12: GPT-3_Embeddings_Similarity_Semantic_Clustering_.ipynb

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      "source": [
        "!pip install openai"
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        "The **embedding is an information dense representation of the semantic meaning of a piece of text**. <br>\n",
        "Each embedding is a vector of floating point numbers, such that the **distance between two embeddings in the vector space is correlated with semantic similarity between two inputs** in the original format. <br>\n",
        "For example, if two texts are similar, then their vector representations should also be similar."
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        "**Use cases:**\n",
        "\n",
        "*   Text Similarity\n",
        "*   Semantic Search\n",
        "*   Classification\n",
        "*   Clustering\n"
      ],
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        "\n",
        "\n",
        "1.   **Similarity embeddings** : These models are good at capturing semantic similarity between two or more pieces of text.\n",
        "2.   **Text search embeddings**: These models help measure whether long documents are relevant to a short search query. There are two types: one for ***embedding the documents*** to be retrieved, and one for ***embedding the search query***.\n",
        "\n",
        "\n"
      ],
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      "source": [
        "import pandas as pd\n",
        "import openai, numpy as np\n",
        "from openai.embeddings_utils import get_embedding, cosine_similarity"
      ],
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      "source": [
        "<h3>Text Similarity</h3>"
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        "resp = openai.Embedding.create(\n",
        "    input=[\"eating food\", \"I am hungry\", \"I am traveling\" , \"exploring new places\"],\n",
        "    engine=\"text-similarity-davinci-001\")"
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              "list"
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            "text/plain": [
              "dict_keys(['object', 'index', 'embedding'])"
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              " ...]"
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_a = resp['data'][0]['embedding']\n",
        "embedding_b = resp['data'][1]['embedding']\n",
        "embedding_c = resp['data'][2]['embedding']\n",
        "embedding_d = resp['data'][3]['embedding']"
      ],
      "metadata": {
        "id": "I5S-imSkUGyI"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.dot(embedding_a, embedding_b)"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "1WlXsaxuUJ78",
        "outputId": "78acab7f-134f-4ba1-9557-48cb9ba69741"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8724587274814626"
            ]
          },
          "metadata": {},
          "execution_count": 11
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    {
      "cell_type": "code",
      "source": [
        "np.dot(embedding_a, embedding_c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "y1_A3ZTVUKhM",
        "outputId": "16cee609-14b9-40e1-fd9d-02e81452d13f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7891928072645734"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "np.dot(embedding_c, embedding_d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qr9yXvzfUM4v",
        "outputId": "31a47080-df41-4b48-bf38-fd0c3b382862"
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8543601927339739"
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          "metadata": {},
          "execution_count": 13
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    },
    {
      "cell_type": "code",
      "source": [
        "# https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews\n",
        "datafile_path = \"https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\"  # for your convenience, we precomputed the embeddings\n",
        "df = pd.read_csv(datafile_path)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KHaFc5ZXbNdA",
        "outputId": "aa6a7f61-db35-442a-f7d1-cfa4552e4bf2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    ProductId          UserId  Score  \\\n",
              "0  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
              "1  B003JK537S  A3JBPC3WFUT5ZP      1   \n",
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              "                                             Summary  \\\n",
              "0  where does one  start...and stop... with a tre...   \n",
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              "2          It isn't blanc mange, but isn't bad . . .   \n",
              "3        These also have SALT and it's not sea salt.   \n",
              "4                             Happy with the product   \n",
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              "                                                Text  \\\n",
              "0  Wanted to save some to bring to my Chicago fam...   \n",
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              "\n",
              "                                            combined  n_tokens  \\\n",
              "0  Title: where does one  start...and stop... wit...        51   \n",
              "1  Title: Arrived in pieces; Content: Not pleased...        35   \n",
              "2  Title: It isn't blanc mange, but isn't bad . ....       277   \n",
              "3  Title: These also have SALT and it's not sea s...       246   \n",
              "4  Title: Happy with the product; Content: My dog...        87   \n",
