I am using LangChain for building some stuff and came across one of the most prominent index-based vector database FAISS. Following is the command of how I am using the FAISS vector database:
from langchain.document_loaders import PyMuPDFLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
loader = PyMuPDFLoader('path/to/pdf_file.pdf')
raw_texts = loader.load_and_split()
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(raw_texts, embeddings)
In the above code, I want to store the vectorstore in a MongoDB database. Is there any way to load these vectorstores on MongoDB and extract them with similarity_search with respect to input prompt?
