From d34583c3a50960faab9e4f837d929ac0005019e5 Mon Sep 17 00:00:00 2001 From: hmorris Date: Tue, 3 Feb 2026 12:47:42 -0500 Subject: [PATCH 1/2] add pgvector doc --- .../marketplace-docs/guides/pgvector/index.md | 79 +++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 docs/marketplace-docs/guides/pgvector/index.md diff --git a/docs/marketplace-docs/guides/pgvector/index.md b/docs/marketplace-docs/guides/pgvector/index.md new file mode 100644 index 00000000000..55fbc7a3a40 --- /dev/null +++ b/docs/marketplace-docs/guides/pgvector/index.md @@ -0,0 +1,79 @@ +--- +title: "Deploy pgvector through the Linode Marketplace" +description: "Deploy Pgvector, an open-source vector extension for Postgresql for similarity search and AI embeddings." +published: 2026-02-03 +keywords: ['pgvector', 'postgresql', 'vector database', 'AI', 'embeddings', 'similarity search'] +tags: ["ubuntu", "marketplace", "developer", "postgresql", "pgvector", "linode platform", "machine learning"] +external_resources: +- '[pgvector GitHub](https://github.com/pgvector/pgvector)' +- '[pgvector Documentation](https://github.com/pgvector/pgvector#readme)' +- '[PostgreSQL Documentation](https://www.postgresql.org/docs/)' +aliases: ['/products/tools/marketplace/guides/pgvector/'] +authors: ["Akamai"] +contributors: ["Akamai"] +license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)' +--- + +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. + + +## Deploying a Marketplace App + +{{% content "deploy-marketplace-apps-shortguide" %}} + +{{% content "marketplace-verify-standard-shortguide" %}} + +{{< note >}} +**Estimated deployment time:** Pgvector should be fully installed within 5-10 minutes after the Compute Instance has finished provisioning. +{{< /note >}} + +## Configuration Options + +- **Supported distributions:** Ubuntu 24.04 LTS +- **Suggested plan:** Dedicated 16GB instance or higher for baseline development and testing with support for GPU instances. Larger plans are recommended for compute + +### Pgvector Options + +{{% content "marketplace-required-limited-user-fields-shortguide" %}} + +{{% content "marketplace-custom-domain-fields-shortguide" %}} + +{{% content "marketplace-special-character-limitations-shortguide" %}} + + +### Obtain the Credentials + +When deployment completes, the system automatically generates credentials to administer your Pgvector instance. These are stored in the limited user’s credentials file. + +1. Log in to your Compute Instance using one of the methods below: + + - **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/). + - **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/). + +2. Run the following command to access the credentials file: + +```command +cat /home/$USERNAME/.credentials +``` + +## Getting Started after Deployment + +You can start by connecting to your Postgresql database + +```command +psql -h localhost -U $POSTGRES_USER -d $POSTGRES_DB +``` +The connection credentials can be found in the credentials file located at `/home/$USERNAME/.credentials`. + +You can then define vector columns and run similarity queries directly in SQL. + +pgvector works with standard PostgreSQL clients and integrates easily with popular AI frameworks and ORMs: + + - **[psycopg](https://www.psycopg.org/)**: PostgreSQL adapter for Python + - **[SQLAlchemy](https://www.sqlalchemy.org/)**: Python ORM with pgvector support + - **[pgvector-node](https://github.com/pgvector/pgvector-node)**: Node.js client helpers + - **[pgvector-go](https://github.com/pgvector/pgvector-go)**: Go utilities for pgvector + +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. + +{{% content "marketplace-update-note-shortguide" %}} \ No newline at end of file From bf5bf3b2f9b9f33dd556aea73ff543a432153617 Mon Sep 17 00:00:00 2001 From: jddocs Date: Mon, 9 Feb 2026 11:21:03 -0500 Subject: [PATCH 2/2] copy edit and dictionary addition --- ci/vale/dictionary.txt | 1 + .../marketplace-docs/guides/pgvector/index.md | 19 +++++++++---------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/ci/vale/dictionary.txt b/ci/vale/dictionary.txt index 10ee9a02a4b..bad8937f269 100644 --- a/ci/vale/dictionary.txt +++ b/ci/vale/dictionary.txt @@ -1910,6 +1910,7 @@ pg_dumpall pg_restore pgAdmin pgpass +Pgvector Phalcon pharmer pharo diff --git a/docs/marketplace-docs/guides/pgvector/index.md b/docs/marketplace-docs/guides/pgvector/index.md index 55fbc7a3a40..84b914475d0 100644 --- a/docs/marketplace-docs/guides/pgvector/index.md +++ b/docs/marketplace-docs/guides/pgvector/index.md @@ -1,7 +1,8 @@ --- title: "Deploy pgvector through the Linode Marketplace" description: "Deploy Pgvector, an open-source vector extension for Postgresql for similarity search and AI embeddings." -published: 2026-02-03 +published: 2026-02-09 +modified: 2026-02-09 keywords: ['pgvector', 'postgresql', 'vector database', 'AI', 'embeddings', 'similarity search'] tags: ["ubuntu", "marketplace", "developer", "postgresql", "pgvector", "linode platform", "machine learning"] external_resources: @@ -16,7 +17,6 @@ license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)' 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. - ## Deploying a Marketplace App {{% content "deploy-marketplace-apps-shortguide" %}} @@ -30,7 +30,7 @@ Pgvector is an open-source PostgreSQL extension that enables vector similarity s ## Configuration Options - **Supported distributions:** Ubuntu 24.04 LTS -- **Suggested plan:** Dedicated 16GB instance or higher for baseline development and testing with support for GPU instances. Larger plans are recommended for compute +- **Suggested plan:** Dedicated 16GB instance or higher for baseline development and testing with support for GPU instances. ### Pgvector Options @@ -40,17 +40,16 @@ Pgvector is an open-source PostgreSQL extension that enables vector similarity s {{% content "marketplace-special-character-limitations-shortguide" %}} - ### Obtain the Credentials -When deployment completes, the system automatically generates credentials to administer your Pgvector instance. These are stored in the limited user’s credentials file. +When deployment completes, the system automatically generates credentials to administer your Pgvector instance. These are stored in the limited user’s `.credentials` file. 1. Log in to your Compute Instance using one of the methods below: - **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/). - **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/). -2. Run the following command to access the credentials file: +2. Run the following command to access the contents of the `.credentials` file: ```command cat /home/$USERNAME/.credentials @@ -58,22 +57,22 @@ cat /home/$USERNAME/.credentials ## Getting Started after Deployment -You can start by connecting to your Postgresql database +You can start by connecting to your PostgreSQL database ```command psql -h localhost -U $POSTGRES_USER -d $POSTGRES_DB ``` -The connection credentials can be found in the credentials file located at `/home/$USERNAME/.credentials`. +The connection credentials can be found in the `.credentials` file located at `/home/$USERNAME/.credentials`. You can then define vector columns and run similarity queries directly in SQL. -pgvector works with standard PostgreSQL clients and integrates easily with popular AI frameworks and ORMs: +Pgvector works with standard PostgreSQL clients and integrates easily with popular AI frameworks and ORMs: - **[psycopg](https://www.psycopg.org/)**: PostgreSQL adapter for Python - **[SQLAlchemy](https://www.sqlalchemy.org/)**: Python ORM with pgvector support - **[pgvector-node](https://github.com/pgvector/pgvector-node)**: Node.js client helpers - **[pgvector-go](https://github.com/pgvector/pgvector-go)**: Go utilities for pgvector -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. +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. {{% content "marketplace-update-note-shortguide" %}} \ No newline at end of file