diff --git a/.github/actions/check-links/check-links.py b/.github/actions/check-links/check-links.py index c93d51ab..a151dafe 100644 --- a/.github/actions/check-links/check-links.py +++ b/.github/actions/check-links/check-links.py @@ -18,6 +18,12 @@ "https://www.pinecone.io", "https://app.pinecone.io", ] + +ignore_links = [ + 'platform.openai.com', # cloudflare blocks requests sometimes + 'nbviewer.org' # nbviewer has a pretty strict rate limit, so we don't want to waste requests +] + known_good_links = set(known_good) for link in known_good: known_good_links.add(f"{link}/") @@ -46,6 +52,9 @@ if links: print(f"\nFile: {filename}") for link in sorted(links): + if any(ignore_link in link for ignore_link in ignore_links): + print(f" ⏭️ {link}") + continue if link in known_good_links: good_links.add(link) print(f" ✅ {link}") diff --git a/docs/onboarding-recommender.ipynb b/docs/onboarding-recommender.ipynb index 5efff0c3..961778ec 100644 --- a/docs/onboarding-recommender.ipynb +++ b/docs/onboarding-recommender.ipynb @@ -1,3916 +1,3892 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "qsGKhkub2LTf" - }, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/onboarding-recommender.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/docs/onboarding-recommender.ipynb)\n", - "\n", - "# Recommendation Systems\n", - "\n", - "Recommendation systems have revolutionized the way we discover and explore new things. These intelligent systems utilize sophisticated algorithms and data analysis to understand individual preferences and provide personalized recommendations.\n", - "

\n", - "By analyzing user data, such as **browsing history**, **purchase patterns**, and **social interactions**, recommendation systems can effectively predict and suggest items that align with users' interests. Whether it's suggesting a new movie to watch, a book to read, or a product to buy, these systems streamline decision-making and enhance the overall user experience.\n", - "
\n", - "With their ability to uncover hidden gems and introduce users to exciting possibilities, recommendation systems have become invaluable tools in navigating the overwhelming abundance of choices in today's digital landscape.\n", - "\n", - "### Recommendation Systems and Vector Databases\n", - "\n", - "In recommendation systems, understanding the similarity between users and items is crucial for generating accurate and personalized recommendations. By leveraging vector databases, these systems can store and organize user and item vectors, which capture the essential characteristics and preferences associated with each user and item.\n", - "

\n", - "The vector database employs advanced indexing techniques, to enable fast retrieval of **similar users or items** based on their **vector representations**. This enables recommendation systems to efficiently process *large-scale datasets* and identify meaningful connections, leading to more precise and relevant recommendations.\n", - "

\n", - "By harnessing the power of vector databases, such as **Pinecone**, recommendation systems can optimize their performance, enhance user satisfaction, and deliver tailored experiences that align with individual preferences.\n", - "


\n", - "Let's take a look at how we can implement one of those use cases!" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "qsGKhkub2LTf" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/onboarding-recommender.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/docs/onboarding-recommender.ipynb)\n", + "\n", + "# Recommendation Systems\n", + "\n", + "Recommendation systems have revolutionized the way we discover and explore new things. These intelligent systems utilize sophisticated algorithms and data analysis to understand individual preferences and provide personalized recommendations.\n", + "

\n", + "By analyzing user data, such as **browsing history**, **purchase patterns**, and **social interactions**, recommendation systems can effectively predict and suggest items that align with users' interests. Whether it's suggesting a new movie to watch, a book to read, or a product to buy, these systems streamline decision-making and enhance the overall user experience.\n", + "
\n", + "With their ability to uncover hidden gems and introduce users to exciting possibilities, recommendation systems have become invaluable tools in navigating the overwhelming abundance of choices in today's digital landscape.\n", + "\n", + "### Recommendation Systems and Vector Databases\n", + "\n", + "In recommendation systems, understanding the similarity between users and items is crucial for generating accurate and personalized recommendations. By leveraging vector databases, these systems can store and organize user and item vectors, which capture the essential characteristics and preferences associated with each user and item.\n", + "

\n", + "The vector database employs advanced indexing techniques, to enable fast retrieval of **similar users or items** based on their **vector representations**. This enables recommendation systems to efficiently process *large-scale datasets* and identify meaningful connections, leading to more precise and relevant recommendations.\n", + "

\n", + "By harnessing the power of vector databases, such as **Pinecone**, recommendation systems can optimize their performance, enhance user satisfaction, and deliver tailored experiences that align with individual preferences.\n", + "


\n", + "Let's take a look at how we can implement one of those use cases!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mxuvFMzo2LTg" + }, + "source": [ + "We start by installing all necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "rvEdmJGF2LTh", + "outputId": "45fe7415-9dee-4597-b368-8ec171426476" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "mxuvFMzo2LTg" - }, - "source": [ - "We start by installing all necessary libraries." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.2/177.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m57.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + 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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 1.13.2 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-functions 1.13.2 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-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 2.0.3 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", + "tensorflow-datasets 4.9.2 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-datasets==1.0.2 \\\n", + " pinecone-notebooks==0.1.1 \\\n", + " pinecone==6.0.2 \\\n", + " tensorflow==2.19.0 \\\n", + " transformers==4.50.1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bo-qDSBZ2LTh" + }, + "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": "fBowTnsb2LTh" + }, + "source": [ + "## Data Preparation\n", + "\n", + "\"Onboarding" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JXFYLvCz2LTi" + }, + "source": [ + "#### Downloading the Dataset\n", + "\n", + "We will download a pre-embedding dataset from `pinecone-datasets`. Allowing us to skip the embedding and any other preprocessing steps.\n", + "

\n", + "When working with your own dataset you will need to perform this embedding step but we have prebuilt the embeddings so we can jump right to the action." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "DNBAE4zf2LTi", + "outputId": "59a8b688-a6b4-4491-d782-6f75e5fd0a85" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rvEdmJGF2LTh", - "outputId": "45fe7415-9dee-4597-b368-8ec171426476" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.2/177.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m57.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m781.3/781.3 kB\u001b[0m \u001b[31m56.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.1/11.1 MB\u001b[0m \u001b[31m51.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.1/143.1 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.3/120.3 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.6/115.6 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.5/115.5 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.1/115.1 kB\u001b[0m \u001b[31m11.8 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[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\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 1.13.2 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-functions 1.13.2 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-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 2.0.3 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", - "tensorflow-datasets 4.9.2 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" - ] - } + "data": { + "text/html": [ + "\n", + "\n", + "
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0tt5027774[-0.12388430535793304, 0.23021861910820007, -0...NoneNone{'imdb_id': 'tt5027774', 'movie_id': 6705, 'po...
