Chapter8_Semantic_Search_and_Rerieval_Augmented_Generation.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/Zohreh6384NKH/LLM_Project/blob/main/Chapter8_Semantic_Search_and_Rerieval_Augmented_Generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Dense Retrieval Example**"
      ],
      "metadata": {
        "id": "j75F2F2Src26"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Getting the text archive and chunking it**"
      ],
      "metadata": {
        "id": "5kwV2DOIrzRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain==0.2.5 faiss-gpu==1.7.2 cohere==5.5.8 langchain-community==0.2.5 rank_bm25==0.2.2 sentence-transformers==3.0.1\n",
        "!CMAKE_ARGS=\"-DLLAMA_CUDA=on\" pip install llama-cpp-python==0.2.78\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfGTvYTZrh9N",
        "outputId": "1fc81e83-e08b-4c7c-a982-8d49ca92b945"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain-community==0.2.5)\n",
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            "Installing collected packages: faiss-gpu, types-requests, tenacity, rank_bm25, parameterized, mypy-extensions, marshmallow, jmespath, httpx-sse, fastavro, typing-inspect, botocore, s3transfer, dataclasses-json, langchain-core, boto3, sentence-transformers, langchain-text-splitters, cohere, langchain, langchain-community\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cohere\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "api_key = 'q8ekvFyupnltoaCdVrsGpOfXR0msTJ7lnAMj23Tc'\n",
        "\n",
        "\n",
        "co = cohere.Client(api_key)"
      ],
      "metadata": {
        "id": "zC-Dh1VztgCy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"\"\n",
        "Interstellar is a 2014 epic science fiction film co-written, directed, and produced by Christopher Nolan.\n",
        "It stars Matthew McConaughey, Anne Hathaway, Jessica Chastain, Bill Irwin, Ellen Burstyn, Matt Damon, and Michael Caine.\n",
        "Set in a dystopian future where humanity is struggling to survive, the film follows a group of astronauts who travel through a wormhole near Saturn in search of a new home for mankind.\n",
        "\n",
        "Brothers Christopher and Jonathan Nolan wrote the screenplay, which had its origins in a script Jonathan developed in 2007.\n",
        "Caltech theoretical physicist and 2017 Nobel laureate in Physics[4] Kip Thorne was an executive producer, acted as a scientific consultant, and wrote a tie-in book, The Science of Interstellar.\n",
        "Cinematographer Hoyte van Hoytema shot it on 35 mm movie film in the Panavision anamorphic format and IMAX 70 mm.\n",
        "Principal photography began in late 2013 and took place in Alberta, Iceland, and Los Angeles.\n",
        "Interstellar uses extensive practical and miniature effects and the company Double Negative created additional digital effects.\n",
        "\n",
        "Interstellar premiered on October 26, 2014, in Los Angeles.\n",
        "In the United States, it was first released on film stock, expanding to venues using digital projectors.\n",
        "The film had a worldwide gross over $677 million (and $773 million with subsequent re-releases), making it the tenth-highest grossing film of 2014.\n",
        "It received acclaim for its performances, direction, screenplay, musical score, visual effects, ambition, themes, and emotional weight.\n",
        "It has also received praise from many astronomers for its scientific accuracy and portrayal of theoretical astrophysics. Since its premiere, Interstellar gained a cult following,[5] and now is regarded by many sci-fi experts as one of the best science-fiction films of all time.\n",
        "Interstellar was nominated for five awards at the 87th Academy Awards, winning Best Visual Effects, and received numerous other accolades\"\"\"\n",
        "\n",
        "#split into a list of sentences\n",
        "texts = text.split('.')\n",
        "\n",
        "#clean up to remove empty spaces and new lines\n",
        "texts = [t.strip('\\n') for t in texts]\n",
        "\n"
      ],
      "metadata": {
        "id": "iErGIFkOs-9Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Embedding the Chunks**"
      ],
      "metadata": {
        "id": "2LXqQvzU0lAH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Get the embeddings\n",
        "response = co.embed(\n",
        "  texts=texts,\n",
        "  input_type=\"search_document\",\n",
        ").embeddings\n",
        "\n",
        "embeds = np.array(response)\n",
        "print(embeds.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pk2RYiIL0lIg",
