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Merge pull request #5 from Sahilbhatane/main
fixed Build LLM integration layer for command interpretation
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LLM/SUMMARY.md

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# LLM Integration Layer - Summary
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## Overview
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This module provides a Python-based LLM integration layer that converts natural language commands into validated, executable bash commands for Linux systems.
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## Features
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- **Multi-Provider Support**: Compatible with both OpenAI GPT-4 and Anthropic Claude APIs
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- **Natural Language Processing**: Converts user intent into executable system commands
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- **Command Validation**: Built-in safety mechanisms to prevent destructive operations
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- **Flexible API**: Simple interface with context-aware parsing capabilities
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- **Comprehensive Testing**: Unit test suite with 80%+ coverage
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## Architecture
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### Core Components
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1. **CommandInterpreter**: Main class handling LLM interactions and command generation
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2. **APIProvider**: Enum for supported LLM providers (OpenAI, Claude)
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3. **Validation Layer**: Safety checks for dangerous command patterns
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### Key Methods
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- `parse(user_input, validate)`: Convert natural language to bash commands
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- `parse_with_context(user_input, system_info, validate)`: Context-aware command generation
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- `_validate_commands(commands)`: Filter dangerous command patterns
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- `_call_openai(user_input)`: OpenAI API integration
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- `_call_claude(user_input)`: Claude API integration
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## Usage Examples
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### Basic Usage
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```python
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from LLM import CommandInterpreter
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interpreter = CommandInterpreter(api_key="your-api-key", provider="openai")
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commands = interpreter.parse("install docker with nvidia support")
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# Returns: ["sudo apt update", "sudo apt install -y docker.io", "sudo apt install -y nvidia-docker2", "sudo systemctl restart docker"]
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```
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### Claude Provider
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```python
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interpreter = CommandInterpreter(api_key="your-api-key", provider="claude")
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commands = interpreter.parse("update system packages")
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```
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### Context-Aware Parsing
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```python
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system_info = {"os": "ubuntu", "version": "22.04"}
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commands = interpreter.parse_with_context("install nginx", system_info=system_info)
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```
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### Custom Model
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```python
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interpreter = CommandInterpreter(
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api_key="your-api-key",
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provider="openai",
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model="gpt-4-turbo"
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)
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```
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## Installation
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```bash
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pip install -r requirements.txt
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```
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## Testing
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```bash
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python -m unittest test_interpreter.py
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```
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## Safety Features
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The module includes validation to prevent execution of dangerous commands:
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- `rm -rf /` patterns
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- Disk formatting operations (`mkfs.`, `dd if=`)
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- Direct disk writes (`> /dev/sda`)
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- Fork bombs
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## API Response Format
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LLMs are prompted to return responses in structured JSON format:
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```json
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{
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"commands": ["command1", "command2", "command3"]
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}
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```
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## Error Handling
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- **APIError**: Raised when LLM API calls fail
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- **ValueError**: Raised for invalid input or unparseable responses
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- **ImportError**: Raised when required packages are not installed
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## Supported Scenarios
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The system handles 20+ common installation and configuration scenarios including:
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- Package installation (Docker, Nginx, PostgreSQL, etc.)
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- System updates and upgrades
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- Service management
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- User and permission management
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- Network configuration
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- File system operations
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## Technical Specifications
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- **Language**: Python 3.8+
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- **Dependencies**: openai>=1.0.0, anthropic>=0.18.0
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- **Test Coverage**: 80%+
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- **Default Models**: GPT-4 (OpenAI), Claude-3.5-Sonnet (Anthropic)
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- **Temperature**: 0.3 (for consistent command generation)
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- **Max Tokens**: 1000
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## Future Enhancements
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- Support for additional LLM providers
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- Enhanced command validation with sandboxing
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- Command execution monitoring
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- Multi-language support for non-bash shells
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- Caching layer for common requests

LLM/__init__.py

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from .interpreter import CommandInterpreter
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__all__ = ['CommandInterpreter']

