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Merge pull request #7418 from nmelehan-akamai/rc-v1.399.0
[Release] v1.399.0
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‎ci/vale/dictionary.txt‎

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pgAdmin
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PGvector
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pgpass
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Pgvector
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pgvector
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Phalcon
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‎docs/marketplace-docs/guides/milvus/index.md‎

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---
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title: "Deploy Milvus through the Linode Marketplace"
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description: "Deploy Milvus, an open-source vector database for AI, similarity search, and embeddings workloads."
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published: 2026-02-09
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modified: 2026-02-09
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keywords: ['milvus', 'vector database', 'AI', 'embeddings', 'similarity search']
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tags: ["ubuntu", "marketplace", "developer", "milvus", "linode platform", "machine learning"]
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external_resources:
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- '[Milvus.io](https://milvus.io/)'
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- '[Milvus Documentation](https://milvus.io/docs)'
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- '[Milvus Quickstart Guide](https://milvus.io/docs/install_standalone-docker.md)'
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aliases: ['/products/tools/marketplace/guides/milvus/']
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authors: ["Akamai"]
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contributors: ["Akamai"]
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license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)'
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---
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Milvus is a high-performance open-source vector database that enables fast similarity search across unstructured data, including image, video, audio, and text embeddings. Designed for AI workloads and large-scale vector indexing, Milvus provides millisecond-level retrieval performance and integrates easily with popular machine learning frameworks and embedding services.
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This guide includes steps for deploying the Milvus Standalone Marketplace App on a Linode Compute Instance using Docker Compose.
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## Deploying a Marketplace App
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{{% content "deploy-marketplace-apps-shortguide" %}}
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{{% content "marketplace-verify-standard-shortguide" %}}
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{{< note >}}
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**Estimated deployment time:** After provisioning completes, the Milvus Standalone container stack initializes in approximately 3–5 minutes.
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{{< /note >}}
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## Configuration Options
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- **Supported distributions:** Ubuntu 24.04 LTS
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- **Recommended minimum plan:** Dedicated 16GB instance or higher for baseline development and testing with support for GPU instances.
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### Milvus Deployment Options
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{{% content "marketplace-required-limited-user-fields-shortguide" %}}
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{{% content "marketplace-custom-domain-fields-shortguide" %}}
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{{% content "marketplace-special-character-limitations-shortguide" %}}
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### Obtain the Credentials
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When deployment completes, the system automatically generates credentials for internal MinIO storage and other parameters needed to administer your Milvus instance. These are stored in the limited user’s `.credentials` file.
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1. Log in to your Compute Instance using one of the methods below:
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- **Lish Console**: Log in to Cloud Manager, click **Linodes**, select your instance, and click **Launch LISH Console**. Log in as `root`. To learn more, see [Using the Lish Console](/docs/products/compute/compute-instances/guides/lish/).
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- **SSH**: Log in to your instance over SSH using the `root` user. To learn how, see [Connecting to a Remote Server Over SSH](/docs/guides/connect-to-server-over-ssh/).
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2. Run the following command to access the `.credentials` file:
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```command
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cat /home/$USERNAME/.credentials
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```
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### Getting Started After Deployment
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Once the Milvus Standalone container is deployed and running, you can connect to the database and begin creating collections, inserting vectors, and performing searches.
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To interact with Milvus, install the a supported client SDK (Python, Go, Java, Node.js).
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- **[pymilvus](https://github.com/milvus-io/pymilvus)**: Python SDK for Milvus Vector Database
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- **[milvus-sdk-node](https://github.com/milvus-io/milvus-sdk-node)**: node.js SDK for Milvus Vector Database
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- **[Milvus Go SDK](https://milvus.io/docs/install-go.md)**: GO SDK for Milvus Vector Database
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- **[Milvus Java SDK](https://milvus.io/docs/install-java.md)**: Java SDK for Milvus Vector Database
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### Verify Running Containers
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First, confirm that all Milvus-related containers are running:
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```command
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docker ps
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```
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You should see containers for Milvus, etcd, and MinIO in a running state.
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### Access the MinIO Dashboard
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Milvus uses MinIO as its object storage backend. You can access the MinIO web interface to verify bucket creation and storage activity.
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To access your MinIO Dashboard, Open a browser and navigate to your Linode rDNS domain `https://203-0-113-0.ip.linodeusercontent.com`. Replace `https://203-0-113-0.ip.linodeusercontent.com` with your [Linode's RDNS domain](/docs/products/compute/compute-instances/guides/manage-ip-addresses/#viewing-ip-addresses). The credentials can be found in the `.credentials` file (/home/$USERNAME/.credentials).
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If you want to learn more about Milvus, checkout [the official Milvus documentation](https://milvus.io/docs) to learn how to further utilize your instance.
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{{% content "marketplace-update-note-shortguide" %}}

