import streamlit as st from langchain.llms import HuggingFaceHub import os from PyPDF2 import PdfReader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import HuggingFaceEmbeddings from langchain.vectorstores import FAISS from langchain.chat_models import ChatOpenAI from langchain.chains.question_answering import load_qa_chain st.set_page_config('preguntaDOC') st.header("Pregunta a tu PDF") OPENAI_API_KEY = st.text_input('OpenAI API Key', type='password') pdf_obj = st.file_uploader("Carga tu documento", type="pdf", on_change=st.cache_resource.clear) @st.cache_resource def create_embeddings(pdf): pdf_reader = PdfReader(pdf) text = "" for page in pdf_reader.pages: text += page.extract_text() text_splitter = RecursiveCharacterTextSplitter( chunk_size=800, chunk_overlap=100, length_function=len ) chunks = text_splitter.split_text(text) # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2") embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2") knowledge_base = FAISS.from_texts(chunks, embeddings) return knowledge_base if pdf_obj: knowledge_base = create_embeddings(pdf_obj) user_question = st.text_input("Haz una pregunta sobre tu PDF:") if user_question: os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY docs = knowledge_base.similarity_search(user_question, 3) # llm = ChatOpenAI(model_name='gpt-3.5-turbo') llm = HuggingFaceHub(repo_id="lmsys/vicuna-7b-v1.1", model_kwargs={"temperature":0.5, "max_length":512}) chain = load_qa_chain(llm, chain_type="stuff") respuesta = chain.run(input_documents=docs, question=user_question) st.write(respuesta)