"""Phase 7: Long-Context Memory Compression.

Compresses years of session transcripts into a hierarchical, searchable "Life Log".
"""

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

    def compress_transcripts(self, transcripts: str) -> Dict[str, Any]:
        """Compresses massive transcripts into a hierarchical memory map."""
        logger.info("Compressing transcripts into hierarchical memory map.")
        
        prompt = f"""
The following are session transcripts spanning a long period:
{transcripts}

Please perform a deep, recursive summarization of these transcripts.
Identify key themes, major decisions, evolving preferences, and significant events.
Create a hierarchical 'Life Log' map and extract high-fidelity symbolic triples for the Knowledge Graph.

Format the output as JSON:
{{
  "summary": "...",
  "hierarchy": {{...}},
  "triples": [{{"s": "subject", "p": "predicate", "o": "object"}}]
}}
"""
        result = self.adapter.generate(
            model="gemini-3.1-pro-preview",
            prompt=prompt,
            system_instruction="You are Timmy's Memory Compressor. Your goal is to turn massive context into structured, searchable wisdom.",
            thinking=True,
            response_mime_type="application/json"
        )
        
        memory_data = json.loads(result["text"])
        self.symbolic.ingest_text(json.dumps(memory_data["triples"]))
        logger.info(f"Ingested {len(memory_data['triples'])} new memory triples.")
        return memory_data