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from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredFileLoader
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings import OpenAIEmbeddings
import pickle
# Load Data
loader = UnstructuredFileLoader("SMR4 publication.txt")
raw_documents = loader.load()
# Split text
text_splitter = RecursiveCharacterTextSplitter()
documents = text_splitter.split_documents(raw_documents)
# Load Data to vectorstore
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(documents, embeddings)
# Save vectorstore
with open("vectorstore.pkl", "wb") as f:
pickle.dump(vectorstore, f)
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