"""
Memory annotators for enriching chunks with metadata.

Adds topic, domain, entities, and other contextual information.
"""

from typing import Any, Dict, List, Optional


class MemoryAnnotator:
    """Annotate memory chunks with metadata."""

    def __init__(self, enable_ner: bool = False, enable_topic: bool = False):
        """
        Initialize annotator.

        Args:
            enable_ner: Enable named entity recognition
            enable_topic: Enable topic classification
        """
        self.enable_ner = enable_ner
        self.enable_topic = enable_topic

    def annotate(self, chunk: Dict[str, Any]) -> Dict[str, Any]:
        """
        Annotate a single memory chunk.

        Args:
            chunk: Memory chunk to annotate

        Returns:
            Annotated chunk with additional metadata
        """
        annotated = chunk.copy()

        # Basic domain inference from keywords
        annotated["domain"] = self._infer_domain(chunk["text"])

        # Optional NER
        if self.enable_ner:
            annotated["entities"] = self._extract_entities(chunk["text"])

        # Optional topic classification
        if self.enable_topic:
            annotated["topic"] = self._classify_topic(chunk["text"])

        return annotated

    def annotate_batch(self, chunks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """
        Annotate a batch of memory chunks.

        Args:
            chunks: List of chunks to annotate

        Returns:
            List of annotated chunks
        """
        return [self.annotate(chunk) for chunk in chunks]

    def _infer_domain(self, text: str) -> str:
        """
        Simple keyword-based domain inference.

        TODO: Replace with proper classification if needed.
        """
        text_lower = text.lower()

        if any(
            word in text_lower
            for word in ["code", "function", "class", "programming", "bug", "api"]
        ):
            return "technical"
        elif any(word in text_lower for word in ["meeting", "project", "deadline", "task"]):
            return "work"
        elif any(word in text_lower for word in ["learn", "study", "research", "understand"]):
            return "learning"
        else:
            return "general"

    def _extract_entities(self, text: str) -> Optional[List[str]]:
        """
        Extract named entities from text.

        TODO: Implement NER using spaCy or similar.
        """
        # Placeholder for NER implementation
        return None

    def _classify_topic(self, text: str) -> Optional[str]:
        """
        Classify topic of text.

        TODO: Implement topic classification.
        """
        # Placeholder for topic classification
        return None

