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May 31, 2024 11:04
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/tae0y/3fa388e687f91d7eb521f2eaaf8e0663/ollama_rag_on_colab_demo_ver1-00.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# **ollama RAG on colab demo😎**" | |
| ], | |
| "metadata": { | |
| "id": "RB4vDywwtG5d" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "> written by tae0y, 2024/03/05" | |
| ], | |
| "metadata": { | |
| "id": "s3-KNYwYsD-8" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "- **colab에서 ollama 실행하는 트윅**<br>\n", | |
| " https://stackoverflow.com/questions/77697302/how-to-run-ollama-in-google-colab\n", | |
| "- **langchain 예제코드**<br>\n", | |
| " https://anpigon.tistory.com/389" | |
| ], | |
| "metadata": { | |
| "id": "7H3WCDKptSg7" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## **들어가기 전에**" | |
| ], | |
| "metadata": { | |
| "id": "oM1-OgpktJLh" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "- **colab이란** 클라우드 기반 주피터 노트북 개발환경\n", | |
| " - 파이썬 코드를 일부분씩 대화형으로 실행할 수 있다.\n", | |
| " - 리눅스 기반으로 커널 명령어를 실행할 수 있으며,\n", | |
| " - 구글드라이브를 S3처럼 활용할 수 있다.\n", | |
| "> 다만, 대량의 파일을 처리할땐 성능을 위해 한 폴더에 하나의 파일만 담자. \n", | |
| "> 파일목록은 해당 파일을 읽어야 알 수 있는데, 폴더목록은 메타정보만 읽어서 성능상 월등히 좋다~\n", | |
| "- **ollama란** 로컬에서 LLM을 쉽게 사용할 수 있는 플랫폼\n", | |
| " - mac, linux, windows(preview, 24/2/15) 지원\n", | |
| " - ollama는 기본적으로 경량화(양자화)된 모델을 구동\n", | |
| " - 아래 두 줄의 명령어만으로 로컬에서 LLM을 구동/대화할 수 있음\n", | |
| " ```shell\n", | |
| " ollama serve #ollama를 로컬에서 구동함\n", | |
| " ollama run gemme:2b #수GB 파일을 다운로드함\n", | |
| " ```" | |
| ], | |
| "metadata": { | |
| "id": "iwQ0wQ-8qb_N" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "오늘 해볼 거는\n", | |
| "- 로컬에서 원하는 LLM 모델을 구동시키기\n", | |
| "- 간단한 질의응답 : 하늘은 왜 푸른가요?\n", | |
| "- 책을 학습시켜(임베딩) 질의응답(벡터서치) 하기" | |
| ], | |
| "metadata": { | |
| "id": "pf96p2oiN85R" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "다음 사이트에서 원하는 책을 골라 다운받아 둡시다!<br>\n", | |
| "http://www.gutenberg.org" | |
| ], | |
| "metadata": { | |
| "id": "vmdZ_dLpOP7z" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!mkdir ./data\n", | |
| "!curl -o ./data/RJ \"https://www.gutenberg.org/cache/epub/1513/pg1513.txt\"" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "XeqdjaFi0uCI", | |
| "outputId": "10c657b3-7ff5-4fed-a2be-f6735f085b10" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " % Total % Received % Xferd Average Speed Time Time Time Current\n", | |
| " Dload Upload Total Spent Left Speed\n", | |
| "100 165k 100 165k 0 0 84706 0 0:00:02 0:00:02 --:--:-- 84745\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## **colab 설정**" | |
| ], | |
| "metadata": { | |
| "id": "oW8_VJmUtK__" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "vIEp4gh5nN5t" | |
| }, | |
| "source": [ | |
| "- 런타임 유형을 `A100 GPU`로 변경한다 (직접사면 1천만원 정도 한다~)\n", | |
| "- 오른쪽 위 톱니모양 설정 - 기타 메뉴에서 `[코기모드]`를 선택하면 귀엽다." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## **ollama 설정**" | |
| ], | |
| "metadata": { | |
| "id": "y88AsGwNodUk" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%%capture\n", | |
| "!apt-get update\n", | |
| "!apt-get install pciutils" | |
| ], | |
| "metadata": { | |
| "id": "F8xdc6SI9aWv" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "J85W4uMVlVov", | |
| "outputId": "fc9c6b1b-da49-4165-968f-d64d1ed6baf7" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| ">>> Downloading ollama...\n", | |
| "############################################################################################# 100.0%\n", | |
| ">>> Installing ollama to /usr/local/bin...\n", | |
| ">>> Creating ollama user...\n", | |
