import logging import os import requests from langchain_community.vectorstores import FAISS from langchain_community.embeddings import HuggingFaceEmbeddings class VectorStore: vectorstore = "index-BAAI_bge-m3-1500-200-recursive_splitter-CA_ES_UE" def __init__(self, embeddings_model): # load vectore store embeddings = HuggingFaceEmbeddings(model_name=embeddings_model, model_kwargs={'device': 'cpu'}) self.vectore_store = FAISS.load_local(self.vectorstore, embeddings, allow_dangerous_deserialization=True) logging.info("RAG loaded!") def get_context(self, instruction, number_of_contexts=2): documentos = self.vectore_store.similarity_search_with_score(instruction, k=number_of_contexts) return self.beautiful_context(documentos) def beautiful_context(self, docs): text_context = "" full_context = "" source_context = [] for doc in docs: text_context += doc[0].page_content full_context += doc[0].metadata["Títol de la norma"] + "\n\n" full_context += doc[0].metadata["url"] + "\n\n" full_context += doc[0].page_content + "\n" source_context.append(doc[0].metadata["url"]) return full_context