Extending test coverage (#434)

* Contract tests

* Testing embeedings

* Agent unit tests

* Knowledge pipeline tests

* Turn on contract tests

tests/unit/test_agent/test_react_processor.py

@@ -0,0 +1,477 @@
+"""
+Unit tests for ReAct processor logic
+
+Tests the core business logic for the ReAct (Reasoning and Acting) pattern
+without relying on external LLM services, focusing on the Think-Act-Observe
+cycle and tool coordination.
+"""
+
+import pytest
+from unittest.mock import Mock, AsyncMock, patch
+import re
+
+
+class TestReActProcessorLogic:
+    """Test cases for ReAct processor business logic"""
+
+    def test_react_cycle_parsing(self):
+        """Test parsing of ReAct cycle components from LLM output"""
+        # Arrange
+        llm_output = """Think: I need to find information about the capital of France.
+Act: knowledge_search: capital of France
+Observe: The search returned that Paris is the capital of France.
+Think: I now have enough information to answer.
+Answer: The capital of France is Paris."""
+        
+        def parse_react_output(text):
+            """Parse ReAct format output into structured steps"""
+            steps = []
+            lines = text.strip().split('\n')
+            
+            for line in lines:
+                line = line.strip()
+                if line.startswith('Think:'):
+                    steps.append({
+                        'type': 'think',
+                        'content': line[6:].strip()
+                    })
+                elif line.startswith('Act:'):
+                    act_content = line[4:].strip()
+                    # Parse "tool_name: parameters" format
+                    if ':' in act_content:
+                        tool_name, params = act_content.split(':', 1)
+                        steps.append({
+                            'type': 'act',
+                            'tool_name': tool_name.strip(),
+                            'parameters': params.strip()
+                        })
+                    else:
+                        steps.append({
+                            'type': 'act',
+                            'content': act_content
+                        })
+                elif line.startswith('Observe:'):
+                    steps.append({
+                        'type': 'observe',
+                        'content': line[8:].strip()
+                    })
+                elif line.startswith('Answer:'):
+                    steps.append({
+                        'type': 'answer',
+                        'content': line[7:].strip()
+                    })
+            
+            return steps
+        
+        # Act
+        steps = parse_react_output(llm_output)
+        
+        # Assert
+        assert len(steps) == 5
+        assert steps[0]['type'] == 'think'
+        assert steps[1]['type'] == 'act'
+        assert steps[1]['tool_name'] == 'knowledge_search'
+        assert steps[1]['parameters'] == 'capital of France'
+        assert steps[2]['type'] == 'observe'
+        assert steps[3]['type'] == 'think'
+        assert steps[4]['type'] == 'answer'
+
+    def test_tool_selection_logic(self):
+        """Test tool selection based on question type and context"""
+        # Arrange
+        test_cases = [
+            ("What is 2 + 2?", "calculator"),
+            ("Who is the president of France?", "knowledge_search"),
+            ("Tell me about the relationship between Paris and France", "graph_rag"),
+            ("What time is it?", "knowledge_search")  # Default to general search
+        ]
+        
+        available_tools = {
+            "calculator": {"description": "Perform mathematical calculations"},
+            "knowledge_search": {"description": "Search knowledge base for facts"},
+            "graph_rag": {"description": "Query knowledge graph for relationships"}
+        }
+        
+        def select_tool(question, tools):
+            """Select appropriate tool based on question content"""
+            question_lower = question.lower()
+            
+            # Math keywords
+            if any(word in question_lower for word in ['+', '-', '*', '/', 'calculate', 'math']):
+                return "calculator"
+            
+            # Relationship/graph keywords
+            if any(word in question_lower for word in ['relationship', 'between', 'connected', 'related']):
+                return "graph_rag"
+            
+            # General knowledge keywords or default case
+            if any(word in question_lower for word in ['who', 'what', 'where', 'when', 'why', 'how', 'time']):
+                return "knowledge_search"
+            
+            return None
+        
+        # Act & Assert
+        for question, expected_tool in test_cases:
+            selected_tool = select_tool(question, available_tools)
+            assert selected_tool == expected_tool, f"Question '{question}' should select {expected_tool}"
+
+    def test_tool_execution_logic(self):
+        """Test tool execution and result processing"""
+        # Arrange
+        def mock_knowledge_search(query):
+            if "capital" in query.lower() and "france" in query.lower():
+                return "Paris is the capital of France."
+            return "Information not found."
+        
