util.py

import os
import json
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores.faiss import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
from dotenv import load_dotenv
load_dotenv()
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))


def extract_text(json_path):
 with open(json_path, 'r') as file:
 data = json.load(file)
 
 text = ""
 for professor in data['professors']:
 professor_id = professor.get('professor_id')
 name = professor.get('name')
 course = professor.get('course')
 reviews = professor.get('reviews', [])
 
 text += f'\nProfessor ID: {professor_id}, Professor Name: {name}, Course: {course}\n '
 if reviews:
 for review in reviews:
 rating = review.get('rating')
 review_text = review.get('review_text')
 text += f"Rating: {rating}, Review: {review_text}\n"
 else:
 print("No reviews available.")
 return text

def split_text_into_chunks(text):
 splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
 text_chunks = splitter.split_text(text)
 return text_chunks

def create_vector_store(chunks):
 embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
 vector_store = FAISS.from_texts(chunks, embedding=embeddings)
 vector_store.save_local("reviews_index")

def main(json_path):
 text = extract_text(json_path)
 chunks = split_text_into_chunks(text)
 create_vector_store(chunks)

json_path = 'reviews.json'
main(json_path)