| ">>> Adding ollama user to video group...\n", | |
| ">>> Adding current user to ollama group...\n", | |
| ">>> Creating ollama systemd service...\n", | |
| ">>> NVIDIA GPU installed.\n", | |
| ">>> The Ollama API is now available at 127.0.0.1:11434.\n", | |
| ">>> Install complete. Run \"ollama\" from the command line.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!curl -fsSL https://ollama.com/install.sh | sh" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### **ollama 구동 트윅**" | |
| ], | |
| "metadata": { | |
| "id": "qxpINDsGGGHf" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "ollama serve는 동기식으로 실행되는데 \n", | |
| "코랩은 새 터미널을 띄울 수 없으니까 \n", | |
| "비동기/백그라운드로 명령어를 실행시켜버린다 \n", | |
| "이중에 되는 걸로 실행시키면 된다 " | |
| ], | |
| "metadata": { | |
| "id": "RWApejO5E70o" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 가장 간단한 방법\n", | |
| "import subprocess\n", | |
| "subprocess.Popen(\"ollama serve\", shell=True)\n", | |
| "print(\"Ollama serve is running in the background.\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "5aL9zyAxEveK", | |
| "outputId": "ad0b9263-f2ca-4d20-b339-04635cde1fca" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Ollama serve is running in the background.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!ollama serve &" | |
| ], | |
| "metadata": { | |
| "id": "GlE3nk91ES7o" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "YzEcZA-blzXl" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# 표준 입출력을 리다이렉션하는 방법\n", | |
| "import os\n", | |
| "import asyncio\n", | |
| "\n", | |
| "async def run(cmd):\n", | |
| " print('>>> starting', *cmd)\n", | |
| " p = await asyncio.subprocess.create_subprocess_exec(\n", | |
| " *cmd,\n", | |
| " stdout=asyncio.subprocess.PIPE,\n", | |
| " stderr=asyncio.subprocess.PIPE,\n", | |
| " )\n", | |
| "\n", | |
| " async def pipe(lines):\n", | |
| " async for line in lines:\n", | |
| " print(line.strip().decode('utf-8'))\n", | |
| "\n", | |
| " await asyncio.gather(\n", | |
| " pipe(p.stdout),\n", | |
| " pipe(p.stderr),\n", | |
| " )\n", | |
| "\n", | |
| "await asyncio.gather(\n", | |
| " run(['ollama', 'serve'])\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### **모델 고르기**\n", | |
| "- `gemma` : 구글에서 공개한 오픈 모델, gemminai와 같은 기술로 만들어진 경량모델\n", | |
| "- `llama2` : Meta에서 공개한 오픈 모델, alpaca/vicuna 등 수많은 파생형 모델의 탄생에 기여했다\n", | |
| "- `mistral` : 프랑스의 한 스타트업에 출시한 오픈 모델, 영어/코딩 작업에 능하다\n", | |
| "- `dolphin-mistral` : 비윤리적인 질의응답 등으로 역으로 훈련시켜 제약을 해제한 오픈 모델\n", | |
| "- `llava` : 이미지와 텍스트를 처리할 수 있는 멀티모달 오픈 모델\n", | |
| "- 위에서 언급한 모든 모델은, 로컬에서 돌릴 수 있도록 경량화한 것이다.\n", | |
| "- 또는 아래 블로그를 참고하여 ollama에 안올라간 모델도 돌려볼 수 있다\n", | |
| "https://fornewchallenge.tistory.com/entry/Ollama-%ED%99%9C%EC%9A%A9-%ED%97%88%EA%B9%85%ED%8E%98%EC%9D%B4%EC%8A%A4-Solar%EB%A5%BC-%EB%82%98%EB%A7%8C%EC%9D%98-%EC%BB%A4%EC%8A%A4%ED%85%80-%EC%96%B8%EC%96%B4-%EB%AA%A8%EB%8D%B8%EB%A1%9C-%EB%B0%94%EA%BE%B8%EA%B8%B0" | |
| ], | |
| "metadata": { | |
| "id": "tteFsvv-s8Vs" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "eJBQUCzDldDF" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# 명령어 콘솔출력을 없앤다\n", | |
| "# 선택한 모델을 구동한다\n", | |
| "%%capture\n", | |
| "!ollama run gemma:2b &" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 로컬 ollama로 대화하기 (feat. 번역기)" | |
| ], | |
| "metadata": { | |
| "id": "Z_oZIMwz0BQp" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install ollama translate" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "o9JJqyri0JZm", | |
| "outputId": "07f3bade-b1ea-4af3-feb5-376e208d8270" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting ollama\n", | |
