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with Data in Azure Cosmos DB for PostgreSQL using Open PSQL Shell.","conversation":{"__ref":"Conversation:conversation:4150930"},"id":"message:4150930","revisionNum":14,"uid":4150930,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1602363"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n \n \n \n \n Developing a multi-tenant app on PostgreSQL? Hitting scaling walls? Discover how Azure Cosmos DB for PostgreSQL extends your beloved PostgreSQL with superpowers: global distribution, effortless scaling, and simplified high availability. This guide equips you to conquer multi-tenancy challenges and build robust, scalable applications \n What covered in this Blog \n \n Challenges with traditional PostgreSQL \n What is Azure Cosmos DB for PostgreSQL \n Citus for PostgreSQL. \n Provisioning Azure Cosmos DB for PostgreSQL via azure Portal. \n Open PSQL Shell on Azure (new feature on preview) \n How to create & query distribute tables in Azure Cosmos DB for PostgreSQL \n \n In this blog, we shall leverage the azure cosmos DB for postgresql which extends PostgreSQl with more enhanced capabilities. It will be a step-by-step guide on how to leverage this service which we are being provided and managed on Azure. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":11319},"postTime":"2024-05-30T00:00:00.035-07:00","lastPublishTime":"2024-05-31T01:14:48.395-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n \n Previously we introduced Getting started with Azure Cosmos Database (A Deep Dive) blog which is an end-to-end introduction of Azure Cosmos DB. In this blog we are going to talk about one of the Azure Cosmos Database API known as Azure Cosmos Database for PostgreSQL. \n \n Imagine a scenario where you are developing a multi-tenant application, as a developer, you have been working with PostgreSQL for years, you have joined a team that is developing an application that needs to handle data for thousands of tenants, each with isolated data. The team decides to use Azure Cosmos DB for PostgreSQL. \n \n What is covered in this Blog \n \n \n Challenges with traditional PostgreSQL \n What is Azure Cosmos DB for PostgreSQL \n Citus for PostgreSQL. \n Provisioning Azure Cosmos DB for PostgreSQL via azure Portal. \n Open PSQL Shell on Azure (new feature on preview) \n How to create & query distribute tables in Azure Cosmos DB for PostgreSQL \n \n In this blog, we shall leverage the azure cosmos DB for PostgreSQL which extends PostgreSQL with more enhanced capabilities. It will be a step-by-step guide on how to leverage this service which we are being provided and managed on Azure. \n \n Challenges with traditional PostgreSQL \n \n \n Scaling Horizontally: As the user base grows, you need to scale your database horizontally to accommodate more tenants. Traditional PostgreSQL doesn’t natively support easy horizontal scaling across multiple nodes. \n Global Distribution: You need a database solution that can seamlessly replicate data across different regions for low-latency access, which is normally not the case with PostgreSQL. \n High Availability and Failover: Traditional PostgreSQL requires manual setup for replication and failover. \n \n \n So, what is Azure Cosmos DB for PostgreSQL? \n \n Azure Cosmos DB for PostgreSQL is a managed service for PostgreSQL extended with Citus superpower of distributed tables. Citus enables you to scale your application as requirements grow. This can be done by distributing them into different nodes of distributed tables. \n Azure cosmosDB for postgreSQL empowers you to build a scalable and globally distributed application while leveraging your existing postgreSQL \n \n \n \n \n Features of Azure Cosmos DB for PostgreSQL. \n \n \n Automatic High availability - Azure Cosmos DB for PostgreSQL ensures that your database remains available even in the face of failures. It automatically replicates data across multiple nodes and regions, minimizing downtime. \n Backups - Azure Cosmos DB for PostgreSQL automatically backs up your data, allowing you to restore it in case of accidental deletions, corruption, or other issues. \n Read-Replicas - Read-replicas allows you to offload read-heavy workloads from the primary database. This improves read performance and ensures that your application can handle high read traffic. \n Easy Monitoring - Monitoring your database’s health and performance is essential. Azure Cosmos DB for PostgreSQL provides built-in monitoring tools. \n Private Endpoint - By using private endpoints, you can restrict access to authorized networks, preventing exposure to the public internet. \n Encryption - Azure Cosmos DB for PostgreSQL encrypts data both at rest and in transit. \n \n \n Citus for PostgreSQL. \n \n Citus for PostgreSQL is a powerful extension that transforms Postgres into a distributed database, allowing you to achieve high performance at any scale. Each tenant’s data can reside on a separate shard, enabling parallel processing and efficient queries. As new tenants join, you can dynamically add shards without downtime. \n We will have a look for more about Citus later in the blog. To learn more about Citus, visit the following link: citus documentation \n \n Create an Azure Cosmos DB for PostgreSQL cluster in Azure portal \n \n Prerequisites \n \n \n An azure account create azure account \n Azure subscription learn more on subscriptions \n \n \n Part A: Basics \n \n Step 1: Create a Cluster. \n \n \n Sign in to your azure account: signin to Azure and you will be taken to the dashboard. \n Create an Azure Cosmsos DB for PostgreSQL cluster using the direct link: Create an Azure Cosmos DB for PostgreSQL cluster or use the search option to find the resource. \n \n \n \n \n \n Step 2: Click on create. \n \n \n \n \n Step 3: select Create on the PostgreSQL tile \n \n \n \n \n Step 4: Fill in the form to provision our cluster \n \n \n \n \n \n \n Options you have: \n \n Subscription - choose the subscription to use. \n Resource Group - create resource group for your resource \n Cluster name - will determine the DNS name of your application. \n Location – Choose location near you. \n PostgreSQL version - choose major option like 15 or the latest you have. \n Database name - you can leave it as citus or change to your preferred name. \n Admin username - must be citus, do not change it. \n Password – choose a password for your access \n Scale – this option allows you to specify the amount of computing you need for your resource, feel free to click configure to adjust. \n \n \n \n \n \n Part B: Networking \n \n Step 5: Select Next to proceed to networking \n \n \n Select Allow public access from Azure services and resources within Azure to this cluster. \n \n For our demo purposes, adding a firewall rule using Add 0.0.0.0-255.255.255.255, It will make your IP address access any host on the internet and it poses a threat. It is advised to use the rule for temporary purposes like testing a feature and later remove the rule. \n \n \n \n \n \n \n Select Review+ Create to do a validation then select Create to create the cluster. It will take a few minutes to deploy your resource, after it is done, click Go to resource. \n \n \n \n \n \n Congratulations, you have Created an Azure Cosmos DB for PostgreSQL cluster. \n Connect to a cluster via Open PSQL Shell on Azure \n \n Open PSQL shell (a feature on preview) is a terminal based front-end to PostgreSQL that enables you to type in queries interactively, issue them to PostgreSQL, and see the query results. With this feature, you don't need to copy your connection strings to connect. To be authenticated, you will be required to enter your password. \n In the image below, click on quick start to open the open PSQL shell window, click on Open PSQL shell icon indicated with number (2) at the top. You will open a terminal which will require you to enter the password you provided earlier. \n To check the available databases, write: \n \n \n \n \n \n Create and distribute tables in Azure Cosmos DB for PostgreSQL \n \n \n As earlier stated, I promised to explain more about citus, let us get a better understanding of citus for PostgreSQL. \n Citus for PostgreSQL is an extension that transforms