RAG_approach_using_llama.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crmmbJH5Bb6I",
        "outputId": "c81ee2bb-ca63-4bbb-f483-eb4f2634371b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pypdf\n",
            "  Downloading pypdf-4.1.0-py3-none-any.whl (286 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.1/286.1 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pypdf\n",
            "Successfully installed pypdf-4.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install pypdf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers einops accelerate langchain bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmdfdPAEBfcp",
        "outputId": "30a56203-e70a-415e-c0cb-3f6e38f7aebd"
      },
      "execution_count": 2,
      "outputs": [
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          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.1/290.1 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m43.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))': /simple/nvidia-cublas-cu12/\u001b[0m\u001b[33m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Embedding\n",
        "!pip install install sentence_transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13h6Jv2IBvDR",
        "outputId": "2ec99ffd-97a8-4661-efc8-7afb1eb02901"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting install\n",
            "  Downloading install-1.3.5-py3-none-any.whl (3.2 kB)\n",
            "Collecting sentence_transformers\n",
            "  Downloading sentence_transformers-2.6.1-py3-none-any.whl (163 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.3/163.3 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.32.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.38.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.66.2)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (2.2.1+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.25.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.11.4)\n",
            "Requirement already satisfied: huggingface-hub>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.20.3)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (9.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (2.31.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (4.10.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (23.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (3.1.3)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.105)\n",
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            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (2.19.3)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence_transformers) (1.3.0)\n",
            "Installing collected packages: install, sentence_transformers\n",
            "Successfully installed install-1.3.5 sentence_transformers-2.6.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install llama_index==0.9.40\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSa8P0kyCI4c",
        "outputId": "3e11df9f-d7d6-4373-f014-35de9933fd40"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama_index==0.9.40\n",
            "  Downloading llama_index-0.9.40-py3-none-any.whl (15.9 MB)\n",
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            "\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))': /simple/deprecated/\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting deprecated>=1.2.9.3 (from llama_index==0.9.40)\n",
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            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx->llama_index==0.9.40) (2024.2.2)\n",
            "Collecting httpcore==1.* (from httpx->llama_index==0.9.40)\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[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx->llama_index==0.9.40)\n",
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            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json->llama_index==0.9.40) (23.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->llama_index==0.9.40) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->llama_index==0.9.40) (2.16.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->llama_index==0.9.40) (1.16.0)\n",
            "Installing collected packages: dirtyjson, h11, deprecated, tiktoken, httpcore, httpx, openai, llama_index\n",
            "Successfully installed deprecated-1.2.14 dirtyjson-1.0.8 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 llama_index-0.9.40 openai-1.14.3 tiktoken-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import VectorStoreIndex,SimpleDirectoryReader,ServiceContext\n",
        "from llama_index.llms import HuggingFaceLLM\n",
        "from llama_index.prompts.prompts import SimpleInputPrompt"
      ],
      "metadata": {
        "id": "5ff24PG5CNFh"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents=SimpleDirectoryReader(\"/content/Data\").load_data()\n",
        "documents"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i7IL16MVCTZE",