        "outputId": "ff269f86-809a-4032-8bb9-2d2547ca9bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(15, 4096)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Building the Search Index**"
      ],
      "metadata": {
        "id": "gQUfQOBN181A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create a search index :this structure allows for fast similarity search of vectors\n",
        "import faiss #import faiss library to python code\n",
        "dim = embeds.shape[1]\n",
        "index = faiss.IndexFlatL2(dim)  #L2 norm used in high dimentional vector comparisons-using l2 distance for similarity\n",
        "index.add(np.float32(embeds))"
      ],
      "metadata": {
        "id": "ygOiLKgu35lB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**search the index**"
      ],
      "metadata": {
        "id": "5RavcG0VAPgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def search(query, number_of_results=3):\n",
        "\n",
        "  # 1. Get the query's embedding\n",
        "  query_embed = co.embed(texts=[query],\n",
        "                input_type=\"search_query\",).embeddings[0]\n",
        "\n",
        "  # 2. Retrieve the nearest neighbors\n",
        "  distances , similar_item_ids = index.search(np.float32([query_embed]), number_of_results)\n",
        "\n",
        "  # 3. Format the results\n",
        "  texts_np = np.array(texts) # Convert texts list to numpy for easier indexing\n",
        "  results = pd.DataFrame(data={'texts': texts_np[similar_item_ids[0]],\n",
        "                              'distance': distances[0]})\n",
        "\n",
        "  # 4. Print and return the results\n",
        "  print(f\"Query:'{query}'\\nNearest neighbors:\")\n",
        "  return results\n"
      ],
      "metadata": {
        "id": "MkISgN1MAPpC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"how precise was the science\"\n",
        "results = search(query)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "4tprxIxmJvla",
        "outputId": "aea72b85-645d-40fd-ca52-ed24489884ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query:'how precise was the science'\n",
            "Nearest neighbors:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               texts      distance\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5c0dc6be-3928-43d4-a54e-9875ff483c21\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"texts\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"It has also received praise from many astronomers for its scientific accuracy and portrayal of theoretical astrophysics\",\n          \"Caltech theoretical physicist and 2017 Nobel laureate in Physics[4] Kip Thorne was an executive producer, acted as a scientific consultant, and wrote a tie-in book, The Science of Interstellar\",\n          \"Interstellar uses extensive practical and miniature effects and the company Double Negative created additional digital effects\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10757.3798828125,\n          11566.1318359375,\n          11922.8330078125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from rank_bm25 import BM25Okapi  # bm stands for best matching\n",
        "from sklearn.feature_extraction import _stop_words\n",
        "import string\n",
        "\n",
        "def bm25_tokenizer(text):\n",
        "    tokenized_doc = []\n",
        "    for token in text.lower().split():\n",
        "        token = token.strip(string.punctuation)\n",
        "\n",
        "        if len(token) > 0 and token not in _stop_words.ENGLISH_STOP_WORDS:\n",
        "            tokenized_doc.append(token)\n",
        "    return tokenized_doc"
      ],
      "metadata": {
        "id": "aX-DeGGly3e1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "tokenized_corpus = []\n",
        "for passage in tqdm(texts):\n",
        "    tokenized_corpus.append(bm25_tokenizer(passage))\n",
        "\n",
        "bm25 = BM25Okapi(tokenized_corpus)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcPalvz1KH8n",
        "outputId": "1253817a-a410-4f8c-eefc-00c7102b29d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 15/15 [00:00<00:00, 43630.07it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def keyword_search(query, top_k=3, num_candidates=15):\n",
        "    print(\"Input question:\", query)\n",
        "\n",
        "    ##### BM25 search (lexical search) #####\n",
        "    bm25_scores = bm25.get_scores(bm25_tokenizer(query))\n",
