from redis import Redis
from redis.commands.search.field import VectorField
from redis.commands.search.field import TextField
from redis.commands.search.field import TagField
from redis.commands.search.result import Result
import numpy as np

def load_vectors(client:Redis, product_metadata, vector_dict):
    p = client.pipeline(transaction=False)
    for index in product_metadata.keys():    
        #hash key
        key='product:'+ str(index)+ ':' + product_metadata[index]['primary_key']
        
        #hash values
        item_metadata = product_metadata[index]
        item_keywords_vector = np.array(vector_dict[index], dtype=np.float32).tobytes()
        item_metadata['item_vector']=item_keywords_vector
        
        # HSET
        p.hset(key,mapping=item_metadata)
            
    p.execute()

def create_flat_index (redis_conn, number_of_vectors, vector_dimensions=512, distance_metric='L2'):
    redis_conn.ft().create_index([
        VectorField('item_vector', "FLAT", {"TYPE": "FLOAT32", "DIM": vector_dimensions, "DISTANCE_METRIC": distance_metric, "INITIAL_CAP": number_of_vectors, "BLOCK_SIZE":number_of_vectors }),
        TagField("product_type"),
        TextField("item_name"),
        TextField("item_keywords"),
        TagField("country")        
    ])