        "outputId": "e0882a5c-4976-4fdd-91cb-5c310f4a4c7c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(id_='dbe836df-0b29-4df8-8d7a-0e05d2f194ed', embedding=None, metadata={'page_label': '1', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Strings\\n15-110 –Wednesday 09/18', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='6a47e1fd-2562-41fd-9b16-0f61fe47c142', embedding=None, metadata={'page_label': '2', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Learning Goals\\nUse programming to specify algorithms to computers\\n•Index and slice into strings as iterable objects to break them up into parts\\n•Use built -in methods to solve problems', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='1c7b36ff-7eb8-4fc7-abe3-0fb6101f9a6c', embedding=None, metadata={'page_label': '3', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='String Operations', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='da660342-d9ea-4b9f-9b90-4b6a659be80e', embedding=None, metadata={'page_label': '4', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=\"String Indexing\\nLast time, we showed how it's possible to find a character in a string based \\non its location with indexing .\\nHow do we get the first character in a string?\\ns[0]\\nHow do we get the last character in a string?\\ns[len(s)-1]\\nWhat happens if we try an index outside of the string?\\ns[len(s)+5] # error!!\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='2ef8e6d1-cf3e-41f2-aaf4-4a0e4c95c8f9', embedding=None, metadata={'page_label': '5', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Activity: guess the index\\nGiven the string \"abc123\", what is the index of...\\n\"b\"?\\n\"1\"?\\n\"3\"?', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='def07cb6-6017-4019-97b1-c0cf5ff75c4d', embedding=None, metadata={'page_label': '6', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='String Slicing\\nWe can also get a whole substring from a string by specifying a slice .\\nSlices are exactly like ranges -they can have a start point, an end point, and a \\nstep. But slices are represented as numbers inside of square brackets , \\nseparated by colons .\\ns = \"abcde\"\\nprint(s[2:len(s):1]) # print \" cde\"\\nprint(s[0:len(s) -1:1]) # prints \" abcd\"\\nprint(s[0:len(s):2]) # prints \"ace\"', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='cdad635e-4b51-4178-b995-c9c0d1daf125', embedding=None, metadata={'page_label': '7', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=\"String Slicing Shorthand\\nLike with range, we don't always need to specify values for the start, end, \\nand step; there are expected defaults (0 for start, len(s) for end, and 1 for \\nstep). But the syntax looks a little different this time.\\ns[:] and s[::] are both the string itself, unchanged\\ns[1:] is the string without the first character\\ns[:-1]is the string without the last character\\ns[::3] is the string with every third character\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='4990d623-3761-4363-bc48-10f2e8cd8aa2', embedding=None, metadata={'page_label': '8', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Activity: find the slice\\nGiven the string \"abcdefghij \", what slice would we need to get the \\nstring \"cfi\"?', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='8140305c-330a-4eb8-a14f-31102b4b72f4', embedding=None, metadata={'page_label': '9', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='String Operations\\nThere are useful string operations outside of indexing and slicing. \\nWe\\'ve already seen concatenation:\\n\"Hello \" + \"World\" -> \"Hello World\"\\nWe can also use multiplication to repeat a string a number of times!\\n\"Hello\" * 3 -> \"HelloHelloHello \"', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='6a2bed97-692f-4139-966b-7d527fc69750', embedding=None, metadata={'page_label': '10', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='New operation: in\\nWhen we have an iterable type (like a string), we can use the in\\noperator to check if a value occurs in the string.\\n\"a\" in \"apple\" # True!\\n\"4\" in \"12345\" # True!\\n\"z\" in \"potato\" # False!\\nThis is much faster to write than our search function from last time!', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='7b2f9d9c-bddc-4306-bfe2-ba5d459fd806', embedding=None, metadata={'page_label': '11', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='String Comparison\\nWe can also compare strings, just like how we compare numbers. When we \\ncompare two strings, we compare the ascii values of each character in order, and \\nthe smaller ascii value goes first.\\nBecause the lowercase letters and uppercase letters are listed in order in the ASCII \\ntable, we can compare lower -to-lower and upper -to-upper lexicographically , the \\norder they\\'d appear in the dictionary. But that won\\'t work if we compare lowercase \\nto uppercase words!\\n\"hello\" > \"goodbye\" # True\\n\"book\" < \"boot\" # True\\n\"APPLE\" < \"BANANA\" # True\\n\"ZEBRA\" > \"aardvark\" # False -lowercase letters are larger!', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='1ba24114-40a5-456a-9b43-261dda5c16d5', embedding=None, metadata={'page_label': '12', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Odds and Ends\\nWe can directly translate from characters to ascii in Python! The function ord(c) returns the ascii \\nnumber of a character, and chr(x) turns an ascii number into its character.\\nord(\"k\") # 107\\nchr(106) # \"j\"\\nFinally, there are a few characters we need to treat specially in Python. Specifically, these are the \\nenter character ( newline ) and the tab character ( tab). We can\\'t type these directly into the string, \\nso we\\'ll use shorthand instead:\\nprint(\"ABC \\\\nDEF\") # newline, or pressing enter/return\\nprint(\"ABC \\\\tDEF\") # tab\\nThe \\\\character designates that it is a special character, or an escape sequence , and it is modified by \\nthe letter that follows it. These two symbols are treated as a single character by the interpreter.