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d9a8b53bc2
* update microagent name and update template.toml * substitute actual micro_agent_name for prompt manager * add python-frontmatter * support micro agent in codeact * add test cases * add instruction from require env var * add draft gh micro agent * update poetry lock * update poetry lock
64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
import os
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from jinja2 import Template
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from openhands.utils.microagent import MicroAgent
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class PromptManager:
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"""
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Manages prompt templates and micro-agents for AI interactions.
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This class handles loading and rendering of system and user prompt templates,
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as well as loading micro-agent specifications. It provides methods to access
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rendered system and initial user messages for AI interactions.
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Attributes:
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prompt_dir (str): Directory containing prompt templates.
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agent_skills_docs (str): Documentation of agent skills.
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micro_agent (MicroAgent | None): Micro-agent, if specified.
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"""
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def __init__(
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self,
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prompt_dir: str,
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agent_skills_docs: str,
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micro_agent: MicroAgent | None = None,
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):
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self.prompt_dir: str = prompt_dir
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self.agent_skills_docs: str = agent_skills_docs
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self.system_template: Template = self._load_template('system_prompt')
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self.user_template: Template = self._load_template('user_prompt')
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self.micro_agent: MicroAgent | None = micro_agent
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def _load_template(self, template_name: str) -> Template:
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template_path = os.path.join(self.prompt_dir, f'{template_name}.j2')
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if not os.path.exists(template_path):
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raise FileNotFoundError(f'Prompt file {template_path} not found')
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with open(template_path, 'r') as file:
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return Template(file.read())
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@property
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def system_message(self) -> str:
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rendered = self.system_template.render(
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agent_skills_docs=self.agent_skills_docs,
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).strip()
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return rendered
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@property
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def initial_user_message(self) -> str:
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"""This is the initial user message provided to the agent
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before *actual* user instructions are provided.
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It is used to provide a demonstration of how the agent
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should behave in order to solve the user's task. And it may
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optionally contain some additional context about the user's task.
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These additional context will convert the current generic agent
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into a more specialized agent that is tailored to the user's task.
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"""
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rendered = self.user_template.render(
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micro_agent=self.micro_agent.content if self.micro_agent else None
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)
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return rendered.strip()
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