PostgreSQL into a distributed database system. It is designed to horizontally scale out PostgreSQL across multiple servers, enabling it to handle larger datasets and higher query loads than a single PostgreSQL instance could manage alone. \n Citus achieves this by partitioning tables and distributing data across a cluster of PostgreSQL servers. It also provides parallel query execution across these servers, allowing queries to be processed more quickly. \n Citus is particularly useful for applications that require high scalability, such as large-scale analytics, real-time data processing, and multi-tenant applications. \n We shall see how citus will enable us to create distributed tables in PostgreSQL, but first let us create tables for our citus database to see its importance. \n \n Create Tables \n \n \n Run the following queries to create github_users and github_events tables: \n \n \n CREATE TABLE github_users ( user_id bigint, url text, login text, avatar_url text, gravatar_id text, display_login text ); \n\nCREATE TABLE github_events(event_id bigint,event_type text,event_public boolean,repo_id bigint,payload jsonb,repo jsonb,user_id bigint,org jsonb,created_at timestamp); \n \n \n \n Create indexes event_type_index and payload_index on github_users and github_events respectively. \n \n The indexes created are used to allow fast querying in the JSONB. \n \n CREATE INDEX event_type_index ON github_events (event_type); \n\nCREATE INDEX payload_index ON github_events USING GIN (payload jsonb_path_ops); \n \n \n You should see the following on executing the commands. \n \n \n Distributed tables \n \n create_distributed_table() is the magic function that Azure Cosmos DB for PostgreSQL provides to distribute tables and use resources across multiple machines. The function decomposes tables into shards, which can be spread across nodes for increased storage and compute performance. \n \n Run the following commands to create distributed tables. \n \n \n SELECT create_distributed_table('github_users', 'user_id'); \n\nSELECT create_distributed_table('github_events', 'user_id'); \n \n Note that if you do not distribute tables, the worker nodes cannot help running your queries. \n \n Load Data into Distributed Tables \n \n To load a large amount of data, we are using data on azure blob storage. To achieve this, we first must create the extension in our database: \n \n SELECT * FROM create_extension('azure_storage'); \n \n \n \n Copy data from Blob Storage: Run the following commands to fetch data from CSV files and load into database tables: \n \n -- download users and store in table \n \n COPY github_users FROM 'https://pgquickstart.blob.core.windows.net/github/users.csv.gz'; \n \n \n -- download events and store in table \n \n COPY github_events FROM 'https://pgquickstart.blob.core.windows.net/github/events.csv.gz'; \n \n You should see 264308 and 126245 has been successfully loaded within a short period of time. \n \n Viewing Distributed Tables \n \n We can review details of our distributed tables, including their sizes. Run the following commands: \n \n SELECT * FROM citus_tables; \n \n \n \n Run queries in Azure Cosmos DB for PostgreSQL \n \n Like any other database, we make use of it by querying to get information. Let us run some queries for our citus database which is powered with distributed tables. \n \n To check count of GitHub users: \n \n count all rows (across shards) \n \n \n \n \n SELECT count(*) FROM github_users; \n \n \n \n \n Azure Cosmos DB for PostgreSQL automatically runs the count on all shards in parallel, and combines the results because data is divided between multiple nodes. \n \n \n To find all events for a single user, run the following command. \n \n \n SELECT created_at, event_type, repo->>'name' AS repo_name FROM github_events WHERE user_id = 3861633; \n \n \n \n \n The results came in within a few seconds because of the power of the distributed tables in Azure Cosmos DB for PostgreSQL. \n I do hope you have learnt from the blog and now have an idea of the power of Azure Cosmos DB for PostgreSQL. It is now time to practice and develop applications that make use AI (Artificial Intelligence) capabilities for fast processing of data. \n \n Read more \n \n \n Introduction to Azure Cosmos DB \n Azure Database for PostgreSQL Training \n Clusters in Azure Cosmos DB for PostgreSQL \n Shard data on worker nodes \n \n \n Available SDKs \n \n \n Connect and run SQL commands using Python \n Connect and run SQL commands using Node.js \n \n \n Connect and run SQL commands using C# \n Connect and run SQL commands using Java \n Connect and run SQL commands using Ruby \n \n 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Azure Cosmos DB for PostgreSQL to your ASP.NET Core application.","conversation":{"__ref":"Conversation:conversation:4265091"},"id":"message:4265091","revisionNum":28,"uid":4265091,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1847250"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n You’re a software developer tasked with creating robust backend web applications for your team. You’re always on the lookout for tools that can enhance performance, scalability, and ease of use. Azure Cosmos DB for PostgreSQL is a powerful, globally distributed database service that seamlessly integrates with your SDKs. In this blog, we’ll explore how to connect Azure Cosmos DB for PostgreSQL to your ASP.NET Core application, unlocking new levels of efficiency and reliability for your projects. \n \n \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":1525},"postTime":"2024-10-17T00:00:00.029-07:00","lastPublishTime":"2024-10-17T00:00:00.029-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n You’re a software developer tasked with creating robust backend web applications for your team. You’re always on the lookout for tools that can enhance performance, scalability, and ease of use. Azure Cosmos DB for PostgreSQL is a powerful, globally distributed database service that seamlessly integrates with your SDKs. In this blog, we’ll explore how to connect Azure Cosmos DB for PostgreSQL to your ASP.NET Core application, unlocking new levels of efficiency and reliability for your projects. \n Topics Covered \n \n Creating an ASP.NET Core Web Application \n Connecting to Azure Cosmos DB for PostgreSQL \n Managing Migrations with Entity Framework Core \n Performing CRUD Operations on data \n \n The source code for the web API we will be developing is available at: CosmosDB-PostgresAPI \n Prerequisites \n To achieve this goal, ensure you have the following: \n \n Understanding of Azure Cosmos DB For PostgreSQL: Familiarize yourself with what Azure Cosmos DB for PostgreSQL is, as covered in our previous blog. \n \n \n Foundations of Azure Cosmos DB: Review the foundational concepts of Azure Cosmos DB, also discussed in our earlier blog. \n \n \n Azure Account with Subscriptions: Make sure you have an active Azure account with the necessary subscriptions. \n \n \n Development Environment: Use Visual Studio Code or Visual Studio as your Integrated Development Environment (IDE). I will be using Visual Studio Code. \n \n \n .NET SDK: Install the .NET SDK to develop and run your ASP.NET Core applications. \n \n Creating an ASP.NET Core Web Application \n \n To check if you have successfully installed .NET SDK, run the following command in your terminal to check the version. \n \n \n dotnet --version \n \n \n I have dotnet 8 installed. \n \n \n In your terminal, run the following commands to create ASP .NET core web Api and open it in visual studio code. \n \n \n dotnet new webapi --use-controllers -o CosmosPostgresApi \ncd CosmosPostgresApi \ncode . \n \n \n \n \n We shall be using Microsoft Entity Framework, an Object-Relational Mapper (ORM) which simplifies data access by allowing developers to interact with databases using .NET objects instead of writing raw SQL queries. We need to install the necessary package from nuget.org in the integrated terminal. Microsoft.EntityFrameworkCore 8.0.8 \n \n \n dotnet add package Microsoft.EntityFrameworkCore \n \n \n The package will be added to CosmosPostgresAPI.csproj \n \n \n \n In your solution explorer, at the root of your project, create Models folder and add a class name