agents/retriever_agent.py

from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
import os

def create_vectorstore():
    loader = TextLoader("data/finance_notes.txt")
    documents = loader.load()
    splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    docs = splitter.split_documents(documents)

    embeddings = HuggingFaceEmbeddings()
    vectorstore = FAISS.from_documents(docs, embeddings)
    return vectorstore