+        def mock_calculator(expression):
+            try:
+                # Simple expression evaluation
+                if '+' in expression:
+                    parts = expression.split('+')
+                    return str(sum(int(p.strip()) for p in parts))
+                return str(eval(expression))
+            except:
+                return "Error: Invalid expression"
+        
+        tools = {
+            "knowledge_search": mock_knowledge_search,
+            "calculator": mock_calculator
+        }
+        
+        def execute_tool(tool_name, parameters, available_tools):
+            """Execute tool with given parameters"""
+            if tool_name not in available_tools:
+                return {"error": f"Tool {tool_name} not available"}
+            
+            try:
+                tool_function = available_tools[tool_name]
+                result = tool_function(parameters)
+                return {"success": True, "result": result}
+            except Exception as e:
+                return {"error": str(e)}
+        
+        # Act & Assert
+        test_cases = [
+            ("knowledge_search", "capital of France", "Paris is the capital of France."),
+            ("calculator", "2 + 2", "4"),
+            ("calculator", "invalid expression", "Error: Invalid expression"),
+            ("nonexistent_tool", "anything", None)  # Error case
+        ]
+        
+        for tool_name, params, expected in test_cases:
+            result = execute_tool(tool_name, params, tools)
+            
+            if expected is None:
+                assert "error" in result
+            else:
+                assert result.get("result") == expected
+
+    def test_conversation_context_integration(self):
+        """Test integration of conversation history into ReAct reasoning"""
+        # Arrange
+        conversation_history = [
+            {"role": "user", "content": "What is 2 + 2?"},
+            {"role": "assistant", "content": "2 + 2 = 4"},
+            {"role": "user", "content": "What about 3 + 3?"}
+        ]
+        
+        def build_context_prompt(question, history, max_turns=3):
+            """Build context prompt from conversation history"""
+            context_parts = []
+            
+            # Include recent conversation turns
+            recent_history = history[-(max_turns*2):] if history else []
+            
+            for turn in recent_history:
+                role = turn["role"]
+                content = turn["content"]
+                context_parts.append(f"{role}: {content}")
+            
+            current_question = f"user: {question}"
+            context_parts.append(current_question)
+            
+            return "\n".join(context_parts)
+        
+        # Act
+        context_prompt = build_context_prompt("What about 3 + 3?", conversation_history)
+        
+        # Assert
+        assert "2 + 2" in context_prompt
+        assert "2 + 2 = 4" in context_prompt
+        assert "3 + 3" in context_prompt
+        assert context_prompt.count("user:") == 3
+        assert context_prompt.count("assistant:") == 1
+
+    def test_react_cycle_validation(self):
+        """Test validation of complete ReAct cycles"""
+        # Arrange
+        complete_cycle = [
+            {"type": "think", "content": "I need to solve this math problem"},
+            {"type": "act", "tool_name": "calculator", "parameters": "2 + 2"},
+            {"type": "observe", "content": "The calculator returned 4"},
+            {"type": "think", "content": "I can now provide the answer"},
+            {"type": "answer", "content": "2 + 2 = 4"}
+        ]
+        
+        incomplete_cycle = [
+            {"type": "think", "content": "I need to solve this"},
+            {"type": "act", "tool_name": "calculator", "parameters": "2 + 2"}
+            # Missing observe and answer steps
+        ]
+        
+        def validate_react_cycle(steps):
+            """Validate that ReAct cycle is complete"""
+            step_types = [step.get("type") for step in steps]
+            
+            # Must have at least one think, act, observe, and answer
+            required_types = ["think", "act", "observe", "answer"]
+            
+            validation_results = {
+                "is_complete": all(req_type in step_types for req_type in required_types),
+                "has_reasoning": "think" in step_types,
+                "has_action": "act" in step_types,
+                "has_observation": "observe" in step_types,
+                "has_answer": "answer" in step_types,
+                "step_count": len(steps)
+            }
+            
+            return validation_results
+        
+        # Act & Assert
+        complete_validation = validate_react_cycle(complete_cycle)
+        assert complete_validation["is_complete"] is True
+        assert complete_validation["has_reasoning"] is True
+        assert complete_validation["has_action"] is True
+        assert complete_validation["has_observation"] is True
+        assert complete_validation["has_answer"] is True
+        
+        incomplete_validation = validate_react_cycle(incomplete_cycle)
+        assert incomplete_validation["is_complete"] is False
+        assert incomplete_validation["has_reasoning"] is True
