51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
"""Phase 16: Sovereign Data Lake & Vector Database Optimization.
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Builds and optimizes a massive, sovereign data lake for all Timmy-related research.
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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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logger = logging.getLogger(__name__)
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class DataLakeOptimizer:
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def __init__(self):
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self.adapter = GeminiAdapter()
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def deep_index_document(self, doc_content: str, metadata: Dict[str, Any]) -> Dict[str, Any]:
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"""Performs deep semantic indexing and metadata generation for a document."""
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logger.info("Performing deep semantic indexing for document.")
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prompt = f"""
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Document Content:
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{doc_content}
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Existing Metadata:
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{json.dumps(metadata, indent=2)}
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Please perform a 'Deep Indexing' of this document.
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Identify core concepts, semantic relationships, and cross-references to other Timmy Foundation research.
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Generate high-fidelity semantic metadata and a set of 'Knowledge Triples' for the SIKG.
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Format the output as JSON:
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{{
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"semantic_summary": "...",
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"key_concepts": [...],
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"cross_references": [...],
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"triples": [{{"s": "subject", "p": "predicate", "o": "object"}}],
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"vector_embedding_hints": "..."
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}}
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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 Data Lake Optimizer. Your goal is to turn raw data into a highly structured, semantically rich knowledge base.",
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thinking=True,
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response_mime_type="application/json"
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)
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indexing_data = json.loads(result["text"])
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return indexing_data
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