---
title: Postgres | Developer Documentation
---

Configure your own Postgres instance as data sink.

## Configure via UI

![postgres](/_astro/postgres.CyNUmbn8_qufye.png)

## Configure Parameters

To configure **Postgres** as a vector store for your LlamaCloud documents you will need the following:

| Parameter           | Description                                   | Example                                           |
| ------------------- | --------------------------------------------- | ------------------------------------------------- |
| Database            | Database name                                 | `llamaindex`                                      |
| Host                | Connection endpoint                           | `my-postgres-cluster.us-east-1.rds.amazonaws.com` |
| User                | Database username                             | `postgres`                                        |
| Password            | Password for database user                    | `*****`                                           |
| Table Name          | Table where embeddings will be stored         | `llamaindex`                                      |
| Schema Name         | Schema in which the database table will exist | `public`                                          |
| Embedding Dimension | Dimension size of embeddings                  | `1536`                                            |
| Port                | Port where Postgres listens                   | `5432`                                            |

## Configure via API / Client

- [Python Client](#tab-panel-275)
- [Typescript Client](#tab-panel-276)

```
from llama_cloud.types import CloudPostgresVectorStore


ds = {
    'name': '<your-data-sink-name>',
    'sink_type': 'POSTGRES',
    'component': CloudPostgresVectorStore(
        database='<database-name>',
        host='<database-host>',
        user='<user>',
        password='<password>',
        port=5432,
        embed_dim=1536,
        schema_name='<schema>',
        table_name='<table>'
    )
}


data_sink = client.data_sinks.create_data_sink(request=ds)
```

```
const ds = {
    'name': '<your-data-sink-name>',
    'sinkType': 'POSTGRES',
    'component': {
        'database': '<database-name>',
        'host': '<database-host>',
        'user': '<user>',
        'password': '<password>',
        'port': 5432,
        'embed_dim': 1536,
        'schema_name': '<schema>',
        'table_name': '<table>'
    }
}


data_sink = await client.dataSinks.createDataSink({
projectId: projectId,
body: ds
})
```