"""
Milvus vector database client.
"""

import logging
from typing import Optional

from pymilvus import Collection, connections, utility

from handler_base.config import Settings
from handler_base.telemetry import create_span

logger = logging.getLogger(__name__)


class MilvusClient:
    """
    Client for Milvus vector database.

    Usage:
        client = MilvusClient()
        await client.connect()
        results = await client.search(embedding, limit=10)
    """

    def __init__(self, settings: Optional[Settings] = None):
        self.settings = settings or Settings()
        self._connected = False
        self._collection: Optional[Collection] = None

    async def connect(self, collection_name: Optional[str] = None) -> None:
        """
        Connect to Milvus and load collection.

        Args:
            collection_name: Collection to use (defaults to settings)
        """
        collection_name = collection_name or self.settings.milvus_collection

        connections.connect(
            alias="default",
            host=self.settings.milvus_host,
            port=self.settings.milvus_port,
        )

        if utility.has_collection(collection_name):
            self._collection = Collection(collection_name)
            self._collection.load()
            logger.info(f"Connected to Milvus collection: {collection_name}")
        else:
            logger.warning(f"Collection {collection_name} does not exist")

        self._connected = True

    async def close(self) -> None:
        """Close Milvus connection."""
        if self._collection:
            self._collection.release()
        connections.disconnect("default")
        self._connected = False
        logger.info("Disconnected from Milvus")

    async def search(
        self,
        embedding: list[float],
        limit: int = 10,
        output_fields: Optional[list[str]] = None,
        filter_expr: Optional[str] = None,
    ) -> list[dict]:
        """
        Search for similar vectors.

        Args:
            embedding: Query embedding vector
            limit: Maximum number of results
            output_fields: Fields to return (default: all)
            filter_expr: Optional filter expression

        Returns:
            List of results with 'id', 'distance', and requested fields
        """
        if not self._collection:
            raise RuntimeError("Not connected to collection")

        with create_span("milvus.search") as span:
            if span:
                span.set_attribute("milvus.collection", self._collection.name)
                span.set_attribute("milvus.limit", limit)

            search_params = {"metric_type": "COSINE", "params": {"nprobe": 10}}

            results = self._collection.search(
                data=[embedding],
                anns_field="embedding",
                param=search_params,
                limit=limit,
                output_fields=output_fields,
                expr=filter_expr,
            )

            # Convert to list of dicts
            hits = []
            for hit in results[0]:
                item = {
                    "id": hit.id,
                    "distance": hit.distance,
                    "score": 1 - hit.distance,  # Convert distance to similarity
                }
                # Add output fields
                if output_fields:
                    for field in output_fields:
                        if hasattr(hit.entity, field):
                            item[field] = getattr(hit.entity, field)
                hits.append(item)

            if span:
                span.set_attribute("milvus.results", len(hits))

            return hits

    async def search_with_texts(
        self,
        embedding: list[float],
        limit: int = 10,
        text_field: str = "text",
        metadata_fields: Optional[list[str]] = None,
    ) -> list[dict]:
        """
        Search and return text content with metadata.

        Args:
            embedding: Query embedding
            limit: Maximum results
            text_field: Name of text field in collection
            metadata_fields: Additional metadata fields to return

        Returns:
            List of results with text and metadata
        """
        output_fields = [text_field]
        if metadata_fields:
            output_fields.extend(metadata_fields)

        return await self.search(embedding, limit, output_fields)

    async def insert(
        self,
        embeddings: list[list[float]],
        data: list[dict],
    ) -> list[int]:
        """
        Insert vectors with data into the collection.

        Args:
            embeddings: List of embedding vectors
            data: List of dicts with field values

        Returns:
            List of inserted IDs
        """
        if not self._collection:
            raise RuntimeError("Not connected to collection")

        with create_span("milvus.insert") as span:
            if span:
                span.set_attribute("milvus.collection", self._collection.name)
                span.set_attribute("milvus.count", len(embeddings))

            # Build insert data
            insert_data = [embeddings]
            for field in self._collection.schema.fields:
                if field.name not in ("id", "embedding"):
                    field_values = [d.get(field.name) for d in data]
                    insert_data.append(field_values)

            result = self._collection.insert(insert_data)
            self._collection.flush()

            return result.primary_keys

    def health(self) -> bool:
        """Check if connected to Milvus."""
        return self._connected and utility.get_connection_addr("default") is not None