def METHOD_NAME(self, documents, max_passages: int = 5): #Works for passages because passages dataset will only have 1 chunk
    self.documents = documents
    self.doc_bodies = [doc.body for doc in self.documents]
    self.passage_to_doc = {} #pasage_id to doc_id
    self.passages = []
    doc_id = 0
    passage_id = 0 
    for doc_counter, doc_body in tqdm(enumerate(self.doc_bodies),total=len(self.doc_bodies)):
        self.es.create(self.index_name, id=doc_id, body={'document': doc_body})
        passages = self.chunk_document(doc_body)
        for i in range(min(len(passages),max_passages)): #NEED to add a chunking strategy first P, Last P, Best P 
            self.passages.append(passages[i])
            input_ids = self.context_tokenizer(passages[i], return_tensors='pt')['input_ids']
            self.faiss_index.add(self.context_model(input_ids).pooler_output.detach().numpy())
            self.passage_to_doc[passage_id] = doc_id
            passage_id += 1
        doc_id += 1