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='498163d0-4143-4928-8a65-3a74a65204ec', embedding=None, metadata={'page_label': '13', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='String Methods', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='bb4be74a-8b77-47d4-9e63-eef8f59f4f1a', embedding=None, metadata={'page_label': '14', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Using string methods\\nString methods work differently from built -in functions. Instead of \\nwriting:\\nisdigit(s)\\nwe have to write:\\ns.isdigit ()\\nThis tells Python to call the built -in function isdigit () on the string s . It \\nwill then return a result normally.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='dbcac4cd-f0a1-4e16-93ec-000d52f2420b', embedding=None, metadata={'page_label': '15', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=\"Don't Memorize -Use the API!\\nThere is a whole library of built -in string functions that have already \\nbeen written; you can find them at \\nhttps://docs.python.org/3.6/library/stdtypes.html#string -methods\\nWe're about to go over a whole lot of methods, and it will be hard to \\nmemorize all of them. Instead, use the Python documentation to \\ncheck the name of a function that you know probably exists.\\nAs long as you can remember which basic actions have already been \\nwritten, you can always look up the name and parameters at need.\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='f3612a5f-85b6-4f3a-9eb7-8501923b0b0b', embedding=None, metadata={'page_label': '16', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=\"Strings Functions that Return Values\\nFirst, let's look at some string functions that return information about \\nthe string.\\ns.isdigit (), s.islower (), and s.isupper ()return True if the \\nstring is all -digits, all -lowercase, or all -uppercase, respectively.\\ns.count(c) returns the number of times the character c occurs in s.\\ns.find(c) returns the index of the character c in s, or -1 if it doesn't \\noccur in s.\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='d1c1147a-aee0-43ba-b377-f6d3b5bb7d47', embedding=None, metadata={'page_label': '17', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=\"Using string methods\\nAs an example of how to use string methods, let's write a function that \\nreturns whether a string is composed entirely of unique characters.\\ndef isUnique (s):\\nfor c in s:\\nif s.count (c)> 1:\\nreturn False\\nreturn True\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='30d0c50b-1e84-4c6c-b165-dc7a116530c0', embedding=None, metadata={'page_label': '18', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='String Methods that Create Strings\\nOther string methods produce a new string based on the original.\\ns.lower()and s.upper()convert a string to all -lowercase or all -\\nuppercase, respectively.\\ns.replace (a, b) produces a new string where all instances of the \\nstring a have been replaced with b.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='b55b0f3a-77b1-4ee1-a6dd-af8121b58823', embedding=None, metadata={'page_label': '19', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Using String Methods\\nWe can use these new methods to make a silly password -generating \\nfunction:\\ndef makePassword (phrase):\\nphrase = phrase.lower ()\\nphrase = phrase.replace (\"a\", \"@\")\\nphrase = phrase.replace (\"o\", \"0\")\\nreturn phrase', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='b4920f5c-c950-4f2f-b2b8-93386ffa5eec', embedding=None, metadata={'page_label': '20', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='One last method: split()\\nFinally, we can use another method, s.split(c), to split up a string \\ninto a new iterable based on a separator character, c. We\\'ll use this \\nmethod directly in a for -each loop for now.\\ndef findName (sentence, name):\\nfor word in sentence.split (\" \"):\\nif word == name:\\nreturn True\\nreturn False', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='5d41a279-f527-4050-bad5-7b42195290d2', embedding=None, metadata={'page_label': '21', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=\"In-Depth Example: Program an Auto -Grader\\nLet's solve a slightly more complicated problem. Pretend you're a very \\nlazy professor of underwater basket weaving, and you want to \\nautomate the process of grading student essays about the correct \\nprocess for weaving a basket while underwater.\\n•What makes a good essay?\\n•How can we map features of a good essay to features of strings we \\ncan calculate?\\n•How can we check whether our auto -grader is good enough?\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='c4392433-f06c-43ff-8630-a04091e2ae03', embedding=None, metadata={'page_label': '22', 'file_name': 'String Slicing.pdf', 'file_path': '/content/Data/String Slicing.pdf', 'file_type': 'application/pdf', 'file_size': 245138, 'creation_date': '2024-03-26', 'last_modified_date': '2024-03-26', 'last_accessed_date': '2024-03-26'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text='Learning Goals\\nUse programming to specify algorithms to computers\\n•Index and slice into strings as iterable objects to break them up into parts\\n•Use built -in methods to solve problems', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt=\"\"\"\n",
        "You are a Q&A assistant. Your goal is to provide correct code as\n",
        "accurately as possible based on the instructions and context provided.\n",
        "\"\"\"\n",
        "## Default format supportable by LLama2\n",
        "query_wrapper_prompt=SimpleInputPrompt(\"<|USER|>{query_str}<|ASSISTANT|>\")"
