43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""Phase 2: Multi-Modal World Modeling.
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Ingests multi-modal data (vision/audio) to build a spatial and temporal
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understanding of Timmy's environment.
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"""
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import logging
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import base64
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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 WorldModeler:
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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 analyze_environment(self, image_data: str, mime_type: str = "image/jpeg"):
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"""Analyzes an image of the environment and updates the world model."""
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# In a real scenario, we'd use Gemini's multi-modal capabilities
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# For now, we'll simulate the vision-to-symbolic extraction
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prompt = f"""
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Analyze the following image of Timmy's environment.
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Identify all key objects, their spatial relationships, and any temporal changes.
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Extract this into a set of symbolic triples for the Knowledge Graph.
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Format: [{{"s": "subject", "p": "predicate", "o": "object"}}]
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"""
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# Simulate multi-modal call (Gemini 3.1 Pro Vision)
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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 World Modeler. Build a high-fidelity spatial/temporal map of the environment.",
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response_mime_type="application/json"
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)
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triples = json.loads(result["text"])
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self.symbolic.ingest_text(json.dumps(triples))
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logger.info(f"Updated world model with {len(triples)} new spatial triples.")
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return triples
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