91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
"""Prompt templates for command generation."""
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from typing import Optional
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class PromptTemplates:
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"""Manages prompt templates for different shells and scenarios."""
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BASE_PROMPT = """You are a shell command expert. Convert the following natural language description into a {shell} command.
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Return ONLY the command in a code block, followed by a brief explanation.
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Description: {description}
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{directory}
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{shell} command:"""
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BASH_SPECIFIC = """Generate a bash command. You can use:
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- Bash-specific syntax like [[ ]], parameter expansion ${{...}}
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- Process substitution <(), >()
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- Arrays and associative arrays
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- Extended globbing patterns
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- Here-strings <<<"""
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ZSH_SPECIFIC = """Generate a zsh command. You can use:
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- Zsh-specific features like glob qualifiers
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- Extended globbing (**,omg, etc.)
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- Zsh arithmetic $((...))
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- Named directories and magic assignments
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- Zsh-specific builtins"""
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GIT_CONTEXT = """Context: Git repository detected. Use git commands when appropriate."""
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DOCKER_CONTEXT = """Context: Docker environment detected. Use docker/docker-compose when appropriate."""
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def generate_prompt(
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self,
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description: str,
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shell: str,
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context: Optional[str] = None,
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) -> str:
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"""Generate a complete prompt for command generation.
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Args:
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description: Natural language description of the task.
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shell: Target shell (bash or zsh).
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context: Optional context information.
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Returns:
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Complete prompt string.
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"""
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shell_specific = self.ZSH_SPECIFIC if shell == "zsh" else self.BASH_SPECIFIC
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directory_info = ""
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if context:
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directory_info = f"Current directory context: {context}"
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prompt = self.BASE_PROMPT.format(
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shell=shell.upper(),
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description=description,
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directory=directory_info,
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)
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prompt += "\n\n" + shell_specific
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return prompt
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def generate_feedback_prompt(
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self,
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original_description: str,
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generated_command: str,
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user_correction: str,
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) -> str:
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"""Generate a prompt to learn from user feedback.
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Args:
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original_description: The original natural language description.
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generated_command: The command that was originally generated.
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user_correction: The user's corrected command.
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Returns:
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Prompt for learning from feedback.
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"""
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return f"""The user wanted to: {original_description}
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I generated: {generated_command}
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The user corrected it to: {user_correction}
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What was wrong with my original command, and what should I learn for similar requests?"""
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