LlamaIndex_Tutorial.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PromtEngineer/Langchain-Tutorilas/blob/main/LlamaIndex_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "nW_0yKusLRWK",
        "outputId": "ccb49701-2714-469a-a04b-e3df1353b4e9"
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          "output_type": "stream",
          "name": "stdout",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m55.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m59.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m107.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m78.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.1/258.1 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q llama-index\n",
        "!pip install -q openai\n",
        "!pip install -q transformers\n",
        "!pip install -q accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\""
      ],
      "metadata": {
        "id": "wwiMDG-2Z3je"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.llms import OpenAI\n",
        "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
        "from IPython.display import Markdown, display"
      ],
      "metadata": {
        "id": "bwdh1lj6g_pz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents = SimpleDirectoryReader(\"data\").load_data()"
      ],
      "metadata": {
        "id": "y0hb-49phJj0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = VectorStoreIndex.from_documents(documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7oTIw4YhJqD",
        "outputId": "060b77e6-7918-40b0-aba1-c1bba9030dd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /tmp/llama_index...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = index.as_query_engine()\n"
      ],
      "metadata": {
        "id": "PjC2iTmhhJzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"What did the author do growing up? \")"
      ],
      "metadata": {
        "id": "UGCwgL2Ng_s1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(Markdown(f\"<b>{response}</b>\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "xF8Yk2-rg_wQ",
        "outputId": "ffc6ed09-4bb5-42ee-ea4b-6d7f5612b502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<b>The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer and started programming on it, writing simple games and a word processor.</b>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Storing and Loading the Index"
      ],
      "metadata": {
        "id": "JNVgRgycmlYv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "index.storage_context.persist()"
      ],
      "metadata": {
        "id": "3bjDrdRag_3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import StorageContext, load_index_from_storage\n",
        "\n",
        "storage_context = StorageContext.from_defaults(persist_dir=\"./storage\")\n",
        "index = load_index_from_storage(storage_context=storage_context)"
      ],
      "metadata": {
        "id": "YjcYRoSlhAAS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nTn8d9UGhALZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### How to Customize"
      ],
      "metadata": {
        "id": "jLO4x48IoHTL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import ServiceContext, set_global_service_context\n"
      ],
      "metadata": {
        "id": "nfDyiWy1Z3m1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define LLM: https://gpt-index.readthedocs.io/en/latest/core_modules/model_modules/llms/usage_custom.html\n",
        "llm = OpenAI(model=\"gpt-3.5-turbo\", temperature=0, max_tokens=256)\n",
        "\n",
        "# configure service context\n",
        "service_context = ServiceContext.from_defaults(llm=llm, chunk_size=800, chunk_overlap=20)\n",
        "# set_global_service_context(service_context)\n",
        "index = VectorStoreIndex.from_documents(documents, service_context=service_context)"
      ],
      "metadata": {
        "id": "q4t_HpOAZ3qL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from llama_index.llms import PaLM\n",
        "# service_context = ServiceContext.from_defaults(llm=PaLM())"
      ],
      "metadata": {
        "id": "MunTVWILl7Bv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = index.as_query_engine(streaming=True)\n",
        "response = query_engine.query(\"What did the author do growing up?\")\n",
        "response.print_response_stream()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTVyuuZcl7E3",
        "outputId": "ca7e26dd-f727-4eef-abfb-e6b54503b637"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer and started programming on it, writing simple games and a word processor."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RdrxRhhPl7IB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = index.as_chat_engine()\n",
        "response = query_engine.chat(\"What is this document about?\")\n",
        "display(Markdown(f\"<b>{response}</b>\"))"
      ],
      "metadata": {
        "id": "V9uzsIaxl7Lr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "06TRYc4Pl7SE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Using a HuggingFace LLM\n",
        "\n",
        "This will NOT work on the Free Google Colab. You will need colab Pro. Video on the quantized models is coming soon....."
      ],
      "metadata": {
        "id": "7KLRgszMrAT5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.llms import HuggingFaceLLM\n",
        "from llama_index.prompts import PromptTemplate\n",
        "\n",
        "system_prompt = \"\"\"<|SYSTEM|># StableLM Tuned (Alpha version)\n",
        "- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.\n",
        "- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n",
        "- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.\n",
        "- StableLM will refuse to participate in anything that could harm a human.\n",
        "\"\"\"\n",
        "\n",
        "# This will wrap the default prompts that are internal to llama-index\n",
        "query_wrapper_prompt = PromptTemplate(\"<|USER|>{query_str}<|ASSISTANT|>\")\n",
        "\n",
        "import torch\n",
        "from llama_index.llms import HuggingFaceLLM\n",
        "llm = HuggingFaceLLM(\n",
        "    context_window=4096,\n",
        "    max_new_tokens=256,\n",
        "    generate_kwargs={\"temperature\": 0.7, \"do_sample\": False},\n",
        "    system_prompt=system_prompt,\n",
        "    query_wrapper_prompt=query_wrapper_prompt,\n",
        "    tokenizer_name=\"StabilityAI/stablelm-tuned-alpha-3b\",\n",
        "    model_name=\"StabilityAI/stablelm-tuned-alpha-3b\",\n",
        "    device_map=\"auto\",\n",
        "    stopping_ids=[50278, 50279, 50277, 1, 0],\n",
        "    tokenizer_kwargs={\"max_length\": 4096},\n",
        "    # uncomment this if using CUDA to reduce memory usage\n",
        "    # model_kwargs={\"torch_dtype\": torch.float16}\n",
        ")\n",
        "service_context = ServiceContext.from_defaults(\n",
        "    chunk_size=1024,\n",
        "    llm=llm,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/606 [00:00<?, ?B/s]"
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      "outputs": []
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}