Update app.py

app.py

@@ -59,8 +59,10 @@ def extract_pdf_content(drive_url):
 
 # Function to create a FAISS vector store
 def create_vector_store(text):
+    # Split the text into sentences and clean it
     sentences = [sentence.strip() for sentence in text.split(". ") if sentence.strip()]
 
+    # Load the model and tokenizer from Hugging Face
     model_name = "sentence-transformers/all-MiniLM-L6-v2"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModel.from_pretrained(model_name)
@@ -71,9 +73,11 @@ def create_vector_store(text):
             embeddings = model(**tokens).last_hidden_state.mean(dim=1).squeeze().numpy()
         return embeddings
 
-    embeddings = [embed(sentence) for sentence in sentences]
-    text_embeddings = [(sentences[i], embeddings[i]) for i in range(len(sentences))]
-    vector_store = FAISS.from_embeddings(text_embeddings)
+    # Create a function that directly returns embeddings
+    embedding_function = lambda x: embed(x)
+
+    # Create a FAISS vector store
+    vector_store = FAISS.from_texts(texts=sentences, embedding=embedding_function)
 
     return vector_store, sentences