        "    top_n = np.argpartition(bm25_scores, -num_candidates)[-num_candidates:]\n",
        "    bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]\n",
        "    bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)\n",
        "\n",
        "    print(f\"Top-3 lexical search (BM25) hits\")\n",
        "    for hit in bm25_hits[0:top_k]:\n",
        "        print(\"\\t{:.3f}\\t{}\".format(hit['score'], texts[hit['corpus_id']].replace(\"\\n\", \" \")))"
      ],
      "metadata": {
        "id": "7lrX6X_GKTje"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Caveas of Dense Retrieval**"
      ],
      "metadata": {
        "id": "2CSPNysQLSyL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What is the mass of the moon?\"\n",
        "results = search(query)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "3TV3PO-0LW3E",
        "outputId": "b4a23999-8e6c-434a-9d1c-386043ec5d69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query:'What is the mass of the moon?'\n",
            "Nearest neighbors:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               texts      distance\n",
              "0  Cinematographer Hoyte van Hoytema shot it on 3...  12854.458984\n",
              "1  The film had a worldwide gross over $677 milli...  13301.030273\n",
              "2  It has also received praise from many astronom...  13332.011719"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d4a03c8f-434a-4ee8-9d5b-283f5f5223e5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cbd2d399-9bac-47bd-b008-9cd01bcd0a9e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c08fb831-d32b-4814-bfc9-dd054852c6d0\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c08fb831-d32b-4814-bfc9-dd054852c6d0 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"texts\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Cinematographer Hoyte van Hoytema shot it on 35 mm movie film in the Panavision anamorphic format and IMAX 70 mm\",\n          \"The film had a worldwide gross over $677 million (and $773 million with subsequent re-releases), making it the tenth-highest grossing film of 2014\",\n          \"It has also received praise from many astronomers for its scientific accuracy and portrayal of theoretical astrophysics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          12854.458984375,\n          13301.0302734375,\n          13332.01171875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Reranking Examples**"
      ],
      "metadata": {
        "id": "S6KW5lZkgs7Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "a cohere API method gets a  list of query and documents. it uses a trained machine learning model to evaluate the semantic similarity between the query and each document."
      ],
      "metadata": {
        "id": "_ZiwqtzAojf0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"how precise was the science\"\n",
        "results = co.rerank(query=query, documents=texts, top_n=3, return_documents=True)\n",
        "results.results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eS9exKjEgtLh",
        "outputId": "08caa53c-66ed-4e66-f568-f5a999246552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[RerankResponseResultsItem(document=RerankResponseResultsItemDocument(text='It has also received praise from many astronomers for its scientific accuracy and portrayal of theoretical astrophysics'), index=12, relevance_score=0.16981852),\n",
              " RerankResponseResultsItem(document=RerankResponseResultsItemDocument(text='The film had a worldwide gross over $677 million (and $773 million with subsequent re-releases), making it the tenth-highest grossing film of 2014'), index=10, relevance_score=0.07004896),\n",
              " RerankResponseResultsItem(document=RerankResponseResultsItemDocument(text='Caltech theoretical physicist and 2017 Nobel laureate in Physics[4] Kip Thorne was an executive producer, acted as a scientific consultant, and wrote a tie-in book, The Science of Interstellar'), index=4, relevance_score=0.0043994132)]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, result in enumerate(results.results):\n",
        "    print(idx, result.relevance_score , result.document.text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XeZAULSanKhy",
        "outputId": "fc0e89cc-9a91-477c-93f1-0cff656c991d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0.16981852 It has also received praise from many astronomers for its scientific accuracy and portrayal of theoretical astrophysics\n",
