financial results?"},
            dependencies=[],
            priority=1,
            created_at=asyncio.get_event_loop().time()
        )
    
    async def test_agent_initialization(self, agentic_rag_service):
        """Test that all agents initialize correctly."""
        # Verify all required agents are present
        assert hasattr(agentic_rag_service, 'agents')
        assert AgentType.RESEARCH in agentic_rag_service.agents
        assert AgentType.ANALYSIS in agentic_rag_service.agents
        assert AgentType.SYNTHESIS in agentic_rag_service.agents
        
        # Verify agent types
        assert isinstance(agentic_rag_service.agents[AgentType.RESEARCH], ResearchAgent)
        assert isinstance(agentic_rag_service.agents[AgentType.ANALYSIS], AnalysisAgent)
        assert isinstance(agentic_rag_service.agents[AgentType.SYNTHESIS], SynthesisAgent)
    
    async def test_agent_memory_management(self, agentic_rag_service):
        """Test agent memory operations."""
        research_agent = agentic_rag_service.agents[AgentType.RESEARCH]
        
        # Test memory update
        research_agent.update_memory("test_key", "test_value")
        memory = research_agent.get_memory()
        assert memory["test_key"] == "test_value"
        
        # Test memory isolation
        analysis_agent = agentic_rag_service.agents[AgentType.ANALYSIS]
        analysis_agent.update_memory("analysis_key", "analysis_value")
        
        research_memory = research_agent.get_memory()
        analysis_memory = analysis_agent.get_memory()
        
        assert "test_key" in research_memory
        assert "test_key" not in analysis_memory
        assert "analysis_key" in analysis_memory
    
    async def test_agent_learning_capabilities(self, agentic_rag_service):
        """Test agent learning from feedback."""
        research_agent = agentic_rag_service.agents[AgentType.RESEARCH]
        
        # Test learning history
        feedback = {"accuracy": 0.9, "relevance": 0.85, "user_satisfaction": 0.95}
        await research_agent.learn(feedback)
        
        assert len(research_agent.learning_history) == 1
        assert research_agent.learning_history[0]["feedback"] == feedback


class TestResearchAgent:
    """Test the Research Agent's autonomous retrieval capabilities."""
    
    @pytest.fixture
    async def research_agent(self):
        """Create a test instance of ResearchAgent."""
        with patch('app.services.agentic_rag_service.VectorService') as mock_vector_service:
            mock_vector_service.return_value = AsyncMock()
            agent = ResearchAgent(mock_vector_service.return_value)
            yield agent
    
    async def test_autonomous_retrieval_strategy_selection(self, research_agent):
        """Test that the agent can autonomously select retrieval strategies."""
        query = "What are our Q4 financial results?"
        context = {"tenant_id": "test-tenant", "user_role": "board_member"}
        
        strategy = await research_agent._determine_retrieval_strategy(query, context)
        
        # Should return a valid strategy
        assert strategy in ["semantic", "hybrid", "structured", "multi_modal"]
    
    async def test_query_analysis_capabilities(self, research_agent):
        """Test query analysis and intent classification."""
        query = "Compare Q3 and Q4 financial performance"
        
        analysis = await research_agent._analyze_query(query)
        
        # Should return structured analysis
        assert "intent" in analysis
        assert "complexity" in analysis
        assert "entities" in analysis
        assert "context_requirements" in analysis
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_semantic_retrieval(self, mock_llm, research_agent):
        """Test semantic retrieval functionality."""
        mock_llm.return_value = "Generated semantic search query"
        
        query = "Financial performance analysis"
        context = {"tenant_id": "test-tenant"}
        
        results = await research_agent._semantic_retrieval(query, context)
        
        # Should return list of results
        assert isinstance(results, list)
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_autonomous_filtering(self, mock_llm, research_agent):
        """Test autonomous filtering of retrieval results."""
        mock_llm.return_value = "Relevant content about financial performance"
        
        # Mock retrieval results
        mock_results = [
            {"content": "Q4 revenue increased by 15%", "score": 0.9},
            {"content": "Weather forecast for next week", "score": 0.3},
            {"content": "Q4 profit margins improved", "score": 0.85}
        ]
        
        query = "Q4 financial results"
        
        filtered_results = await research_agent._autonomous_filtering(mock_results, query)
        
        # Should filter out irrelevant results
        assert len(filtered_results) <= len(mock_results)
        # Should maintain high-scoring relevant results
        assert any("Q4" in result["content"] for result in filtered_results)


class TestAnalysisAgent:
    """Test the Analysis Agent's advanced reasoning capabilities."""
    
