360 lines
15 KiB
Python
360 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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AI Behavior Test - Tests actual AI responses with different prompt structures
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"""
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import asyncio
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import json
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import sys
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import os
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from typing import Dict, Any, List
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# Add the gateway to path
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sys.path.append(os.path.dirname(__file__))
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# Import the service initialization
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from modules.features.chatPlayground.mainChatPlayground import getServices
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from modules.datamodels.datamodelAi import AiCallOptions, OperationType
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from modules.datamodels.datamodelUam import User
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# The test uses the AI service which handles JSON template internally
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class AIBehaviorTester:
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def __init__(self):
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# Create a minimal user context for testing
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testUser = User(
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id="test_user",
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username="test_user",
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email="test@example.com",
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fullName="Test User",
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language="en",
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mandateId="test_mandate"
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)
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# Initialize services using the existing system
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self.services = getServices(testUser, None) # Test user, no workflow
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self.testResults = []
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async def initialize(self):
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"""Initialize the AI service."""
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# Set logging level to DEBUG to see debug messages
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import logging
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logging.getLogger().setLevel(logging.DEBUG)
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# The AI service needs to be recreated with proper initialization
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from modules.services.serviceAi.mainServiceAi import AiService
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self.services.ai = await AiService.create(self.services)
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# Create a minimal workflow context
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from modules.datamodels.datamodelChat import ChatWorkflow
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import uuid
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self.services.currentWorkflow = ChatWorkflow(
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id=str(uuid.uuid4()),
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name="Test Workflow",
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status="running",
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startedAt=self.services.utils.timestampGetUtc(),
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lastActivity=self.services.utils.timestampGetUtc(),
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currentRound=1,
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currentTask=0,
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currentAction=0,
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totalTasks=0,
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totalActions=0,
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mandateId="test_mandate",
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messageIds=[],
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workflowMode="React",
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maxSteps=5
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)
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async def testPromptBehavior(self, promptName: str, prompt: str, maxIterations: int = 2) -> Dict[str, Any]:
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"""Test actual AI behavior with a specific prompt structure."""
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print(f"\n{'='*60}")
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print(f"TESTING AI BEHAVIOR: {promptName}")
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print(f"{'='*60}")
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print(f"User prompt: {prompt}")
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print(f"Prompt length: {len(prompt)} characters")
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accumulatedContent = []
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# Use the AI service directly with the user prompt - it will build the generation prompt internally
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try:
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# Use the existing AI service with JSON format - it handles looping internally
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response = await self.services.ai.coreAi.callAiDocuments(
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prompt=prompt, # Use the raw user prompt directly
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documents=None,
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outputFormat="json",
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title="Prime Numbers Test",
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loopInstructionFormat="json" # Use the JSON loop instructions
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)
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if isinstance(response, dict):
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result = json.dumps(response, indent=2)
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else:
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result = str(response)
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print(f"Response length: {len(result)} characters")
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print(f"Response preview: {result[:200]}...")
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# If we got an error response, try to extract the actual AI content from debug files
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if isinstance(response, dict) and not response.get("success", True):
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# The AI service wrapped the response in an error format
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# We need to get the actual AI content from the debug files
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print("⚠️ AI returned error response, but may have generated content")
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# Try to read the actual AI response from debug files
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debug_content = self._getLatestDebugResponse()
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if debug_content:
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result = debug_content
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print(f"📄 Found debug content: {len(result)} characters")
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print(f"📄 Debug preview: {result[:200]}...")
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# Parse and analyze response
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parsed_result = self._parseJsonResponse(result)
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if parsed_result:
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# Check if continuation
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if parsed_result.get("continuation") is not None:
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continuation_text = parsed_result.get("continuation", "")
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print(f"✅ Continuation detected: {continuation_text[:100]}...")