      ],
      "metadata": {
        "id": "FCvo1Im2C2p2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41-m52iYb-7R",
        "outputId": "da5bd018-272f-4f9b-8efd-7672bbcba421"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) y\n",
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "llm = HuggingFaceLLM(\n",
        "    context_window=4096,\n",
        "    max_new_tokens=256,\n",
        "    generate_kwargs={\"temperature\": 0.0, \"do_sample\": False},\n",
        "    system_prompt=system_prompt,\n",
        "    query_wrapper_prompt=query_wrapper_prompt,\n",
        "    tokenizer_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    model_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    device_map=\"auto\",\n",
        "    # uncomment this if using CUDA to reduce memory usage\n",
        "    model_kwargs={\"torch_dtype\": torch.float16 , \"load_in_8bit\":True}\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406,
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        },
        "id": "sIFTizSdcBEA",
        "outputId": "577a9f4e-22c1-42d8-9927-7fc84d7bdec9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1588a27b60e4789928c1a7733cc4a47"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
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        {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8dd9f7b6987842948d1dc06a37695ff1"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec560cdf92b6457f9957239656ca69d3"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8eab1c46e46f46ab946c0a6c41ae5631"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8f16b017af44610aeed32c37d7b7ded"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "14612f597483419dae967b1b50076bd9"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from llama_index import ServiceContext\n",
        "from llama_index.embeddings import LangchainEmbedding\n",
        "\n",
        "embed_model=LangchainEmbedding(\n",
        "    HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\"))"
      ],
      "metadata": {
        "colab": {
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    {
      "cell_type": "code",
      "source": [
        "service_context=ServiceContext.from_defaults(\n",
        "    chunk_size=1024,\n",
        "    llm=llm,\n",
        "    embed_model=embed_model\n",
        ")"
      ],
      "metadata": {
        "id": "awuHS3vVdAah"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "service_context"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bz8WQJ21dDc8",
        "outputId": "5755e3ed-333f-43fb-e88f-b57514bb147c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ServiceContext(llm_predictor=LLMPredictor(system_prompt=None, query_wrapper_prompt=None, pydantic_program_mode=<PydanticProgramMode.DEFAULT: 'default'>), prompt_helper=PromptHelper(context_window=4096, num_output=256, chunk_overlap_ratio=0.1, chunk_size_limit=None, separator=' '), embed_model=LangchainEmbedding(model_name='sentence-transformers/all-mpnet-base-v2', embed_batch_size=10, callback_manager=<llama_index.callbacks.base.CallbackManager object at 0x7ba58fa86560>), transformations=[SentenceSplitter(include_metadata=True, include_prev_next_rel=True, callback_manager=<llama_index.callbacks.base.CallbackManager object at 0x7ba58fa86560>, id_func=<function default_id_func at 0x7ba590b937f0>, chunk_size=1024, chunk_overlap=200, separator=' ', paragraph_separator='\\n\\n\\n', secondary_chunking_regex='[^,.;。？！]+[,.;。？！]?')], llama_logger=<llama_index.logger.base.LlamaLogger object at 0x7ba4a48dff70>, callback_manager=<llama_index.callbacks.base.CallbackManager object at 0x7ba58fa86560>)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index=VectorStoreIndex.from_documents(documents,service_context=service_context)"
      ],
      "metadata": {
        "id": "2IdGJFAFdFfq"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBFDyN6gdIQc",
        "outputId": "70dcdcff-84aa-4972-f1ad-90c434f2e160"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<llama_index.indices.vector_store.base.VectorStoreIndex at 0x7ba4a48de9e0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine=index.as_query_engine()"
      ],
      "metadata": {
        "id": "8aPfgM37dLbf"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response=query_engine.query(\"Write me a code for exctracting using string slicing all elements in string 'MYnAMEIS' which are present at odd indices \")"
      ],
      "metadata": {
        "id": "ufQyjWRydP0D"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tt_IdYJdfe2",
        "outputId": "bdcbf109-8bf0-4ba3-b294-7b8632bf3476"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sure! Here is an example of how you could use string slicing to extract all elements in a string that are present at odd indices:\n",
            "```\n",
            "string = \"MYnAMEIS\"\n",
            "odd_elements = []\n",
            "for i in range(1, len(string), 2):\n",
            "    odd_elements.append(string[i])\n",
            "print(odd_elements)\n",
            "```\n",
            "This code will iterate over the characters in the string, starting at the second character (since we're indexing from 1) and skipping every other character. The resulting list of characters will be the odd indices of the string.\n",
            "\n",
            "I hope this helps! Let me know if you have any questions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "anvSD-w8dw0B"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}