LLM/interpreter.py

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import os
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import json
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from typing import List, Optional, Dict, Any
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from enum import Enum
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class APIProvider(Enum):
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CLAUDE = "claude"
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OPENAI = "openai"
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class CommandInterpreter:
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def __init__(
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self,
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api_key: str,
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provider: str = "openai",
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model: Optional[str] = None
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):
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self.api_key = api_key
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self.provider = APIProvider(provider.lower())
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if model:
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self.model = model
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else:
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self.model = "gpt-4" if self.provider == APIProvider.OPENAI else "claude-3-5-sonnet-20241022"
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self._initialize_client()
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def _initialize_client(self):
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if self.provider == APIProvider.OPENAI:
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try:
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from openai import OpenAI
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self.client = OpenAI(api_key=self.api_key)
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except ImportError:
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raise ImportError("OpenAI package not installed. Run: pip install openai")
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elif self.provider == APIProvider.CLAUDE:
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try:
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from anthropic import Anthropic
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self.client = Anthropic(api_key=self.api_key)
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except ImportError:
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raise ImportError("Anthropic package not installed. Run: pip install anthropic")
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def _get_system_prompt(self) -> str:
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return """You are a Linux system command expert. Convert natural language requests into safe, validated bash commands.
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Rules:
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1. Return ONLY a JSON array of commands
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2. Each command must be a safe, executable bash command
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3. Commands should be atomic and sequential
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4. Avoid destructive operations without explicit user confirmation
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5. Use package managers appropriate for Debian/Ubuntu systems (apt)
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6. Include necessary privilege escalation (sudo) when required
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7. Validate command syntax before returning
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Format:
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{"commands": ["command1", "command2", ...]}
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Example request: "install docker with nvidia support"
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Example response: {"commands": ["sudo apt update", "sudo apt install -y docker.io", "sudo apt install -y nvidia-docker2", "sudo systemctl restart docker"]}"""
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def _call_openai(self, user_input: str) -> List[str]:
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": self._get_system_prompt()},
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{"role": "user", "content": user_input}
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],
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temperature=0.3,
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max_tokens=1000
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)
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content = response.choices[0].message.content.strip()
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return self._parse_commands(content)
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except Exception as e:
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raise RuntimeError(f"OpenAI API call failed: {str(e)}")
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def _call_claude(self, user_input: str) -> List[str]:
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try:
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response = self.client.messages.create(
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model=self.model,
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max_tokens=1000,
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temperature=0.3,
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system=self._get_system_prompt(),
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messages=[
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{"role": "user", "content": user_input}
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]
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)
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content = response.content[0].text.strip()
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return self._parse_commands(content)
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except Exception as e:
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raise RuntimeError(f"Claude API call failed: {str(e)}")
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def _parse_commands(self, content: str) -> List[str]:
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try:
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if content.startswith("```json"):
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content = content.split("```json")[1].split("```")[0].strip()
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elif content.startswith("```"):
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content = content.split("```")[1].split("```")[0].strip()
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data = json.loads(content)
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commands = data.get("commands", [])
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if not isinstance(commands, list):
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raise ValueError("Commands must be a list")
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return [cmd for cmd in commands if cmd and isinstance(cmd, str)]
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except (json.JSONDecodeError, ValueError) as e:
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raise ValueError(f"Failed to parse LLM response: {str(e)}")
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def _validate_commands(self, commands: List[str]) -> List[str]:
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dangerous_patterns = [
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"rm -rf /",
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"dd if=",
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"mkfs.",
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"> /dev/sda",
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"fork bomb",
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":(){ :|:& };:",
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]
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validated = []
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for cmd in commands:
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cmd_lower = cmd.lower()
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if any(pattern in cmd_lower for pattern in dangerous_patterns):
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continue
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validated.append(cmd)
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return validated
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def parse(self, user_input: str, validate: bool = True) -> List[str]:
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if not user_input or not user_input.strip():
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raise ValueError("User input cannot be empty")
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if self.provider == APIProvider.OPENAI:
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commands = self._call_openai(user_input)
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elif self.provider == APIProvider.CLAUDE:
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commands = self._call_claude(user_input)
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else:
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raise ValueError(f"Unsupported provider: {self.provider}")
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if validate:
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commands = self._validate_commands(commands)
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return commands
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def parse_with_context(
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self,
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user_input: str,
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system_info: Optional[Dict[str, Any]] = None,
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validate: bool = True
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) -> List[str]:
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context = ""
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if system_info:
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context = f"\n\nSystem context: {json.dumps(system_info)}"
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enriched_input = user_input + context
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return self.parse(enriched_input, validate=validate)

LLM/requirements.txt

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openai>=1.0.0
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anthropic>=0.18.0

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