"""
Basic LLM Manager for testing and development
"""
import asyncio
import json
from typing import Dict, Any, Optional
from dataclasses import dataclass
@dataclass
class LLMResponse:
"""LLM response wrapper"""
content: str
model: str
metadata: Dict[str, Any]
class LLMManager:
"""
Basic LLM Manager for testing purposes
In production, this would integrate with actual LLM services
"""
def __init__(self):
self.available_models = [
"claude-3-sonnet",
"gpt-4",
"gpt-3.5-turbo"
]
async def generate_completion(
self,
prompt: str,
model: str = "claude-3-sonnet",
temperature: float = 0.7,
max_tokens: int = 1000,
metadata: Optional[Dict[str, Any]] = None
) -> LLMResponse:
"""
Generate completion for given prompt
For testing, returns mock responses based on prompt content
"""
# Simulate processing time
await asyncio.sleep(0.1)
# Mock different responses based on prompt content
if "syntax error" in prompt.lower():
content = json.dumps({
"issues": [{
"type": "syntax_error",
"severity": 8,
"description": "Missing closing bracket",
"line_number": 15,
"suggested_fixes": ["Add closing bracket at line 15"]
}]
})
elif "analyze" in prompt.lower():
content = json.dumps({
"root_cause": "Missing import statement",
"impact_assessment": "High - prevents module loading",
"urgency": "high",
"recommendations": ["Add import statement at top of file"]
})
elif "solution" in prompt.lower():
content = json.dumps({
"feasibility_score": 0.9,
"risk_assessment": "Low risk",
"implementation_complexity": "Simple",
"estimated_success_rate": 0.95
})
else:
content = json.dumps({
"analysis": "Code appears functional",
"recommendations": ["Continue monitoring"]
})
return LLMResponse(
content=content,
model=model,
metadata=metadata or {}
)
async def generate_response(
self,
prompt: str,
model: str = "claude-3-sonnet",
**kwargs
) -> Dict[str, Any]:
"""
Alternative interface for response generation
Returns dict instead of LLMResponse object
"""
response = await self.generate_completion(prompt, model, **kwargs)
try:
return json.loads(response.content)
except json.JSONDecodeError:
return {"response": response.content}