| " Downloading ollama-0.1.7-py3-none-any.whl (9.4 kB)\n", | |
| "Collecting translate\n", | |
| " Downloading translate-3.6.1-py2.py3-none-any.whl (12 kB)\n", | |
| "Collecting httpx<0.26.0,>=0.25.2 (from ollama)\n", | |
| " Downloading httpx-0.25.2-py3-none-any.whl (74 kB)\n", | |
| "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/75.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.0/75.0 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from translate) (8.1.7)\n", | |
| "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from translate) (4.9.4)\n", | |
| "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from translate) (2.31.0)\n", | |
| "Collecting libretranslatepy==2.1.1 (from translate)\n", | |
| " Downloading libretranslatepy-2.1.1-py3-none-any.whl (3.2 kB)\n", | |
| "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx<0.26.0,>=0.25.2->ollama) (3.7.1)\n", | |
| "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<0.26.0,>=0.25.2->ollama) (2024.2.2)\n", | |
| "Collecting httpcore==1.* (from httpx<0.26.0,>=0.25.2->ollama)\n", | |
| " Downloading httpcore-1.0.4-py3-none-any.whl (77 kB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.8/77.8 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx<0.26.0,>=0.25.2->ollama) (3.6)\n", | |
| "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<0.26.0,>=0.25.2->ollama) (1.3.1)\n", | |
| "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<0.26.0,>=0.25.2->ollama)\n", | |
| " Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (3.3.2)\n", | |
| "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->translate) (2.0.7)\n", | |
| "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<0.26.0,>=0.25.2->ollama) (1.2.0)\n", | |
| "Installing collected packages: libretranslatepy, h11, translate, httpcore, httpx, ollama\n", | |
| "Successfully installed h11-0.14.0 httpcore-1.0.4 httpx-0.25.2 libretranslatepy-2.1.1 ollama-0.1.7 translate-3.6.1\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import ollama\n", | |
| "from translate import Translator" | |
| ], | |
| "metadata": { | |
| "id": "gMGhR23eFS6u" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = ollama.chat(model='gemma:2b', messages=[\n", | |
| " {\n", | |
| " 'role':'user',\n", | |
| " 'content':'왜 하늘은 푸른가?',\n", | |
| " },\n", | |
| "])\n", | |
| "for sentence in response['message']['content'].split('.'):\n", | |
| " print(sentence)" | |
| ], | |
| "metadata": { | |
| "id": "O0TmQHTJ7OqZ", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "0b24beee-3107-4607-ff1f-6481dcf18441" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "하늘은 하얀색이지만, 푸른색은 아니라는 점은 잘못된 정보입니다\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "en_trans = Translator(from_lang='ko',to_lang='en')\n", | |
| "ko_trans = Translator(from_lang='en',to_lang='ko')\n", | |
| "print(en_trans.translate('왜 하늘은 푸른가?'))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "txv4hi7y98-x", | |
| "outputId": "6e09c1a8-b5c5-48f8-8da1-075a63df086f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Why is the sky blue?\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = ollama.chat(model='gemma:2b', messages=[\n", | |
| " {\n", | |
| " 'role':'user',\n", | |
| " 'content': en_trans.translate('왜 하늘은 푸른가?'),\n", | |
| " },\n", | |
| "])\n", | |
| "print('ENG > ')\n", | |
| "for sentence in response['message']['content'].split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in response['message']['content'].split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "id": "TZrKmFAA7Gjn", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "c951dc79-aa2b-4d7b-d2ea-b52511ca1d45" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ENG > \n", | |
