vector_db.py

from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS

loader = PyPDFLoader(
    "C:/Users/Admin/Desktop/SQL Mentor/MySQLNotesForProfessionals (1) (1).pdf"
)
documents = loader.load()

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)

docs = splitter.split_documents(documents)

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

db = FAISS.from_documents(
    docs,
    embeddings
)

db.save_local("vectorstore")

print("Vector DB Created")