{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from main import chat_with_pdf, print_stream_and_return_full_answer\n", "from dotenv import load_dotenv\n", "\n", "load_dotenv()\n", "\n", "bert_paper_url = \"https://arxiv.org/pdf/1810.04805.pdf\"\n", "questions = [\n", " \"what is BERT?\",\n", " \"what NLP tasks does it perform well?\",\n", " \"is BERT suitable for NER?\",\n", " \"is it better than GPT\",\n", " \"when was GPT come up?\",\n", " \"when was BERT come up?\",\n", " \"so about same time?\",\n", "]\n", "\n", "history = []\n", "for q in questions:\n", " stream, context = chat_with_pdf(q, bert_paper_url, history)\n", " print(\"User: \" + q, flush=True)\n", " print(\"Bot: \", end=\"\", flush=True)\n", " answer = print_stream_and_return_full_answer(stream)\n", " history = history + [\n", " {\"role\": \"user\", \"content\": q},\n", " {\"role\": \"assistant\", \"content\": answer},\n", " ]" ] } ], "metadata": { "kernelspec": { "display_name": "pf", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.17" }, "stage": "development" }, "nbformat": 4, "nbformat_minor": 2 }