            "1 0.07004896 The film had a worldwide gross over $677 million (and $773 million with subsequent re-releases), making it the tenth-highest grossing film of 2014\n",
            "2 0.0043994132 Caltech theoretical physicist and 2017 Nobel laureate in Physics[4] Kip Thorne was an executive producer, acted as a scientific consultant, and wrote a tie-in book, The Science of Interstellar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def keyword_and_reranking_search(query, top_k=3, num_candidates=10):\n",
        "    print(\"Input question:\", query)\n",
        "\n",
        "    ##### BM25 search (lexical search) #####\n",
        "    bm25_scores = bm25.get_scores(bm25_tokenizer(query))\n",
        "    top_n = np.argpartition(bm25_scores, -num_candidates)[-num_candidates:]\n",
        "    bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]\n",
        "    bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)\n",
        "\n",
        "    print(f\"Top-3 lexical search (BM25) hits\")\n",
        "    for hit in bm25_hits[0:top_k]:\n",
        "        print(\"\\t{:.3f}\\t{}\".format(hit['score'], texts[hit['corpus_id']].replace(\"\\n\", \" \")))\n",
        "\n",
        "    #Add re-ranking\n",
        "    docs = [texts[hit['corpus_id']] for hit in bm25_hits]\n",
        "\n",
        "    print(f\"\\nTop-3 hits by rank-API ({len(bm25_hits)} BM25 hits re-ranked)\")\n",
        "    results = co.rerank(query=query, documents=docs, top_n=top_k, return_documents=True)\n",
        "    for hit in results.results:\n",
        "        print(\"\\t{:.3f}\\t{}\".format(hit.relevance_score, hit.document.text.replace(\"\\n\", \" \")))\n"
      ],
      "metadata": {
        "id": "YqypDWMiq7QD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keyword_and_reranking_search(query = \"how precise was the science\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "_Rel1ZMy7c1Y",
        "outputId": "c2671016-1089-4e60-d766-576f0deb0ad7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input question: how precise was the science\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'bm25' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-88a86350205b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkeyword_and_reranking_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"how precise was the science\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-14-55d19fb6ec1b>\u001b[0m in \u001b[0;36mkeyword_and_reranking_search\u001b[0;34m(query, top_k, num_candidates)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m##### BM25 search (lexical search) #####\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mbm25_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbm25\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbm25_tokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mtop_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbm25_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mnum_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mnum_candidates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mbm25_hits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'corpus_id'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'score'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbm25_scores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtop_n\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'bm25' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Retrieval Augmented Generation**"
      ],
      "metadata": {
        "id": "5sAFjHrH7iBx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"income generated\"\n",
        "\n",
        "# 1- Retrieval\n",
        "# We'll use embedding search. But ideally we'd do hybrid\n",
        "results = search(query)\n",
        "\n",
        "# 2- Grounded Generation\n",
        "docs_dict = [{'text': text} for text in results['texts']]\n",
        "response = co.chat(\n",
        "    message = query,\n",
        "    documents=docs_dict\n",
        ")\n",
        "\n",
        "print(response.text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9D-9W8a47nt6",
        "outputId": "d285e84c-083c-484f-a042-9e5eeccbd5c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query:'income generated'\n",
            "Nearest neighbors:\n",
            "The film had a worldwide gross of over $677 million, and $773 million with subsequent re-releases.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICkQPdIRbcWa",
