과제2. 챗봇 모델 완성하기.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"cVB9pY-v8uv5"},"source":["# **과제2 : 챗봇 모델 완성하기**"]},{"cell_type":"markdown","metadata":{"id":"qTa9LlwZD_aa"},"source":["## **1.환경준비**"]},{"cell_type":"markdown","metadata":{"id":"gIN5d51gtker"},"source":["### (1) 라이브러리 Import"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"GVvCef6A8uv6"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import os\n","import sqlite3\n","import openai\n","\n","from langchain.chat_models import ChatOpenAI\n","from langchain.schema import HumanMessage, SystemMessage, Document\n","from langchain.embeddings import OpenAIEmbeddings\n","from langchain.vectorstores import Chroma\n","from langchain.chains import RetrievalQA, ConversationalRetrievalChain\n","from langchain.memory import ConversationBufferMemory\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"]},{"cell_type":"markdown","metadata":{},"source":["### (2) OpenAI API Key 확인\n","* 환경변수로 등록된 Key 확인하기"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qIUl4r69hHcN"},"outputs":[],"source":["# 환경변수에서 키 불러오기\n","api_key = os.getenv('OPENAI_API_KEY')\n","print(api_key) "]},{"cell_type":"markdown","metadata":{},"source":["* 만약 환경변수 키 설정이 잘 안된다면 아래 코드셀의 주석을 해제하고, 자신의 api key를 입력하고 실행\n","    * 아래 코드는 키 지정을 **임시**로 수행함.\n","    * 파이썬 파일(.ipynb, .py)안에서 매번 수행해야 함."]},{"cell_type":"code","execution_count":62,"metadata":{},"outputs":[],"source":["# os.environ['OPENAI_API_KEY'] = '여러분의 OpenAI API키' \n","# openai.api_key = os.getenv('OPENAI_API_KEY')"]},{"cell_type":"markdown","metadata":{"id":"mnk1VvHm2Ka0"},"source":["## **2.Vector DB 만들기**"]},{"cell_type":"markdown","metadata":{},"source":["* 데이터 로딩 \n","    * 1일차에서 제공한 파일의 구조를 그대로 이용\n","    * 에이블스쿨 홈페이지 FAQ 데이터 수집(https://aivle.kt.co.kr/home/brd/faq/main?mcd=MC00000056)\n","        * 모든 질문을 csv 형태로 저장\n","    * 데이터프레임으로 저장하기"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data = pd.read_csv('aivleschool_qa.csv', encoding='utf-8')\n","data.head()"]},{"cell_type":"markdown","metadata":{},"source":["* 벡터 데이터베이스\n","    * 1일차 벡터 데이터베이스를 그대로 이용\n","        * Embedding 모델 : text-embedding-ada-002\n","        * DB 경로 : ./database\n","\n"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"Xj5yt-Ufi1jU"},"outputs":[],"source":["embeddings = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n","database = Chroma(persist_directory=\"./database\", embedding_function = embeddings )"]},{"cell_type":"markdown","metadata":{},"source":["* 데이터 입력\n","    * 이미 입력된 데이터는 제외하고 나머지 입력하기"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# 각 행의 데이터를 Document 객체로 변환 \n","documents = [Document(page_content=text) for text in data['QA'].tolist()]\n","\n","# 데이터프레임에서 문서 추가\n","database.add_documents(documents)"]},{"cell_type":"markdown","metadata":{},"source":["* 입력된 데이터 조회"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sxAdBcYtvUtF"},"outputs":[],"source":["database.get()"]},{"cell_type":"markdown","metadata":{},"source":["## **3.RAG+LLM모델**"]},{"cell_type":"markdown","metadata":{},"source":["* 모델 선언 : ConversationalRetrievalChain\n","    * LLM 모델 : gpt-3.5-turbo\n","    * retriever : 벡터DB\n","        * 유사도 높은 문서 5개 가져오도록 설정\n","    * memory\n","* 결과\n","    * 유사도 높은 문장 3개와 유사도 점수도 함께 출력하도록 코드 작성\n","    * 질문 history 관리를 위한 이력 저장\n","        * timestamp\n","        * 질문\n","        * 유사도 3개\n","        * 답변\n","        * 그외\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["chat = ChatOpenAI(model=\"gpt-3.5-turbo\")\n","\n","k=3\n","retriever = database.as_retriever(search_kwargs={\"k\": k})\n","\n","# 대화 메모리 생성\n","memory = ConversationBufferMemory(memory_key=\"chat_history\", input_key=\"question\", output_key=\"answer\", \n","                                  return_messages=True)\n","\n","# ConversationalRetrievalQA 체인 생성\n","qa = ConversationalRetrievalChain.from_llm(llm=chat, retriever=retriever, memory=memory, \n","                                           return_source_documents=True,  output_key=\"answer\")"]},{"cell_type":"markdown","metadata":{},"source":["* 모델 사용하기\n","    * 에이블스쿨 지원 자격 관련 질문을 하고 답을 확인해 봅시다."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["query = '지원하는데 나이 제한이 있나요?'\n","result = qa(query)\n","\n","print(result[\"answer\"])"]}],"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3","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":"3.11.9"}},"nbformat":4,"nbformat_minor":0}