Pharmacy.cs. Add the following code to your class. \n \n \n \n Copy the code and paste it in Pharmacy.cs class \n \n \n \n using System; \nnamespace CosmosPostgresApi.Models; \npublic class Pharmacy \n\n{ \n public int PharmacyId { get; set; } \n\n public required string PharmacyName { get; set; } \n\n public required string City { get; set; } \n\n public required string State { get; set; } \n\n public int ZipCode { get; set; } \n} \n \n \n The above code will help map data from the database to the object and vice versa. Entity Framework will use it to create a database table with columns, PharmacyId, PharmacyName, City, State, and ZipCode. \n Create another file AppDbContext.cs in the Models folder and add the following code. \n \n \n using System; \n\nusing Microsoft.EntityFrameworkCore; \n\nnamespace CosmosPostgresApi.Models; \n\npublic class AppDbContext : DbContext \n\n{ \n public AppDbContext(DbContextOptions<AppDbContext> options) : base(options) { } \n\n public DbSet<Pharmacy> Pharmacies { get; set; } \n\n protected override void OnModelCreating(ModelBuilder modelBuilder) \n\n { \n\n base.OnModelCreating(modelBuilder); \n\n modelBuilder.Entity<Pharmacy>() \n\n .ToTable(\"pharmacies\") \n\n .HasKey(p => p.PharmacyId); \n } \n\n public async Task DistributeTableAsync() \n\n { \n\n await Database.ExecuteSqlRawAsync(\"SELECT create_distributed_table('pharmacies', 'PharmacyId');\"); \n\n } \n} \n \n \n \n This code snippet means that AppDbContext class which is a class that is inherited from the DbContext class. \n The AppDbContext class is used to interact with the database and represents a session with the database. \n It contains property Pharmacies of type DbSet<Pharmacy> which represents the collection of pharmacies in the database. \n The OnModelCreating method is used to configure the entity mappings and relationships in the database. It creates a table named pharmacies and sets the primary key to the PharmacyId property of the Pharmacy class. \n \n \n DistributeTableAsync method is used to distribute the pharmacies table across the distributed database. Learn more about distribution of Azure Cosmos DB for PostgreSQL \n \n Connecting to Azure Cosmos DB for PostgreSQL \n \n To be able to connect to Azure Cosmos DB for PostgreSQL into our web API, you will be required to create a cluster in Azure Portal. We covered this in our previous blog. \n While creating the cluster, do not forget your database name, password and admin User. \n Once a cluster is created, navigate to the resource you just created. \n I have created a cluster called csharp-postgres-sdk \n \n \n Let's go back to the Visual Studio code and connect to the created database. \n Since we need the credentials to connect to the database we just created, we shall store them in appsettings.json file as a connection string. \n Copy this code and paste it in appsettings.json. Replace <uniqueID>, <cluster>, <password> with the correct values. \n \n \n \"ConnectionStrings\": { \n \"CosmosPostgres\": \"c-<cluster>.<uniqueID>.postgres.cosmos.azure.com; Database = citus; Port = 5432; User Id = citus; Password = <password>; Ssl Mode = Require; Pooling = true; Minimum Pool Size=0; Maximum Pool Size =50;\" \n }, \n \n \n \n Your connection string should be placed as follows: \n \n \n We shall install some few packages like PostgreSQL provider and others which will help us generate code for CRUD operations and Migration using Entity Framework. \n \n Use the terminal to install the following packages: \n \n \n dotnet add package Npgsql.EntityFrameworkCore.PostgreSQL \n\ndotnet add package Microsoft.VisualStudio.Web.CodeGeneration.Design \n\ndotnet add package Microsoft.EntityFrameworkCore.Design \n\ndotnet add package Microsoft.EntityFrameworkCore.SqlServer \n\ndotnet add package Microsoft.EntityFrameworkCore.Tools \n\ndotnet tool install -g dotnet-aspnet-codegenerator \n \n \n \n To confirm all the packages has been added check ItemGroup. \n \n \n \n Before we run migrations lets register the AddDbContext class as a service in the dependency injection container and configure it to use a PostgreSQL database. \n \n \n Now we can Create a controller, I will use the following code which helps quickly set up a controller with CRUD operations for the Pharmacy model, using asynchronous methods and integrating with the specified database context. \n \n Run the code in your terminal: \n \n \n dotnet aspnet-codegenerator controller -name PharmacyController -async -api -m Pharmacy -dc AppDbContext -outDir Controllers \n \n \n \n You should be able to see the CRUD operations generated in the Controllers folder named PharmacyController.cs \n \n \n Managing Migrations with Entity Framework Core \n Entity Framework Core allows us to generate SQL code directly from our C# objects, providing the advantage of using an Object-Relational Mapper (ORM) to simplify database interactions. \n In your terminal run the following command which creates a new migration named “InitialCreate” in your project. This migration will contain the necessary code to create the initial database schema based on your current data model. \n \n \n dotnet ef migrations add InitialCreate \n \n \n \n A new folder Migrations is generated with initial SQL code that will be used to create the table. \n \n \n \n To apply the following changes to the database you need to run update command: \n \n \n \n dotnet ef database update \n \n \n Navigate to Azure portal, Quick start (Preview) under your created resource, launch PostgreSQL Shell a command line interface to interact with your database. Enter your password when prompted. \n \n Run this command to see the tables: \n \n \n \\dt \n \n \n \n This should be the results you find: \n \n \n \n The table pharmacies have been created and we can now perform some CRUD operations. \n In your visual studio code, press ctr + F5 to run your code. \n \n The project will launch the swagger in the browser, and you can start testing the endpoints. I will be using Rest Client to test the API. For this, you should have Rest Client extension installed in visual studio code. \n \n \n To POST a Pharmacy: (Create a Pharmacy) \n \n \n \n To GET all a Pharmacies: \n \n \n \n To GET single a Pharmacy: \n \n \n \n To PUT a Pharmacy (Update) \n \n \n \n To DELETE a Pharmacy: \n \n \n \n In this blog, we’ve successfully demonstrated how to persist data in Azure Cosmos DB for PostgreSQL. I hope you found the steps clear and easy to follow. Thank you for reading, and happy coding! \n Read more \n Use Python to connect and run SQL commands on Azure Cosmos DB for PostgreSQL \n Use Node.js to connect and run SQL commands on Azure Cosmos DB for PostgreSQL \n Java app to connect and run SQL commands on Azure Cosmos DB for PostgreSQL \n Use Ruby to connect and run SQL commands on Azure Cosmos DB for PostgreSQL \n Create a web API with ASP.NET Core \n Entity Framework Core 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Development using Azure Cosmos DB Emulator at no Cost","conversation":{"__ref":"Conversation:conversation:4153822"},"id":"message:4153822","revisionNum":2,"uid":4153822,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1602363"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n \n Discover the Azure Cosmos DB Emulator – your key to a smooth, cost-effective local development experience. This guide equips you with everything you need: installation steps, using the Data Explorer, connecting with Azure Data Studio, and consuming Cosmos DB with JavaScript/TypeScript client libraries. Boost your development efficiency and build robust applications with confidence. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":17006},"postTime":"2024-06-03T00:00:00.032-07:00","lastPublishTime":"2024-06-03T00:00:00.032-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n \n In our previous blogs we introduced you to what is Azure Cosmos DB and Azure Cosmos DB APIs like Azure Cosmos DB for PostgreSQL, Azure Cosmos DB for Apache Gremlin, Azure Cosmos DB for MongoDB. In this blog we are going to introduce you Azure Cosmos DB Emulator which will help to run Azure Cosmos DB offline and do local development with ease. \n \n Benefits of Azure Cosmos DB Emulator \n \n \n Local Development Environment: The emulator provides a local environment that closely emulates the Azure Cosmos DB service. You can use it to develop and test your applications without needing an active Azure subscription or incurring any service costs. \n Cost-Free Testing: By using the emulator, you can write and test code that interacts with Azure Cosmos DB APIs even when you have no network connection. This allows you to avoid any costs associated with using the actual cloud service \n Automation Workflow Support: Incorporating the emulator into your automation workflows enables consistent testing across local development machines and remote continuous integration jobs. \n \n \n Prerequisites \n \n \n .NET 6 or later, Node.js LTS, or Python 3.7 or later \n \n \n Ensure that all required executables are available in your PATH. \n \n \n Windows emulator \n \n \n 64-bit Windows Server 2016, 2019, Windows 10, or Windows 11. \n Minimum hardware requirements: \n \n \n 2-GB RAM \n 10-GB available hard disk space \n \n \n What will be covered \n \n \n Installation process of Azure Cosmos DB Emulator \n Using Explorer in Azure Cosmos DB Emulator \n Connecting Azure Cosmos DB Emulator with Azure Data Studio \n Consuming Azure Cosmos DB using Azure Cosmos DB client library for JavaScript/TypeScript \n \n \n Installation process of Azure Cosmos DB Emulator \n \n \n Visit this page \n Under choose an API, select your preferred API, I will go with NoSQL. \n You can run the emulator in docker(both linux & windows) or windows via local installation which we are going to do. \n Download the Azure Cosmos DB emulator. \n Run the installer on your local machine with administrative privileges. \n The emulator automatically installs the appropriate developer certificates and configures firewall rules on your local machine. \n \n \n Start the emulator \n \n \n Start the emulator by selecting the application in the Windows Start menu. \n Alternatively, you can start the emulator's executable (Microsoft.Azure.Cosmos.Emulator.exe) at the %ProgramFiles%\\Azure Cosmos DB Emulator path. \n Also, you can start the emulator from the command-line. Use these parameters to configure the emulator ie you can specify the port to use for the API for NoSQL endpoint. More about emulator cli commands \n \n Microsoft.Azure.Cosmos.Emulator.exe /Port=65000 \n \n \n The emulator automatically opens the data explorer using the URL https://localhost:8081/_explorer/index.html \n If you're using docker you must configure emulator's TLS/SSL certificate but Windows local installation of the emulator automatically imports the TLS/SSL certificates. No further action is necessary. \n You should be welcomed by this familiar screen. \n \n \n \n \n \n Using Explorer in Azure Cosmos DB Emulator \n \n \n Create a database, container, select database throughput, database max RU/s and partition key. Then press okey \n \n \n \n \n You can confirm that you have created the database and container by \n \n \n \n \n \n Let’s create an item in our container. Click on items then new item and paste below json object and save. \n \n \n { \n\n \n\n \"eventId\": \"unique_event_id\", \n\n \n\n \"eventName\": \"Marathon\", \n\n \n\n \"eventDate\": \"2024-05-20\", \n\n \n\n \"participants\": [ \n\n \n\n { \n\n \n\n \"participantId\": \"participant1\", \n\n \n\n \"name\": \"Alice\", \n\n \n\n \"score\": 1200 \n\n \n\n }, \n\n { \n\n \"participantId\": \"participant2\", \n \"name\": \"Bob\", \n \"score\": 1100 \n\n } \n ] \n\n} \n \n \n \n \n \n \n Run query to select all items in our container. Click on run query, write your query and execute the query. \n \n \n \n \n \n You can access the Query Stats like RUs usage. You can also export the stats. \n \n \n \n \n Connecting Azure Cosmos DB Emulator with Azure Data Studio \n \n \n Download Azure data studio here and install it in your machine. \n \n \n Go to Azure Cosmos DB Emulator quickstart page and copy the Primary Connection String \n \n \n \n \n \n Open Azure data studio. Under extensions search Azure Cosmos DB and install the extension. \n \n \n \n \n \n Click on connections, create new connection, then select Azure Cosmos DB for NoSQL, select connection string, the paste the Primary Connection String we copied in the emulator. Press connect. \n \n \n \n \n \n The connection is successful, and you can easily use Azure Data Studio if its your preferred tool and create databases, containers make queries etc. \n \n \n \n \n \n U can take advantage of the import sample data button to get sample data to get started. A new container will be created with data. \n \n \n \n \n Consuming Azure Cosmos DB using Azure Cosmos DB client library for JavaScript/TypeScript \n \n \n We are going to use Azure Cosmos DB client library for JavaScript/TypeScript which is an SDK intended for JavaScript/TypeScript applications to interact with Azure Cosmos DB APIs. With it we can \n \n Create Cosmos DB databases and modify their settings. \n Create and modify containers to store collections of JSON documents. \n Create, read, update, and delete the items (JSON documents) in your containers. \n Query the documents in your database using SQL-like syntax \n \n \n You can read more about this SDK here. \n I have prepared a small workshop here, to help you interact with Azure Cosmos DB Emulator with a simple API starter code which you can hook up with hono or express or your favorite JavaScript / Typescript backend. \n \n \n \n \n \n In client.config.ts, we have just initialized our cosmosClient and passed in endpoint and key stored in .env file. NB: I have added agent property to allow us to use the \"https:\" from our emulator smoothly in our local development. \n I have imported client.config.ts into db.config.ts to help us create our database and container programtically. Lastly I’m using data.ts to store sample objects we can use to save in our container. \n \n \n \n \n \n In my index.ts, I have included several functions to help interact with our emulator. If you run pnpm run dev\n \n or npm run dev you will get [ ] meaning our container is empty but it have been created together with the database. \n \n \n \n \n \n If you uncomment createFamilyMember(familyMember) and alsorun you code. You should get one family member create \n \n \n \n \n \n You can confirm this on the Emulators explorer. \n \n \n \n \n Read more \n \n \n Optimize development and testing cost in Azure Cosmos DB \n Understand your Azure Cosmos DB bill \n Optimize request cost in Azure Cosmos DB \n Estimate RU/s using the Azure Cosmos DB capacity planner - Azure Cosmos DB for NoSQL \n \n \n 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up vector search in Azure Cosmos DB for PostgreSQL with pgvector indexes","conversation":{"__ref":"Conversation:conversation:4087077"},"id":"message:4087077","revisionNum":2,"uid":4087077,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1164279"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n Explore the IVFFlat and the HNSW index of pgvector for approximate nearest neighbor search on Azure Cosmos DB for PostgreSQL. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":1783},"postTime":"2024-03-19T00:00:00.063-07:00","lastPublishTime":"2024-03-19T00:00:00.063-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n In the previous post, you developed an image similarity search app using Jupyter Notebook. Utilizing the pgvector extension on Azure Cosmos DB for PostgreSQL, you were able to detect images that are semantically similar to a reference image or a text prompt. \n \n By default, pgvector performs exact nearest neighbor search, calculating the similarity between the query vector and every vector in the database. While this type of search provides perfect recall, it often leads to longer search times. To enhance efficiency for large datasets, you should create indexes to enable approximate nearest neighbor search, which trades off result quality for speed. Pgvector supports two types of approximate indexes: \n \n Inverted File with Flat Compression (IVFFlat) index \n Hierarchical Navigable Small World (HNSW) index \n \n \n In this tutorial, you will: \n \n Create an IVFFlat and an HNSW index in an Azure Cosmos DB for PostgreSQL table. \n Write SQL queries to detect similar images based on a text prompt or a reference image, utilizing the index. \n Investigate the execution plan of a similarity search query. \n \n \n The entire functional project is available in my GitHub repository. If want to follow along, just fork the repository and clone it to have it locally available. \n \n Prerequisites \n \n To proceed with this tutorial, ensure that you have the following prerequisites installed and configured: \n \n An Azure subscription - Create an Azure free account or an Azure for Students account. \n Python 3.10.x, Visual Studio Code, and Jupyter Notebook \n \n Additionally, make sure you have set up your workspace according to the guidelines provided on the project’s repository. \n \n Inverted File with Flat Compression (IVFFlat) index \n \n How the IVFFlat index works \n \n The IVFFlat algorithm accelerates vector search by grouping the vectors in the dataset into clusters (also known as Voronoi regions or cells) and limiting the search scope to the few nearest clusters for each query rather than the entire dataset. \n \n Let’s gain an intuitive understanding of how IVFFlat works. Consider that we place our high-dimensional vectors in a two-dimensional vector space. We then apply k-means clustering to compute the cluster centroids. After identifying the centroids, we assign each vector in our dataset to the closest centroid based on proximity. This process results in the partition of the vector space into several non-intersecting regions (Voronoi Diagram), as depicted in the following image: \n \n \n \n Now, each vector falls within a region. In the context of similarity search, each region consists of vectors that are semantically similar. Since vectors within the same region are more likely to be similar to each other than those in different regions, IVFFlat makes the search process more efficient. \n \n Let’s consider the query vector, as shown in the following image. To find the nearest neighbors to this query vector using IVFFlat, we perform the following steps: \n \n Calculate the distance between the query vector and each centroid. \n Identify the cell whose centroid is closest to the query vector and limit the search scope to that cell. \n Compute the distance between the query vector and every vector within the selected cell. \n Choose the vectors with the smallest distance as the nearest neighbors to the query vector. \n \n \n \n Although the IVFFlat algorithm accelerates the search process and provides good search quality, it can lead to errors. For example, the above image illustrates the scenario where the query vector resides at the edge of two cells. Despite the query vector being close to a datapoint in the blue region, this vector will not be considered a nearest neighbor candidate since the search scope is limited to the orange region. \n \n To address this issue and enhance search quality, we can expand the search scope by selecting several regions to search for nearest neighbor candidates. However, this approach comes with a trade-off: it increases search time. \n \n Create an IVFFlat index \n \n The code for creating an IVFFlat index and inserting data into a PostgreSQL table can be found at data_upload/upload_data_to_postgresql_ivfflat.py. \n \n To create an IVFFlat index using pgvector, two parameters need to be specified: \n \n Distance: The pgvector extension provides 3 methods for calculating the distance between vectors: Euclidean (L2), inner product, and cosine. These methods are identified by vector_l2_ops , vector_ip_ops , and vector_cosine_ops , respectively. It is essential to select the same distance metric for both the creation and querying of the index. \n Number of clusters: The lists parameter specifies the number of clusters that will be created. Pgvector suggests that an appropriate number of lists is rows/1000 for datasets with up to 1 million rows and sqrt(rows) for larger datasets. It is also advisable to create at least 10 clusters. \n \n To create an IVFFlat index in a PostgreSQL table, you can use the following statement: \n \n \n CREATE INDEX ON <table_name> USING ivfflat (<vector_column_name> <distance_method>) WITH (lists = <number_of_clusters>); \n \n \n It is important to note that the index should be created once the table is populated with data. If new vectors are added to the table, the index should be rebuilt to update the cluster centroids and accurately represent the new dataset. \n \n Search using the IVFFlat index \n \n The number of regions to consider during search is determined by the probes parameter. According to pgvector documentation, the recommended value for the probes parameter is sqrt(lists) . The default value is 1. To specify the value of the probes parameter, you can execute the following statement: \n \n \n SET ivfflat.probes = <number_of_probes>; \n \n \n To specify the number of probes to use for a single query, you should use the LOCAL keyword. \n \n Hierarchical Navigable Small World (HNSW) index \n \n How the HNSW index works \n \n The HNSW index is based on the construction of a multi-layered graph structure, that is optimized for performing approximate nearest neighbor search. In this graph structure, the datapoints (also referred to as nodes or vertices) are connected to each other by edges, which make it possible to navigate through the graph by following these edges. \n \n The base layer of the multi-layer graph essentially represents the entire dataset, while the higher layers consist of fewer nodes, providing a simplified overview of the layers below. The higher layers contain longer links, allowing for longer jumps between nodes for faster search, while the lower layers contain shorter links, enabling more accurate search. Nearest neighbor search begins at the top layer, where the longest links are present. We then navigate to the nearest node and gradually move to lower layers until a local minimum is found. This process is illustrated in the following image: \n \n \n \n The search process in an HNSW graph can be compared to the process of planning a trip between two cities. Much like how we start our journey with major roads and gradually transition to smaller ones as we approach our destination, the HNSW search procedure begins with longer links at the top layer and gradually moves to lower layers as we approach the desired data points. \n \n The HNSW algorithm is based on two fundamentals techniques: the probability skip list and the navigable small world (NSW) graphs. A detailed explanation of the process of constructing an index and search through the graph is beyond the scope of this article. For further information, refer to the resources provided at the end of this article. \n \n Compared to the IVFFlat index, the HNSW index generally provides better query performance in terms of the tradeoff between recall and speed, but at the expense of higher build time and more memory usage. Additionally, it doesn't require a training step to build the index. This means you can create an HNSW index even before any data is inserted into the table, unlike the IVFFlat index, which needs to be rebuilt when data changes to accurately represent new cluster centroids. \n \n Create an HNSW index \n \n The code for creating an HNSW index and inserting data into a PostgreSQL table can be found at data_upload/upload_data_to_postgresql_hnsw.py. \n \n To create an HNSW index through the pgvector extension, three parameters need to be specified: \n \n Distance: The pgvector extension provides 3 methods for calculating the distance between vectors: Euclidean (L2), inner product, and cosine. These methods are identified by vector_l2_ops , vector_ip_ops , and vector_cosine_ops , respectively. It is essential to select the same distance metric for both the creation and querying of the index. \n m: The parameter m specifies the maximum number of connections with neighboring datapoints per point per layer. Its default value is 16 . \n ef_construction: The parameter ef_construction defines the size of list that holds the nearest neighbor candidates when building the index. The default value is 64 . \n \n To create an HNSW index in a PostgreSQL table, you can use the following statement: \n \n \n CREATE INDEX ON <table_name> USING