"""Phase 16: Sovereign Data Lake & Vector Database Optimization.

Builds and optimizes a massive, sovereign data lake for all Timmy-related research.
"""

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

logger = logging.getLogger(__name__)

class DataLakeOptimizer:
    def __init__(self):
        self.adapter = GeminiAdapter()

    def deep_index_document(self, doc_content: str, metadata: Dict[str, Any]) -> Dict[str, Any]:
        """Performs deep semantic indexing and metadata generation for a document."""
        logger.info("Performing deep semantic indexing for document.")
        
        prompt = f"""
Document Content:
{doc_content}

Existing Metadata:
{json.dumps(metadata, indent=2)}

Please perform a 'Deep Indexing' of this document.
Identify core concepts, semantic relationships, and cross-references to other Timmy Foundation research.
Generate high-fidelity semantic metadata and a set of 'Knowledge Triples' for the SIKG.

Format the output as JSON:
{{
  "semantic_summary": "...",
  "key_concepts": [...],
  "cross_references": [...],
  "triples": [{{"s": "subject", "p": "predicate", "o": "object"}}],
  "vector_embedding_hints": "..."
}}
"""
        result = self.adapter.generate(
            model="gemini-3.1-pro-preview",
            prompt=prompt,
            system_instruction="You are Timmy's Data Lake Optimizer. Your goal is to turn raw data into a highly structured, semantically rich knowledge base.",
            thinking=True,
            response_mime_type="application/json"
        )
        
        indexing_data = json.loads(result["text"])
        return indexing_data