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Release/v1.2 (#457)
* Bump setup.py versions for 1.1 * PoC MCP server (#419) * Very initial MCP server PoC for TrustGraph * Put service on port 8000 * Add MCP container and packages to buildout * Update docs for API/CLI changes in 1.0 (#421) * Update some API basics for the 0.23/1.0 API change * Add MCP container push (#425) * Add command args to the MCP server (#426) * Host and port parameters * Added websocket arg * More docs * MCP client support (#427) - MCP client service - Tool request/response schema - API gateway support for mcp-tool - Message translation for tool request & response - Make mcp-tool using configuration service for information about where the MCP services are. * Feature/react call mcp (#428) Key Features - MCP Tool Integration: Added core MCP tool support with ToolClientSpec and ToolClient classes - API Enhancement: New mcp_tool method for flow-specific tool invocation - CLI Tooling: New tg-invoke-mcp-tool command for testing MCP integration - React Agent Enhancement: Fixed and improved multi-tool invocation capabilities - Tool Management: Enhanced CLI for tool configuration and management Changes - Added MCP tool invocation to API with flow-specific integration - Implemented ToolClientSpec and ToolClient for tool call handling - Updated agent-manager-react to invoke MCP tools with configurable types - Enhanced CLI with new commands and improved help text - Added comprehensive documentation for new CLI commands - Improved tool configuration management Testing - Added tg-invoke-mcp-tool CLI command for isolated MCP integration testing - Enhanced agent capability to invoke multiple tools simultaneously * Test suite executed from CI pipeline (#433) * Test strategy & test cases * Unit tests * Integration tests * Extending test coverage (#434) * Contract tests * Testing embeedings * Agent unit tests * Knowledge pipeline tests * Turn on contract tests * Increase storage test coverage (#435) * Fixing storage and adding tests * PR pipeline only runs quick tests * Empty configuration is returned as empty list, previously was not in response (#436) * Update config util to take files as well as command-line text (#437) * Updated CLI invocation and config model for tools and mcp (#438) * Updated CLI invocation and config model for tools and mcp * CLI anomalies * Tweaked the MCP tool implementation for new model * Update agent implementation to match the new model * Fix agent tools, now all tested * Fixed integration tests * Fix MCP delete tool params * Update Python deps to 1.2 * Update to enable knowledge extraction using the agent framework (#439) * Implement KG extraction agent (kg-extract-agent) * Using ReAct framework (agent-manager-react) * ReAct manager had an issue when emitting JSON, which conflicts which ReAct manager's own JSON messages, so refactored ReAct manager to use traditional ReAct messages, non-JSON structure. * Minor refactor to take the prompt template client out of prompt-template so it can be more readily used by other modules. kg-extract-agent uses this framework. * Migrate from setup.py to pyproject.toml (#440) * Converted setup.py to pyproject.toml * Modern package infrastructure as recommended by py docs * Install missing build deps (#441) * Install missing build deps (#442) * Implement logging strategy (#444) * Logging strategy and convert all prints() to logging invocations * Fix/startup failure (#445) * Fix loggin startup problems * Fix logging startup problems (#446) * Fix logging startup problems (#447) * Fixed Mistral OCR to use current API (#448) * Fixed Mistral OCR to use current API * Added PDF decoder tests * Fix Mistral OCR ident to be standard pdf-decoder (#450) * Fix Mistral OCR ident to be standard pdf-decoder * Correct test * Schema structure refactor (#451) * Write schema refactor spec * Implemented schema refactor spec * Structure data mvp (#452) * Structured data tech spec * Architecture principles * New schemas * Updated schemas and specs * Object extractor * Add .coveragerc * New tests * Cassandra object storage * Trying to object extraction working, issues exist * Validate librarian collection (#453) * Fix token chunker, broken API invocation (#454) * Fix token chunker, broken API invocation (#455) * Knowledge load utility CLI (#456) * Knowledge loader * More tests
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209
tests/unit/test_agent/conftest.py
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tests/unit/test_agent/conftest.py
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"""
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Shared fixtures for agent unit tests
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"""
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import pytest
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from unittest.mock import Mock, AsyncMock
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# Mock agent schema classes for testing
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class AgentRequest:
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def __init__(self, question, conversation_id=None):
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self.question = question
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self.conversation_id = conversation_id
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class AgentResponse:
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def __init__(self, answer, conversation_id=None, steps=None):
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self.answer = answer
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self.conversation_id = conversation_id
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self.steps = steps or []
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class AgentStep:
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def __init__(self, step_type, content, tool_name=None, tool_result=None):
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self.step_type = step_type # "think", "act", "observe"
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self.content = content
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self.tool_name = tool_name
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self.tool_result = tool_result
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@pytest.fixture
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def sample_agent_request():
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"""Sample agent request for testing"""
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return AgentRequest(
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question="What is the capital of France?",
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conversation_id="conv-123"
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)
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@pytest.fixture
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def sample_agent_response():
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"""Sample agent response for testing"""
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steps = [
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AgentStep("think", "I need to find information about France's capital"),
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AgentStep("act", "search", tool_name="knowledge_search", tool_result="Paris is the capital of France"),
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AgentStep("observe", "I found that Paris is the capital of France"),
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AgentStep("think", "I can now provide a complete answer")
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]
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return AgentResponse(
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answer="The capital of France is Paris.",
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conversation_id="conv-123",
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steps=steps
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)
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@pytest.fixture
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def mock_llm_client():
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"""Mock LLM client for agent reasoning"""
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mock = AsyncMock()
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mock.generate.return_value = "I need to search for information about the capital of France."
