🔍 Code Extractor

A dataclass representing an individual step in an analysis process, tracking execution details, scripts, outputs, and errors for a specific analysis operation.

Purpose

AnalysisStep serves as a data container for tracking individual steps within a multi-step analysis workflow. It captures metadata about each step including its type (data loading, script generation, execution, debugging, or reporting), the generated scripts, execution results, and any errors encountered. This class is designed to maintain a complete audit trail of analysis operations within a session, enabling debugging, reporting, and workflow reconstruction.

Source Code

class AnalysisStep:
    """Individual step in analysis process"""
    step_id: str
    session_id: str
    step_number: int
    step_type: str  # "data_load", "script_generation", "execution", "debugging", "reporting"
    input_data: Dict[str, Any]
    generated_script: str = ""
    execution_output: str = ""
    execution_error: str = ""
    execution_success: bool = False
    created_at: datetime = None
    
    def __post_init__(self):
        if self.created_at is None:
            self.created_at = datetime.now()
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary with proper serialization"""
        return {
            'step_id': self.step_id,
            'session_id': self.session_id,
            'step_number': self.step_number,
            'step_type': self.step_type,
            'input_data': self.input_data,
            'generated_script': self.generated_script,
            'execution_output': self.execution_output,
            'execution_error': self.execution_error,
            'execution_success': self.execution_success,
            'created_at': self.created_at.isoformat() if self.created_at else None
        }

Parameters

Name	Type	Default	Kind
bases	-	-

Parameter Details

step_id: Unique identifier for this analysis step, typically a UUID string

session_id: Identifier linking this step to a parent analysis session

step_number: Sequential number indicating the order of this step within the session (integer)

step_type: Type of analysis step being performed. Valid values: 'data_load', 'script_generation', 'execution', 'debugging', 'reporting'

input_data: Dictionary containing input parameters and data for this step. Structure varies based on step_type

generated_script: The script code generated or used in this step (default: empty string)

execution_output: Standard output captured from script execution (default: empty string)

execution_error: Error messages or stack traces if execution failed (default: empty string)

execution_success: Boolean flag indicating whether the step executed successfully (default: False)

created_at: Timestamp when the step was created. Auto-populated with current datetime if not provided (default: None, set in __post_init__)

Return Value

Instantiation returns an AnalysisStep object with all specified attributes. The to_dict() method returns a dictionary representation with all fields serialized, including the created_at timestamp converted to ISO format string.

Class Interface

Methods

Attributes

Name	Type	Description	Scope
step_id	str	Unique identifier for this analysis step	instance
session_id	str	Identifier of the parent analysis session this step belongs to	instance
step_number	int	Sequential number indicating the position of this step in the analysis workflow	instance
step_type	str	Type of analysis operation: 'data_load', 'script_generation', 'execution', 'debugging', or 'reporting'	instance
input_data	Dict[str, Any]	Dictionary containing input parameters and data specific to this step's operation	instance
generated_script	str	The script code generated or executed in this step (default: empty string)	instance
execution_output	str	Standard output captured from script execution (default: empty string)	instance
execution_error	str	Error messages or stack traces if execution failed (default: empty string)	instance
execution_success	bool	Flag indicating whether the step executed successfully (default: False)	instance
created_at	datetime	Timestamp when the step was created, automatically set to current time if not provided	instance

Dependencies

• datetime
• typing

Required Imports

from datetime import datetime
from typing import Dict, Any
from dataclasses import dataclass

Usage Example

from datetime import datetime
from typing import Dict, Any
from dataclasses import dataclass

