Update util.py

util.py

@@ -1,51 +1,51 @@
-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'
+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
+
+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):
+ load_dotenv()
+ genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+ text = extract_text(json_path)
+ chunks = split_text_into_chunks(text)
+ create_vector_store(chunks)
+
+json_path = 'reviews.json'
 main(json_path)