| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from langchain_huggingface import HuggingFaceEmbeddings | |
| embedding = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') | |
| def vectorStore(data, embedding): | |
| splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50) | |
| chunks = splitter.split_documents(data) | |
| vector = FAISS.from_documents(chunks, embedding) | |
| retriever = vector.as_retriever() | |
| return retriever | |