removed chatbot tests
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2 changed files with 0 additions and 220 deletions
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# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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"""Chatbot functional tests."""
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#!/usr/bin/env python3
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# Copyright (c) 2025 Patrick Motsch
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# All rights reserved.
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"""
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Chatbot Functional Tests
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Tests the chatbot implementation to ensure:
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1. Chatbot initialization works correctly
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2. Streaming events are emitted properly
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3. Tool calls execute correctly
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4. Messages are stored in database
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5. No infinite loops occur
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"""
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import asyncio
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import os
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import sys
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from pathlib import Path
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# Add the gateway to path (go up 2 levels from tests/functional/chatbot/)
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_gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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if _gateway_path not in sys.path:
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sys.path.insert(0, _gateway_path)
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import pytest
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from modules.features.chatbot.chatbot import Chatbot
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from modules.features.chatbot.chatbotAIBridge import AICenterChatModel
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from modules.features.chatbot.chatbotMemory import DatabaseCheckpointer
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from modules.features.chatbot.config import load_chatbot_config_from_dict
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from modules.features.chatbot.streamingHelper import ChatStreamingHelper
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from modules.datamodels.datamodelUam import User
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from modules.datamodels.datamodelAi import OperationTypeEnum, ProcessingModeEnum
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class TestChatbot:
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"""Test suite for chatbot functionality."""
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@pytest.fixture
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def test_user(self):
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"""Create a test user."""
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return User(
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id="test_user_chatbot",
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username="test_chatbot",
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email="test@example.com",
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fullName="Test Chatbot User",
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language="de",
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mandateId="test_mandate",
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)
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@pytest.fixture
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def workflow_id(self):
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"""Generate a test workflow ID."""
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import uuid
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return str(uuid.uuid4())
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@pytest.mark.asyncio
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async def test_chatbot_initialization(self, test_user, workflow_id):
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"""Test that chatbot can be initialized correctly."""
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# Load config
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config = load_chatbot_config_from_dict({}, config_id="test")
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# Create system prompt
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from datetime import datetime
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system_prompt = config.systemPrompt.replace(
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"{{DATE}}",
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datetime.now().strftime("%d.%m.%Y")
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)
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# Create AI center model
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operation_type = OperationTypeEnum[config.model.operationType]
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processing_mode = ProcessingModeEnum[config.model.processingMode]
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model = AICenterChatModel(
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user=test_user,
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operation_type=operation_type,
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processing_mode=processing_mode
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)
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# Create memory/checkpointer
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memory = DatabaseCheckpointer(user=test_user, workflow_id=workflow_id)
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# Create chatbot instance
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chatbot = await Chatbot.create(
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model=model,
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memory=memory,
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system_prompt=system_prompt,
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workflow_id=workflow_id
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)
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assert chatbot is not None
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assert chatbot.model is not None
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assert chatbot.memory is not None
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assert chatbot.app is not None
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assert chatbot.system_prompt == system_prompt
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@pytest.mark.asyncio
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async def test_streaming_helper_role_from_message(self):
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"""Test ChatStreamingHelper.role_from_message."""
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from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
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human_msg = HumanMessage(content="Hello")
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assert ChatStreamingHelper.role_from_message(msg=human_msg) == "user"
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ai_msg = AIMessage(content="Hi there")
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assert ChatStreamingHelper.role_from_message(msg=ai_msg) == "assistant"
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system_msg = SystemMessage(content="You are a helpful assistant")
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assert ChatStreamingHelper.role_from_message(msg=system_msg) == "system"
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@pytest.mark.asyncio
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async def test_streaming_helper_flatten_content(self):
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"""Test ChatStreamingHelper.flatten_content."""
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# Test string
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assert ChatStreamingHelper.flatten_content(content="Hello") == "Hello"
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# Test list
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content_list = [{"type": "text", "text": "Hello"}, {"type": "text", "text": "World"}]
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result = ChatStreamingHelper.flatten_content(content=content_list)
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assert "Hello" in result
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assert "World" in result
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# Test dict
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content_dict = {"text": "Simple message"}
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assert ChatStreamingHelper.flatten_content(content=content_dict) == "Simple message"
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# Test None
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assert ChatStreamingHelper.flatten_content(content=None) == ""
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@pytest.mark.asyncio
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async def test_streaming_helper_message_to_dict(self):
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"""Test ChatStreamingHelper.message_to_dict."""
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from langchain_core.messages import HumanMessage
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msg = HumanMessage(content="Hello there")
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result = ChatStreamingHelper.message_to_dict(msg=msg)
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assert result["role"] == "user"
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assert result["content"] == "Hello there"
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@pytest.mark.asyncio
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async def test_streaming_helper_extract_messages_from_output(self):
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"""Test ChatStreamingHelper.extract_messages_from_output."""
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# Test dict with messages
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output_dict = {"messages": [{"role": "user", "content": "Hello"}]}
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messages = ChatStreamingHelper.extract_messages_from_output(output_obj=output_dict)
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assert len(messages) == 1
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# Test None
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messages = ChatStreamingHelper.extract_messages_from_output(output_obj=None)
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assert len(messages) == 0
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# Test object with messages attribute
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class MockOutput:
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def __init__(self):
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self.messages = [{"role": "assistant", "content": "Hi"}]
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mock_output = MockOutput()
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messages = ChatStreamingHelper.extract_messages_from_output(output_obj=mock_output)
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assert len(messages) == 1
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@pytest.mark.asyncio
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async def test_chatbot_should_continue_logic(self, test_user, workflow_id):
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"""Test that should_continue logic works correctly (no infinite loops)."""
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# Load config
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config = load_chatbot_config_from_dict({}, config_id="test")
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# Create system prompt
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from datetime import datetime
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system_prompt = config.systemPrompt.replace(
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"{{DATE}}",
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datetime.now().strftime("%d.%m.%Y")
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)
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# Create AI center model
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operation_type = OperationTypeEnum[config.model.operationType]
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processing_mode = ProcessingModeEnum[config.model.processingMode]
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model = AICenterChatModel(
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user=test_user,
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operation_type=operation_type,
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processing_mode=processing_mode
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)
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# Create memory/checkpointer
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memory = DatabaseCheckpointer(user=test_user, workflow_id=workflow_id)
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# Create chatbot instance
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chatbot = await Chatbot.create(
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model=model,
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memory=memory,
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system_prompt=system_prompt,
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workflow_id=workflow_id
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)
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# The should_continue logic is internal to the workflow
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# We can test that the workflow compiles successfully
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assert chatbot.app is not None
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# Test that we can invoke the workflow (this will test should_continue internally)
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# Use a simple message that shouldn't cause infinite loops
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try:
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result = await chatbot.chat(
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message="Hallo",
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chat_id=workflow_id
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)
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# Should return messages without infinite loop
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assert result is not None
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assert isinstance(result, list)
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except Exception as e:
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# If there's an error, it shouldn't be an infinite loop error
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# (infinite loops would timeout or hit max iterations)
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assert "infinite" not in str(e).lower()
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assert "loop" not in str(e).lower()
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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