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fc10d08 e07061d fc10d08 e07061d fc10d08 e07061d fc10d08 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | from typing import List, Dict, Any
from app.services.agent_orchestrator import BaseAgent
class ImprovementAgent(BaseAgent):
def generate_improvements(self, weaknesses: List[str]) -> Dict[str, Any]:
system_prompt = """You are an AI research scientist and senior architect with a focus on modular, API-driven design.
Your goal is to generate impactful ideas for improving a codebase or designing a new one by integrating high-value external components.
Key Principles:
- Decompose the project into independent, modular components that communicate via FastAPI endpoints.
- Identify state-of-the-art Hugging Face Models/Spaces or GitHub projects that can serve as functional building blocks.
- **Strict Critical Judgment**: Only suggest an external project if it is exceptionally useful and provides significant advantages over a custom implementation.
- Focus on reducing custom code and maintenance by leveraging established open-source ecosystems."""
user_prompt = f"Given these weaknesses or project requirements:\n{weaknesses}\n\nPropose a strategic improvement roadmap focusing on modularity and external integrations. Respond in JSON with format:\n{{\n 'improvements': [\n {{\n 'component_or_weakness': 'the target component or identified weakness',\n 'proposal': 'detailed plan for integration or improvement',\n 'justification': 'critical reasoning for why this external project or approach is high-value',\n 'replacement_search_query': 'specific query for Hugging Face or GitHub discovery',\n 'utility_score': 1-10,\n 'feasibility': 1-10\n }}\n ]\n}}"
return self._get_response(system_prompt, user_prompt, response_format={"type": "json_object"})
def _mock_response(self, system_prompt: str) -> Any:
return {
"improvements": [
{
"component_or_weakness": "Memory Management",
"proposal": "Integrate a specialized Rust-based memory optimizer via a Python wrapper.",
"justification": "Significant reduction in overhead for high-throughput data processing.",
"replacement_search_query": "python rust memory management",
"utility_score": 8,
"feasibility": 7
},
{
"component_or_weakness": "Sentiment Analysis",
"proposal": "Replace custom rule-based system with a hosted Hugging Face Space API.",
"justification": "LLM-based models provide far superior accuracy for nuanced text.",
"replacement_search_query": "sentiment analysis space",
"utility_score": 9,
"feasibility": 10
}
]
}
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