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accumulatedContent.append(result)
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# Analyze continuation quality
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continuation_quality = self._analyzeContinuationQuality(continuation_text)
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print(f" Continuation quality: {continuation_quality['score']}/10")
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print(f" Issues: {', '.join(continuation_quality['issues'])}")
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else:
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print("✅ Final response received")
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accumulatedContent.append(result)
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else:
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print("❌ Invalid JSON response")
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accumulatedContent.append(result)
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except Exception as e:
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print(f"❌ Error in AI call: {str(e)}")
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accumulatedContent.append("")
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# Analyze results
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result = self._analyzeBehaviorResults(promptName, accumulatedContent)
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self.testResults.append(result)
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return result
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def _extractContinuationInstruction(self, response: str) -> str:
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"""Extract continuation instruction from response."""
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try:
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parsed = json.loads(response)
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return parsed.get("continuation", "")
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except:
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return ""
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def _getLatestDebugResponse(self) -> str:
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"""Get the latest AI response from debug files."""
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try:
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import glob
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import os
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# Look for the most recent debug response file
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debug_pattern = "local/logs/debug/prompts/*document_generation_response*.txt"
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debug_files = glob.glob(debug_pattern)
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if debug_files:
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# Sort by modification time, get the most recent
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latest_file = max(debug_files, key=os.path.getmtime)
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with open(latest_file, 'r', encoding='utf-8') as f:
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return f.read()
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return ""
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except Exception as e:
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print(f"Error reading debug file: {e}")
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return ""
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def _parseJsonResponse(self, response: str) -> Dict[str, Any]:
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"""Parse JSON response."""
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try:
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# First try direct JSON parsing
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return json.loads(response)
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except:
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try:
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# Try extracting JSON from markdown code blocks
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if "```json" in response:
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start = response.find("```json") + 7
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end = response.find("```", start)
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if end > start:
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json_str = response[start:end].strip()
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return json.loads(json_str)
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elif "```" in response:
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start = response.find("```") + 3
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end = response.find("```", start)
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if end > start:
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json_str = response[start:end].strip()
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return json.loads(json_str)
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return None
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except:
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return None
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def _analyzeContinuationQuality(self, continuation_text: str) -> Dict[str, Any]:
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"""Analyze the quality of continuation instructions."""
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score = 10
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issues = []
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try:
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# Parse the continuation object
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if isinstance(continuation_text, str):
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continuation_obj = json.loads(continuation_text)
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else:
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continuation_obj = continuation_text
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# Check for required fields
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if not isinstance(continuation_obj, dict):
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score -= 5
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issues.append("Not a valid object")
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return {"score": max(0, score), "issues": issues}
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# Check for last_data_items
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if "last_data_items" not in continuation_obj:
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score -= 3
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issues.append("Missing last_data_items")
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elif not continuation_obj["last_data_items"]:
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score -= 2
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issues.append("Empty last_data_items")
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# Check for next_instruction
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if "next_instruction" not in continuation_obj:
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score -= 3
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issues.append("Missing next_instruction")
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elif not continuation_obj["next_instruction"]:
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score -= 2
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issues.append("Empty next_instruction")
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# Check for specific data points in last_data_items
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if "last_data_items" in continuation_obj:
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last_items = continuation_obj["last_data_items"]
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if not any(char.isdigit() for char in str(last_items)):
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score -= 1
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issues.append("No specific numbers in last_data_items")
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# Check for clear instruction in next_instruction
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if "next_instruction" in continuation_obj:
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instruction = continuation_obj["next_instruction"]
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if "continue" not in instruction.lower():
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score -= 1
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issues.append("No 'continue' in next_instruction")
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except (json.JSONDecodeError, TypeError):
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score -= 5
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issues.append("Invalid JSON format")
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return {
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"score": max(0, score),
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"issues": issues
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}
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def _analyzeBehaviorResults(self, promptName: str, accumulatedContent: List[str]) -> Dict[str, Any]:
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"""Analyze AI behavior results."""