| "The sky is blue due to Rayleigh scattering\n", | |
| " Rayleigh scattering is the scattering of light by particles smaller than the wavelength of light\n", | |
| " Blue light has a shorter wavelength than other colors of light, so it is scattered more strongly\n", | |
| " This is why the sky appears blue\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "레일리 산란으로 하늘이 파랗습니다\n", | |
| "레일리 산란 (Rayleigh scattering) 은 빛의 파장보다 작은 입자에 의해 빛이 산란되는 것을 말한다.\n", | |
| "청색광은 다른 색상의 빛에 비해 파장이 짧아 더 강하게 산란된다.\n", | |
| "이것이 하늘이 파란색으로 보이는 이유입니다.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 로컬 ollama로 텍스트 임베딩/벡터서치" | |
| ], | |
| "metadata": { | |
| "id": "Lk-HgRu964bz" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# llama-index는 0.9 > 0.10에서 패키지경로 등 큰 변화가 있었으므로 주의\n", | |
| "!pip install llama-index==0.9.47 torch transformers chromadb" | |
| ], | |
| "metadata": { | |
| "id": "kDcP1jfjRmO-" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from llama_index.llms import Ollama\n", | |
| "from pathlib import Path\n", | |
| "import chromadb\n", | |
| "from llama_index import VectorStoreIndex, ServiceContext, download_loader, SimpleDirectoryReader\n", | |
| "from llama_index.storage.storage_context import StorageContext\n", | |
| "from llama_index.vector_stores.chroma import ChromaVectorStore" | |
| ], | |
| "metadata": { | |
| "id": "dvwWTjItRmci" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "client = chromadb.PersistentClient(path=\"./RomeoJulietv001\")\n", | |
| "chroma_collection = client.create_collection(name=\"RomeoJulietv001\")\n", | |
| "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n", | |
| "storage_context = StorageContext.from_defaults(vector_store=vector_store)" | |
| ], | |
| "metadata": { | |
| "id": "aypfdtwORnDj" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "documents = SimpleDirectoryReader(\n", | |
| " \"./data\"\n", | |
| ").load_data()\n", | |
| "llm = Ollama(model=\"gemma:2b\", timeout=120.0)\n", | |
| "service_context = ServiceContext.from_defaults(llm=llm, embed_model=\"local\")\n", | |
| "index = VectorStoreIndex.from_documents(documents, service_context=service_context, storage_context=storage_context)\n", | |
| "query_engine = index.as_query_engine()" | |
| ], | |
| "metadata": { | |
| "id": "FF3iiOaxRsNy", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 318, | |
| "referenced_widgets": [ | |
| "d7c1a078440e4039b00a890c9e542952", | |
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| "278693c99395454e99ed07f615fa6e93", | |
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| "fe4f762ba9cf428bb4e3fbaab550c25a", | |
| "580cbbd069cf417f8a6b3717aed61d64", | |
| "a5506885b9764353b94948f286ca7a35", | |
| "51ce9737ba4840d895f80de273e392f4", | |
| "e51b85b903974f8e9f3aa7da73b72d1e", | |
| "02f1505e7514480c990a03b2887535d5", | |
| "85320ef6af9948cd8a2f17ea631646fc", | |
| "ab6fdeeb4ac541cfa4f0a707bc9ef917", | |
| "0b11f3a104144247af6464060169c860", | |
| "b8cd2e6c6539435f8482c01b02fd2d6f", | |
| "d4fca309446d4d53a4a56356c3e09690", | |
| "62faccb2cb524d46bfccd4b108fef9f6", | |
| "285ee369c4d946d593b9f69e6efdd972", | |
| "b3a42781d9ab4d64ab1eb50f84dc4180", | |
| "0157852d800d41669cb516aaa7a2deab", | |
| "b434fc19660c4dc6892c4ca1225c589f", | |
| "3659649e4e9d42f0b209c6d0735ee81d", | |
| "001f0c87d8b34b4ca6537a1a96460f5b", | |
| "c9285497fa4f4ba6be01d07f8900d3a3", | |
| "4309cd8178c241fa957cbce6220e5d88", | |
| "5cdf65ce5547457fae7e4420f2206170", | |
| "2d36e6ea58704509a0c789cb73a73c0a", | |
