User: im looking for some jewelry for my girlfriendBot: Based on your query, I would recommend checking out these Women's Engravable 14k Gold Plated 925 Sterling Silver Polished Disc Charm on 18" Chain Pendant Necklace. They might make a great gift for your girlfriend if she likes elegant and stylish jewelry. However, if you are looking for shoes, you might want to check out Amazon Brand - The Fix Women's Lizzie Block Heel Ruffled Sandal Heeled, Dove Suede, 9 B US. These shoes are great for spring and summer, and perfect for a date night or a fancy event. Another option for shoes could be Amazon Brand - The Fix Women's Giana Open Toe Bootie with Pearl Buckle, which comes in bright white leather and sizes up to 9.5 B US. I hope this helps!User: tell me more about the necklaceBot: The Women's Engravable 14k Gold Plated 925 Sterling Silver Polished Disc Charm on 18" Chain Pendant Necklace is a beautiful and elegant piece of jewelry. The pendant is made of 925 sterling silver and is plated with 14k gold for a luxurious look. The pendant is disc-shaped and can be engraved with a personal message or initials to make it even more special. The chain is 18 inches long and adds a delicate touch to the necklace. This necklace is perfect for everyday wear or for special occasions.User: what's the product idBot: The product ID for the Women's Engravable 14k Gold Plated 925 Sterling Silver Polished Disc Charm on 18" Chain Pendant Necklace is B07CJCSYQF.

pip install redis pandaspip install -U sentence-transformerspip install openaipip install langchain

import numpy as npimport pandas as pdimport timefrom redis.commands.search.field import VectorFieldfrom redis.commands.search.field import TextFieldfrom redis.commands.search.field import TagFieldfrom redis.commands.search.query import Queryimport redis

MAX_TEXT_LENGTH=512NUMBER_PRODUCTS=1000def auto_truncate(val):    return val[:MAX_TEXT_LENGTH]#Load Product data and truncate long text fieldsall_prods_df = pd.read_csv("product_data.csv", converters={'bullet_point': auto_truncate,'item_keywords':auto_truncate,'item_name':auto_truncate})all_prods_df['primary_key'] = all_prods_df['item_id'] + '-' + all_prods_df['domain_name']all_prods_df['item_keywords'].replace('', np.nan, inplace=True)all_prods_df.dropna(subset=['item_keywords'], inplace=True)all_prods_df.reset_index(drop=True,inplace=True)#get the first 1000 products with non-empty item keywordsproduct_metadata = all_prods_df.head(NUMBER_PRODUCTS).to_dict(orient='index')all_prods_df.head()

def load_vectors(client, product_metadata, vector_dict, vector_field_name):    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 = vector_dict[index].astype(np.float32).tobytes()        item_metadata[vector_field_name]=item_keywords_vector                # HSET        p.hset(key,mapping=item_metadata)                p.execute()def create_flat_index (redis_conn,vector_field_name,number_of_vectors, vector_dimensions=512, distance_metric='L2'):    redis_conn.ft().create_index([        VectorField(vector_field_name, "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")            ]) 

ITEM_KEYWORD_EMBEDDING_FIELD='item_keyword_vector'TEXT_EMBEDDING_DIMENSION=768NUMBER_PRODUCTS=1000print ('Loading and Indexing + ' +  str(NUMBER_PRODUCTS) + ' products')#flush all dataredis_conn.flushall()#create flat index & load vectorscreate_flat_index(redis_conn, ITEM_KEYWORD_EMBEDDING_FIELD,NUMBER_PRODUCTS,TEXT_EMBEDDING_DIMENSION,'COSINE')load_vectors(redis_conn,product_metadata,item_keywords_vectors,ITEM_KEYWORD_EMBEDDING_FIELD)     

from langchain.prompts import PromptTemplatefrom langchain.llms import OpenAIllm = OpenAI(model_name="gpt-3.5-turbo", temperature=0.3, openai_api_key="sk-9xxxxxxxxxx4")prompt = PromptTemplate(    input_variables=["product_description"],    template="Create comma seperated product keywords to perform a query on a amazon dataset for this user input: {product_description}",)from langchain.chains import LLMChainchain = LLMChain(llm=llm, prompt=prompt)

userinput = input("Hey im a E-commerce Chatbot, how can i help you today? ")print("User:", userinput)# Run the chain only specifying the input variable.keywords = chain.run(userinput)

topK = 3# Vectorize the queryquery_vector = model.encode(keywords).astype(np.float32).tobytes()# Prepare the queryq = Query(f'*=>[KNN {topK} @{ITEM_KEYWORD_EMBEDDING_FIELD} $vec_param AS vector_score]').sort_by('vector_score').paging(0, topK).return_fields('vector_score', 'item_name', 'item_id', 'item_keywords').dialect(2)params_dict = {"vec_param": query_vector}# Execute the queryresults = redis_conn.ft().search(q, query_params=params_dict)full_result_string = ''for product in results.docs:    full_result_string += product.item_name + ' ' + product.item_keywords + ' ' + product.item_id + "\n\n\n"

from langchain.memory import ConversationBufferMemorytemplate = """You are a chatbot. Be kind, detailed and nice. Present the given queried search result in a nice way as answer to the user input. dont ask questions back! just take the given context{chat_history}Human: {user_msg}Chatbot:"""prompt = PromptTemplate(    input_variables=["chat_history", "user_msg"],    template=template)memory = ConversationBufferMemory(memory_key="chat_history")llm_chain = LLMChain(    llm=OpenAI(model_name="gpt-3.5-turbo", temperature=0.8, openai_api_key="sk-9xxxxxxxxxxxxxxxxxxxx4"),    prompt=prompt,    verbose=False,    memory=memory,)answer = llm_chain.predict(user_msg=f"{full_result_string} ---\n\n {userinput}")print("Bot:", answer)time.sleep(0.5)while True:    follow_up = input("Anything else you want to ask about this topic?")    print("User:", follow_up)    answer = llm_chain.predict(        user_msg=follow_up    )    print("Bot:", answer)    time.sleep(0.5)