1tt5463162[0.008479624055325985, 0.3665461540222168, -0....NoneNone{'imdb_id': 'tt5463162', 'movie_id': 7966, 'po...
2tt4007502[-0.0022702165879309177, 0.5886886715888977, -...NoneNone{'imdb_id': 'tt4007502', 'movie_id': 1614, 'po...
3tt4209788[0.08350061625242233, 0.4322584867477417, -0.2...NoneNone{'imdb_id': 'tt4209788', 'movie_id': 7022, 'po...
4tt2948356[-0.1614755392074585, 0.41389355063438416, -0....NoneNone{'imdb_id': 'tt2948356', 'movie_id': 3571, 'po...
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\n" ], - "source": [ - "!pip install -qU \\\n", - " pinecone-client==3.1.0 \\\n", - " pinecone-datasets==0.7.0 \\\n", - " transformers==4.30.2 \\\n", - " tensorflow==2.11.1 \\\n", - " pinecone-notebooks==0.1.1" + "text/plain": [ + " id values sparse_values \\\n", + "0 tt5027774 [-0.12388430535793304, 0.23021861910820007, -0... None \n", + "1 tt5463162 [0.008479624055325985, 0.3665461540222168, -0.... None \n", + "2 tt4007502 [-0.0022702165879309177, 0.5886886715888977, -... None \n", + "3 tt4209788 [0.08350061625242233, 0.4322584867477417, -0.2... None \n", + "4 tt2948356 [-0.1614755392074585, 0.41389355063438416, -0.... None \n", + "\n", + " metadata blob \n", + "0 None {'imdb_id': 'tt5027774', 'movie_id': 6705, 'po... \n", + "1 None {'imdb_id': 'tt5463162', 'movie_id': 7966, 'po... \n", + "2 None {'imdb_id': 'tt4007502', 'movie_id': 1614, 'po... \n", + "3 None {'imdb_id': 'tt4209788', 'movie_id': 7022, 'po... \n", + "4 None {'imdb_id': 'tt2948356', 'movie_id': 3571, 'po... " ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pinecone_datasets import load_dataset\n", + "\n", + "dataset_name = \"movielens-user-ratings\"\n", + "dataset = load_dataset(dataset_name)\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "I3rEGmwR2LTi", + "outputId": "15c0036f-db4f-45b9-f751-4d43e014be90" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "bo-qDSBZ2LTh" - }, - "source": [ - "---\n", - "\n", - "🚨 _Note: the above `pip install` is formatted for Jupyter notebooks. If running elsewhere you may need to drop the `!`._\n", - "\n", - "---" + "data": { + "text/plain": [ + "970582" ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RPp6NQRhxXWo" + }, + "source": [ + "We can limit the number of records within our `dataset` if on the Standard Tier of Pinecone (for paid users, you can index the full dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ovQo1JIGxXxr", + "outputId": "7fd396ed-9d75-4472-817c-fd774c59c06d" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "fBowTnsb2LTh" - }, - "source": [ - "## Data Preparation\n", - "\n", - "\"Onboarding" + "data": { + "text/plain": [ + "10000" ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = dataset.head(10_000)\n", + "len(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NwpTG_eJ2LTi" + }, + "source": [ + "#### Reformatting the Dataset\n", + "\n", + "A `pinecone-dataset` always contains `id`, `values`, `sparse_values`, `metadata`, and `blob`. All we need are the IDs, vector embeddings (stored in `values`), and some metadata (which is actually stored in `blob`). Let's reformat the dataset ready for adding to Pinecone. We also drop `sparse_values` as they are not needed for this example.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "FSFtJSfL2LTi", + "outputId": "b11f041f-7246-421d-a401-7b627d3859f4" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "JXFYLvCz2LTi" - }, - "source": [ - "#### Downloading the Dataset\n", - "\n", - "We will download a pre-embedding dataset from `pinecone-datasets`. Allowing us to skip the embedding and any other preprocessing steps.\n", - "

\n", - "When working with your own dataset you will need to perform this embedding step but we have prebuilt the embeddings so we can jump right to the action." + "data": { + "text/html": [ + "\n", + "\n", + "
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idvaluesmetadata
0tt5027774[-0.12388430535793304, 0.23021861910820007, -0...{'imdb_id': 'tt5027774', 'movie_id': 6705, 'po...