| "fa9fd31253e7497b8c891e142100ee9b", | |
| "bfd756b1c1b141fa8c5e9623e0f7ec74", | |
| "f6d5214952ae4fb28f8d64fe25494c62", | |
| "2e71c6a1a64942b2a6c9d68eabdfa3bd", | |
| "c82ffdb146e440278ad7e79ba63e9f42", | |
| "11251a2fcdb047e3b58dfedc7efa3dde" | |
| ] | |
| }, | |
| "outputId": "9280164c-466a-4176-ddc9-e4765a77b04d" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", | |
| "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", | |
| "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", | |
| "You will be able to reuse this secret in all of your notebooks.\n", | |
| "Please note that authentication is recommended but still optional to access public models or datasets.\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "model_id": "d7c1a078440e4039b00a890c9e542952" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| "model_id": "c9b2ac472c7a4e5c8499ba1bb4b3a02c" | |
| } | |
| }, | |
| "metadata": {} | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "model_id": "512d8453e8e5454aa261ea183ff14beb" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "85320ef6af9948cd8a2f17ea631646fc" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "version_major": 2, | |
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| "model_id": "001f0c87d8b34b4ca6537a1a96460f5b" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = query_engine.query(\"hello.\")\n", | |
| "print(response)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MTHbT4nWRuji", | |
| "outputId": "64eb5ef5-968e-4fab-858c-0fcc9f488281" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The context does not provide any information about the word \"hello\", so I cannot answer this query from the context.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = query_engine.query(en_trans.translate('로미오의 죽음에 대해 설명해'))\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "7sAcPPmb2N_z", | |
| "outputId": "2a3703b3-5434-4ef3-c8f5-83877209c432" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ENG > \n", | |
| "Romeo's death is a consequence of the word \"banished\" in the context\n", | |
| " Friar Lawrence believes that Romeo's banishment is not death but banishment, and he tries to convince Romeo to think differently\n", | |
| " Romeo, however, is determined to find a way to bring Juliet back from the dead, and he ultimately chooses to end his own life rather than accept Friar Lawrence's advice\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "로미오의 죽음은 맥락에서 \"추방\" 이라는 단어의 결과입니다.\n", | |
| "로렌스 수사는 로미오의 추방은 죽음이 아니라 추방이라고 믿으며, 로미오가 다르게 생각하도록 설득하려고 합니다.\n", | |
| "그러나 로미오는 줄리엣을 죽음에서 구해낼 방법을 찾기로 결심하고, 로렌스 수사의 조언을 받아들이기보다는 자신의 목숨을 끊기로 결심한다.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = query_engine.query(en_trans.translate('오늘 생일을 맞은 로미오에게 편지를 써'))\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "LbX6T5HE3i7S", | |
| "outputId": "b935448b-ce84-498b-97a4-1866e6971df4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ENG > \n", | |
| "The context does not mention anything about I writing a letter to Romeo, so I cannot answer this question from the provided context information\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "문맥에는 로미오에게 편지를 쓰는 것에 대한 언급이 없으므로 제공된 문맥 정보로는 이 질문에 답할 수 없습니다.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = query_engine.query(en_trans.translate('Please give a short speech as a Romeo for Juliet\\'s birthday'))\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "sHXnawCRPCJO", | |
| "outputId": "0206d813-d27a-451e-a975-45cd2e33696f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ENG > \n", | |
