49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
"""Phase 11: Sovereign Intersymbolic Reasoning Engine (SIRE).
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Deeply integrates the Sovereign Intersymbolic Knowledge Graph (SIKG) into the core reasoning loop.
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"""
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import logging
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import json
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from typing import List, Dict, Any
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from agent.gemini_adapter import GeminiAdapter
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from agent.symbolic_memory import SymbolicMemory
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logger = logging.getLogger(__name__)
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class SIREEngine:
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def __init__(self):
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self.adapter = GeminiAdapter()
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self.symbolic = SymbolicMemory()
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def graph_augmented_reasoning(self, query: str) -> Dict[str, Any]:
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"""Performs graph-first reasoning for a given query."""
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logger.info(f"Performing SIRE reasoning for query: {query}")
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# 1. Perform symbolic lookup (multi-hop)
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symbolic_context = self.symbolic.search(query, depth=3)
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# 2. Augment neural reasoning with symbolic context
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prompt = f"""
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Query: {query}
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Symbolic Context (from Knowledge Graph):
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{json.dumps(symbolic_context, indent=2)}
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Please provide a high-fidelity response using the provided symbolic context as the ground truth.
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Validate every neural inference against these symbolic constraints.
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If there is a conflict, prioritize the symbolic context.
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"""
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result = self.adapter.generate(
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model="gemini-3.1-pro-preview",
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prompt=prompt,
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system_instruction="You are Timmy's SIRE Engine. Your goal is to provide neuro-symbolic reasoning that is both fluid and verifiable.",
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thinking=True
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)
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return {
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"query": query,
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"symbolic_context": symbolic_context,
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"response": result["text"]
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}
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