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Semantic Search w/ Pinecone
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/Daethyra/08e5087e17f02db37a1a41bbe239cc56/copy-of-semantic-search.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "k7Lc9I6taO3k" | |
| }, | |
| "source": [ | |
| "[](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/semantic-search.ipynb) [](https://nbviewer.org/github/pinecone-io/examples/blob/master/docs/semantic-search.ipynb)\n", | |
| "\n", | |
| "# Semantic Search\n", | |
| "\n", | |
| "In this walkthrough we will see how to use Pinecone for semantic search. To begin we must install the required prerequisite libraries:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "q03L1BYEZQfe", | |
| "outputId": "3f5b4223-2c01-4c4b-b588-9b3e650015ac" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.2/177.2 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
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| "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
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| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.1/115.1 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.6/114.6 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25h Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "google-cloud-bigquery 3.10.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-cloud-bigquery-connection 1.12.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n", | |
| "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-cloud-bigquery-storage 2.22.0 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n", | |
| "google-cloud-bigquery-storage 2.22.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-cloud-datastore 2.15.2 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n", | |
| "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-cloud-firestore 2.11.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n", | |
| "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-cloud-functions 1.13.3 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n", | |
| "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-cloud-language 2.9.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n", | |
| "google-cloud-language 2.9.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-cloud-translate 3.11.3 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n", | |
| "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.1 which is incompatible.\n", | |
| "grpc-google-iam-v1 0.12.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "pandas-gbq 0.17.9 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 11.0.0 which is incompatible.\n", | |
| "tensorboard 2.13.0 requires protobuf>=3.19.6, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "tensorflow 2.13.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "tensorflow-datasets 4.9.3 requires protobuf>=3.20, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "tensorflow-hub 0.14.0 requires protobuf>=3.19.6, but you have protobuf 3.19.3 which is incompatible.\n", | |
| "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.3 which is incompatible.\u001b[0m\u001b[31m\n", | |
| "\u001b[0m" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install -qU \\\n", | |
| " \"pinecone-client[grpc]\"==2.2.1 \\\n", | |
| " pinecone-datasets=='0.5.0rc11' \\\n", | |
| " sentence-transformers==2.2.2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ePtLFjA02iM-" | |
| }, | |
| "source": [ | |
| "---\n", | |
| "\n", | |
| "🚨 _Note: the above `pip install` is formatted for Jupyter notebooks. If running elsewhere you may need to drop the `!`._\n", | |
| "\n", | |
| "---" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hrSfFiIC5roI" | |
| }, | |
| "source": [ | |
| "## Data Download" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "kujS_e8s55oJ" | |
| }, | |
| "source": [ | |
| "In this notebook we will skip the data preparation steps as they can be very time consuming and jump straight into it with the prebuilt dataset from *Pinecone Datasets*. If you'd rather see how it's all done, please refer to [this notebook](https://colab.research.google.com/github/pinecone-io/examples/blob/master/search/semantic-search/semantic-search.ipynb).\n", | |
| "\n", | |
| "Let's go ahead and download the dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "lOgjRG52Zqqz", | |
| "outputId": "d6c29697-de08-4b0d-e0c5-b9a89518246d" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " id values \\\n", | |
| "240000 515997 [-0.00531694, 0.06937869, -0.0092854, 0.003286... \n", | |
| "240001 515998 [-0.09243751, 0.065432355, -0.06946959, 0.0669... \n", | |
| "240002 515999 [-0.021924071, 0.032280188, -0.020190848, 0.07... \n", | |
| "240003 516000 [-0.120020054, 0.024080949, 0.10693012, -0.018... \n", | |
| "240004 516001 [-0.095293395, -0.048446465, -0.017618902, -0.... \n", | |
| "\n", | |
| " sparse_values \\\n", | |
| "240000 {'indices': [845, 1657, 13677, 20780, 27058, 2... \n", | |
| "240001 {'indices': [2110, 6324, 9754, 13677, 15207, 2... \n", | |
| "240002 {'indices': [2110, 4949, 23579, 23758, 27058, ... \n", | |
| "240003 {'indices': [22014, 24734, 24773, 25791, 25991... \n", | |
| "240004 {'indices': [307, 2110, 5785, 12969, 12971, 13... \n", | |
| "\n", | |
| " metadata \n", | |
| "240000 {'text': ' Why is a \"law of sciences\" importan... \n", | |
| "240001 {'text': ' Is it possible to format a BitLocke... \n", | |
| "240002 {'text': ' Can formatting a hard drive stress ... \n", | |
| "240003 {'text': ' Are the new Samsung Galaxy J7 and J... \n", | |
| "240004 {'text': ' I just watched an add for Indonesia... " | |
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| " <td>515999</td>\n", | |
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| " <td>{'indices': [307, 2110, 5785, 12969, 12971, 13...</td>\n", | |