1tt5463162[0.008479624055325985, 0.3665461540222168, -0....{'imdb_id': 'tt5463162', 'movie_id': 7966, 'po...
2tt4007502[-0.0022702165879309177, 0.5886886715888977, -...{'imdb_id': 'tt4007502', 'movie_id': 1614, 'po...
3tt4209788[0.08350061625242233, 0.4322584867477417, -0.2...{'imdb_id': 'tt4209788', 'movie_id': 7022, 'po...
4tt2948356[-0.1614755392074585, 0.41389355063438416, -0....{'imdb_id': 'tt2948356', 'movie_id': 3571, 'po...
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idvaluessparse_valuesmetadatablob
0tt5027774[-0.12388430535793304, 0.23021861910820007, -0...NoneNone{'imdb_id': 'tt5027774', 'movie_id': 6705, 'po...
1tt5463162[0.008479624055325985, 0.3665461540222168, -0....NoneNone{'imdb_id': 'tt5463162', 'movie_id': 7966, 'po...
2tt4007502[-0.0022702165879309177, 0.5886886715888977, -...NoneNone{'imdb_id': 'tt4007502', 'movie_id': 1614, 'po...
3tt4209788[0.08350061625242233, 0.4322584867477417, -0.2...NoneNone{'imdb_id': 'tt4209788', 'movie_id': 7022, 'po...
4tt2948356[-0.1614755392074585, 0.41389355063438416, -0....NoneNone{'imdb_id': 'tt2948356', 'movie_id': 3571, 'po...
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\n" - ], - "text/plain": [ - " id values sparse_values \\\n", - "0 tt5027774 [-0.12388430535793304, 0.23021861910820007, -0... None \n", - "1 tt5463162 [0.008479624055325985, 0.3665461540222168, -0.... None \n", - "2 tt4007502 [-0.0022702165879309177, 0.5886886715888977, -... None \n", - "3 tt4209788 [0.08350061625242233, 0.4322584867477417, -0.2... None \n", - "4 tt2948356 [-0.1614755392074585, 0.41389355063438416, -0.... None \n", - "\n", - " metadata blob \n", - "0 None {'imdb_id': 'tt5027774', 'movie_id': 6705, 'po... \n", - "1 None {'imdb_id': 'tt5463162', 'movie_id': 7966, 'po... \n", - "2 None {'imdb_id': 'tt4007502', 'movie_id': 1614, 'po... \n", - "3 None {'imdb_id': 'tt4209788', 'movie_id': 7022, 'po... \n", - "4 None {'imdb_id': 'tt2948356', 'movie_id': 3571, 'po... " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pinecone_datasets import load_dataset\n", - "\n", - "dataset_name = \"movielens-user-ratings\"\n", - "dataset = load_dataset(dataset_name)\n", - "dataset.head()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "{'imdb_id': 'tt5027774',\n", + " 'movie_id': 6705,\n", + " 'poster': 'https://m.media-amazon.com/images/M/MV5BMjI0ODcxNzM1N15BMl5BanBnXkFtZTgwMzIwMTEwNDI@._V1_SX300.jpg',\n", + " 'rating': 4.0,\n", + " 'title': 'Three Billboards Outside Ebbing, Missouri (2017)',\n", + " 'user_id': 4556}\n" + ] + } + ], + "source": [ + "from pprint import pp\n", + "\n", + "pp(dataset[\"metadata\"][0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XU3yufR02LTi" + }, + "source": [ + "Now we move on to initializing our Pinecone vector database." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "if not os.environ.get(\"PINECONE_API_KEY\"):\n", + " from pinecone_notebooks.colab import Authenticate\n", + "\n", + " Authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pinecone import Pinecone\n", + "\n", + "api_key = os.environ.get(\"PINECONE_API_KEY\")\n", + "\n", + "# Configure the Pinecone client\n", + "pc = Pinecone(api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we setup our index specification, this allows us to define the cloud provider and region where we want to deploy our index. You can find a list of all [available providers and regions here](https://docs.pinecone.io/guides/projects/understanding-projects)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RyjrDHL32LTj" + }, + "source": [ + "In order to create a new index, we need to specify the index name, similarity metric, as well as the dimension of the vectors stored in that index.\n", + "
\n", + "We will assign these values here.\n", + "
\n", + "Note that the dimension parameter has to match the embedding dimensions provided in the dataset (or the model that outputs those embeddings)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "56Yt1eV72LTj", + "outputId": "c4ef1867-dc97-4f7b-bb26-8d5cf50d2db9" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "I3rEGmwR2LTi", - "outputId": "15c0036f-db4f-45b9-f751-4d43e014be90" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "970582" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(dataset)" + "data": { + "text/plain": [ + "32" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_dimensions = len(dataset[\"values\"][0])\n", + "embedding_dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "Rw-HJOBA2LTj", + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "index_name = \"onboarding-recommender\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BLIgf3Wl2LTj" + }, + "source": [ + "First, we need to check if the index already exists (for example, if you have already run part of this demo before). \n", + "\n", + "If the index does not exist, we create one." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "z78OHM0j2LTj" + }, + "outputs": [], + "source": [ + "from pinecone import ServerlessSpec\n", + "\n", + "if not pc.has_index(name=index_name):\n", + " pc.create_index(\n", + " name=index_name,\n", + " dimension=embedding_dimensions,\n", + " metric=\"cosine\",\n", + " spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\"),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yuh_KmEI2LTj" + }, + "source": [ + "We are going to initialize an index variable so that we can use it later on to describe the index and perform vector upsert." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "HSA6BAfl2LTj" + }, + "outputs": [], + "source": [ + "index = pc.Index(index_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VeBky_c75vEA" + }, + "source": [ + "Initially the index will be empty:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "QG8unV5R2LTj", + "outputId": "2beda05b-86b8-4f6e-a550-9f092f98c813" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "RPp6NQRhxXWo" - }, - "source": [ - "We can limit the number of records within our `dataset` if on the Standard Tier of Pinecone (for paid users, you can index the full dataset)." + "data": { + "text/plain": [ + "{'dimension': 32,\n", + " 'index_fullness': 0.0,\n", + " 'namespaces': {'': {'vector_count': 0}},\n", + " 'total_vector_count': 0}" ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index.describe_index_stats()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dEp-wZjc5y9b" + }, + "source": [ + "We upsert like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 99, + "referenced_widgets": [ + "89cf37ba2570433fb09b391a3c440e57", + "0815f9bdc73a47be8e841d26ac0febc1", + "96a1d8e445be41ef976162bb1b3b2a20", + "cb98b1d54e4b465885561fa1f6829ead", + "1c5aab255eb6470a8e789e040063bc98", + "3424a2fa338c4c00afbdcb36288b2772", + "f6177e6539064b8ba6a006385de45366", + "87685e96656f4b2cb56bed23fd264213", + "cfca72a8877c4b7c8f3b8abd5f4dbb30", + "e2930a83573948a1860149d0e87d0b10", + "2b979e3a7fcf47b0914b2143c90bb112", + "49cb34618589477b91d64200892dd04b", + "bed866473b984701bb1e228b0d98ed44", + "b69378488293410b99db580c21196135", + "846e78ae161148ff930eb2d1d23e96cd", + "a0104a08845f4f2196b0e1dc24cc8fbd", + "f28627a7d1ca48d3b9e5ecde1f13d116", + "abe30251e8be4c0aa8c3285b44a31270", + "619f0310234f4a25b8664b90a2ef0bc5", + "47de64ce9ba54cb280721891fcfc91f3", + "6474a1a1a92148bf9bda91b8390e3806", + "68cdf187417740a68ff2c68ce13af3f8" + ] }, + "id": "2yr3as-82LTj", + "outputId": "aafa92cf-ed12-4f29-e192-4da2762cba20" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ovQo1JIGxXxr", - "outputId": "7fd396ed-9d75-4472-817c-fd774c59c06d" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "89cf37ba2570433fb09b391a3c440e57", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "10000" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset = dataset.head(10_000)\n", - "len(dataset)" + "text/plain": [ + "sending upsert requests: 0%| | 0/10000 [00:00\n", - "
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\n", - " \n" - ], - "text/plain": [ - " id values \\\n", - "0 tt5027774 [-0.12388430535793304, 0.23021861910820007, -0... \n", - "1 tt5463162 [0.008479624055325985, 0.3665461540222168, -0.... \n", - "2 tt4007502 [-0.0022702165879309177, 0.5886886715888977, -... \n", - "3 tt4209788 [0.08350061625242233, 0.4322584867477417, -0.2... \n", - "4 tt2948356 [-0.1614755392074585, 0.41389355063438416, -0.... \n", - "\n", - " metadata \n", - "0 {'imdb_id': 'tt5027774', 'movie_id': 6705, 'po... \n", - "1 {'imdb_id': 'tt5463162', 'movie_id': 7966, 'po... \n", - "2 {'imdb_id': 'tt4007502', 'movie_id': 1614, 'po... \n", - "3 {'imdb_id': 'tt4209788', 'movie_id': 7022, 'po... \n", - "4 {'imdb_id': 'tt2948356', 'movie_id': 3571, 'po... " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.drop(['sparse_values', 'metadata'], axis=1, inplace=True)\n", - "dataset.rename(columns={'blob': 'metadata'}, inplace=True)\n", - "\n", - "dataset.head()" + "data": { + "text/plain": [ + "upserted_count: 10000" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index.upsert_from_dataframe(dataset, batch_size=1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zSaHJ1YE50gl" + }, + "source": [ + "Now we should see 10K vectors in our index (note, the `total_vector_count` may take some time to fully update, paritcularly when using the Pinecone *Standard Tier*)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "iNmaRTdV2LTj", + "outputId": "0c4209fe-24cc-4c91-8f92-c114482d08e4" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "LJa96KyT2LTi" - }, - "source": [ - "Here is an example of the metadata value." + "data": { + "text/plain": [ + "{'dimension': 32,\n", + " 'index_fullness': 0.02066,\n", + " 'namespaces': {'': {'vector_count': 2066}},\n", + " 'total_vector_count': 2066}" ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index.describe_index_stats()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O8ybkrRC2LTj" + }, + "source": [ + "## Querying the Index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p2wPJwtj2LTj" + }, + "source": [ + "Now, when the index is populated, we can perform queries on it to find the most relevant recommendations.\n", + "
\n", + "To do that, we need to instantiate our embedding models so that we can create vectors from our input user or input item objects." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Ux4ZCIH2LTj" + }, + "source": [ + "### Getting the Model\n", + "\n", + "We will download the models from the HuggingFace Hub. We will use one model to embed the *example user* and another model to embed the *example item*.