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return mock
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@pytest.fixture
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def mock_knowledge_search_tool():
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"""Mock knowledge search tool"""
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def search_tool(query):
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if "capital" in query.lower() and "france" in query.lower():
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return "Paris is the capital and largest city of France."
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return "No relevant information found."
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return search_tool
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@pytest.fixture
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def mock_graph_rag_tool():
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"""Mock graph RAG tool"""
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def graph_rag_tool(query):
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return {
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"entities": ["France", "Paris"],
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"relationships": [("Paris", "capital_of", "France")],
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"context": "Paris is the capital city of France, located in northern France."
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}
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return graph_rag_tool
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@pytest.fixture
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def mock_calculator_tool():
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"""Mock calculator tool"""
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def calculator_tool(expression):
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# Simple mock calculator
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try:
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# Very basic expression evaluation for testing
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if "+" in expression:
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parts = expression.split("+")
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return str(sum(int(p.strip()) for p in parts))
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elif "*" in expression:
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parts = expression.split("*")
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result = 1
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for p in parts:
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result *= int(p.strip())
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return str(result)
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return str(eval(expression)) # Simplified for testing
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except:
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return "Error: Invalid expression"
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return calculator_tool
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@pytest.fixture
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def available_tools(mock_knowledge_search_tool, mock_graph_rag_tool, mock_calculator_tool):
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"""Available tools for agent testing"""
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return {
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"knowledge_search": {
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"function": mock_knowledge_search_tool,
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"description": "Search knowledge base for information",
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"parameters": ["query"]
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},
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"graph_rag": {
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"function": mock_graph_rag_tool,
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"description": "Query knowledge graph with RAG",
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"parameters": ["query"]
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},
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"calculator": {
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"function": mock_calculator_tool,
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"description": "Perform mathematical calculations",
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"parameters": ["expression"]
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}
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}
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@pytest.fixture
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def sample_conversation_history():
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"""Sample conversation history for multi-turn testing"""
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return [
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{
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"role": "user",
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"content": "What is 2 + 2?",
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"timestamp": "2024-01-01T10:00:00Z"
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},
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{
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"role": "assistant",
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"content": "2 + 2 = 4",
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"steps": [
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{"step_type": "think", "content": "This is a simple arithmetic question"},
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{"step_type": "act", "content": "calculator", "tool_name": "calculator", "tool_result": "4"},
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{"step_type": "observe", "content": "The calculator returned 4"},
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{"step_type": "think", "content": "I can provide the answer"}
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],
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"timestamp": "2024-01-01T10:00:05Z"
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},
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{
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"role": "user",
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"content": "What about 3 + 3?",
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"timestamp": "2024-01-01T10:01:00Z"
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}
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]
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@pytest.fixture
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def react_prompts():
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"""ReAct prompting templates for testing"""
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return {
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"system_prompt": """You are a helpful AI assistant that uses the ReAct (Reasoning and Acting) pattern.
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For each question, follow this cycle:
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1. Think: Analyze the question and plan your approach
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2. Act: Use available tools to gather information
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3. Observe: Review the tool results
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4. Repeat if needed, then provide final answer
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Available tools: {tools}
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Format your response as:
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Think: [your reasoning]
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Act: [tool_name: parameters]
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Observe: [analysis of results]
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Answer: [final response]""",
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"think_prompt": "Think step by step about this question: {question}\nPrevious context: {context}",
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"act_prompt": "Based on your thinking, what tool should you use? Available tools: {tools}",
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"observe_prompt": "You used {tool_name} and got result: {tool_result}\nHow does this help answer the question?",
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"synthesize_prompt": "Based on all your steps, provide a complete answer to: {question}"
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}
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@pytest.fixture
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def mock_agent_processor():
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"""Mock agent processor for testing"""
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class MockAgentProcessor:
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def __init__(self, llm_client=None, tools=None):
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self.llm_client = llm_client
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self.tools = tools or {}
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self.conversation_history = {}
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async def process_request(self, request):
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# Mock processing logic
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return AgentResponse(
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answer="Mock response",
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conversation_id=request.conversation_id,
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steps=[]
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)
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return MockAgentProcessor
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