    @pytest.fixture
    async def analysis_agent(self):
        """Create a test instance of AnalysisAgent."""
        agent = AnalysisAgent()
        yield agent
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_chain_of_thought_analysis(self, mock_llm, analysis_agent):
        """Test Chain of Thought reasoning."""
        mock_llm.return_value = """
        Step 1: Analyze Q4 revenue data
        Step 2: Compare with Q3 performance
        Step 3: Identify key drivers
        Step 4: Assess market conditions
        Conclusion: Q4 shows strong growth due to new product launch
        """
        
        query = "Analyze Q4 financial performance"
        data = [{"content": "Q4 revenue: $10M", "source": "financial_report.pdf"}]
        
        result = await analysis_agent._chain_of_thought_analysis(query, data)
        
        # Should return structured analysis
        assert "reasoning_steps" in result
        assert "conclusion" in result
        assert "confidence" in result
        assert result["confidence"] > 0.0
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_tree_of_thoughts_analysis(self, mock_llm, analysis_agent):
        """Test Tree of Thoughts reasoning."""
        mock_llm.side_effect = [
            "Path 1: Revenue growth analysis",
            "Path 2: Cost structure analysis", 
            "Path 3: Market share analysis",
            "Evaluation: Path 1 is most relevant",
            "Synthesis: Combined insights from all paths"
        ]
        
        query = "Comprehensive Q4 analysis"
        data = [{"content": "Q4 financial data", "source": "report.pdf"}]
        
        result = await analysis_agent._tree_of_thoughts_analysis(query, data)
        
        # Should return multi-path analysis
        assert "reasoning_paths" in result
        assert "evaluation" in result
        assert "synthesis" in result
        assert len(result["reasoning_paths"]) > 1
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_multi_step_analysis(self, mock_llm, analysis_agent):
        """Test Multi-Step reasoning."""
        mock_llm.side_effect = [
            "Step 1: Data validation - All data is accurate",
            "Step 2: Trend analysis - Revenue growing 15% QoQ",
            "Step 3: Risk assessment - Low risk factors identified",
            "Step 4: Future projection - Continued growth expected"
        ]
        
        query = "Multi-step financial analysis"
        data = [{"content": "Financial data", "source": "data.pdf"}]
        
        result = await analysis_agent._multi_step_analysis(query, data)
        
        # Should return step-by-step analysis
        assert "steps" in result
        assert "validation" in result
        assert "final_analysis" in result
        assert len(result["steps"]) > 1
    
    async def test_reasoning_path_evaluation(self, analysis_agent):
        """Test reasoning path evaluation and ranking."""
        paths = [
            {"content": "Path 1 analysis", "confidence": 0.8},
            {"content": "Path 2 analysis", "confidence": 0.9},
            {"content": "Path 3 analysis", "confidence": 0.7}
        ]
        
        query = "Test query"
        context = "Test context"
        
        evaluation = await analysis_agent._evaluate_reasoning_path(paths[0], query, context)
        
        # Should return evaluation metrics
        assert "quality_score" in evaluation
        assert "relevance_score" in evaluation
        assert "overall_score" in evaluation


class TestSynthesisAgent:
    """Test the Synthesis Agent's response generation capabilities."""
    
    @pytest.fixture
    async def synthesis_agent(self):
        """Create a test instance of SynthesisAgent."""
        agent = SynthesisAgent()
        yield agent
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_information_synthesis(self, mock_llm, synthesis_agent):
        """Test information synthesis from multiple sources."""
        mock_llm.return_value = "Synthesized response combining research and analysis"
        
        query = "Q4 financial summary"
        research_results = {"data": "Research data", "confidence": 0.9}
        analysis_results = {"insights": "Analysis insights", "confidence": 0.85}
        context = {"tenant_id": "test-tenant"}
        
        result = await synthesis_agent._synthesize_information(
            query, research_results, analysis_results, context
        )
        
        # Should return synthesized information
        assert "synthesis" in result
        assert "key_insights" in result
        assert "confidence" in result
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_response_generation(self, mock_llm, synthesis_agent):
        """Test final response generation."""
        mock_llm.return_value = "Generated response with proper formatting and citations"
        
        query = "Financial performance summary"
        synthesis = {"content": "Synthesized content", "insights": ["insight1", "insight2"]}
        context = {"user_role": "board_member"}
        
        response = await synthesis_agent._generate_response(query, synthesis, context)
        
        # Should return well-formatted response
        assert isinstance(response, str)
        assert len(response) > 0
    
    async def test_metadata_addition(self, synthesis_agent):
        """Test metadata addition to responses."""
        response = "Q4 revenue increased by 15%"
        research_results = {"sources": ["report1.pdf", "report2.pdf"]}
        analysis_results = {"confidence": 0.9, "methodology": "CoT"}
        
        metadata = await synthesis_agent._add_metadata(response, research_results, analysis_results)
        
        # Should include comprehensive metadata
        assert "sources" in metadata
        assert "confidence" in metadata
        assert "methodology" in metadata
        assert "timestamp" in metadata


class TestAgenticRAGService:
    """Test the complete Agentic RAG Service orchestration."""
    
