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Connect PostgreSQL to your preprocessing pipeline, and use the Unstructured Ingest CLI or the Unstructured Ingest Python library to batch process all your documents and store structured outputs locally on your filesystem.

The requirements are as follows.

The following video shows how to set up Amazon RDS for PostgreSQL:

The following video shows how to set up Azure Database for PostgreSQL:

  • A PostgreSQL instance.

  • The host name and port number for the instance.

    • For Amazon RDS for PostgreSQL, learn how to get the host name and port number.
    • For Azure Database for PostgreSQL, learn how to get the host. The port number is 5432.
    • For local PostgreSQL installations, these values are in the postgresql.conf file’s listen_addresses and port settings. This file should be on the same machine as the instance. These values might also already be set as environment variables named PGHOST and PGPORT on the same machine as the instance.
    • For other installation types, see your PostgreSQL provider’s documentation.

  • Depending on your network security requirements, you might need to allow access to your instance only from specific IP addresses.

    To get Unstructured’s IP address ranges, go to https://assets.p6m.u10d.net/publicitems/ip-prefixes.json and allow all of the ip_prefix fields’ values that are listed.

    These IP address ranges are subject to change. You can always find the latest ones in the preceding file.

    To learn how to allow these IP address ranges, see your PostgreSQL provider’s documentation, for example with Amazon RDS for PostgreSQL or Azure Database for PostgreSQL.

  • A database in the instance.

    • For Amazon RDS for PostgreSQL and Azure Database for PostgreSQL, the default database name is postgres unless a custom database name was specified during the instance creation process.
    • For local PostgreSQL installations, learn how to create a database.
    • For other installation types, see your PostgreSQL provider’s documentation.

  • A table in the database. Learn how to create a table.

    The table’s schema must match the schema of the documents that Unstructured produces. Unstructured cannot provide a schema that is guaranteed to work in all circumstances. This is because these schemas will vary based on your source files’ types; how you want Unstructured to partition, chunk, and generate embeddings; any custom post-processing code that you run; and other factors.

    You can adapt the following table schema example for your own needs:

    See also:

  • A user in the database, and a password for the user.

    • For Amazon RDS for PostgreSQL, learn how to create a user.
    • For Azure Database for PostgreSQL, learn how to create a user.
    • For local PostgreSQL installations, learn how to create a user.
    • For other installation types, see your PostgreSQL provider’s documentation.

  • Database access for the user.

The PostgreSQL connector dependencies:

CLI, Python
pip install "unstructured-ingest[postgres]"

You might also need to install additional dependencies, depending on your needs. Learn more.

The following environment variables:

  • PGHOST - The host name, represented by --host (CLI) or host (Python).
  • PGPORT - The port number, represented by --port (CLI) or port (Python).
  • PGUSER - The username, represented by --username (CLI) or username (Python).
  • PGPASSWORD - The user’s password, represented by --password (CLI) or password (Python).
  • PGDATABASE - The name of the database, represented by --database (CLI) or database (Python).

These environment variables:

  • UNSTRUCTURED_API_KEY - Your Unstructured API key value.
  • UNSTRUCTURED_API_URL - Your Unstructured API URL.

Now call the Unstructured CLI or Python SDK. The destination connector can be any of the ones supported. This example uses the local destination connector:

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