        "outputId": "1e72ef58-968f-4b86-da62-b957f983cdf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NonStreamedChatResponse(text='The film had a worldwide gross of over $677 million, and $773 million with subsequent re-releases.', generation_id='893f6af9-82c5-4954-bce1-04a8090113ba', citations=[ChatCitation(start=15, end=98, text='worldwide gross of over $677 million, and $773 million with subsequent re-releases.', document_ids=['doc_0'])], documents=[{'id': 'doc_0', 'text': 'The film had a worldwide gross over $677 million (and $773 million with subsequent re-releases), making it the tenth-highest grossing film of 2014'}], is_search_required=None, search_queries=None, search_results=None, finish_reason='COMPLETE', tool_calls=None, chat_history=[Message_User(message='income generated', tool_calls=None, role='USER'), Message_Chatbot(message='The film had a worldwide gross of over $677 million, and $773 million with subsequent re-releases.', tool_calls=None, role='CHATBOT')], prompt=None, meta=ApiMeta(api_version=ApiMetaApiVersion(version='1', is_deprecated=None, is_experimental=None), billed_units=ApiMetaBilledUnits(input_tokens=106, output_tokens=26, search_units=None, classifications=None), tokens=ApiMetaTokens(input_tokens=800, output_tokens=71), warnings=None), response_id='39a1a960-d520-4dd5-917c-7943bad0d857')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response.citations\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "st816d6FbgEU",
        "outputId": "ac590b9b-7290-4d97-ee28-d3ae4bee6883"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[ChatCitation(start=15, end=98, text='worldwide gross of over $677 million, and $773 million with subsequent re-releases.', document_ids=['doc_0'])]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Rag with Local Models**"
      ],
      "metadata": {
        "id": "svw18B6YbpQm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/main/Phi-3-mini-4k-instruct-fp16.gguf\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9KRfQfibu5Y",
        "outputId": "6a272938-aa26-4f2a-b538-4941c8206991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-11-18 00:09:20--  https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/main/Phi-3-mini-4k-instruct-fp16.gguf\n",
            "Resolving huggingface.co (huggingface.co)... 3.165.160.61, 3.165.160.12, 3.165.160.11, ...\n",
            "Connecting to huggingface.co (huggingface.co)|3.165.160.61|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://cdn-lfs-us-1.hf.co/repos/41/c8/41c860f65b01de5dc4c68b00d84cead799d3e7c48e38ee749f4c6057776e2e9e/5d99003e395775659b0dde3f941d88ff378b2837a8dc3a2ea94222ab1420fad3?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27Phi-3-mini-4k-instruct-fp16.gguf%3B+filename%3D%22Phi-3-mini-4k-instruct-fp16.gguf%22%3B&Expires=1732147760&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczMjE0Nzc2MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzQxL2M4LzQxYzg2MGY2NWIwMWRlNWRjNGM2OGIwMGQ4NGNlYWQ3OTlkM2U3YzQ4ZTM4ZWU3NDlmNGM2MDU3Nzc2ZTJlOWUvNWQ5OTAwM2UzOTU3NzU2NTliMGRkZTNmOTQxZDg4ZmYzNzhiMjgzN2E4ZGMzYTJlYTk0MjIyYWIxNDIwZmFkMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=l8SOS%7EV20AcZsbJSZ0RkYmeiDozl-VLy2OxYvmSyRNUkfAtExSR2OtowFPlGo6NSbspWOCfKmlNSm8zpddP9fKKSAtdKC5qwDDxVH-8jB8KQXw%7EOPyWjId7Uk2XT864no-Z3jziRmY6t3fnVFA%7EAh6BbyRKa6zpuPJDq6bSGZhFrsJMCA3buYisMZZeHFx6OIVqRPs8L-brC3MsIM6H8bwP31i3HoCRNx1tkOisCOdX5ydphULg6sH%7EL8RO7Qzr-EM85YQq6CuAUwuj7NEO0yciFTbh51bTuYb5hXZi7bAswhcHtT6ScV6LmT%7EiFY6bJPVyg8RK%7EOqRcLBCfuC3ipQ__&Key-Pair-Id=K24J24Z295AEI9 [following]\n",
            "--2024-11-18 00:09:20--  https://cdn-lfs-us-1.hf.co/repos/41/c8/41c860f65b01de5dc4c68b00d84cead799d3e7c48e38ee749f4c6057776e2e9e/5d99003e395775659b0dde3f941d88ff378b2837a8dc3a2ea94222ab1420fad3?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27Phi-3-mini-4k-instruct-fp16.gguf%3B+filename%3D%22Phi-3-mini-4k-instruct-fp16.gguf%22%3B&Expires=1732147760&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczMjE0Nzc2MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzQxL2M4LzQxYzg2MGY2NWIwMWRlNWRjNGM2OGIwMGQ4NGNlYWQ3OTlkM2U3YzQ4ZTM4ZWU3NDlmNGM2MDU3Nzc2ZTJlOWUvNWQ5OTAwM2UzOTU3NzU2NTliMGRkZTNmOTQxZDg4ZmYzNzhiMjgzN2E4ZGMzYTJlYTk0MjIyYWIxNDIwZmFkMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=l8SOS%7EV20AcZsbJSZ0RkYmeiDozl-VLy2OxYvmSyRNUkfAtExSR2OtowFPlGo6NSbspWOCfKmlNSm8zpddP9fKKSAtdKC5qwDDxVH-8jB8KQXw%7EOPyWjId7Uk2XT864no-Z3jziRmY6t3fnVFA%7EAh6BbyRKa6zpuPJDq6bSGZhFrsJMCA3buYisMZZeHFx6OIVqRPs8L-brC3MsIM6H8bwP31i3HoCRNx1tkOisCOdX5ydphULg6sH%7EL8RO7Qzr-EM85YQq6CuAUwuj7NEO0yciFTbh51bTuYb5hXZi7bAswhcHtT6ScV6LmT%7EiFY6bJPVyg8RK%7EOqRcLBCfuC3ipQ__&Key-Pair-Id=K24J24Z295AEI9\n",
