122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
from abc import ABC, abstractmethod
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from typing import Any, Dict, Optional
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import requests
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class LLMClient(ABC):
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@abstractmethod
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def get_recommendation(self, config_type: str, issues: list) -> str:
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pass
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class OllamaClient(LLMClient):
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def __init__(self, endpoint: str = "http://localhost:11434", model: str = "llama3"):
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self.endpoint = endpoint
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self.model = model
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def is_available(self) -> bool:
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try:
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response = requests.get(f"{self.endpoint}/api/tags", timeout=5)
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return response.status_code == 200
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except Exception:
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return False
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def get_recommendation(self, config_type: str, issues: list) -> str:
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prompt = self._build_prompt(config_type, issues)
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try:
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response = requests.post(
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f"{self.endpoint}/api/generate",
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json={
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"model": self.model,
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"prompt": prompt,
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"stream": False
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},
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timeout=30
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)
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if response.status_code == 200:
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result = response.json()
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return result.get("response", "No recommendation available")
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except Exception as e:
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return f"LLM unavailable: {str(e)}. Falling back to rule-based suggestions."
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return "Unable to get recommendation from LLM"
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def _build_prompt(self, config_type: str, issues: list) -> str:
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issues_text = "\n".join([f"- {i.get('message', '')}" for i in issues]) if issues else "No issues found"
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prompt = f"""You are a configuration expert. Analyze the following {config_type} configuration issues and provide recommendations:
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Issues found:
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{issues_text}
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Please provide:
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1. A brief explanation of the issues
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2. Specific fix recommendations with code examples
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3. Best practices for this configuration type
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Format your response clearly with sections."""
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return prompt
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class FallbackClient(LLMClient):
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RECOMMENDATIONS = {
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"deprecated-package": "Replace deprecated packages with their modern equivalents. Check the official documentation for migration guides.",
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"outdated-version": "Consider updating dependencies to stable versions. Major version 0.x packages may have breaking changes.",
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"missing-type-checking": "Enable strict mode in TypeScript for better type safety and improved developer experience.",
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"missing-scripts": "Add standard scripts (test, build) to improve development workflow and CI/CD integration.",
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"security-vulnerability": "Review security settings and ensure production configurations don't expose sensitive data.",
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}
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def get_recommendation(self, config_type: str, issues: list) -> str:
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recommendations = []
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for issue in issues:
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category = issue.get("category", "")
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if category in self.RECOMMENDATIONS:
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recommendations.append(self.RECOMMENDATIONS[category])
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if recommendations:
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return "\n".join(recommendations)
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return "Review the configuration for common best practices in your project type."
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class LLMProvider:
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def __init__(self, endpoint: str = "http://localhost:11434", model: str = "llama3"):
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self.endpoint = endpoint
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self.model = model
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self._client: Optional[LLMClient] = None
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def get_client(self) -> LLMClient:
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if self._client is None:
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try:
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client = OllamaClient(self.endpoint, self.model)
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if client.is_available():
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self._client = client
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else:
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self._client = FallbackClient()
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except Exception:
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self._client = FallbackClient()
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return self._client
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def get_recommendation(self, config_type: str, issues: list) -> str:
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client = self.get_client()
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return client.get_recommendation(config_type, issues)
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def load_config() -> Dict[str, Any]:
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try:
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with open("config.yaml", "r") as f:
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import yaml
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return yaml.safe_load(f) or {}
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except FileNotFoundError:
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return {}
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def get_llm_config() -> Dict[str, Any]:
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config = load_config()
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return config.get("llm", {"endpoint": "http://localhost:11434", "model": "llama3"})
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