\n", + "This will allow us to retrieve the most relevant items for a specific user or find the most similar items to a specific item." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 188, + "referenced_widgets": [ + "7ab6935f6be04636bca56c1b18f48871", + "6b9f37b8ae114c71bdd86fa591736f1a", + "9f224dd22b134275803908cc3dc2da6d", + "0b4193e93f954c578fef7b4c1b8e1c4e", + "5c618580d7124f0fa1acdb03ed674acf", + "e4fa89b2a5fe4eb98369e7bbec9462d0", + "6c04949a90824faf8eee03fd5e721f62", + "3ea3c0945c4549bab21e73c2c52dc3c0", + "c35efb3b6e3b4fa5a84a0ec6aecf4290", + "11bb72df0ba146958adaa1efddb90f71", + "7bf9059dd14d4de5acf9e7636c4663df", + "76a1782667ac467aa0cda9951c8fc6c7", + "9bcd137b1c7e40c8a780e08a60d6fd0b", + "5814caf95df24271a693caa383be4f54", + "9147099e53d54b5eb1796ed062d819a1", + "86305c15a8ff46d9bcac59e3ff5d068d", + "0b78150578324fb5aa57c6c761404581", + "6ee1c17b0155422fb7b33dc3a5421caa", + "231d892286d8430c8dd6cad129d6e150", + "fa65d5ef820947a2a09388eedda6d6dd", + "ef55dfd53ed64e50a8ec69d6a75ae852", + "a91a8079fa9a4b48840c42efd34401e6" + ] }, + "id": "wiAeOiCt2LTj", + "outputId": "d5f08d22-8163-44f0-bc3b-6058db7b2a66" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OPN0JTfx2LTi", - "outputId": "0665127c-f667-48b0-d150-ccaf6751d62e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'imdb_id': 'tt5027774',\n", - " 'movie_id': 6705,\n", - " 'poster': 'https://m.media-amazon.com/images/M/MV5BMjI0ODcxNzM1N15BMl5BanBnXkFtZTgwMzIwMTEwNDI@._V1_SX300.jpg',\n", - " 'rating': 4.0,\n", - " 'title': 'Three Billboards Outside Ebbing, Missouri (2017)',\n", - " 'user_id': 4556}\n" - ] - } - ], - "source": [ - "from pprint import pp\n", - "\n", - "pp(dataset['metadata'][0])" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "config.json not found in HuggingFace Hub.\n", + "WARNING:huggingface_hub.hub_mixin:config.json not found in HuggingFace Hub.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "XU3yufR02LTi" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7ab6935f6be04636bca56c1b18f48871", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "Now we move on to initializing our Pinecone vector database." + "text/plain": [ + "Fetching 7 files: 0%| | 0/7 [00:00\n", + "
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imdb_idmovie_idposterratingtitleuser_id
0tt50277746705https://m.media-amazon.com/images/M/MV5BMjI0OD...4.0Three Billboards Outside Ebbing, Missouri (2017)4556
1tt54631627966https://m.media-amazon.com/images/M/MV5BMDkzNm...3.5Deadpool 2 (2018)20798
2tt40075021614https://m.media-amazon.com/images/M/MV5BMjY3YT...4.5Frozen Fever (2015)26543
3tt42097887022https://m.media-amazon.com/images/M/MV5BNTkzMz...4.0Molly's Game (2017)4106
4tt29483563571https://m.media-amazon.com/images/M/MV5BOTMyMj...4.0Zootopia (2016)15259
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\n", - "We will assign these values here.\n", - "
\n", - "Note that the dimension parameter has to match the embedding dimensions provided in the dataset (or the model that outputs those embeddings)." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Recommendations: \n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "56Yt1eV72LTj", - "outputId": "c4ef1867-dc97-4f7b-bb26-8d5cf50d2db9" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "32" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + "\n", + "
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moviesscores
0Avengers: Infinity War - Part I (2018)0.998588
1Avengers: Infinity War - Part II (2019)0.987156
2Thor: Ragnarok (2017)0.981000
3Captain America: Civil War (2016)0.979533
4Guardians of the Galaxy (2014)0.976064
5Guardians of the Galaxy 2 (2017)0.960372
6Avengers: Age of Ultron (2015)0.944494
7Untitled Spider-Man Reboot (2017)0.944431
8Logan (2017)0.936184
9Spider-Man: Into the Spider-Verse (2018)0.930237
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\n" ], - "source": [ - "# embedding dimensions\n", - "len(dataset['values'][0])" + "text/plain": [ + " movies scores\n", + "0 Avengers: Infinity War - Part I (2018) 0.998588\n", + "1 Avengers: Infinity War - Part II (2019) 0.987156\n", + "2 Thor: Ragnarok (2017) 0.981000\n", + "3 Captain America: Civil War (2016) 0.979533\n", + "4 Guardians of the Galaxy (2014) 0.976064\n", + "5 Guardians of the Galaxy 2 (2017) 0.960372\n", + "6 Avengers: Age of Ultron (2015) 0.944494\n", + "7 Untitled Spider-Man Reboot (2017) 0.944431\n", + "8 Logan (2017) 0.936184\n", + "9 Spider-Man: Into the Spider-Verse (2018) 0.930237" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(\n", + " {\n", + " \"movies\": [record.metadata[\"title\"] for record in movie_query_results.matches],\n", + " \"scores\": [record.score for record in movie_query_results.matches],\n", + " }\n", + ")\n", + "print(\"Recommendations: \")\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sXvf7mU22LTs" + }, + "source": [ + "We can observe that it is doing an excellent job in finding similar movies, and it is accomplishing this task very quickly." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m-cwktMp2LTs" + }, + "source": [ + "#### User Recommendations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "axrgCNTH2LTs" + }, + "source": [ + "Now, let's observe how our vector database behaves when we query it using the user vector.\n", + "
