Extending test coverage (#434)

* Contract tests

* Testing embeedings

* Agent unit tests

* Knowledge pipeline tests

* Turn on contract tests

tests/unit/test_agent/conftest.py

@@ -0,0 +1,209 @@
+"""
+Shared fixtures for agent unit tests
+"""
+
+import pytest
+from unittest.mock import Mock, AsyncMock
+
+
+# Mock agent schema classes for testing
+class AgentRequest:
+    def __init__(self, question, conversation_id=None):
+        self.question = question
+        self.conversation_id = conversation_id
+
+
+class AgentResponse:
+    def __init__(self, answer, conversation_id=None, steps=None):
+        self.answer = answer
+        self.conversation_id = conversation_id
+        self.steps = steps or []
+
+
+class AgentStep:
+    def __init__(self, step_type, content, tool_name=None, tool_result=None):
+        self.step_type = step_type  # "think", "act", "observe"
+        self.content = content
+        self.tool_name = tool_name
+        self.tool_result = tool_result
+
+
+@pytest.fixture
+def sample_agent_request():
+    """Sample agent request for testing"""
+    return AgentRequest(
+        question="What is the capital of France?",
+        conversation_id="conv-123"
+    )
+
+
+@pytest.fixture
+def sample_agent_response():
+    """Sample agent response for testing"""
+    steps = [
+        AgentStep("think", "I need to find information about France's capital"),
+        AgentStep("act", "search", tool_name="knowledge_search", tool_result="Paris is the capital of France"),
+        AgentStep("observe", "I found that Paris is the capital of France"),
+        AgentStep("think", "I can now provide a complete answer")
+    ]
+    
+    return AgentResponse(
+        answer="The capital of France is Paris.",
+        conversation_id="conv-123",
+        steps=steps
+    )
+
+
+@pytest.fixture
+def mock_llm_client():
+    """Mock LLM client for agent reasoning"""
+    mock = AsyncMock()
+    mock.generate.return_value = "I need to search for information about the capital of France."
+    return mock
+
+
+@pytest.fixture
+def mock_knowledge_search_tool():
+    """Mock knowledge search tool"""
+    def search_tool(query):
+        if "capital" in query.lower() and "france" in query.lower():
+            return "Paris is the capital and largest city of France."
+        return "No relevant information found."
+    
+    return search_tool
+
+
+@pytest.fixture
+def mock_graph_rag_tool():
+    """Mock graph RAG tool"""
+    def graph_rag_tool(query):
+        return {
+            "entities": ["France", "Paris"],
+            "relationships": [("Paris", "capital_of", "France")],
+            "context": "Paris is the capital city of France, located in northern France."
+        }
+    
+    return graph_rag_tool
+
+
+@pytest.fixture
+def mock_calculator_tool():
+    """Mock calculator tool"""
+    def calculator_tool(expression):
+        # Simple mock calculator
+        try:
+            # Very basic expression evaluation for testing
+            if "+" in expression:
+                parts = expression.split("+")
+                return str(sum(int(p.strip()) for p in parts))
+            elif "*" in expression:
+                parts = expression.split("*")
+                result = 1
+                for p in parts:
+                    result *= int(p.strip())
+                return str(result)
+            return str(eval(expression))  # Simplified for testing
+        except:
+            return "Error: Invalid expression"
+    
+    return calculator_tool
+
+
+@pytest.fixture
+def available_tools(mock_knowledge_search_tool, mock_graph_rag_tool, mock_calculator_tool):
+    """Available tools for agent testing"""
+    return {
+        "knowledge_search": {
+            "function": mock_knowledge_search_tool,
+            "description": "Search knowledge base for information",
+            "parameters": ["query"]
+        },
+        "graph_rag": {
+            "function": mock_graph_rag_tool,
+            "description": "Query knowledge graph with RAG",
+            "parameters": ["query"]
+        },
+        "calculator": {
+            "function": mock_calculator_tool,
+            "description": "Perform mathematical calculations",
+            "parameters": ["expression"]
+        }
+    }
+
+
+@pytest.fixture
+def sample_conversation_history():
+    """Sample conversation history for multi-turn testing"""
+    return [
+        {
+            "role": "user",
+            "content": "What is 2 + 2?",
+            "timestamp": "2024-01-01T10:00:00Z"
+        },
+        {
+            "role": "assistant", 
+            "content": "2 + 2 = 4",
+            "steps": [
+                {"step_type": "think", "content": "This is a simple arithmetic question"},
+                {"step_type": "act", "content": "calculator", "tool_name": "calculator", "tool_result": "4"},
+                {"step_type": "observe", "content": "The calculator returned 4"},
+                {"step_type": "think", "content": "I can provide the answer"}
+            ],
+            "timestamp": "2024-01-01T10:00:05Z"
+        },
+        {
+            "role": "user",
+            "content": "What about 3 + 3?",
+            "timestamp": "2024-01-01T10:01:00Z"
+        }
+    ]
+
+
+@pytest.fixture
+def react_prompts():
+    """ReAct prompting templates for testing"""
+    return {
+        "system_prompt": """You are a helpful AI assistant that uses the ReAct (Reasoning and Acting) pattern.
+
+For each question, follow this cycle:
+1. Think: Analyze the question and plan your approach
+2. Act: Use available tools to gather information 
+3. Observe: Review the tool results
+4. Repeat if needed, then provide final answer
+
+Available tools: {tools}
+
+Format your response as:
+Think: [your reasoning]
+Act: [tool_name: parameters]
+Observe: [analysis of results]
+Answer: [final response]""",
+
+        "think_prompt": "Think step by step about this question: {question}\nPrevious context: {context}",
+        
+        "act_prompt": "Based on your thinking, what tool should you use? Available tools: {tools}",
+        
+        "observe_prompt": "You used {tool_name} and got result: {tool_result}\nHow does this help answer the question?",
+        
+        "synthesize_prompt": "Based on all your steps, provide a complete answer to: {question}"
+    }
+
+
+@pytest.fixture
+def mock_agent_processor():
+    """Mock agent processor for testing"""
+    class MockAgentProcessor:
+        def __init__(self, llm_client=None, tools=None):
+            self.llm_client = llm_client
+            self.tools = tools or {}
+            self.conversation_history = {}
+        
+        async def process_request(self, request):
+            # Mock processing logic
+            return AgentResponse(
+                answer="Mock response",
+                conversation_id=request.conversation_id,
+                steps=[]
+            )
+    
+    return MockAgentProcessor