| " <td>{'text': ' I just watched an add for Indonesia...</td>\n", | |
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| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0cec84f0-6ac0-4914-8f69-74db2aa7f4d7')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
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| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
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| " display: none;\n", | |
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| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert: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", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert: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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| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-0cec84f0-6ac0-4914-8f69-74db2aa7f4d7');\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", | |
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| " --disabled-bg-color: #DDD;\n", | |
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| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
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| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
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| " 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", | |
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| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
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| "\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", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
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| " border-left-color: var(--fill-color);\n", | |
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| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
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| " border-bottom-color: var(--fill-color);\n", | |
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| "\n", | |
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| " 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", | |
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| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-768f09a0-1212-48c1-8c7d-8820e9bb1013 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
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| "</div>\n", | |
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| " </div>\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 2 | |
| } | |
| ], | |
| "source": [ | |
| "from pinecone_datasets import load_dataset\n", | |
| "\n", | |
| "dataset = load_dataset('quora_all-MiniLM-L6-bm25')\n", | |
| "# we drop sparse_values as they are not needed for this example\n", | |
| "dataset.documents.drop(['metadata'], axis=1, inplace=True)\n", | |
| "dataset.documents.rename(columns={'blob': 'metadata'}, inplace=True)\n", | |
| "# we will use 80K rows of the dataset between rows 240K -> 320K\n", | |
| "dataset.documents.drop(dataset.documents.index[320_000:], inplace=True)\n", | |
| "dataset.documents.drop(dataset.documents.index[:240_000], inplace=True)\n", | |
| "dataset.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "conuh2Uo-mwR", | |
| "outputId": "632495de-9b89-4fdf-f73c-2ea2a8f293fc" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "80000\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(len(dataset))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ebd7XSamfMsC" | |
| }, | |
| "source": [ | |
| "## Creating an Index\n", | |
| "\n", | |
| "Now the data is ready, we can set up our index to store it.\n", | |
| "\n", | |
| "We begin by initializing our connection to Pinecone. To do this we need a [free API key](https://app.pinecone.io)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "id": "mc66NEBAcQHY" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import os\n", | |
| "import pinecone\n", | |
| "\n", | |
| "# get api key from app.pinecone.io\n", | |
| "PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY') or 'api-key'\n", | |
| "# find your environment next to the api key in pinecone console\n", | |
| "PINECONE_ENV = os.environ.get('PINECONE_ENVIRONMENT') or 'us-central1-gcp'\n", | |
| "\n", | |
| "pinecone.init(\n", | |
| " api_key=PINECONE_API_KEY,\n", | |
| " environment=PINECONE_ENV\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "SdaTip6CfllN" | |
| }, | |
| "source": [ | |
| "Now we create a new index called `semantic-search-fast`. It's important that we align the index `dimension` and `metric` parameters with those required by the `MiniLM-L6` model." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "tags": [ | |
| "parameters" | |
| ], | |
| "id": "hYgrmwRy2iNA" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "index_name = 'semantic-search-fast'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "id": "p1pyQh8gfm2-" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import time\n", | |
| "\n", | |
| "# only create index if it doesn't exist\n", | |
| "if index_name not in pinecone.list_indexes():\n", | |
| " pinecone.create_index(\n", | |
| " name=index_name,\n", | |
| " dimension=len(dataset.documents.iloc[0]['values']),\n", | |
| " metric='cosine'\n", | |
| " )\n", | |
| " # wait a moment for the index to be fully initialized\n", | |
| " time.sleep(1)\n", | |
| "\n", | |
| "# now connect to the index\n", | |
| "index = pinecone.GRPCIndex(index_name)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "YUd1VGg6i108" | |
| }, | |
| "source": [ | |
| "Upsert the data:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "id": "RhR6WOi1huXZ" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "for batch in dataset.iter_documents(batch_size=100):\n", | |
| " index.upsert(batch)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "VrK_IN079Vuu" | |
| }, | |
| "source": [ | |
