create_memory_for_llm.py

from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from dotenv import load_dotenv, find_dotenv

load_dotenv(find_dotenv())

# Load raw PDF(s)
DATA_PATH = "data/"
def load_pdf_files(data):
    loader = DirectoryLoader(data,
                             glob='*.pdf',
                             loader_cls=PyPDFLoader)
    documents = loader.load()
    return documents

documents = load_pdf_files(data=DATA_PATH)
# print("Lenght of PDF pages: ",len(documents))


# Create Chunks
def create_chunks(extracted_data):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500,
                                                   chunk_overlap=50)
    text_chunk = text_splitter.split_documents(extracted_data)
    return text_chunk

text_chunks = create_chunks(extracted_data=documents)
# print("Length of text Chunks: ", len(text_chunks))


# Create Vector Embeddings
def get_embedding_model():
    embedding_model=HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    return embedding_model

embedding_model = get_embedding_model()


# Store embeddings in FAISS
DB_FAISS_PATH = "vectorstore/db_faiss"
db = FAISS.from_documents(text_chunks, embedding_model)
db.save_local(DB_FAISS_PATH)