            "Resolving cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)... 3.165.160.77, 3.165.160.38, 3.165.160.3, ...\n",
            "Connecting to cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)|3.165.160.77|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7643295904 (7.1G) [binary/octet-stream]\n",
            "Saving to: ‘Phi-3-mini-4k-instruct-fp16.gguf’\n",
            "\n",
            "Phi-3-mini-4k-instr 100%[===================>]   7.12G  40.1MB/s    in 3m 1s   \n",
            "\n",
            "2024-11-18 00:12:22 (40.2 MB/s) - ‘Phi-3-mini-4k-instruct-fp16.gguf’ saved [7643295904/7643295904]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import LlamaCpp\n",
        "\n",
        "# Make sure the model path is correct for your system!\n",
        "llm = LlamaCpp(\n",
        "    model_path=\"Phi-3-mini-4k-instruct-fp16.gguf\",\n",
        "    n_gpu_layers=-1,\n",
        "    max_tokens=500,\n",
        "    n_ctx=2048,\n",
        "    seed=42,\n",
        "    verbose=False\n",
        ")"
      ],
      "metadata": {
        "id": "Amf8h6xQbzyj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Loading the Embedding Model**"
      ],
      "metadata": {
        "id": "Cx3s-XK2b6PG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "# Embedding Model for converting text to numerical representations\n",
        "embedding_model = HuggingFaceEmbeddings(\n",
        "    model_name='thenlper/gte-small'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531,
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          ]
        },
        "id": "9KD5AU6Jb_ZO",
        "outputId": "a1f3f009-a7ae-4b73-ac17-c74e5c26c6c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-3b1d61103f79>:4: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
            "  embedding_model = HuggingFaceEmbeddings(\n",
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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": [
              "modules.json:   0%|          | 0.00/385 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/68.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/57.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "config.json:   0%|          | 0.00/583 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/66.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/394 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2402907477f464c901f838ed4800a65"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Preparing the Vector Database**"
      ],
      "metadata": {
        "id": "zGQU3whTcIef"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.vectorstores import FAISS\n",
        "\n",
        "# Create a local vector database\n",
        "db = FAISS.from_texts(texts, embedding_model)"
      ],
      "metadata": {
        "id": "OlEDvk_ocM0g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**the RAG Prompt**\n"
      ],
      "metadata": {
        "id": "eWpv7zDpcTj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate\n",
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "\n",
        "# Create a prompt template\n",
        "template = \"\"\"<|user|>\n",
        "Relevant information:\n",
        "{context}\n",
        "\n",
        "Provide a concise answer the following question using the relevant information provided above:\n",
        "{question}<|end|>\n",
        "<|assistant|>\"\"\"\n",
        "prompt = PromptTemplate(\n",
        "    template=template,\n",
        "    input_variables=[\"context\", \"question\"]\n",
        ")\n",
        "\n",
        "# RAG Pipeline\n",
        "rag = RetrievalQA.from_chain_type(\n",
        "    llm=llm,\n",
        "    chain_type='stuff',\n",
        "    retriever=db.as_retriever(),\n",
        "    chain_type_kwargs={\n",
        "        \"prompt\": prompt\n",
        "    },\n",
        "    verbose=True\n",
        ")"
      ],
      "metadata": {
        "id": "j9si5_xDcUBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rag.invoke('Income generated')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJM36V7Fcch3",
        "outputId": "feb765b3-bafc-4b11-9d27-e3882d065027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'query': 'Income generated',\n",
              " 'result': ' The Inception (2010) movie grossed over $829 million worldwide.\\n\\nThe Interstellar movie, shot by Hoyte van Hoytema on 35 mm and IMAX formats, grossed a total of over $677 million in its theatrical run. It became the tenth-highest grossing film of 2014 after subsequent re-releases.'}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    }
  ]
}