Update agent.py

agent.py

@@ -357,7 +357,6 @@ vector_store = FAISS.from_documents(docs, embedding_model)
 
 # Now, you can use the embeddings with FAISS or other retrieval systems
 # For example, with FAISS:
-from langchain.vectorstores import FAISS
 
 # Assuming 'docs' contains your list of documents and 'embedding_model' is the model you created
 vector_store = FAISS.from_documents(docs, embedding_model)