              "\n",
              "                                  babbage_similarity  \\\n",
              "0  [-0.01274053193628788, 0.010849879123270512, -...   \n",
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              "                                      babbage_search  \n",
              "0  [-0.01880764216184616, 0.019457539543509483, -...  \n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-207c78ad-0fb1-4ce3-8c18-54e6f5ef41c3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-207c78ad-0fb1-4ce3-8c18-54e6f5ef41c3');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df.loc[0]['babbage_search'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN194_mDmIy_",
        "outputId": "32fd8971-2b76-49a4-be58-5e19f6881922"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bL4X-TmZbmJk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"babbage_search\"] = df.babbage_search.apply(eval).apply(np.array)\n",
        "df[\"babbage_similarity\"] = df.babbage_similarity.apply(eval).apply(np.array)"
      ],
      "metadata": {
        "id": "dLSy8ojhbPmo"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(df.loc[0]['babbage_search'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKRJCIz7mgXe",
        "outputId": "8f48d686-8fe1-4db0-89eb-f3dedae00e63"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>Semantic Search</h3>"
      ],
      "metadata": {
        "id": "r_hCLQt9V527"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# search through the reviews for a specific product\n",
        "def search_reviews(df, search_query, n=3):\n",
        "    embedding = get_embedding(\n",
        "        search_query,\n",
        "        engine=\"text-search-babbage-query-001\"\n",
        "    )\n",
        "    df[\"similarities\"] = df.babbage_search.apply(lambda x: cosine_similarity(x, embedding))\n",
        "\n",
        "    top_n =df.sort_values(\"similarities\", ascending=False).head(n)\n",
        "    # res = top_n.combined.str.replace(\"Title: \", \"\").str.replace(\"; Content:\", \": \")\n",
        "    return top_n"
      ],
      "metadata": {
        "id": "LeKKERLFcs4Q"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res = search_reviews(df, \"delicious beans\", n=3)\n",
        "res['combined'].to_list()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SA7xS9bKcGJ2",
        "outputId": "885b1e8c-b086-4be9-f89a-b8f2f451f055"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Title: Fantastic Instant Refried beans; Content: Fantastic Instant Refried Beans have been a staple for my family now for nearly 20 years.  All 7 of us love it and my grown kids are passing on the tradition.',\n",
              " 'Title: Jamaican Blue beans; Content: Excellent coffee bean for roasting. Our family just purchased another 5 pounds for more roasting. Plenty of flavor and mild on acidity when roasted to a dark brown bean and before any oil appears on the bean itself (455F @ 17 minutes).',\n",
              " \"Title: Delicious!; Content: I enjoy this white beans seasoning, it gives a rich flavor to the beans I just love it, my mother in law didn't know about this Zatarain's brand and now she is traying different seasoning and she likes it very much.<br />Thank you Amazon for having it because now I can't find it in stores, I like to have this 12 boxes because I can made it whenever my family want it.\"]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>Classification</h3>"
      ],
      "metadata": {
        "id": "pdM1bDg9WAcx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        " \n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    list(df.babbage_similarity.values),\n",
        "    df.Score,\n",
        "    test_size = 0.2,\n",
        "    random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "eAcdOnDGeyP5"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        " \n",
        "clf = RandomForestClassifier(n_estimators=100)\n",
        "clf.fit(X_train, y_train)\n",
        "preds = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "ieOg8bVRldiM"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AoSIs40elsIC",
        "outputId": "d9697637-409c-4e96-e2ce-4f0f7a4acc4e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.65      0.72      0.68        18\n",
            "           2       1.00      0.35      0.52        17\n",
            "           3       0.50      0.12      0.20         8\n",
            "           4       0.83      0.38      0.53        26\n",
            "           5       0.81      0.99      0.89       131\n",
            "\n",
            "    accuracy                           0.80       200\n",
            "   macro avg       0.76      0.52      0.57       200\n",
            "weighted avg       0.80      0.80      0.77       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N0A2najnKFJ",
        "outputId": "165e0d38-b905-49b0-b682-ce13d2349c97"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Score'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCIPgmienray",
        "outputId": "a2519d50-3657-4d52-c777-ba61dab9e597"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    0.651\n",
              "4    0.138\n",
              "1    0.087\n",
              "3    0.075\n",
              "2    0.049\n",
              "Name: Score, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>Clustering</h3>"
      ],
      "metadata": {
        "id": "jI9wUPHSVzHn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# source: https://stackoverflow.com/questions/55619176/how-to-cluster-similar-sentences-using-bert\n",
        "from sklearn.cluster import KMeans\n",