    @pytest.fixture
    async def agentic_service(self):
        """Create a test instance of AgenticRAGService."""
        with patch('app.services.agentic_rag_service.VectorService') as mock_vector_service:
            mock_vector_service.return_value = AsyncMock()
            service = AgenticRAGService()
            yield service
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_autonomous_workflow_execution(self, mock_llm, agentic_service):
        """Test complete autonomous workflow execution."""
        mock_llm.side_effect = [
            "Research strategy: semantic search",
            "Research results: Q4 data found",
            "Analysis: Chain of Thought reasoning",
            "Analysis results: Strong performance identified",
            "Synthesis: Comprehensive summary generated"
        ]
        
        result = await agentic_service.answer(
            tenant_id="test-tenant",
            query="Analyze Q4 financial performance",
            reasoning_type=ReasoningType.CHAIN_OF_THOUGHT,
            enable_autonomous_workflow=True
        )
        
        # Should return complete response
        assert "answer" in result
        assert "sources" in result
        assert "confidence" in result
        assert "metadata" in result
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_simple_workflow_execution(self, mock_llm, agentic_service):
        """Test simple workflow execution."""
        mock_llm.return_value = "Simple response generated"
        
        result = await agentic_service.answer(
            tenant_id="test-tenant",
            query="What is our revenue?",
            reasoning_type=ReasoningType.CHAIN_OF_THOUGHT,
            enable_autonomous_workflow=False
        )
        
        # Should return response using simple workflow
        assert "answer" in result
        assert "sources" in result
    
    async def test_agent_status_monitoring(self, agentic_service):
        """Test agent status monitoring."""
        status = await agentic_service.get_agent_status()
        
        # Should return status for all agents
        assert "research_agent" in status
        assert "analysis_agent" in status
        assert "synthesis_agent" in status
        
        # Each agent should have status information
        for agent_status in status.values():
            assert "status" in agent_status
            assert "memory_usage" in agent_status
            assert "last_activity" in agent_status
    
    async def test_agent_memory_reset(self, agentic_service):
        """Test agent memory reset functionality."""
        # Test reset all agents
        success = await agentic_service.reset_agent_memory()
        assert success is True
        
        # Test reset specific agent
        success = await agentic_service.reset_agent_memory(AgentType.RESEARCH)
        assert success is True


class TestIntegration:
    """Integration tests for the complete agentic RAG system."""
    
    @pytest.fixture
    async def integration_service(self):
        """Create a service instance for integration testing."""
        with patch('app.services.agentic_rag_service.VectorService') as mock_vector_service:
            mock_vector_service.return_value = AsyncMock()
            service = AgenticRAGService()
            yield service
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_end_to_end_agentic_workflow(self, mock_llm, integration_service):
        """Test complete end-to-end agentic workflow."""
        mock_llm.side_effect = [
            "Research: Financial data analysis",
            "Analysis: Performance evaluation",
            "Synthesis: Executive summary"
        ]
        
        # Test complex query with autonomous workflow
        result = await integration_service.answer(
            tenant_id="test-tenant",
            query="Provide comprehensive analysis of Q4 performance including risks and opportunities",
            reasoning_type=ReasoningType.TREE_OF_THOUGHTS,
            enable_autonomous_workflow=True
        )
        
        # Verify complete response structure
        assert "answer" in result
        assert "sources" in result
        assert "confidence" in result
        assert "metadata" in result
        assert "reasoning_type" in result["metadata"]
        assert result["metadata"]["reasoning_type"] == "tree_of_thoughts"
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_error_recovery_and_fallback(self, mock_llm, integration_service):
        """Test error recovery and fallback mechanisms."""
        # Simulate LLM failure
        mock_llm.side_effect = Exception("LLM service unavailable")
        
        # Should gracefully handle errors and provide fallback
        result = await integration_service.answer(
            tenant_id="test-tenant",
            query="Test query",
            reasoning_type=ReasoningType.CHAIN_OF_THOUGHT,
            enable_autonomous_workflow=True
        )
        
