"""Phase 11: Sovereign Intersymbolic Reasoning Engine (SIRE).

Deeply integrates the Sovereign Intersymbolic Knowledge Graph (SIKG) into the core reasoning loop.
"""

import logging
import json
from typing import List, Dict, Any
from agent.gemini_adapter import GeminiAdapter
from agent.symbolic_memory import SymbolicMemory

logger = logging.getLogger(__name__)

class SIREEngine:
    def __init__(self):
        self.adapter = GeminiAdapter()
        self.symbolic = SymbolicMemory()

    def graph_augmented_reasoning(self, query: str) -> Dict[str, Any]:
        """Performs graph-first reasoning for a given query."""
        logger.info(f"Performing SIRE reasoning for query: {query}")
        
        # 1. Perform symbolic lookup (multi-hop)
        symbolic_context = self.symbolic.search(query, depth=3)
        
        # 2. Augment neural reasoning with symbolic context
        prompt = f"""
Query: {query}

Symbolic Context (from Knowledge Graph):
{json.dumps(symbolic_context, indent=2)}

Please provide a high-fidelity response using the provided symbolic context as the ground truth.
Validate every neural inference against these symbolic constraints.
If there is a conflict, prioritize the symbolic context.
"""
        result = self.adapter.generate(
            model="gemini-3.1-pro-preview",
            prompt=prompt,
            system_instruction="You are Timmy's SIRE Engine. Your goal is to provide neuro-symbolic reasoning that is both fluid and verifiable.",
            thinking=True
        )
        
        return {
            "query": query,
            "symbolic_context": symbolic_context,
            "response": result["text"]
        }