        "# Corpus with example sentences\n",
        "corpus = ['A man is eating food.',\n",
        "          'A man is eating a piece of bread.',\n",
        "          'Horse is eating grass.',\n",
        "          'A man is eating pasta.',\n",
        "          'A Woman is eating Biryani.',\n",
        "          'The girl is carrying a baby.',\n",
        "          'The baby is carried by the woman',\n",
        "          'A man is riding a horse.',\n",
        "          'A man is riding a white horse on an enclosed ground.',\n",
        "          'A monkey is playing drums.',\n",
        "          'Someone in a gorilla costume is playing a set of drums.',\n",
        "          'A cheetah is running behind its prey.',\n",
        "          'A cheetah chases prey on across a field.',\n",
        "          'The cheetah is chasing a man who is riding the horse.',\n",
        "          'man and women with their baby are watching cheetah in zoo'\n",
        "          ]"
      ],
      "metadata": {
        "id": "1qUlu0JRvW2g"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = openai.Embedding.create(\n",
        "    input=corpus,\n",
        "    model=\"text-similarity-babbage-001\"\n",
        ")"
      ],
      "metadata": {
        "id": "j1HnsmaqwyJk"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(response['data'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEajN013w7_z",
        "outputId": "fb03f3e5-4073-4907-dad2-9dc2a621cc9a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corpus_embeddings = [ d['embedding'] for d in response['data']]\n",
        "# Normalize the embeddings to unit length\n",
        "corpus_embeddings = corpus_embeddings /  np.linalg.norm(corpus_embeddings, axis=1, keepdims=True)"
      ],
      "metadata": {
        "id": "jyDrgM8jw1wH"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clustering_model = KMeans(n_clusters=3)\n",
        "clustering_model.fit(corpus_embeddings)\n",
        "cluster_assignment = clustering_model.labels_\n",
        "print(cluster_assignment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejRizWSdw5jR",
        "outputId": "52d673df-3b87-458c-b945-67202ae3dc86"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 1 0 0 2 2 1 1 2 2 2 2 1 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_sentences = {}\n",
        "for sentence_id, cluster_id in enumerate(cluster_assignment):\n",
        "    if cluster_id not in clustered_sentences:\n",
        "        clustered_sentences[cluster_id] = []\n",
        "\n",
        "    clustered_sentences[cluster_id].append(corpus[sentence_id])\n",
        "clustered_sentences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNjism-SxQHW",
        "outputId": "82db35f2-4ea4-49e2-e0a9-7089e0cb1028"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: ['A man is eating food.',\n",
              "  'A man is eating a piece of bread.',\n",
              "  'A man is eating pasta.',\n",
              "  'A Woman is eating Biryani.'],\n",
              " 1: ['Horse is eating grass.',\n",
              "  'A man is riding a horse.',\n",
              "  'A man is riding a white horse on an enclosed ground.',\n",
              "  'The cheetah is chasing a man who is riding the horse.'],\n",
              " 2: ['The girl is carrying a baby.',\n",
              "  'The baby is carried by the woman',\n",
              "  'A monkey is playing drums.',\n",
              "  'Someone in a gorilla costume is playing a set of drums.',\n",
              "  'A cheetah is running behind its prey.',\n",
              "  'A cheetah chases prey on across a field.',\n",
              "  'man and women with their baby are watching cheetah in zoo']}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clustering_model = KMeans(n_clusters=4)\n",
        "clustering_model.fit(corpus_embeddings)\n",
        "cluster_assignment = clustering_model.labels_\n",
        "print(cluster_assignment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lrg1QtCSxRe-",
        "outputId": "0b757e58-8c0a-45a8-9739-87c3cd2d2116"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 2 2 2 0 0 3 3 1 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_sentences = {}\n",
        "for sentence_id, cluster_id in enumerate(cluster_assignment):\n",
        "    if cluster_id not in clustered_sentences:\n",
        "        clustered_sentences[cluster_id] = []\n",
        "\n",
        "    clustered_sentences[cluster_id].append(corpus[sentence_id])\n",
        "clustered_sentences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6naeu1RYxx5A",
        "outputId": "b23675e0-5a54-49f7-e13c-83f8e4ccd37c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: ['A man is eating food.',\n",
              "  'A man is eating a piece of bread.',\n",
              "  'Horse is eating grass.',\n",
              "  'A man is eating pasta.',\n",
              "  'A man is riding a horse.',\n",
              "  'A man is riding a white horse on an enclosed ground.'],\n",
              " 1: ['A cheetah is running behind its prey.',\n",
              "  'A cheetah chases prey on across a field.',\n",
              "  'The cheetah is chasing a man who is riding the horse.',\n",
              "  'man and women with their baby are watching cheetah in zoo'],\n",
              " 2: ['A Woman is eating Biryani.',\n",
              "  'The girl is carrying a baby.',\n",
              "  'The baby is carried by the woman'],\n",
              " 3: ['A monkey is playing drums.',\n",
              "  'Someone in a gorilla costume is playing a set of drums.']}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    }
  ]
}