hnsw (<vector_column_name> <distance_method>) WITH (m = <m>, ef_construction = <ef_construction>); \n \n \n Search using the HNSW index \n \n For approximate nearest neighbor search, an additional parameter needs to be considered to use the HNSW index. The ef_search parameter specifies the size of the list that holds the nearest neighbor candidates during query execution. The default value is set to 40 . The ef_search parameter can be altered (for a single query or a session) using the following command: \n \n \n SET hnsw.ef_search = <value>; \n \n \n Detect similar images using the pgvector indexes \n \n To search for similar images through the HNSW index of the pgvector extension, we can use SQL SELECT statements and the built-in distance operators. The structure of a SELECT statement was explained in the Exact Nearest Neighbor Search blog post. \n \n It's important to note that PostgreSQL does not guarantee the use of an approximate index, as it may determine that a sequential scan could be more efficient for a query. To check whether PostgreSQL utilizes the index in a query, you can prefix the SELECT statement with the EXPLAIN ANALYZE keywords. \n \n \n EXPLAIN ANALYZE SELECT image_title, artist_name FROM paintings ORDER BY vector <=> '[0.001363333, …, -0.0010466448]' LIMIT 12; \n \n \n An example of a query plan that utilizes the IVFFlat index is provided below: \n \n Limit (cost=1537.64..1539.15 rows=12 width=72) (actual time=1.803..1.984 rows=12 loops=1) -> Index Scan using paintings_ivfflat_vector_idx on paintings (cost=1537.64..2951.85 rows=11206 width=72) (actual time=1.801..1.981 rows=12 loops=1) Order By: (vector <=> '[0.001363333, ..., -0.0010466448]'::vector) Planning Time: 0.135 ms Execution Time: 2.014 ms \n \n In the Jupyter Notebooks for the IVFFlat index and the HNSW index, you'll explore text-to-image and image-to-image search scenarios. You will use the same text prompts and reference images as in the Exact Nearest Neighbors search example, allowing for a comparison of the accuracy of the results. \n \n Here is an example of the images retrieved using the IVFFlat and the HNSW index. \n \n Left image: The IVFFlat index with probes=1 retrieved 9 out of the 12 paintings obtained with exact search. Right image: The HNSW index retrieved all the paintings obtained with exact search. \n \n Next steps \n \n The pgvector extension and PostgreSQL provide additional features that you can leverage to build AI-powered search applications. For example, you can integrate vector search with conventional keyword-based search methods into hybrid search systems, which generally have better performance. \n \n If you want to learn more about the HNSW algorithm, check out these learning resources: \n \n Alexander Ponomarenko, Yury Malkov, Andrey Logvinov, Vladimir Krylov, Approximate Nearest Neighbor Search Small World Approach (2011) \n Yury Malkov, Alexander Ponomarenko, Andrey Logvinov, Vladimir Krylov, Approximate nearest neighbor algorithm based on navigable small world graphs (2014) \n Yury Malkov, Dmitry Yashunin, Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2020) \n How to optimize performance when using pgvector on Azure Cosmos DB for PostgreSQL – Microsoft Docs \n Vector Similarity Search and Faiss Course by James Briggs \n Official GitHub repository of the pgvector extension \n 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and query embeddings in Azure Cosmos DB for PostgreSQL with pgvector","conversation":{"__ref":"Conversation:conversation:4070907"},"id":"message:4070907","revisionNum":2,"uid":4070907,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1164279"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n Set up Azure Cosmos DB for PostgreSQL as a vector database and search for images that are similar to a reference image or text prompt using pgvector. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3761},"postTime":"2024-03-05T00:00:00.030-08:00","lastPublishTime":"2024-03-05T00:00:00.030-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n In the previous post, you learned how to generate vector embeddings for images and text using the multi-modal embeddings APIs of Azure AI Vision and computed the embeddings of the images in our dataset. In this post, we will use Azure Blob Storage to store the images and Azure Cosmos DB for PostgreSQL to store our vector embeddings. Then, we will perform a similarity search on our embeddings by utilizing the pgvector extension. \n \n The workflow is illustrated in the following image: \n \n \n \n In this tutorial, you will learn how to: \n \n Upload images to an Azure Blob Storage container using the Python SDK. \n Store vector embeddings on an Azure Cosmos DB for PostgreSQL table. \n Write SQL queries to detect similar images based on a text prompt or a reference image. \n \n \n The entire functional project is available in my GitHub repository. If you're keen on trying it out, just fork the repository and clone it to have it locally available. \n \n Prerequisites \n \n To proceed with this tutorial, ensure that you have the following prerequisites installed and configured: \n \n An Azure subscription - Create an Azure free account or an Azure for Students account. \n An Azure Storage account - Create an Azure Storage account using the Azure CLI. \n An Azure Cosmos DB for PostgreSQL cluster - Create an Azure Cosmos DB for PostgreSQL cluster in the Azure portal. \n Python 3.10.x, Visual Studio Code, and Jupyter Notebook \n \n \n Upload images to Azure Blob Storage \n \n The code for uploading images to an Azure Blob Storage container can be found at data_upload/upload_images_to_blob.py. \n \n Azure Blob Storage is a cloud storage service that is optimized for storing large amounts of unstructured data, such as images. It offers three types of resources: \n \n The storage account that contains all your Azure Storage data objects. Every object that is stored in Azure Storage is identified by a unique address. \n Containers in the storage account which are similar to directories in a file system. \n Blobs that are organized in the containers. \n \n The following diagram illustrates the relationship between these resources: \n \n \n \n Azure Blob Storage resources: storage account, container, and blobs. (Image source: Azure Blob Storage Object model – Microsoft Docs) \n \n The Azure Blob Storage client library for Python provides the following classes to manage blobs and containers: \n \n BlobServiceClient : We will use the BlobServiceClient class to interact with the Azure Storage account and create a container. \n ContainerClient : We will use the ContainerClient class to interact with our container and the blobs inside the container. \n BlobClient : We will use the BlobClient class to upload a blob to our container. \n \n The process of uploading our images to Azure Blob Storage can be summarized as follows: \n \n Create a new container to store our images. \n Retrieve the filenames of the images in the dataset. \n Upload the images in the container, utilizing multiple threads via the ThreadPoolExecutor class. Additionally, use the tqdm library to display progress bars for better visualizing the image uploading process. \n \n \n \n import os\nimport csv\nimport sys\nfrom dotenv import load_dotenv\nfrom azure.storage.blob import BlobServiceClient, ContainerClient, ContentSettings\nfrom azure.core.exceptions import ResourceExistsError\nfrom concurrent.futures import ThreadPoolExecutor\nfrom tqdm import tqdm\n\n# Constants\nMAX_WORKERS = 4\nIMAGE_FILE_CSV_COLUMN_NAME = \"image_file\"\n\n# Directories\ncurrent_dir = os.path.dirname(os.path.realpath(__file__))\nparent_dir = os.path.dirname(current_dir)\n\n# Load environemt file\nload_dotenv(os.path.join(parent_dir, \".env\"), override=True)\n# Azure Blob Storage credentials\nblob_account_name = os.getenv(\"BLOB_ACCOUNT_NAME\")\nblob_account_key = os.getenv(\"BLOB_ACCOUNT_KEY\")\nblob_endpoint_suffix = os.getenv(\"BLOB_ENDPOINT_SUFFIX\")\nblob_connection_string = (\n f\"DefaultEndpointsProtocol=https;AccountName={blob_account_name};\"\n f\"AccountKey={blob_account_key};EndpointSuffix={blob_endpoint_suffix}\"\n)\ncontainer_name = os.getenv(\"CONTAINER_NAME\")\n\n# Dataset's folder\ndataset_folder = os.path.join(parent_dir, \"dataset\")\ndataset_filepath = os.path.join(dataset_folder, \"dataset_embeddings.csv\")\n\n# Images' folder\nimages_folder = os.path.join(parent_dir, \"semart_dataset\", \"images\")\n\n# Content-Type for blobs\ncontent_settings = ContentSettings(content_type=\"image/jpeg\")\n\n\ndef main():\n # Create Azure Blob Storage client\n blob_service_client = BlobServiceClient.from_connection_string(conn_str=blob_connection_string)\n\n # Create a new container\n try:\n container_client = blob_service_client.create_container(name=container_name, public_access=\"blob\")\n except ResourceExistsError:\n sys.exit(f\"A container with name {container_name} already exists.