+        assert incomplete_validation["has_action"] is True
+        assert incomplete_validation["has_observation"] is False
+        assert incomplete_validation["has_answer"] is False
+
+    def test_multi_step_reasoning_logic(self):
+        """Test multi-step reasoning chains"""
+        # Arrange
+        complex_question = "What is the population of the capital of France?"
+        
+        def plan_reasoning_steps(question):
+            """Plan the reasoning steps needed for complex questions"""
+            steps = []
+            
+            question_lower = question.lower()
+            
+            # Check if question requires multiple pieces of information
+            if "capital of" in question_lower and ("population" in question_lower or "how many" in question_lower):
+                steps.append({
+                    "step": 1,
+                    "action": "find_capital",
+                    "description": "First find the capital city"
+                })
+                steps.append({
+                    "step": 2,
+                    "action": "find_population",
+                    "description": "Then find the population of that city"
+                })
+            elif "capital of" in question_lower:
+                steps.append({
+                    "step": 1,
+                    "action": "find_capital",
+                    "description": "Find the capital city"
+                })
+            elif "population" in question_lower:
+                steps.append({
+                    "step": 1,
+                    "action": "find_population",
+                    "description": "Find the population"
+                })
+            else:
+                steps.append({
+                    "step": 1,
+                    "action": "general_search",
+                    "description": "Search for relevant information"
+                })
+            
+            return steps
+        
+        # Act
+        reasoning_plan = plan_reasoning_steps(complex_question)
+        
+        # Assert
+        assert len(reasoning_plan) == 2
+        assert reasoning_plan[0]["action"] == "find_capital"
+        assert reasoning_plan[1]["action"] == "find_population"
+        assert all("step" in step for step in reasoning_plan)
+
+    def test_error_handling_in_react_cycle(self):
+        """Test error handling during ReAct execution"""
+        # Arrange
+        def execute_react_step_with_errors(step_type, content, tools=None):
+            """Execute ReAct step with potential error handling"""
+            try:
+                if step_type == "think":
+                    # Thinking step - validate reasoning
+                    if not content or len(content.strip()) < 5:
+                        return {"error": "Reasoning too brief"}
+                    return {"success": True, "content": content}
+                
+                elif step_type == "act":
+                    # Action step - validate tool exists and execute
+                    if not tools or not content:
+                        return {"error": "No tools available or no action specified"}
+                    
+                    # Parse tool and parameters
+                    if ":" in content:
+                        tool_name, params = content.split(":", 1)
+                        tool_name = tool_name.strip()
+                        params = params.strip()
+                        
+                        if tool_name not in tools:
+                            return {"error": f"Tool {tool_name} not available"}
+                        
+                        # Execute tool
+                        result = tools[tool_name](params)
+                        return {"success": True, "tool_result": result}
+                    else:
+                        return {"error": "Invalid action format"}
+                
+                elif step_type == "observe":
+                    # Observation step - validate observation
+                    if not content:
+                        return {"error": "No observation provided"}
+                    return {"success": True, "content": content}
+                
+                else:
+                    return {"error": f"Unknown step type: {step_type}"}
+                    
+            except Exception as e:
+                return {"error": f"Execution error: {str(e)}"}
+        
+        # Test cases
+        mock_tools = {
+            "calculator": lambda x: str(eval(x)) if x.replace('+', '').replace('-', '').replace('*', '').replace('/', '').replace(' ', '').isdigit() else "Error"
+        }
+        
+        test_cases = [
+            ("think", "I need to calculate", {"success": True}),
+            ("think", "", {"error": True}),  # Empty reasoning
+            ("act", "calculator: 2 + 2", {"success": True}),
+            ("act", "nonexistent: something", {"error": True}),  # Tool doesn't exist
+            ("act", "invalid format", {"error": True}),  # Invalid format
+            ("observe", "The result is 4", {"success": True}),
+            ("observe", "", {"error": True}),  # Empty observation
+            ("invalid_step", "content", {"error": True})  # Invalid step type
+        ]
+        
+        # Act & Assert
