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Merge pull request #384 from pinecone-io/bulk-import-notebook
Add bulk import notebook
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‎docs/pinecone-bulk-import.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "5ePFLZDtbWB9"
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},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/pinecone-bulk-import.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/pinecone-bulk-import.ipynb)\n",
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"\n",
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"# Pinecone Bulk Import"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "lKAHnDD0Zeiw"
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},
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"source": [
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"## Scenario: Ingesting Embedded Parquet Data from S3 to Pinecone\n",
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"\n",
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"In this scenario, you are tasked with ingesting pre-generated vector embeddings stored in Parquet files located in an S3 bucket into a Pinecone index. The embeddings have been precomputed by a third-party vendor and are ready to be indexed for future vector similarity search or other downstream tasks.\n",
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"\n",
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"### Problem Overview\n",
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"The goal is to seamlessly move the data from S3 to Pinecone so that it can be used for future tasks such as semantic search, recommendations, and anomaly detection.\n",
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"\n",
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"### Solution steps\n",
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"1. **Access the S3 Bucket**: You will access the S3 bucket where the Parquet files are stored. These files contain the embeddings and metadata needed for indexing.\n",
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" \n",
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"2. **Read and Extract Embeddings**: Once the Parquet files are accessed, you will extract the embeddings and any necessary metadata (e.g., unique document IDs or other attributes).\n",
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" \n",
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"3. **Upload Embeddings to Pinecone**: After extracting the data, you will upload the embeddings to a Pinecone index, associating each embedding with its respective identifier. This process allows the embeddings to be efficiently queried or analyzed later.\n",
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"\n",
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"This approach allows you to efficiently transfer embedded parquet files from S3 storage to Pinecone to support vector search. Please see our official [Understanding Imports in Pinecone Documentation](https://docs.pinecone.io/guides/data/understanding-imports)\n",
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" for additional information.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "azHQh9CugZHU"
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},
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"source": [
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"## Install required libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "gcofp6aAwlgR"
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},
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"outputs": [],
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"source": [
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"!pip install pinecone-client\n",
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"!pip install pinecone_notebooks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true,
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"id": "LC6v4kqda7dN"
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},
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"outputs": [],
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"source": [
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"from pinecone import Pinecone, ServerlessSpec\n",
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"import time\n",
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"import os\n",
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"from datetime import datetime\n",
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"import json"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "UYm71QsCEwfD"
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},
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"source": [
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"## Get Pinecone API key"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "BIh83-IXwXgU"
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},
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"outputs": [],
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"source": [
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"from pinecone_notebooks.colab import Authenticate\n",
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"Authenticate()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true,
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"id": "xyfAuSi5bGoN"
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},
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"outputs": [],
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"source": [
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"api_key = os.getenv('PINECONE_API_KEY')\n",
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"\n",
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"# Configure Pinecone client\n",
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"pc = Pinecone(api_key=api_key)\n",
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"\n",
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"# Get cloud and region settings\n",
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"cloud = os.getenv('PINECONE_CLOUD', 'aws')\n",
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"region = os.getenv('PINECONE_REGION', 'us-east-1')\n",
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"\n",
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"# Define serverless specifications\n",
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"spec = ServerlessSpec(cloud=cloud, region=region)\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wPrCU2PabgTg"
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},
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"source": [
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"## Create a serverless index\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "7TA1uqEQbLiT"
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},
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"outputs": [],
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"source": [
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"\n",
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"index_name = \"pinecone-bulk-import\"\n",
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"dimension = 1536\n",
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"\n",
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"if not pc.has_index(index_name):\n",
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" pc.create_index(\n",
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" name=index_name,\n",
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" dimension=dimension,\n",
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" metric=\"cosine\",\n",
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" spec=ServerlessSpec(cloud=\"aws\", region=\"us-west-2\")\n",
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" )\n",
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"\n",
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"index = pc.Index(name=index_name)\n",
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"\n",
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"print(f\"Index '{index_name}' created successfully.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3552NYEDcBos"
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},
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"source": [
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"## Start import task\n",
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"\n",
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"This sample dataset contains:\n",
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"\n",
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"* **Dimensions**: 1536\n",
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"* **Rows**: 10,000\n",
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"* **Files**: 10 parquet files\n",
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"* **Size per file**: ~12.58 MB\n",
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"* **Total size**: ~125.8\n",
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"\n",
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"Each file contains:\n",
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"\n",
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"* **id**: Unique identifier\n",
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"* **Values**: Embedded vectors\n",
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"* **metadata**: JSON-formatted dictionary with metadata\n",
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"\n",
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"***Note***: *This task may take 10 minutes or more to complete. And Each import request can import up 1TB of data, or 100,000,000 records into a maximum of 100 namespaces, whichever limit is met first.*"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "pwVvY9fRlZYj"
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},
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"source": [
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"## Specify AWS S3 folder and start task"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "FV8DGtmtnKpj"
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},
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"outputs": [],
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"source": [
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"root = \"s3://dev-bulk-import-datasets-pub/10k-1536/\"\n",
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"op = index.start_import(uri=root, error_mode=\"CONTINUE\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "CUoMQXImncaU"
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},
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"source": [
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"## Check the status of the import"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ARJlKVtmpY73"
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},
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"outputs": [],
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"source": [
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"index.describe_index_stats()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Qq9qL3hRcEWv"
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},
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"source": [
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"## List import operations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "K_ig20UBbPeu"
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},
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"outputs": [],
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"source": [
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"imports = list(index.list_imports())\n",
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"if imports:\n",
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" for i in imports:\n",
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" print(i)\n",
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"else:\n",
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" print(\"No imports found in the index.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "els_rMBhcFTa"
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},
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"source": [
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"## Describe a specific import"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "OXgTNgVAbRps"
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},
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"outputs": [],
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"source": [
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"index.describe_import(\"1\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "79NM6VDtcME7"
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},
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"source": [
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"## Cancel the Import (if needed)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "87M3vXgsvbxs"
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},
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"outputs": [],
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"source": [
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"# Check if operation status and cancel running instance\n",
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"op_status = index.describe_import(op.id)\n",
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"print(f\"Operation status: {op_status}\")\n",
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"\n",
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"if op_status in ['in_progress', 'pending']:\n",
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" try:\n",
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" cancel_response = index.cancel_import(op.id)\n",
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" print(f\"Import operation {op.id} cancelled.\")\n",
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" except Exception as e:\n",
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" print(f\"Error cancelling import: {e}\")\n",
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"else:\n",
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" print(f\"Cannot cancel operation {op.id} because its status is: {op_status}\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "a1euqHZocS1F"
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},
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"source": [
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"## Delete the index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "jofVBQHycWxt"
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},
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"outputs": [],
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"source": [
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"pc.delete_index(index_name)\n",
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"print(f\"Index '{index_name}' deleted.\")"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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