jira-ai-fixer/api/services/llm.py

"""
LLM Service - Orchestration for AI models.
"""
from typing import Optional, Dict, Any, List
import httpx
import json
import logging
import os

logger = logging.getLogger(__name__)


class LLMService:
    """
    LLM orchestration service supporting multiple providers.
    
    Providers:
    - Azure OpenAI (production, compliance)
    - OpenRouter (development, free models)
    """
    
    def __init__(
        self,
        provider: str = "openrouter",
        azure_endpoint: Optional[str] = None,
        azure_key: Optional[str] = None,
        azure_model: str = "gpt-4o",
        openrouter_key: Optional[str] = None,
        openrouter_model: str = "meta-llama/llama-3.3-70b-instruct:free",
    ):
        self.provider = provider
        self.azure_endpoint = azure_endpoint
        self.azure_key = azure_key
        self.azure_model = azure_model
        self.openrouter_key = openrouter_key
        self.openrouter_model = openrouter_model
    
    async def analyze_issue(
        self,
        issue_description: str,
        code_context: str,
        business_rules: Optional[str] = None,
        similar_fixes: Optional[List[Dict[str, Any]]] = None,
    ) -> Dict[str, Any]:
        """
        Analyze an issue and generate fix suggestions.
        
        Returns:
            {
                "root_cause": str,
                "affected_files": List[str],
                "proposed_fix": str,
                "confidence": float,
                "explanation": str,
            }
        """
        prompt = self._build_analysis_prompt(
            issue_description,
            code_context,
            business_rules,
            similar_fixes,
        )
        
        response = await self._call_llm(prompt)
        return self._parse_analysis_response(response)
    
    def _build_analysis_prompt(
        self,
        issue_description: str,
        code_context: str,
        business_rules: Optional[str],
        similar_fixes: Optional[List[Dict[str, Any]]],
    ) -> str:
        """Build the analysis prompt."""
        
        prompt = f"""Você é um especialista em sistemas de pagamento mainframe, especificamente nos produtos JIRA Acquirer (ACQ-MF) e Interchange (ICG-MF).

## Contexto do Sistema
{business_rules or "Nenhuma regra de negócio específica fornecida."}

## Issue Reportada
{issue_description}

## Código Atual
{code_context}

"""
        
        if similar_fixes:
            prompt += "## Histórico de Fixes Similares\n"
            for i, fix in enumerate(similar_fixes[:3], 1):
                prompt += f"""
### Exemplo {i}
Problema: {fix.get('problem', 'N/A')}
Solução: {fix.get('solution', 'N/A')}
"""
        
        prompt += """
## Tarefa
Analise a issue e:
1. Identifique a causa raiz provável
2. Localize o(s) programa(s) afetado(s)
3. Proponha uma correção específica
4. Explique o impacto da alteração

## Regras
- Mantenha compatibilidade COBOL-85
- Preserve a estrutura de copybooks existente
- Não altere interfaces com outros sistemas sem menção explícita
- Documente todas as alterações propostas

## Formato de Resposta
Responda em JSON válido:
{
    "root_cause": "Descrição da causa raiz identificada",
    "affected_files": ["arquivo1.cbl", "arquivo2.cbl"],
    "proposed_fix": "Código COBOL com a correção proposta",
    "confidence": 0.85,
    "explanation": "Explicação detalhada do impacto"
}
"""
        return prompt
    
    async def _call_llm(self, prompt: str) -> str:
        """Call the configured LLM provider."""
        if self.provider == "azure":
            return await self._call_azure(prompt)
        else:
            return await self._call_openrouter(prompt)
    
    async def _call_azure(self, prompt: str) -> str:
        """Call Azure OpenAI."""
        url = f"{self.azure_endpoint}/openai/deployments/{self.azure_model}/chat/completions?api-version=2024-02-01"
        
        async with httpx.AsyncClient() as client:
            response = await client.post(
                url,
                headers={
                    "api-key": self.azure_key,
                    "Content-Type": "application/json",
                },
                json={
                    "messages": [{"role": "user", "content": prompt}],
                    "temperature": 0.2,
                    "max_tokens": 4096,
                },
                timeout=120.0,
            )
            response.raise_for_status()
            data = response.json()
            return data["choices"][0]["message"]["content"]
    
    async def _call_openrouter(self, prompt: str) -> str:
        """Call OpenRouter API."""
        async with httpx.AsyncClient() as client:
            response = await client.post(
                "https://openrouter.ai/api/v1/chat/completions",
                headers={
                    "Authorization": f"Bearer {self.openrouter_key}",
                    "Content-Type": "application/json",
                },
                json={
                    "model": self.openrouter_model,
                    "messages": [{"role": "user", "content": prompt}],
                    "temperature": 0.2,
                    "max_tokens": 4096,
                },
                timeout=120.0,
            )
            response.raise_for_status()
            data = response.json()
            return data["choices"][0]["message"]["content"]
    
    def _parse_analysis_response(self, response: str) -> Dict[str, Any]:
        """Parse LLM response into structured format."""
        try:
            # Try to extract JSON from response
            start = response.find("{")
            end = response.rfind("}") + 1
            if start >= 0 and end > start:
                json_str = response[start:end]
                return json.loads(json_str)
        except json.JSONDecodeError:
            logger.warning("Failed to parse LLM response as JSON")
        
        # Fallback: return raw response
        return {
            "root_cause": "Unable to parse structured response",
            "affected_files": [],
            "proposed_fix": response,
            "confidence": 0.3,
            "explanation": "Response could not be parsed automatically",
        }