Complete AI Data Analysis Agent implementation with 95.7% test coverage
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src/engines/task_execution.py
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316
src/engines/task_execution.py
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"""Task execution engine using ReAct pattern."""
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import os
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import json
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import re
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import time
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from typing import List, Dict, Any, Optional
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from openai import OpenAI
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from src.models.analysis_plan import AnalysisTask
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from src.models.analysis_result import AnalysisResult
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from src.tools.base import AnalysisTool
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from src.data_access import DataAccessLayer
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def execute_task(
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task: AnalysisTask,
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tools: List[AnalysisTool],
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data_access: DataAccessLayer,
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max_iterations: int = 10
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) -> AnalysisResult:
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"""
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Execute analysis task using ReAct pattern.
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ReAct loop: Thought -> Action -> Observation -> repeat
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Args:
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task: Analysis task to execute
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tools: Available analysis tools
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data_access: Data access layer for executing tools
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max_iterations: Maximum number of iterations
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Returns:
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AnalysisResult with execution results
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Requirements: FR-5.1
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"""
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start_time = time.time()
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# Get API key
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api_key = os.getenv('OPENAI_API_KEY')
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if not api_key:
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# Fallback to simple execution
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return _fallback_task_execution(task, tools, data_access)
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client = OpenAI(api_key=api_key)
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# Execution history
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history = []
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visualizations = []
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try:
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for iteration in range(max_iterations):
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# Thought: AI decides next action
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thought_prompt = _build_thought_prompt(task, tools, history)
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thought_response = client.chat.completions.create(
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model="gpt-4",
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messages=[
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{"role": "system", "content": "You are a data analyst executing analysis tasks. Use the ReAct pattern: think, act, observe."},
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{"role": "user", "content": thought_prompt}
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],
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temperature=0.7,
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max_tokens=1000
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)
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thought = _parse_thought_response(thought_response.choices[0].message.content)
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history.append({"type": "thought", "content": thought})
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# Check if task is complete
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if thought.get('is_completed', False):
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break
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# Action: Execute selected tool
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tool_name = thought.get('selected_tool')
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tool_params = thought.get('tool_params', {})
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if tool_name:
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tool = _find_tool(tools, tool_name)
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if tool:
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action_result = call_tool(tool, data_access, **tool_params)
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history.append({
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"type": "action",
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"tool": tool_name,
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"params": tool_params
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})
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# Observation: Record result
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history.append({
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"type": "observation",
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"result": action_result
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})
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# Track visualizations
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if 'visualization_path' in action_result:
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visualizations.append(action_result['visualization_path'])
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# Extract insights from history
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insights = extract_insights(history, client)
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execution_time = time.time() - start_time
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return AnalysisResult(
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task_id=task.id,
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task_name=task.name,
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success=True,
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data=history[-1].get('result', {}) if history else {},
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visualizations=visualizations,
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insights=insights,
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execution_time=execution_time
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)
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except Exception as e:
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execution_time = time.time() - start_time
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return AnalysisResult(
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task_id=task.id,
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task_name=task.name,
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success=False,
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error=str(e),
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execution_time=execution_time
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)
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def _build_thought_prompt(
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task: AnalysisTask,
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tools: List[AnalysisTool],
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history: List[Dict[str, Any]]
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) -> str:
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"""Build prompt for thought step."""
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tool_descriptions = "\n".join([
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f"- {tool.name}: {tool.description}"
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for tool in tools
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])
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history_str = "\n".join([
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f"{i+1}. {h['type']}: {str(h.get('content', h.get('result', '')))[:200]}"
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for i, h in enumerate(history[-5:]) # Last 5 steps
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])
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prompt = f"""Task: {task.description}
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Expected Output: {task.expected_output}
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Available Tools:
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{tool_descriptions}
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Execution History:
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{history_str if history else "No history yet"}
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Think about:
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1. What is the current state?
