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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) |