import os
from qdrant_client import QdrantClient
from qdrant_client.http import models
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

class VectorDB:
    def __init__(self, collection_name: str = "pro_rag_container"):
        self.collection_name = collection_name
        
        # --- 1. CLOUD vs LOCAL LOGIC ---
        qdrant_url = os.getenv("QDRANT_URL")
        qdrant_key = os.getenv("QDRANT_API_KEY")

        if qdrant_url and qdrant_key:
            print("☁️ Connecting to Qdrant Cloud...")
            self.client = QdrantClient(url=qdrant_url, api_key=qdrant_key)
        else:
            print("🏠 Connecting to Local Docker...")
            self.client = QdrantClient(url="http://localhost:6333")

    # --- 2. THE MISSING FUNCTION ---
    def create_collection(self, vector_size: int = 3072):
        """
        Creates the collection if it doesn't exist.
        Using 3072 dimensions for OpenAI text-embedding-3-large.
        """
        # Check if collection exists
        if self.client.collection_exists(collection_name=self.collection_name):
            print(f"ℹ️ Collection '{self.collection_name}' already exists. Skipping creation.")
            return

        print(f"⚙️ Creating collection '{self.collection_name}' with size {vector_size}...")
        
        # Create Collection with Cosine Similarity
        self.client.create_collection(
            collection_name=self.collection_name,
            vectors_config=models.VectorParams(
                size=vector_size,
                distance=models.Distance.COSINE
            )
        )
        print(f"✅ Collection '{self.collection_name}' created successfully!")

    def reset_database(self):
        """
        Deletes the collection.
        """
        self.client.delete_collection(collection_name=self.collection_name)
        print(f"⚠️ Collection '{self.collection_name}' has been DELETED.")


# import os
# from qdrant_client import QdrantClient
# from qdrant_client.http import models
# from dotenv import load_dotenv

# # Load environment variables (API Keys, etc.)
# load_dotenv()

# class VectorDB:
#     def __init__(self, collection_name: str = "pro_rag_v1"):
#         """
#         Initialize connection to Qdrant (Docker).
#         """
#         self.collection_name = collection_name
#         self.client = QdrantClient(url="http://localhost:6333")
        
#         # Verify connection immediately
#         try:
#             self.client.get_collections()
#             print(f"✅ Connected to Qdrant Database at http://localhost:6333")
#         except Exception as e:
#             print(f"❌ Could not connect to Qdrant. Is Docker running? Error: {e}")

#     def create_collection(self, vector_size: int = 3072):
#         """
#         Creates the collection if it doesn't exist.
#         Using 3072 dimensions for OpenAI text-embedding-3-large.
#         """
#         # Check if collection exists
#         if self.client.collection_exists(collection_name=self.collection_name):
#             print(f"ℹ️ Collection '{self.collection_name}' already exists. Skipping creation.")
#             return

#         print(f"⚙️ Creating collection '{self.collection_name}' with size {vector_size}...")
        
#         # Create Collection with Cosine Similarity
#         self.client.create_collection(
#             collection_name=self.collection_name,
#             vectors_config=models.VectorParams(
#                 size=vector_size,
#                 distance=models.Distance.COSINE
#             )
#         )
#         print(f"✅ Collection '{self.collection_name}' created successfully!")

#     def reset_database(self):
#         """
#         DANGEROUS: Deletes the collection. Used for restarting the POC.
#         """
#         self.client.delete_collection(collection_name=self.collection_name)
#         print(f"⚠️ Collection '{self.collection_name}' has been DELETED.")