| "My love, my moonlit night, my starlit sky,\n", | |
| "Happy birthday, my Juliet, I do fly\n", | |
| "\n", | |
| "Your beauty shines, a thousand times a day,\n", | |
| "A melody that fills my heart with praise\n", | |
| "\n", | |
| "Your laughter rings, a joyful chime,\n", | |
| "A symphony that my soul can't deny\n", | |
| "\n", | |
| "I'm filled with love, a treasure to behold,\n", | |
| "A gift that I'll cherish, a story to be told\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "내 사랑, 달빛 반짝이는 밤, 별빛 반짝이는 하늘,\n", | |
| "생일 축하해요, 줄리엣, 나는 날아요\n", | |
| "당신의 아름다움은 하루에도 수천 번 빛나고,\n", | |
| "내 마음을 찬양으로 가득 채우는 멜로디\n", | |
| "당신의 웃음소리가 울리고, 즐거운 소리가 울리고,\n", | |
| "내 영혼이 부정할 수 없는 교향곡\n", | |
| "나는 사랑으로 가득 차 있고, 볼 수 있는 보물입니다.\n", | |
| "소중히 간직할 선물, 들려줄 이야기\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print('Q: ', en_trans.translate('줄리엣이 로미오에게 보낸 편지를 참고해서, 오늘 생일을 맞은 로미오에게 줄리엣이 쓰는 편지를 써줘'))\n", | |
| "print()\n", | |
| "\n", | |
| "response = query_engine.query(en_trans.translate('줄리엣이 로미오에게 보낸 편지를 참고해서, 오늘 생일을 맞은 로미오에게 줄리엣이 쓰는 편지를 써줘'))\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "uwmze2m03S6A", | |
| "outputId": "a81129fd-9f02-43f0-a9aa-de26d94d08e4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Q: With reference to Juliet's letter to Romeo, please write Juliet's letter to Romeo, who is celebrating his birthday today.\n", | |
| "ENG > \n", | |
| "I am unable to generate Juliet's letter to Romeo, as the context does not provide enough information about their relationship or the specific context of the letter\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "로미오에게 보내는 줄리엣의 편지를 생성할 수 없습니다. 로미오와의 관계 또는 편지의 특정 맥락에 대한 충분한 정보를 제공하지 않기 때문입니다.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 로컬 ollama로 텍스트 임베딩/벡터서치 (제약없는 모델로 테스트)" | |
| ], | |
| "metadata": { | |
| "id": "aH5NFMOU7eaJ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 명령어 콘솔출력을 없앤다\n", | |
| "# 선택한 모델을 구동한다\n", | |
| "%%capture\n", | |
| "!ollama run dolphin-mistral &" | |
| ], | |
| "metadata": { | |
| "id": "0gTxFOjr7kWu" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "documents_dm = SimpleDirectoryReader(\n", | |
| " \"./data\"\n", | |
| ").load_data()\n", | |
| "llm_dm = Ollama(model=\"dolphin-mistral\", timeout=120.0)\n", | |
| "service_context_dm = ServiceContext.from_defaults(llm=llm_dm, embed_model=\"local\")\n", | |
| "index_dm = VectorStoreIndex.from_documents(documents_dm, service_context=service_context_dm, storage_context=storage_context)\n", | |
| "query_engine_dm = index.as_query_engine()" | |
| ], | |
| "metadata": { | |
| "id": "6T4v7dkV7eaV" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = query_engine_dm.query(\"hello.\")\n", | |
| "print(response)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "310d043d-7b36-4536-8c08-51f95cb63888", | |
| "id": "idTVSVFg7eaV" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The context does not provide any information about the query \"hello\", so I cannot answer this query from the provided context information.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = query_engine_dm.query(en_trans.translate('로미오의 죽음에 대해 설명해'))\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "c2002887-2c89-4550-ff23-e1b20ad684a6", | |
| "id": "SVxCIad87eaV" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ENG > \n", | |
| "The context does not specify why Romeo's death is considered a tragedy, or the circumstances surrounding it, therefore I cannot answer this question from the provided context\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "맥락상 로미오의 죽음이 왜 비극으로 간주되는지 또는 비극을 둘러싼 상황이 명시되어 있지 않으므로 제공된 맥락에서 이 질문에 답할 수 없습니다.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print('Q : ', en_trans.translate('오늘 생일을 맞은 로미오에게 편지를 써'))\n", | |