\")\n\n # Find the URLs of the images in the dataset\n images = load_image_filenames()\n\n print(f\"Number of images in the dataset: {len(images)}\")\n print(f\"Uploading images to container '{container_name}'\")\n\n # Upload images to blob storage\n upload_images(images=images, container_client=container_client)\n\n\ndef load_image_filenames() -> list[str]:\n with open(dataset_filepath, \"r\") as csv_file:\n csv_reader = csv.DictReader(csv_file, delimiter=\"\\t\", skipinitialspace=True)\n image_filenames = [row[IMAGE_FILE_CSV_COLUMN_NAME] for row in csv_reader]\n\n return image_filenames\n\n\ndef upload_blob_from_local_file(\n image_filepath: str,\n container_client: ContainerClient,\n) -> None:\n blob_name = os.path.basename(image_filepath)\n try:\n blob_client = container_client.get_blob_client(blob=blob_name)\n with open(image_filepath, mode=\"rb\") as data:\n blob_client.upload_blob(data=data, overwrite=True, content_settings=content_settings)\n except Exception as e:\n print(f\"Couldn't upload image {blob_name} to Azure Storage Account due to error: {e}\")\n\n\ndef upload_images(images: list[str], container_client: ContainerClient) -> None:\n with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:\n list(\n tqdm(\n executor.map(\n lambda x: upload_blob_from_local_file(\n image_filepath=os.path.join(images_folder, x),\n container_client=container_client,\n ),\n images,\n ),\n total=len(images),\n )\n ) \n \n \n Store embeddings in Azure Cosmos DB for PostgreSQL \n \n The code for inserting vector embeddings into an Azure Cosmos DB for PostgreSQL table can be found at data_upload/upload_data_to_postgresql.py. \n \n Activate the pgvector extension \n \n The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt: \n \n \n SELECT CREATE_EXTENSION('vector'); \n \n \n The pgvector extension introduces a data type called VECTOR that can be used during the creation of a table to indicate that a column will hold vector embeddings. When creating the column, it’s essential to specify the dimension of the vectors. In our scenario, Azure AI Vision generates 1024-dimensional vectors. \n \n Insert data into a PostgreSQL table \n \n To insert data into an Azure Cosmos DB for PostgreSQL table, we will proceed as follows: \n \n Create a table to store the filenames of the images, their embeddings, and their associated metadata. All information is saved in a CSV file, as presented in the previous post. \n Insert the data from the CSV file into the table using the PostgreSQL COPY command. \n \n \n \n import os\nimport psycopg2\nfrom psycopg2 import pool\nfrom dotenv import load_dotenv\n\n# Constants\nIMAGE_FILE_COLUMN_NAME = \"image_file\"\nDESCRIPTION_COLUMN_NAME = \"description\"\nAUTHOR_COLUMN_NAME = \"author\"\nTITLE_COLUMN_NAME = \"title\"\nTECHNIQUE_COLUMN_NAME = \"technique\"\nTYPE_COLUMN_NAME = \"type\"\nTIMEFRAME_COLUMN_NAME = \"timeframe\"\nVECTOR_COLUMN_NAME = \"vector\"\n\n# Directories\ncurrent_dir = os.path.dirname(os.path.realpath(__file__))\nparent_dir = os.path.dirname(current_dir)\n\n# Load environemt file\nload_dotenv(os.path.join(parent_dir, \".env\"), override=True)\n# Azure CosmosDB for PostgreSQL credentials\npostgres_host = os.getenv(\"POSTGRES_HOST\")\npostgres_database_name = os.getenv(\"POSTGRES_DB_NAME\")\npostgres_user = os.getenv(\"POSTGRES_USER\")\npostgres_password = os.getenv(\"POSTGRES_PASSWORD\")\nsslmode = \"require\"\ntable_name = os.getenv(\"POSTGRES_TABLE_NAME\")\npostgres_connection_string = (\n f\"host={postgres_host} user={postgres_user} \"\n f\"dbname={postgres_database_name} \"\n f\"password={postgres_password} sslmode={sslmode}\"\n)\n\n# Dataset's folder\ndataset_folder = os.path.join(parent_dir, \"dataset\")\ndataset_filepath = os.path.join(dataset_folder, \"dataset_embeddings.csv\")\n\n\ndef main():\n postgresql_pool = psycopg2.pool.SimpleConnectionPool(1, 20, postgres_connection_string)\n if (postgresql_pool):\n print(\"Connection pool created successfully\")\n\n # Get a connection from the connection pool\n conn = postgresql_pool.getconn()\n cursor = conn.cursor()\n\n print(\"Creating a table...\")\n cursor.execute(f\"DROP TABLE IF EXISTS {table_name};\")\n cursor.execute(\n f\"CREATE TABLE {table_name} (\"\n f\"{IMAGE_FILE_COLUMN_NAME} TEXT PRIMARY KEY,\"\n f\"{DESCRIPTION_COLUMN_NAME} TEXT NOT NULL,\"\n f\"{AUTHOR_COLUMN_NAME} TEXT NOT NULL,\"\n f\"{TITLE_COLUMN_NAME} TEXT NOT NULL,\"\n f\"{TECHNIQUE_COLUMN_NAME} TEXT,\"\n f\"{TYPE_COLUMN_NAME} TEXT,\"\n f\"{TIMEFRAME_COLUMN_NAME} TEXT,\"\n f\"{VECTOR_COLUMN_NAME} VECTOR(1024) NOT NULL);\"\n )\n\n print(\"Saving data to table...\")\n with open(dataset_filepath) as csv_file:\n cursor.copy_expert(\n f\"COPY {table_name} FROM STDIN WITH \"\n f\"(FORMAT csv, DELIMITER '\\t', HEADER MATCH);\",\n csv_file\n )\n\n conn.commit()\n\n # Fetch all rows from table\n cursor.execute(f\"SELECT * FROM {table_name};\")\n rows = cursor.fetchall()\n print(f\"Number of records in the table: {len(rows)}\")\n\n # Close the connection\n cursor.close()\n conn.close() \n \n \n Detect similar images using the pgvector extension \n \n The image similarity search workflow that we will follow is summarized as follows: \n \n Use the Azure AI Vision Vectorize Image API or the Vectorize Text API to generate the vector embedding of a reference image or text prompt, respectively. It is crucial to employ the same embedding model for queries as the one used to generate embeddings for the images in the dataset. \n To calculate similarity and retrieve images, utilize SQL SELECT statements and the built-in vector operators of the PostgreSQL database. Specifically, cosine similarity will be used as the similarity metric. \n The similarity search will produce a list of vectors that are most similar to the query vector. The raw data associated with each vector can then be accessed. \n Download the images from the Azure Blob Storage container and display them using the matplotlib package. \n \n \n This workflow is illustrated in the following diagram: \n \n \n \n Nearest neighbor search using pgvector \n \n Given the vector embedding of the query, we can use SQL SELECT statements to search for similar images. Let’s understand how a simple SELECT statement works. Consider the following query: \n \n \n SELECT * FROM table ORDER BY embedding <=> '[0.003, …, 0.034]' LIMIT 5 \n \n \n This query computes the cosine distance ( <=> ) between the given vector ( [0.003, …, 0.034] ) and the vectors stored in the table, sorts the results by the calculated distance, and returns the five most similar images ( LIMIT 5 ). Additionally, you can obtain the cosine similarity between the query vector and the retrieved vectors by modifying the SELECT statement as follows: \n \n \n SELECT image_title, 1 - (vector <=> '[0.003, …, 0.034]') AS cosine_similarity FROM table ORDER BY cosine_similarity DESC LIMIT 5; \n \n \n The pgvector extension provides 3 operators that can be used to calculate similarity: \n \n \n \n \n Operator \n Description \n \n \n <-> \n Euclidean distance \n \n \n <#> \n Negative inner product \n \n \n <=> \n Cosine distance \n \n \n \n \n Code sample: Image similarity search \n \n In the Jupyter Notebook provided on my GitHub repository, you'll explore the following scenarios: \n \n Text-to-image search: You will use a text prompt to search for and identify paintings that are semantically similar, relying solely on the vector embeddings without utilizing image metadata, such as the title or description of the painting. \n Image-to-image search: You will use a painting as a reference to search for similar ones