\n", + "We expect to receive movies that closely resemble the ones that the user rated highly." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "id": "M8IPoq1Z2LTs" + }, + "outputs": [], + "source": [ + "user_id = 825\n", + "user_vector = user_model(user_id).numpy().tolist()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8sz0bmOz2LTs" + }, + "source": [ + "Here, we are defining a function that allows us to easily display the movies that the user rated in the past." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "id": "azkQAcNh2LTs" + }, + "outputs": [], + "source": [ + "def top_movies_user_rated(user):\n", + " # Retrieve the list of movies rated by the user\n", + " user_movies = movies_details[movies_details[\"user_id\"] == user]\n", + " # Sort by the highest rated movies\n", + " top_rated = user_movies.sort_values(by=[\"rating\"], ascending=False)\n", + " # Return the top 14 movies\n", + " return pd.DataFrame(\n", + " {\n", + " \"movies\": top_rated[\"title\"].tolist()[:14],\n", + " \"ratings\": top_rated[\"rating\"].tolist()[:14],\n", + " }\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 }, + "id": "IcvnRxMT2LTs", + "outputId": "13a662b6-37af-4589-82a3-d061cdd230ca" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "Rw-HJOBA2LTj", - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "index_name = 'onboarding-recommender'" + "data": { + "text/html": [ + "\n", + "\n", + "
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moviesratings
0Swiss Army Man (2016)4.0
1Blackfish (2013)3.5
2Raman Raghav 2.0 (2016)3.5
3Loveless (2017)3.5
4Ae Dil Hai Mushkil (2016)3.0
5Ted 2 (2015)3.0
6Inside Llewyn Davis (2013)3.0
7Drinking Buddies (2013)2.5
8Yamla Pagla Deewana 2 (2013)1.5
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moviesscores
012 Years a Slave (2013)0.868902
1Bridegroom (2013)0.850303
2Capital C (2015)0.850294
3Dangal (2016)0.843546
4The Crew (2016)0.841317
5Capernaum (2018)0.840019
6Shaadi Mein Zaroor Aana (2017)0.832370
7Tanu Weds Manu Returns (2015)0.829788
8Spider-Man: Far from Home (2019)0.826584
9Avengers: Infinity War - Part II (2019)0.824928
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\n" + ], + "text/plain": [ + " movies scores\n", + "0 12 Years a Slave (2013) 0.868902\n", + "1 Bridegroom (2013) 0.850303\n", + "2 Capital C (2015) 0.850294\n", + "3 Dangal (2016) 0.843546\n", + "4 The Crew (2016) 0.841317\n", + "5 Capernaum (2018) 0.840019\n", + "6 Shaadi Mein Zaroor Aana (2017) 0.832370\n", + "7 Tanu Weds Manu Returns (2015) 0.829788\n", + "8 Spider-Man: Far from Home (2019) 0.826584\n", + "9 Avengers: Infinity War - Part II (2019) 0.824928" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(\n", + " {\n", + " \"movies\": [record.metadata[\"title\"] for record in query_results.matches],\n", + " \"scores\": [record.score for record in query_results.matches],\n", + " }\n", + ")\n", + "print(\"Recommendations: \")\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "91FgZa1g2LTt" + }, + "source": [ + "Using this method, we can identify movies similar to those the user rated highly, while avoiding those the user rated lowly." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3j6Zgk8k2LTt" + }, + "source": [ + "## Summary\n", + "\n", + "The notebook demonstrated the step-by-step process of creating and populating an index in the vector database. It covered aspects such as specifying the index name, similarity metric, and vector dimensions. The example also included instructions on checking if an index exists, deleting and creating new indexes when necessary.\n", + "\n", + "Furthermore, the notebook illustrated the usage of embedding models to generate vector representations of both users and items. The results showed that the recommendations closely resembled the movies that the user rated highly, and dissimilar movies were not included.\n", + "\n", + "Overall, this example showcased the power and efficiency of vector databases in recommendation systems. It is important to note that the benefits of vector databases extend beyond movies and can be applied to various types of items, making them a valuable tool in building effective recommendation systems." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "pinecone-onboarding", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0815f9bdc73a47be8e841d26ac0febc1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3424a2fa338c4c00afbdcb36288b2772", + "placeholder": "​", + "style": "IPY_MODEL_f6177e6539064b8ba6a006385de45366", + "value": "sending upsert requests: 100%" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "yuh_KmEI2LTj" - }, - "source": [ - "We are going to initialize an index variable so that we can use it later on to describe the index and perform vector upsert." - ] + "0b4193e93f954c578fef7b4c1b8e1c4e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_11bb72df0ba146958adaa1efddb90f71", + "placeholder": "​", + "style": "IPY_MODEL_7bf9059dd14d4de5acf9e7636c4663df", + "value": " 7/7 [00:00<00:00, 268.30it/s]" + } }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "HSA6BAfl2LTj" - }, - "outputs": [], - "source": [ - "index = pc.Index(index_name)" - ] + "0b78150578324fb5aa57c6c761404581": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "VeBky_c75vEA" - }, - "source": [ - "Initially the index will be empty:" - ] + "11bb72df0ba146958adaa1efddb90f71": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - 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{ - "cell_type": "markdown", - "metadata": { - "id": "dEp-wZjc5y9b" - }, - "source": [ - "We upsert like so:" - ] + "231d892286d8430c8dd6cad129d6e150": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - 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}, - "id": "2yr3as-82LTj", - "outputId": "aafa92cf-ed12-4f29-e192-4da2762cba20" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "89cf37ba2570433fb09b391a3c440e57", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "sending upsert requests: 0%| | 0/10000 [00:00\n", - "To do that, we need to instantiate our embedding models so that we can create vectors from our input user or input item objects." - ] + "5c618580d7124f0fa1acdb03ed674acf": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "-Ux4ZCIH2LTj" - }, - "source": [ - "### Getting the Model\n", - "\n", - "We will download the models from the HuggingFace Hub. We will use one model to embed the *example user* and another model to embed the *example item*.