@dataclass
class AnalysisStep:
    step_id: str
    session_id: str
    step_number: int
    step_type: str
    input_data: Dict[str, Any]
    generated_script: str = ""
    execution_output: str = ""
    execution_error: str = ""
    execution_success: bool = False
    created_at: datetime = None
    
    def __post_init__(self):
        if self.created_at is None:
            self.created_at = datetime.now()
    
    def to_dict(self) -> Dict[str, Any]:
        return {
            'step_id': self.step_id,
            'session_id': self.session_id,
            'step_number': self.step_number,
            'step_type': self.step_type,
            'input_data': self.input_data,
            'generated_script': self.generated_script,
            'execution_output': self.execution_output,
            'execution_error': self.execution_error,
            'execution_success': self.execution_success,
            'created_at': self.created_at.isoformat() if self.created_at else None
        }

# Create a new analysis step
step = AnalysisStep(
    step_id='step-001',
    session_id='session-abc',
    step_number=1,
    step_type='data_load',
    input_data={'file_path': '/data/input.csv', 'format': 'csv'}
)

# Update execution results
step.generated_script = 'import pandas as pd\ndf = pd.read_csv("/data/input.csv")'
step.execution_output = 'Successfully loaded 1000 rows'
step.execution_success = True

# Serialize to dictionary
step_dict = step.to_dict()
print(step_dict['created_at'])  # ISO format timestamp

Best Practices

• Always provide step_id and session_id to maintain proper tracking and relationships between steps
• Use consistent step_type values from the defined set: 'data_load', 'script_generation', 'execution', 'debugging', 'reporting'
• Set execution_success to True only after successful execution, and populate execution_error if failures occur
• The created_at timestamp is automatically set if not provided, ensuring all steps have creation timestamps
• Use to_dict() method for serialization to JSON or database storage, as it properly handles datetime conversion
• Keep input_data structure consistent for the same step_type to facilitate processing and analysis
• Update execution_output and execution_error fields immediately after script execution for accurate tracking
• Step_number should be sequential within a session to maintain proper ordering of analysis workflow

Tags

Similar Components

AI-powered semantic similarity - components with related functionality:

• class AnalysisStep 97.7% similar

  A dataclass representing an individual step in an analysis process, tracking execution details, scripts, outputs, and metadata for each step in a data analysis workflow.

  From: /tf/active/vicechatdev/vice_ai/smartstat_models.py

• class DataAnalysisSession_v1 71.8% similar

  A dataclass representing a statistical analysis session that is linked to specific document sections, managing analysis state, messages, plots, and configuration.

  From: /tf/active/vicechatdev/vice_ai/models.py

• class AnalysisResult_v1 67.6% similar

  A dataclass that encapsulates the results from statistical analysis operations, including metadata, file paths, and timestamps.

  From: /tf/active/vicechatdev/vice_ai/models.py

• class DataSection 67.1% similar

  A dataclass representing a dedicated data analysis section that stores analysis results, plots, dataset information, and conclusions separately from text content.

  From: /tf/active/vicechatdev/vice_ai/models.py

• class DataAnalysisSession 66.2% similar

  A dataclass representing a data analysis session that is linked to a specific text section within a document, managing conversation messages, analysis results, plots, and configuration.

  From: /tf/active/vicechatdev/vice_ai/models.py

class AnalysisStep:
    """Individual step in analysis process"""
    step_id: str
    session_id: str
    step_number: int
    step_type: str  # "data_load", "script_generation", "execution", "debugging", "reporting"
    input_data: Dict[str, Any]
    generated_script: str = ""
    execution_output: str = ""
    execution_error: str = ""
    execution_success: bool = False
    created_at: datetime = None
    
    def __post_init__(self):
        if self.created_at is None:
            self.created_at = datetime.now()
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary with proper serialization"""
        return {
            'step_id': self.step_id,
            'session_id': self.session_id,
            'step_number': self.step_number,
            'step_type': self.step_type,
            'input_data': self.input_data,
            'generated_script': self.generated_script,
            'execution_output': self.execution_output,
            'execution_error': self.execution_error,
            'execution_success': self.execution_success,
            'created_at': self.created_at.isoformat() if self.created_at else None
        }