products_recommend.py

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores.faiss import FAISS
from langchain.chains import RetrievalQA
from langchain_openai import OpenAI
from dotenv import load_dotenv

load_dotenv();


# Get question
question="I would like to be a teacher, can you recommend an activity?";

# Load from local storage
embeddings = OpenAIEmbeddings()
persisted_vectorstore = FAISS.load_local("_rise_product_db", embeddings)

# Use RetrievalQA chain for orchestration
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=persisted_vectorstore.as_retriever())
result = qa.invoke(question)
print(result)