Switch to standard FAISS library

agents/demystifier_agent.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, Field
 # --- Core LangChain & Document Processing Imports ---
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from core_utils.simple_vectorstore import SimpleVectorStore
+from langchain_community.vectorstores import FAISS
 from langchain_core.prompts import PromptTemplate
 from langchain_core.runnables import RunnablePassthrough
 from langchain_core.output_parsers import StrOutputParser
@@ -30,11 +30,8 @@ def process_document_for_demystification(file_path: str):
     splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
     chunks = splitter.split_documents(documents)
     
-    print("--- Creating Simple vector store (NumPy) for Q&A ---")
-    vectorstore = SimpleVectorStore.from_documents(chunks, embedding=embedding_model)
-    # SimpleVectorStore doesn't support as_retriever directly in the same way as FAISS without modification, 
-    # but we can wrap it or just use it as a retriever if we implemented as_retriever.
-    # Actually, VectorStore base class has as_retriever.
+    print("--- Creating FAISS vector store for Q&A ---")
+    vectorstore = FAISS.from_documents(chunks, embedding=embedding_model)
     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
     rag_chain = create_rag_chain(retriever)