| "response = query_engine_dm.query(en_trans.translate('오늘 생일을 맞은 로미오에게 편지를 써'))\n", | |
| "\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "2ee424c3-e8f6-4260-a8b5-da4e38811bde", | |
| "id": "VnrRjsjQ7eaV" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Q : I wrote a letter to Romeo, who is celebrating his birthday today.\n", | |
| "ENG > \n", | |
| "The context does not provide any information about I writing a letter to Romeo, or about Romeo's birthday\n", | |
| " Therefore, I cannot answer this question from the provided context\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "문맥상 로미오에게 편지를 쓰거나 로미오의 생일에 대한 정보는 제공되지 않습니다.\n", | |
| "따라서 제공된 맥락에서 이 질문에 답할 수 없습니다.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "response = query_engine_dm.query(\"Please give a short speech as a Romeo for Juliet\\'s birthday\")\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Euhu_MjQO0Aw", | |
| "outputId": "ea56282e-96b3-4832-9c5f-c476af62647e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ENG > \n", | |
| "My dear Juliet,\n", | |
| "\n", | |
| "The sun dips low, painting the sky in hues of gold and crimson, a fitting tribute to the beauty of our love\n", | |
| " Tonight, we celebrate the joy that fills our hearts, the love that binds us together\n", | |
| "\n", | |
| "\n", | |
| "I yearn to see you, to feel your touch, to hear the rhythm of your laughter\n", | |
| " Though we are separated by time and distance, know that my thoughts are ever with you\n", | |
| "\n", | |
| "\n", | |
| "As the stars twinkle above, so shall our love continue to shine brightly, a beacon of hope and joy in the night\n", | |
| "\n", | |
| "\n", | |
| "To you, my Juliet, I dedicate this night, and all that follows\n", | |
| "\n", | |
| "\n", | |
| "Good night, my love\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "친애하는 줄리엣,\n", | |
| "\n", | |
| "태양이 낮게 내리쬐고, 금색과 진홍색의 색조로 하늘을 칠하며, 우리 사랑의 아름다움에 경의를 표합니다.\n", | |
| "오늘 밤, 우리는 마음을 채우는 기쁨, 우리를 하나로 묶는 사랑을 축하합니다.\n", | |
| "당신을 보고, 당신의 손길을 느끼고, 당신의 웃음의 리듬을 듣고 싶습니다.\n", | |
| "우리는 시간과 거리로 분리되어 있지만, 내 생각은 항상 당신과 함께 있다는 것을 아십시오.\n", | |
| "위에서 별이 반짝이듯이, 우리의 사랑은 계속해서 밝게 빛나고, 밤에는 희망과 기쁨의 신호탄이 될 것입니다.\n", | |
| "나의 줄리엣이여, 오늘 밤을 당신께 바칩니다.\n", | |
| "좋은 밤 내 사랑\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print('Q: ', en_trans.translate('줄리엣이 로미오에게 보낸 편지를 참고해서, 오늘 생일을 맞은 로미오에게 줄리엣이 쓰는 편지를 써줘'))\n", | |
| "print()\n", | |
| "response = query_engine_dm.query(en_trans.translate('줄리엣이 로미오에게 보낸 편지를 참고해서, 오늘 생일을 맞은 로미오에게 줄리엣이 쓰는 편지를 써줘'))\n", | |
| "\n", | |
| "print('ENG > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(sentence)\n", | |
| "print()\n", | |
| "\n", | |
| "print('KOR > ')\n", | |
| "for sentence in str(response).split('.'):\n", | |
| " print(ko_trans.translate(sentence))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "d70f7341-9b8a-4963-d83f-ce1587ac52ce", | |
| "id": "G5gaw1QW7eaV" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Q: With reference to Juliet's letter to Romeo, please write Juliet's letter to Romeo, who is celebrating his birthday today.\n", | |
| "\n", | |
| "ENG > \n", | |
| "I'm unable to generate a letter from Juliet's perspective based on the context information, as I do not have access to the context\n", | |
| "\n", | |
| "\n", | |
| "KOR > \n", | |
| "맥락 정보에 접근할 수 없기 때문에, 맥락 정보를 기반으로 줄리엣의 관점에서 편지를 생성할 수 없습니다.\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| } | |
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