import logging import os import requests from langchain_community.vectorstores import FAISS from langchain_community.embeddings import HuggingFaceEmbeddings class RAG: NO_ANSWER_MESSAGE: str = "Ho sento, no he pogut respondre la teva pregunta." vectorstore = "index-intfloat_multilingual-e5-small-500-100-CA-ES" # mixed #vectorstore = "vectorestore" # CA only def __init__(self, hf_token, embeddings_model, model_name): self.model_name = model_name self.hf_token = hf_token # load vectore store embeddings = HuggingFaceEmbeddings(model_name=embeddings_model, model_kwargs={'device': 'cpu'}) self.vectore_store = FAISS.load_local("index-intfloat_multilingual-e5-small-500-100-CA-ES", embeddings, allow_dangerous_deserialization=True)#, allow_dangerous_deserialization=True) logging.info("RAG loaded!") def get_context(self, instruction, number_of_contexts=1): documentos = self.vectore_store.similarity_search_with_score(instruction, k=number_of_contexts) return documentos def predict(self, instruction, context, model_parameters): api_key = os.getenv("HF_TOKEN") headers = { "Accept" : "application/json", "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } query = f"### Instruction\n{instruction}\n\n### Context\n{context}\n\n### Answer\n " #prompt = "You are a helpful assistant. Answer the question using only the context you are provided with. If it is not possible to do it with the context, just say 'I can't answer'. <|endoftext|>" payload = { "inputs": query, "parameters": model_parameters } response = requests.post(self.model_name, headers=headers, json=payload) return response.json()[0]["generated_text"].split("###")[-1][8:] 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].page_content + "\n" full_context += doc[0].metadata["Títol de la norma"] + "\n\n" full_context += doc[0].metadata["url"] + "\n\n" source_context.append(doc[0].metadata["url"]) return text_context, full_context, source_context def get_response(self, prompt: str, model_parameters: dict) -> str: docs = self.get_context(prompt, model_parameters["NUM_CHUNKS"]) text_context, full_context, source = self.beautiful_context(docs) del model_parameters["NUM_CHUNKS"] response = self.predict(prompt, text_context, model_parameters) if not response: return self.NO_ANSWER_MESSAGE return response, full_context, source