embedding.py

from langchain_community.document_loaders import PyPDFLoader,DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS

loader = DirectoryLoader('ipc-data', glob="./*.pdf", loader_cls=PyPDFLoader)
documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=200)
texts = text_splitter.split_documents(documents)

embeddings = HuggingFaceEmbeddings(
    model_name="nomic-ai/nomic-embed-text-v1",
    model_kwargs={"trust_remote_code": True, "revision": "289f532e14dbbbd5a04753fa58739e9ba766f3c7"},
)

# Creates vector embeddings and saves it in the FAISS DB
faiss_db = FAISS.from_documents(texts, embeddings)

# Saves and export the vector embeddings databse
faiss_db.save_local("ipc_vector_db")