\n", - "This will allow us to retrieve the most relevant items for a specific user or find the most similar items to a specific item." - ] + "619f0310234f4a25b8664b90a2ef0bc5": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 188, - "referenced_widgets": [ - "7ab6935f6be04636bca56c1b18f48871", - "6b9f37b8ae114c71bdd86fa591736f1a", - "9f224dd22b134275803908cc3dc2da6d", - "0b4193e93f954c578fef7b4c1b8e1c4e", - "5c618580d7124f0fa1acdb03ed674acf", - "e4fa89b2a5fe4eb98369e7bbec9462d0", - "6c04949a90824faf8eee03fd5e721f62", - "3ea3c0945c4549bab21e73c2c52dc3c0", - "c35efb3b6e3b4fa5a84a0ec6aecf4290", - "11bb72df0ba146958adaa1efddb90f71", - "7bf9059dd14d4de5acf9e7636c4663df", - "76a1782667ac467aa0cda9951c8fc6c7", - "9bcd137b1c7e40c8a780e08a60d6fd0b", - "5814caf95df24271a693caa383be4f54", - "9147099e53d54b5eb1796ed062d819a1", - "86305c15a8ff46d9bcac59e3ff5d068d", - "0b78150578324fb5aa57c6c761404581", - "6ee1c17b0155422fb7b33dc3a5421caa", - "231d892286d8430c8dd6cad129d6e150", - "fa65d5ef820947a2a09388eedda6d6dd", - "ef55dfd53ed64e50a8ec69d6a75ae852", - "a91a8079fa9a4b48840c42efd34401e6" - ] - }, - "id": "wiAeOiCt2LTj", - "outputId": "d5f08d22-8163-44f0-bc3b-6058db7b2a66" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "config.json not found in HuggingFace Hub.\n", - "WARNING:huggingface_hub.hub_mixin:config.json not found in HuggingFace Hub.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7ab6935f6be04636bca56c1b18f48871", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fetching 7 files: 0%| | 0/7 [00:00\n", - "
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imdb_idmovie_idposterratingtitleuser_id
0tt50277746705https://m.media-amazon.com/images/M/MV5BMjI0OD...4.0Three Billboards Outside Ebbing, Missouri (2017)4556
1tt54631627966https://m.media-amazon.com/images/M/MV5BMDkzNm...3.5Deadpool 2 (2018)20798
2tt40075021614https://m.media-amazon.com/images/M/MV5BMjY3YT...4.5Frozen Fever (2015)26543
3tt42097887022https://m.media-amazon.com/images/M/MV5BNTkzMz...4.0Molly's Game (2017)4106
4tt29483563571https://m.media-amazon.com/images/M/MV5BOTMyMj...4.0Zootopia (2016)15259
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\n", - " \n" - ], - "text/plain": [ - " imdb_id movie_id poster \\\n", - "0 tt5027774 6705 https://m.media-amazon.com/images/M/MV5BMjI0OD... \n", - "1 tt5463162 7966 https://m.media-amazon.com/images/M/MV5BMDkzNm... \n", - "2 tt4007502 1614 https://m.media-amazon.com/images/M/MV5BMjY3YT... \n", - "3 tt4209788 7022 https://m.media-amazon.com/images/M/MV5BNTkzMz... \n", - "4 tt2948356 3571 https://m.media-amazon.com/images/M/MV5BOTMyMj... \n", - "\n", - " rating title user_id \n", - "0 4.0 Three Billboards Outside Ebbing, Missouri (2017) 4556 \n", - "1 3.5 Deadpool 2 (2018) 20798 \n", - "2 4.5 Frozen Fever (2015) 26543 \n", - "3 4.0 Molly's Game (2017) 4106 \n", - "4 4.0 Zootopia (2016) 15259 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "movies_details = pd.DataFrame(\n", - " dataset['metadata'].values.tolist()\n", - ")\n", - "movies_details.head()" - ] + "6b9f37b8ae114c71bdd86fa591736f1a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_e4fa89b2a5fe4eb98369e7bbec9462d0", + "placeholder": "​", + "style": "IPY_MODEL_6c04949a90824faf8eee03fd5e721f62", + "value": "Fetching 7 files: 100%" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "qu1fHhaK2LTk" - }, - "source": [ - "#### Item Similarity" - ] + "6c04949a90824faf8eee03fd5e721f62": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "hX_Gq_HM2LTk" - }, - "source": [ - "First, we can check how our vector database behaves when returning the most similar movies upon querying it using the movie vector created using the `movie_model` loaded above." - ] + "6ee1c17b0155422fb7b33dc3a5421caa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - 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0Avengers: Infinity War - Part I (2018)0.998588
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2Thor: Ragnarok (2017)0.981000
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"source": [ - "df = pd.DataFrame(\n", - " {\n", - " 'movies': [record.metadata['title'] for record in movie_query_results.matches],\n", - " 'scores': [record.score for record in movie_query_results.matches]\n", - " }\n", - ")\n", - "print(\"Recommendations: \")\n", - "display(df)" - ] + "layout": "IPY_MODEL_1c5aab255eb6470a8e789e040063bc98" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "sXvf7mU22LTs" - }, - "source": [ - "We can observe that it is doing an excellent job in finding similar movies, and it is accomplishing this task very quickly." - ] + "9147099e53d54b5eb1796ed062d819a1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ef55dfd53ed64e50a8ec69d6a75ae852", + "placeholder": "​", + "style": "IPY_MODEL_a91a8079fa9a4b48840c42efd34401e6", + "value": " 7/7 [00:00<00:00, 387.35it/s]" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "m-cwktMp2LTs" - }, - "source": [ - "#### User Recommendations" - ] + "96a1d8e445be41ef976162bb1b3b2a20": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_87685e96656f4b2cb56bed23fd264213", + "max": 10000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_cfca72a8877c4b7c8f3b8abd5f4dbb30", + "value": 10000 + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "axrgCNTH2LTs" - }, - "source": [ - "Now, let's observe how our vector database behaves when we query it using the user vector.