from __future__ import annotations
import numpy as np
from typing import Any, List, Dict
from chromadb import PersistentClient
from pkg.vector.vdb import VectorDatabase
from pkg.core import app


class ChromaVectorDatabase(VectorDatabase):
    def __init__(self, ap: app.Application, base_path: str = './data/chroma'):
        self.ap = ap
        self.client = PersistentClient(path=base_path)
        self._collections = {}

    def get_or_create_collection(self, collection: str):
        if collection not in self._collections:
            self._collections[collection] = self.client.get_or_create_collection(name=collection)
            self.ap.logger.info(f"Chroma collection '{collection}' accessed/created.")
        return self._collections[collection]

    def add_embeddings(
        self,
        collection: str,
        ids: List[str],
        embeddings: np.ndarray,
        metadatas: List[Dict[str, Any]],
        documents: List[str],
    ) -> None:
        col = self.get_or_create_collection(collection)
        col.add(embeddings=embeddings.tolist(), ids=ids, metadatas=metadatas, documents=documents)
        self.ap.logger.info(f"Added {len(ids)} embeddings to Chroma collection '{collection}'.")

    def search(self, collection: str, query_embedding: np.ndarray, k: int = 5) -> Dict[str, Any]:
        col = self.get_or_create_collection(collection)
        results = col.query(
            query_embeddings=query_embedding.tolist(),
            n_results=k,
            include=['metadatas', 'distances', 'documents'],
        )
        self.ap.logger.debug(f"Chroma search in '{collection}' returned {len(results.get('ids', [[]])[0])} results.")
        return results

    def delete_by_metadata(self, collection: str, where: Dict[str, Any]) -> None:
        col = self.get_or_create_collection(collection)
        col.delete(where=where)
        self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' with filter: {where}")