Pgvector Marketplace doc (#7388)

* add pgvector doc

* copy edit and dictionary addition

* Vale fix

---------

Co-authored-by: jddocs <jdutton@akamai.com>
Co-authored-by: nmelehan-akamai <nmelehan@akamai.com>

Author: 3 people

ci/vale/dictionary.txt

@@ -1919,6 +1919,7 @@ pg_restore
 pgAdmin
 PGvector
 pgpass
+Pgvector
 pgvector
 Phalcon
 pharmer

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

@@ -0,0 +1,78 @@
+---
+title: "Deploy pgvector through the Linode Marketplace"
+description: "Deploy Pgvector, an open-source vector extension for PostgreSQL for similarity search and AI embeddings."
+published: 2026-02-09
+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:
+- '[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.
+
+### 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 contents of 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" %}}