\n", - 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\n" - ], - "text/plain": [ - " movies ratings\n", - "0 Swiss Army Man (2016) 4.0\n", - "1 Blackfish (2013) 3.5\n", - "2 Raman Raghav 2.0 (2016) 3.5\n", - "3 Loveless (2017) 3.5\n", - "4 Ae Dil Hai Mushkil (2016) 3.0\n", - "5 Ted 2 (2015) 3.0\n", - "6 Inside Llewyn Davis (2013) 3.0\n", - "7 Drinking Buddies (2013) 2.5\n", - "8 Yamla Pagla Deewana 2 (2013) 1.5" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(top_movies_user_rated(user_id))" - ] + "abe30251e8be4c0aa8c3285b44a31270": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "aFzlItA92LTs" - }, - "source": [ - "And now we can pass our `user_vector` to the query to get the recommendations." - ] + "b69378488293410b99db580c21196135": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_619f0310234f4a25b8664b90a2ef0bc5", + "max": 10, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_47de64ce9ba54cb280721891fcfc91f3", + "value": 10 + } }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": { - "id": "4MctHWEU2LTs" - }, - "outputs": [], - "source": [ - "query_results = index.query(vector=\n", - " user_vector,\n", - " top_k=10,\n", - " include_metadata=True\n", - ")" - ] + "bed866473b984701bb1e228b0d98ed44": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f28627a7d1ca48d3b9e5ecde1f13d116", + "placeholder": "​", + "style": "IPY_MODEL_abe30251e8be4c0aa8c3285b44a31270", + "value": "collecting async responses: 100%" + } }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 381 - }, - "id": "1pvCntTP2LTs", - "outputId": "0d0a2262-0645-44d2-8567-de3f379d1804" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Recommendations: \n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
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moviesscores
012 Years a Slave (2013)0.868902
1Bridegroom (2013)0.850303
2Capital C (2015)0.850294
3Dangal (2016)0.843546
4The Crew (2016)0.841317
5Capernaum (2018)0.840019
6Shaadi Mein Zaroor Aana (2017)0.832370
7Tanu Weds Manu Returns (2015)0.829788
8Spider-Man: Far from Home (2019)0.826584
9Avengers: Infinity War - Part II (2019)0.824928
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\n" - ], - "text/plain": [ - " movies scores\n", - "0 12 Years a Slave (2013) 0.868902\n", - "1 Bridegroom (2013) 0.850303\n", - "2 Capital C (2015) 0.850294\n", - "3 Dangal (2016) 0.843546\n", - "4 The Crew (2016) 0.841317\n", - "5 Capernaum (2018) 0.840019\n", - "6 Shaadi Mein Zaroor Aana (2017) 0.832370\n", - "7 Tanu Weds Manu Returns (2015) 0.829788\n", - "8 Spider-Man: Far from Home (2019) 0.826584\n", - "9 Avengers: Infinity War - Part II (2019) 0.824928" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pd.DataFrame(\n", - " {\n", - " 'movies': [record.metadata['title'] for record in query_results.matches],\n", - " 'scores': [record.score for record in query_results.matches]\n", - " }\n", - ")\n", - "print(\"Recommendations: \")\n", - "display(df)" - ] + "c35efb3b6e3b4fa5a84a0ec6aecf4290": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "91FgZa1g2LTt" - }, - "source": [ - "Using this method, we can identify movies similar to those the user rated highly, while avoiding those the user rated lowly." - ] + "cb98b1d54e4b465885561fa1f6829ead": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_e2930a83573948a1860149d0e87d0b10", + "placeholder": "​", + "style": "IPY_MODEL_2b979e3a7fcf47b0914b2143c90bb112", + "value": " 10000/10000 [00:07<00:00, 9671.36it/s]" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "3j6Zgk8k2LTt" - }, - "source": [ - "## Summary\n", - "\n", - "The notebook demonstrated the step-by-step process of creating and populating an index in the vector database. It covered aspects such as specifying the index name, similarity metric, and vector dimensions. The example also included instructions on checking if an index exists, deleting and creating new indexes when necessary.\n", - "\n", - "Furthermore, the notebook illustrated the usage of embedding models to generate vector representations of both users and items. The results showed that the recommendations closely resembled the movies that the user rated highly, and dissimilar movies were not included.\n", - "\n", - "Overall, this example showcased the power and efficiency of vector databases in recommendation systems. 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