import json
from typing import Optional, List, Dict, Any
from pydantic import BaseModel

try:
    import psycopg2
    from psycopg2.extras import execute_values
except ImportError:
    raise ImportError("PGVector requires extra dependencies. Install with `pip install psycopg2`") from None


from mem0.vector_stores.base import VectorStoreBase

class OutputData(BaseModel):
    id: Optional[str]
    score: Optional[float]
    payload: Optional[dict]


class PGVector(VectorStoreBase):
    def __init__(
        self,
        dbname,
        collection_name,
        embedding_model_dims,
        user,
        password,
        host,
        port
    ):
        """
        Initialize the PGVector database.

        Args:
            dbname (str): Database name
            collection_name (str): Collection name
            embedding_model_dims (int): Dimension of the embedding vector
            user (str): Database user
            password (str): Database password
            host (str, optional): Database host
            port (int, optional): Database port
        """
        self.collection_name = collection_name

        self.conn = psycopg2.connect(
            dbname=dbname,
            user=user,
            password=password,
            host=host,
            port=port
        )
        self.cur = self.conn.cursor()

        collections = self.list_cols()
        if collection_name not in collections:
            self.create_col(embedding_model_dims)
        
    def create_col(self, embedding_model_dims):
        """
        Create a new collection (table in PostgreSQL).

        Args:
            name (str): Name of the collection.
            embedding_model_dims (int, optional): Dimension of the embedding vector.
        """
        self.cur.execute(f"""
            CREATE TABLE IF NOT EXISTS {self.collection_name} (
                id UUID PRIMARY KEY,
                vector vector({embedding_model_dims}),
                payload JSONB
            );
        """)
        self.conn.commit()

    def insert(self, vectors, payloads = None, ids = None):
        """
        Insert vectors into a collection.

        Args:
            vectors (List[List[float]]): List of vectors to insert.
            payloads (List[Dict], optional): List of payloads corresponding to vectors.
            ids (List[str], optional): List of IDs corresponding to vectors.
        """
        json_payloads = [json.dumps(payload) for payload in payloads]

        data = [(id, vector, payload) for id, vector, payload in zip(ids, vectors, json_payloads)]
        execute_values(self.cur, f"INSERT INTO {self.collection_name} (id, vector, payload) VALUES %s", data)
        self.conn.commit()

    def search(self, query, limit = 5, filters = None):
        """
        Search for similar vectors.

        Args:
            query (List[float]): Query vector.
            limit (int, optional): Number of results to return. Defaults to 5.
            filters (Dict, optional): Filters to apply to the search. Defaults to None.

        Returns:
            list: Search results.
        """
        filter_conditions = []
        filter_params = []

        if filters:
            for k, v in filters.items():
                filter_conditions.append(f"payload->>%s = %s")
                filter_params.extend([k, str(v)])

        filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else ""

        self.cur.execute(f"""
            SELECT id, vector <-> %s::vector AS distance, payload
            FROM {self.collection_name}
            {filter_clause}
            ORDER BY distance
            LIMIT %s
        """, (query, *filter_params, limit))

        results = self.cur.fetchall()
        return [OutputData(id=str(r[0]), score=float(r[1]), payload=r[2]) for r in results]

    def delete(self, vector_id):
        """
        Delete a vector by ID.

        Args:
            vector_id (str): ID of the vector to delete.
        """
        self.cur.execute(f"DELETE FROM {self.collection_name} WHERE id = %s", (vector_id,))
        self.conn.commit()

    def update(self, vector_id, vector = None, payload = None):
        """
        Update a vector and its payload.

        Args:
            vector_id (str): ID of the vector to update.
            vector (List[float], optional): Updated vector.
            payload (Dict, optional): Updated payload.
        """
        if vector:
            self.cur.execute(f"UPDATE {self.collection_name} SET vector = %s WHERE id = %s", (vector, vector_id))
        if payload:
            self.cur.execute(f"UPDATE {self.collection_name} SET payload = %s WHERE id = %s", (psycopg2.extras.Json(payload), vector_id))
        self.conn.commit()

    def get(self, vector_id) -> OutputData:
        """
        Retrieve a vector by ID.

        Args:
            vector_id (str): ID of the vector to retrieve.

        Returns:
            OutputData: Retrieved vector.
        """
        self.cur.execute(f"SELECT id, vector, payload FROM {self.collection_name} WHERE id = %s", (vector_id,))
        result = self.cur.fetchone()
        if not result:
            return None
        return OutputData(id=str(result[0]), score=None, payload=result[2])

    def list_cols(self) -> List[str]:
        """
        List all collections.

        Returns:
            List[str]: List of collection names.
        """
        self.cur.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'")
        return [row[0] for row in self.cur.fetchall()]

    def delete_col(self):
        """ Delete a collection. """
        self.cur.execute(f"DROP TABLE IF EXISTS {self.collection_name}")
        self.conn.commit()

    def col_info(self):
        """
        Get information about a collection.

        Returns:
            Dict[str, Any]: Collection information.
        """
        self.cur.execute(f"""
            SELECT 
                table_name, 
                (SELECT COUNT(*) FROM {self.collection_name}) as row_count,
                (SELECT pg_size_pretty(pg_total_relation_size('{self.collection_name}'))) as total_size
            FROM information_schema.tables 
            WHERE table_schema = 'public' AND table_name = %s
        """, (self.collection_name,))
        result = self.cur.fetchone()
        return {
            "name": result[0],
            "count": result[1],
            "size": result[2]
        }

    def list(self, filters = None, limit = 100):
        """
        List all vectors in a collection.

        Args:
            filters (Dict, optional): Filters to apply to the list.
            limit (int, optional): Number of vectors to return. Defaults to 100.

        Returns:
            List[OutputData]: List of vectors.
        """
        filter_conditions = []
        filter_params = []

        if filters:
            for k, v in filters.items():
                filter_conditions.append(f"payload->>%s = %s")
                filter_params.extend([k, str(v)])

        filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else ""

        query = f"""
            SELECT id, vector, payload
            FROM {self.collection_name}
            {filter_clause}
            LIMIT %s
        """

        self.cur.execute(query, (*filter_params, limit))

        results = self.cur.fetchall()
        return [[OutputData(id=str(r[0]), score=None, payload=r[2]) for r in results]]

    def __del__(self):
        """
        Close the database connection when the object is deleted.
        """
        if hasattr(self, 'cur'):
            self.cur.close()
        if hasattr(self, 'conn'):
            self.conn.close()