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totalContentLength = 0
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iterations = len(accumulatedContent)
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continuationInstructions = []
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continuationQualities = []
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for i, content in enumerate(accumulatedContent):
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parsed = self._parseJsonResponse(content)
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if parsed:
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# Count content length in the response
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contentLength = len(content)
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totalContentLength += contentLength
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continuation = parsed.get("continuation")
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if continuation:
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continuationInstructions.append(continuation)
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quality = self._analyzeContinuationQuality(continuation)
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continuationQualities.append(quality)
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# Calculate averages
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avgContinuationQuality = sum(q["score"] for q in continuationQualities) / len(continuationQualities) if continuationQualities else 0
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return {
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"promptName": promptName,
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"iterations": iterations,
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"totalContentLength": totalContentLength,
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"continuationInstructions": continuationInstructions,
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"avgContinuationQuality": avgContinuationQuality,
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"success": totalContentLength > 0,
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"efficiency": totalContentLength / iterations if iterations > 0 else 0
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}
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def _countPrimesInResponse(self, parsed: Dict[str, Any]) -> int:
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"""Count prime numbers in the parsed response."""
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count = 0
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if "documents" in parsed:
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for doc in parsed["documents"]:
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if "sections" in doc:
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for section in doc["sections"]:
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if section.get("content_type") == "table" and "elements" in section:
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for element in section["elements"]:
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if "rows" in element:
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for row in element["rows"]:
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for cell in row:
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if isinstance(cell, (str, int)) and str(cell).isdigit():
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count += 1
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return count
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def printBehaviorResults(self):
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"""Print AI behavior test results."""
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print(f"\n{'='*80}")
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print("AI BEHAVIOR TEST RESULTS")
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print(f"{'='*80}")
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for result in self.testResults:
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print(f"\n{result['promptName']}:")
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print(f" Iterations: {result['iterations']}")
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print(f" Total Content Length: {result['totalContentLength']}")
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print(f" Efficiency: {result['efficiency']:.1f} chars/iteration")
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print(f" Avg Continuation Quality: {result['avgContinuationQuality']:.1f}/10")
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print(f" Success: {'✅' if result['success'] else '❌'}")
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if result['continuationInstructions']:
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print(f" Continuation Instructions:")
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for i, instruction in enumerate(result['continuationInstructions']):
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print(f" {i+1}: {instruction[:80]}...")
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# Find best performing prompt
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if self.testResults:
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bestEfficiency = max(self.testResults, key=lambda x: x['efficiency'])
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bestQuality = max(self.testResults, key=lambda x: x['avgContinuationQuality'])
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print(f"\n{'='*80}")
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print("BEST PERFORMERS")
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print(f"{'='*80}")
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print(f"🏆 Best Efficiency: {bestEfficiency['promptName']} ({bestEfficiency['efficiency']:.1f} chars/iteration)")
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print(f"🎯 Best Continuation Quality: {bestQuality['promptName']} ({bestQuality['avgContinuationQuality']:.1f}/10)")
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# Test prompt scenarios for GENERIC continuation behavior
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# These test different approaches to handle ANY user prompt and ANY data type
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PROMPT_SCENARIOS = {
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"Prime Numbers Test": """Generate the first 5000 prime numbers in a table with 10 columns per row.""",
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"Text Content": """Generate a comprehensive guide about how to bring a new product to market in 10 sections, each containing detailed explanations and examples."""
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}
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async def main():
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"""Run AI behavior testing."""
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tester = AIBehaviorTester()
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print("Starting AI Behavior Testing...")
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print("Initializing AI service...")
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await tester.initialize()
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print(f"Testing {len(PROMPT_SCENARIOS)} different prompt scenarios")
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for promptName, prompt in PROMPT_SCENARIOS.items():
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try:
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await tester.testPromptBehavior(promptName, prompt, maxIterations=2)
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except Exception as e:
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print(f"❌ Failed to test {promptName}: {str(e)}")
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tester.printBehaviorResults()
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if __name__ == "__main__":
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asyncio.run(main())
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