+        for step_type, content, expected in test_cases:
+            result = execute_react_step_with_errors(step_type, content, mock_tools)
+            
+            if expected.get("error"):
+                assert "error" in result, f"Expected error for step {step_type}: {content}"
+            else:
+                assert "success" in result, f"Expected success for step {step_type}: {content}"
+
+    def test_response_synthesis_logic(self):
+        """Test synthesis of final response from ReAct steps"""
+        # Arrange
+        react_steps = [
+            {"type": "think", "content": "I need to find the capital of France"},
+            {"type": "act", "tool_name": "knowledge_search", "tool_result": "Paris is the capital of France"},
+            {"type": "observe", "content": "The search confirmed Paris is the capital"},
+            {"type": "think", "content": "I have the information needed to answer"}
+        ]
+        
+        def synthesize_response(steps, original_question):
+            """Synthesize final response from ReAct steps"""
+            # Extract key information from steps
+            tool_results = []
+            observations = []
+            reasoning = []
+            
+            for step in steps:
+                if step["type"] == "think":
+                    reasoning.append(step["content"])
+                elif step["type"] == "act" and "tool_result" in step:
+                    tool_results.append(step["tool_result"])
+                elif step["type"] == "observe":
+                    observations.append(step["content"])
+            
+            # Build response based on available information
+            if tool_results:
+                # Use tool results as primary information source
+                primary_info = tool_results[0]
+                
+                # Extract specific answer from tool result
+                if "capital" in original_question.lower() and "Paris" in primary_info:
+                    return "The capital of France is Paris."
+                elif "+" in original_question and any(char.isdigit() for char in primary_info):
+                    return f"The answer is {primary_info}."
+                else:
+                    return primary_info
+            else:
+                # Fallback to reasoning if no tool results
+                return "I need more information to answer this question."
+        
+        # Act
+        response = synthesize_response(react_steps, "What is the capital of France?")
+        
+        # Assert
+        assert "Paris" in response
+        assert "capital of france" in response.lower()
+        assert len(response) > 10  # Should be a complete sentence
+
+    def test_tool_parameter_extraction(self):
+        """Test extraction and validation of tool parameters"""
+        # Arrange
+        def extract_tool_parameters(action_content, tool_schema):
+            """Extract and validate parameters for tool execution"""
+            # Parse action content for tool name and parameters
+            if ":" not in action_content:
+                return {"error": "Invalid action format - missing tool parameters"}
+            
+            tool_name, params_str = action_content.split(":", 1)
+            tool_name = tool_name.strip()
+            params_str = params_str.strip()
+            
+            if tool_name not in tool_schema:
+                return {"error": f"Unknown tool: {tool_name}"}
+            
+            schema = tool_schema[tool_name]
+            required_params = schema.get("required_parameters", [])
+            
+            # Simple parameter extraction (for more complex tools, this would be more sophisticated)
+            if len(required_params) == 1 and required_params[0] == "query":
+                # Single query parameter
+                return {"tool_name": tool_name, "parameters": {"query": params_str}}
+            elif len(required_params) == 1 and required_params[0] == "expression":
+                # Single expression parameter  
+                return {"tool_name": tool_name, "parameters": {"expression": params_str}}
+            else:
+                # Multiple parameters would need more complex parsing
+                return {"tool_name": tool_name, "parameters": {"input": params_str}}
+        
+        tool_schema = {
+            "knowledge_search": {"required_parameters": ["query"]},
+            "calculator": {"required_parameters": ["expression"]},
+            "graph_rag": {"required_parameters": ["query"]}
+        }
+        
+        test_cases = [
+            ("knowledge_search: capital of France", "knowledge_search", {"query": "capital of France"}),
+            ("calculator: 2 + 2", "calculator", {"expression": "2 + 2"}),
+            ("invalid format", None, None),  # No colon
+            ("unknown_tool: something", None, None)  # Unknown tool
+        ]
+        
+        # Act & Assert
+        for action_content, expected_tool, expected_params in test_cases:
+            result = extract_tool_parameters(action_content, tool_schema)
+            
+            if expected_tool is None:
+                assert "error" in result
+            else:
+                assert result["tool_name"] == expected_tool
+                assert result["parameters"] == expected_params