        # Should still return a response (even if it's an error message)
        assert "answer" in result
        assert "error" in result or "fallback" in result
    
    async def test_tenant_isolation(self, integration_service):
        """Test that agents maintain tenant isolation."""
        # Test with different tenants
        tenant1_result = await integration_service.answer(
            tenant_id="tenant-1",
            query="Test query 1",
            reasoning_type=ReasoningType.CHAIN_OF_THOUGHT
        )
        
        tenant2_result = await integration_service.answer(
            tenant_id="tenant-2", 
            query="Test query 2",
            reasoning_type=ReasoningType.CHAIN_OF_THOUGHT
        )
        
        # Results should be different for different tenants
        assert tenant1_result != tenant2_result


class TestPerformance:
    """Performance tests for the agentic RAG system."""
    
    @pytest.fixture
    async def performance_service(self):
        """Create a service instance for performance testing."""
        with patch('app.services.agentic_rag_service.VectorService') as mock_vector_service:
            mock_vector_service.return_value = AsyncMock()
            service = AgenticRAGService()
            yield service
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_response_time_performance(self, mock_llm, performance_service):
        """Test that responses are generated within acceptable time limits."""
        mock_llm.return_value = "Performance test response"
        
        import time
        start_time = time.time()
        
        result = await performance_service.answer(
            tenant_id="test-tenant",
            query="Performance test query",
            reasoning_type=ReasoningType.CHAIN_OF_THOUGHT
        )
        
        end_time = time.time()
        response_time = end_time - start_time
        
        # Should complete within reasonable time (adjust threshold as needed)
        assert response_time < 10.0  # 10 seconds max
        assert "answer" in result
    
    async def test_memory_usage_optimization(self, performance_service):
        """Test that memory usage is optimized."""
        # Get initial memory status
        initial_status = await performance_service.get_agent_status()
        
        # Perform multiple operations
        for i in range(5):
            await performance_service.answer(
                tenant_id=f"test-tenant-{i}",
                query=f"Test query {i}",
                reasoning_type=ReasoningType.CHAIN_OF_THOUGHT
            )
        
        # Get final memory status
        final_status = await performance_service.get_agent_status()
        
        # Memory usage should be reasonable (not growing exponentially)
        for agent_type in ["research_agent", "analysis_agent", "synthesis_agent"]:
            initial_memory = initial_status[agent_type]["memory_usage"]
            final_memory = final_status[agent_type]["memory_usage"]
            
            # Memory growth should be reasonable
            assert final_memory <= initial_memory * 2  # Max 2x growth


class TestReasoningTypes:
    """Test different reasoning types and their effectiveness."""
    
    @pytest.fixture
    async def reasoning_service(self):
        """Create a service instance for reasoning type testing."""
        with patch('app.services.agentic_rag_service.VectorService') as mock_vector_service:
            mock_vector_service.return_value = AsyncMock()
            service = AgenticRAGService()
            yield service
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_chain_of_thought_reasoning(self, mock_llm, reasoning_service):
        """Test Chain of Thought reasoning effectiveness."""
        mock_llm.return_value = "Step-by-step reasoning with clear logic"
        
        result = await reasoning_service.answer(
            tenant_id="test-tenant",
            query="Explain the reasoning behind Q4 performance",
            reasoning_type=ReasoningType.CHAIN_OF_THOUGHT
        )
        
        assert result["metadata"]["reasoning_type"] == "chain_of_thought"
        assert "answer" in result
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_tree_of_thoughts_reasoning(self, mock_llm, reasoning_service):
        """Test Tree of Thoughts reasoning effectiveness."""
        mock_llm.return_value = "Multi-path exploration with synthesis"
        
        result = await reasoning_service.answer(
            tenant_id="test-tenant",
            query="Explore multiple perspectives on Q4 performance",
            reasoning_type=ReasoningType.TREE_OF_THOUGHTS
        )
        
        assert result["metadata"]["reasoning_type"] == "tree_of_thoughts"
        assert "answer" in result
    
    @patch('app.services.agentic_rag_service.llm_service.generate')
    async def test_multi_step_reasoning(self, mock_llm, reasoning_service):
        """Test Multi-Step reasoning effectiveness."""
        mock_llm.return_value = "Sequential analysis with validation"
        
        result = await reasoning_service.answer(
            tenant_id="test-tenant",
            query="Perform detailed step-by-step analysis",
            reasoning_type=ReasoningType.MULTI_STEP
        )
        
        assert result["metadata"]["reasoning_type"] == "multi_step"
        assert "answer" in result