by comparing the vector embedding of the reference image with those in the collection. \n Metadata filtering: Filtering enables users to narrow down search results, such as searching for paintings by a specific artist. However, implementing accurate and fast metadata filtering in vector search systems is a challenging task. You can read the article The Missing WHERE Clause in Vector Search on the Pinecone blog to learn about the two fundamental approaches for metadata filtering and understand the complexities involved in implementing such filters into vector search applications. \n \n \n \n \n Next steps \n \n In this post, you explored the basic vector similarity search features offered by the pgvector extension. This type of vector search is referred to as exact nearest neighbor search, as it computes the similarity between the query vector and every vector in the database. In the upcoming post, you will explore approximate nearest neighbor search, which trades off result quality for speed. \n \n If you want to explore pgvector's features, check out these learning resources: \n \n How to use pgvector on Azure Cosmos DB for PostgreSQL – Microsoft Docs \n Official GitHub repository of the pgvector extension \n 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a vector similarity search app with Azure AI Vision and PostgreSQL","conversation":{"__ref":"Conversation:conversation:3954936"},"id":"message:3954936","revisionNum":3,"uid":3954936,"depth":0,"board":{"__ref":"Blog:board:EducatorDeveloperBlog"},"author":{"__ref":"User:user:1164279"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n Explore the world of vector search and its incredible potential for enabling next-generation search experiences. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3805},"postTime":"2023-10-17T00:00:00.038-07:00","lastPublishTime":"2023-10-17T00:00:00.038-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n Have you ever found yourself in a situation where you remember what happened in a movie but struggle to recall its name and can’t come up with the right words to search for it? Or, maybe you’ve had that moment when you have a picture of a product you wish to purchase but aren't sure how to find it online. \n Unlike traditional search systems that rely on mentions of keywords, tags, or other metadata, vector search leverages machine learning to capture the meaning of data, allowing you to search by what you mean. Consider the second scenario from the paragraph above as an example. In a vector search system, you can simply use the image of a product to find similar ones instead of struggling to come up with the proper search words. This method is also known as \"reverse image search.\" Vector search works by transforming data, such as text, images, videos, and audio, into a numerical representation that is called vector embedding and applying nearest neighbor algorithms to find similar data. \n \n About me \n \n Hi, I am Foteini Savvidou, a Microsoft Learn Student Ambassador and Microsoft MVP from Greece! \n \n \n \n I am a senior Electrical and Computer Engineering student at the Aristotle University of Thessaloniki (Greece) interested in wireless communications, IoT, AI, cloud technologies, and biomedical engineering. Always passionate about teaching and learning new things, I love helping people expand their technical skills through sharing articles on my blog. \n \n Introduction \n \n A few days ago, I had a wonderful time delivering a session about building a vector search system using Azure Cosmos DB for PostgreSQL and Azure AI Vision at the monthly Azure Cosmos DB Usergroup’s virtual show hosted by Jay Gordon and Microsoft Reactor. In this article, I will document the main points of my session and provide you with learning resources and code examples. \n \n You will: \n \n Understand the concept of vector embeddings and how a vector search similarity system works. \n Use the Azure AI Vision Image Retrieval APIs for converting images and text into a vector representation. \n Build a vector search system with Azure Cosmos DB for PostgreSQL and Azure AI Vision. \n \n \n Before you start, follow these steps to set up your workspace: \n \n Sign up for either an Azure free account or an Azure for Students account. If you already have an active subscription, you can use it. \n Install Python 3.x, Visual Studio Code, Jupyter Notebook and Jupyter Extension for Visual Studio Code. \n Create a Cognitive Services resource in the Azure portal. \n Create an Azure Cosmos DB for PostgreSQL cluster and an Azure Blob Storage container. \n \n \n What are vector embeddings? \n \n In simple terms, vector embeddings are numerical representations of data, such as images, text, videos, and audio. These vectors are high-dimensional dense vectors, with each dimension containing information about the original content. By translating data into vectors, computers can capture the meaning of the data and understand the semantic similarity between two objects. We can quantify semantic similarity of two objects by their proximity in a vector space. \n \n \n \n We can measure the semantic similarity by using a distance metric such as Euclidean distance, inner product, or cosine distance. In the following examples, we will use the cosine similarity, which is defined as the cosine of the angle between the two vectors. \n There are numerous embedding models available, including OpenAI, Hugging Face, and Azure AI Vision. Azure AI Vision (formerly known as Azure Computer Vision) provides two Image Retrieval APIs for vectorizing image and text queries: the Vectorize Image API and the Vectorize Text API. This vectorization converts images and text into coordinates in a 1024-dimensional vector space, enabling users to search a collection of images using text and/or images without the need for metadata, such as image tags, labels, or captions. \n \n How vector similarity search works? \n \n A vector search system works by comparing the vector embedding of a user’s query with a set of pre-stored vector embeddings to find a list of vectors that are the most similar to the query vector. The diagram below illustrates the workflow. \n \n \n \n Vector embeddings can be stored in a vector database, which is a specialized type of database optimized for storing and querying vectors with a large number of dimensions. \n \n In the following video, you can learn more about vector embeddings and vector similarity search with Azure Cosmos DB for PostgreSQL and Azure AI Vision and walk through the Jupyter Notebooks that are available on my GitHub repository. \n \n \n \n Python code samples \n \n In my GitHub repository, you can find some Jupyter Notebooks to help you gain hands-on experience with the concepts introduced in the above video. You may want to refer to the README of the project for instructions on how to set up your Azure resources. \n \n Quickstart \n \n In the quickstart, you will explore the Image Retrieval APIs of Azure AI Vision and the basics of the pgvector extension. You will build a simple app to search a collection of images from a wide range of natural scenes. The images were taken from Kaggle. \n \n Example scenario: Vector search with Azure Cosmos DB for PostgreSQL, Azure Blob Storage and Azure AI Vision \n \n In this extended scenario, you will: \n \n Use Azure Blob Storage to store approximately 8.2k images of paintings that were taken from the Paintings Dataset of the Visual Geometry Group - University of Oxford. \n Generate the vector embedding of each image in the container using the Vectorize Image API of Azure AI Vision. \n Store the vector embeddings along with a reference to the corresponding image file in a PostgreSQL table. \n Execute SQL queries to search for images that are most similar to a reference image or a text prompt. \n \n \n Resources \n \n If you’d like to dive deeper into this topic, here are some helpful resources. \n \n Azure Computer Vision Image Retrieval – Microsoft Docs \n How to use pgvector on Azure Cosmos DB for PostgreSQL – Microsoft Docs \n Official GitHub repository of the pgvector extension \n Image Vector Similarity Search with Azure Computer Vision and PostgreSQL \n 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