| "## Making Queries" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "rr4unPAq9alb" | |
| }, | |
| "source": [ | |
| "Now that our index is populated we can begin making queries. We are performing a semantic search for *similar questions*, so we should embed and search with another question. Let's begin." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
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| "SentenceTransformer(\n", | |
| " (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel \n", | |
| " (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n", | |
| " (2): Normalize()\n", | |
| ")" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ], | |
| "source": [ | |
| "from sentence_transformers import SentenceTransformer\n", | |
| "import torch\n", | |
| "\n", | |
| "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", | |
| "\n", | |
| "model = SentenceTransformer('all-MiniLM-L6-v2', device=device)\n", | |
| "model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "MP2-unZ--XJ9" | |
| }, | |
| "source": [ | |
| "Now let's query." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "JWcO7jAK-N_1", | |
| "outputId": "353c3e81-031c-493d-a987-0608538d4974" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "{'matches': [{'id': '69331',\n", | |
| " 'metadata': {'text': \" What's the world's largest city?\"},\n", | |
| " 'score': 0.78565526,\n", | |
| " 'sparse_values': {'indices': [], 'values': []},\n", | |
| " 'values': []},\n", | |
| " {'id': '69332',\n", | |
| " 'metadata': {'text': ' What is the biggest city?'},\n", | |
| " 'score': 0.72713953,\n", | |
| " 'sparse_values': {'indices': [], 'values': []},\n", | |
| " 'values': []},\n", | |
| " {'id': '84749',\n", | |
| " 'metadata': {'text': \" What are the world's most advanced \"\n", | |
| " 'cities?'},\n", | |
| " 'score': 0.7092115,\n", | |
| " 'sparse_values': {'indices': [], 'values': []},\n", | |
| " 'values': []},\n", | |
| " {'id': '109231',\n", | |
| " 'metadata': {'text': ' Where is the most beautiful city in the '\n", | |
| " 'world?'},\n", | |
| " 'score': 0.696055,\n", | |
| " 'sparse_values': {'indices': [], 'values': []},\n", | |
| " 'values': []},\n", | |
| " {'id': '109230',\n", | |
| " 'metadata': {'text': ' What is the greatest, most beautiful city '\n", | |
| " 'in the world?'},\n", | |
| " 'score': 0.6574446,\n", | |
| " 'sparse_values': {'indices': [], 'values': []},\n", | |
| " 'values': []}],\n", | |
| " 'namespace': ''}" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 10 | |
| } | |
| ], | |
| "source": [ | |
| "query = \"which city has the highest population in the world?\"\n", | |
| "\n", | |
| "# create the query vector\n", | |
| "xq = model.encode(query).tolist()\n", | |
| "\n", | |
| "# now query\n", | |
| "xc = index.query(xq, top_k=5, include_metadata=True)\n", | |
| "xc" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "-XwOWcgo_QtI" | |
| }, | |
| "source": [ | |
| "In the returned response `xc` we can see the most relevant questions to our particular query — we don't have any exact matches but we can see that the returned questions are similar in the topics they are asking about. We can reformat this response to be a little easier to read:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "id": "gy7isg_f-vWg", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "a1bbbbee-ca99-40bd-de06-ed90ff59c140" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0.79: What's the world's largest city?\n", | |
| "0.73: What is the biggest city?\n", | |
| "0.71: What are the world's most advanced cities?\n", | |
| "0.7: Where is the most beautiful city in the world?\n", | |
| "0.66: What is the greatest, most beautiful city in the world?\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for result in xc['matches']:\n", | |
| " print(f\"{round(result['score'], 2)}: {result['metadata']['text']}\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1JK5yApl_5fE" | |
| }, | |
| "source": [ | |
| "These are good results, let's try and modify the words being used to see if we still surface similar results." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "id": "dJbjE-iq_yMr", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "e4a28017-c66e-48a7-cdb0-d3799441a0af" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "0.64: What is the biggest city?\n", | |
| "0.6: What is the most dangerous city in USA?\n", | |
| "0.59: What's the world's largest city?\n", | |
| "0.59: What is the most dangerous city in USA? Why?\n", | |
| "0.58: What are the world's most advanced cities?\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "query = \"which metropolis has the highest number of people?\"\n", | |
| "\n", | |
| "# create the query vector\n", | |
| "xq = model.encode(query).tolist()\n", | |
| "\n", | |
| "# now query\n", | |
| "xc = index.query(xq, top_k=5, include_metadata=True)\n", | |
| "for result in xc['matches']:\n", | |
| " print(f\"{round(result['score'], 2)}: {result['metadata']['text']}\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "HIAxOPb-A2w_" | |
| }, | |
| "source": [ | |
| "Here we used different terms in our query than that of the returned documents. We substituted **\"city\"** for **\"metropolis\"** and **\"populated\"** for **\"number of people\"**.\n", | |
| "\n", | |
| "Despite these very different terms and *lack* of term overlap between query and returned documents — we get highly relevant results — this is the power of *semantic search*.\n", | |
| "\n", | |
| "You can go ahead and ask more questions above. When you're done, delete the index to save resources:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "id": "-cWdeKzhAtww" | |
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| "source": [ | |
| "pinecone.delete_index(index_name)" | |
| ] | |
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