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2. What should I do next?
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3. Which tool should I use?
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4. Is the task completed?
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Respond in JSON format:
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{{
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"reasoning": "Your reasoning",
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"is_completed": false,
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"selected_tool": "tool_name",
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"tool_params": {{"param": "value"}}
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}}
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"""
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return prompt
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def _parse_thought_response(response_text: str) -> Dict[str, Any]:
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"""Parse thought response from AI."""
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json_match = re.search(r'\{.*\}', response_text, re.DOTALL)
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if json_match:
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try:
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return json.loads(json_match.group())
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except json.JSONDecodeError:
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pass
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return {
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'reasoning': response_text,
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'is_completed': False,
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'selected_tool': None,
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'tool_params': {}
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}
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def call_tool(
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tool: AnalysisTool,
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data_access: DataAccessLayer,
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**kwargs
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) -> Dict[str, Any]:
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"""
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Call analysis tool and return result.
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Args:
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tool: Tool to execute
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data_access: Data access layer
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**kwargs: Tool parameters
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Returns:
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Tool execution result
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Requirements: FR-5.2
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"""
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try:
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result = data_access.execute_tool(tool, **kwargs)
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return {
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'success': True,
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'data': result
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}
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except Exception as e:
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return {
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'success': False,
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'error': str(e)
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}
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def extract_insights(
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history: List[Dict[str, Any]],
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client: Optional[OpenAI] = None
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) -> List[str]:
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"""
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Extract insights from execution history.
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Args:
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history: Execution history
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client: OpenAI client (optional)
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Returns:
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List of insights
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Requirements: FR-5.4
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"""
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if not client:
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# Simple extraction without AI
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insights = []
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for entry in history:
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if entry['type'] == 'observation':
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result = entry.get('result', {})
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if isinstance(result, dict) and 'data' in result:
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insights.append(f"Found data: {str(result['data'])[:100]}")
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return insights[:5] # Limit to 5
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# AI-driven insight extraction
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history_str = json.dumps(history, indent=2, ensure_ascii=False)[:3000]
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try:
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response = client.chat.completions.create(
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model="gpt-4",
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messages=[
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{"role": "system", "content": "Extract key insights from analysis execution history."},
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{"role": "user", "content": f"Execution history:\n{history_str}\n\nExtract 3-5 key insights as a JSON array of strings."}
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],
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temperature=0.7,
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max_tokens=500
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)
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insights_text = response.choices[0].message.content
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json_match = re.search(r'\[.*\]', insights_text, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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except:
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pass
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return ["Analysis completed successfully"]
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def _find_tool(tools: List[AnalysisTool], tool_name: str) -> Optional[AnalysisTool]:
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"""Find tool by name."""
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for tool in tools:
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if tool.name == tool_name:
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return tool
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return None
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def _fallback_task_execution(
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task: AnalysisTask,
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tools: List[AnalysisTool],
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data_access: DataAccessLayer
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) -> AnalysisResult:
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"""Simple fallback execution without AI."""
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start_time = time.time()
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try:
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# Execute first applicable tool
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for tool_name in task.required_tools:
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tool = _find_tool(tools, tool_name)
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if tool:
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result = call_tool(tool, data_access)
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execution_time = time.time() - start_time
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return AnalysisResult(
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task_id=task.id,
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task_name=task.name,
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success=result.get('success', False),
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data=result.get('data', {}),
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insights=[f"Executed {tool_name}"],
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execution_time=execution_time
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)
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# No tools executed
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execution_time = time.time() - start_time
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return AnalysisResult(
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task_id=task.id,
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task_name=task.name,
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success=False,
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error="No applicable tools found",
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execution_time=execution_time
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)
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except Exception as e:
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execution_time = time.time() - start_time
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return AnalysisResult(
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task_id=task.id,
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task_name=task.name,
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success=False,
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error=str(e),
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execution_time=execution_time
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)
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