‎docs/marketplace-docs/guides/pgvector/index.md‎

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---
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title: "Deploy pgvector through the Linode Marketplace"
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description: "Deploy Pgvector, an open-source vector extension for PostgreSQL for similarity search and AI embeddings."
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published: 2026-02-09
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modified: 2026-02-09
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keywords: ['pgvector', 'postgresql', 'vector database', 'AI', 'embeddings', 'similarity search']
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tags: ["ubuntu", "marketplace", "developer", "postgresql", "pgvector", "linode platform", "machine learning"]
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external_resources:
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- '[pgvector GitHub](https://github.com/pgvector/pgvector)'
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- '[pgvector Documentation](https://github.com/pgvector/pgvector#readme)'
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- '[PostgreSQL Documentation](https://www.postgresql.org/docs/)'
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aliases: ['/products/tools/marketplace/guides/pgvector/']
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authors: ["Akamai"]
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contributors: ["Akamai"]
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license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)'
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---
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Pgvector is an open-source PostgreSQL extension that enables vector similarity search directly inside a relational database. It allows you to store embeddings alongside structured data and perform nearest-neighbor searches using cosine similarity, inner product, or Euclidean distance—making it well-suited for AI, semantic search, and retrieval-augmented generation (RAG) workloads.
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## Deploying a Marketplace App
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{{% content "deploy-marketplace-apps-shortguide" %}}
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{{% content "marketplace-verify-standard-shortguide" %}}
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{{< note >}}
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**Estimated deployment time:** Pgvector should be fully installed within 5-10 minutes after the Compute Instance has finished provisioning.
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{{< /note >}}
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## Configuration Options
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- **Supported distributions:** Ubuntu 24.04 LTS
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- **Suggested plan:** Dedicated 16GB instance or higher for baseline development and testing with support for GPU instances.
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### Pgvector Options
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{{% content "marketplace-required-limited-user-fields-shortguide" %}}
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{{% content "marketplace-custom-domain-fields-shortguide" %}}
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{{% content "marketplace-special-character-limitations-shortguide" %}}
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### Obtain the Credentials
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When deployment completes, the system automatically generates credentials to administer your Pgvector instance. These are stored in the limited user’s `.credentials` file.
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1. Log in to your Compute Instance using one of the methods below:
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- **Lish Console**: Log in to Cloud Manager, click **Linodes**, select your instance, and click **Launch LISH Console**. Log in as `root`. To learn more, see [Using the Lish Console](/docs/products/compute/compute-instances/guides/lish/).
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- **SSH**: Log in to your instance over SSH using the `root` user. To learn how, see [Connecting to a Remote Server Over SSH](/docs/guides/connect-to-server-over-ssh/).
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2. Run the following command to access the contents of the `.credentials` file:
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```command
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cat /home/$USERNAME/.credentials
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```
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## Getting Started after Deployment
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You can start by connecting to your PostgreSQL database
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```command
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psql -h localhost -U $POSTGRES_USER -d $POSTGRES_DB
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```
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The connection credentials can be found in the `.credentials` file located at `/home/$USERNAME/.credentials`.
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You can then define vector columns and run similarity queries directly in SQL.
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Pgvector works with standard PostgreSQL clients and integrates easily with popular AI frameworks and ORMs:
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- **[psycopg](https://www.psycopg.org/)**: PostgreSQL adapter for Python
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- **[SQLAlchemy](https://www.sqlalchemy.org/)**: Python ORM with pgvector support
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- **[pgvector-node](https://github.com/pgvector/pgvector-node)**: Node.js client helpers
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- **[pgvector-go](https://github.com/pgvector/pgvector-go)**: Go utilities for pgvector
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If you want to learn more about Pgvector, check out the [official Pgvector documentation](https://github.com/pgvector/pgvector?tab=readme-ov-file#getting-started) to explore indexing strategies, performance tuning, and advanced query patterns.
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{{% content "marketplace-update-note-shortguide" %}}

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