Some or all of the information on this page might not apply to Cloud de Confiance by S3NS. See Differences from Google Cloud for more details.

Parse PDFs in a retrieval-augmented generation pipeline

This tutorial guides you through the process of creating a retrieval-augmented generation (RAG) pipeline based on parsed PDF content.

PDF files, such as financial documents, can be challenging to use in RAG pipelines because of their complex structure and mix of text, figures, and tables. This tutorial shows you how to use the ML.PROCESS_DOCUEMNT function in combination with Document AI's layout parser to build a RAG pipeline based on key information extracted from a PDF file.

Objectives

This tutorial covers the following tasks:

Creating a Cloud resource connection so that you can connect to Cloud Storage and Vertex AI from BigQuery.
Create a Cloud Storage bucket and upload a sample PDF file.
Creating an object table over the PDF file to make the PDF file available in BigQuery.
Create a Document AI processor that you can use to parse the PDF file.
Creating a remote model that lets you use the Document AI API to access the document processor from BigQuery.
Using the remote model with the ML.PROCESS_DOCUMENT function to parse the PDF contents into chunks and then write that content to a BigQuery table.
Extracting PDF content from the JSON data returned by the ML.PROCESS_DOCUMENT function, and then writing that content to a BigQuery table.
Generate embeddings from the parsed PDF content, and then write those embeddings to a BigQuery table. Embeddings are numerical representations of the PDF content that enable you to perform semantic search and retrieval on the PDF content.
Use the VECTOR_SEARCH function on the embeddings to identify semantically similar PDF content.
Perform retrieval-augmented generation (RAG) by using the AI.GENERATE function to generate text, using vector search results to augment the prompt input and improve results.

Costs

In this document, you use the following billable components of Cloud de Confiance by S3NS:

BigQuery: You incur costs for the data that you process in BigQuery.
Gemini Enterprise Agent Platform: You incur costs for calls to Agent Platform models.
Document AI: You incur costs for calls to the Document AI API.
Cloud Storage: You incur costs for object storage in Cloud Storage.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

Console

In the Cloud de Confiance console, on the project selector page, select or create a Cloud de Confiance project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
- Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector
Verify that billing is enabled for your Cloud de Confiance project.
Enable the BigQuery, BigQuery Connection, Vertex AI, Document AI, and Cloud Storage APIs.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.
Enable the APIs

Make sure that you have the following role or roles on the project: Storage Admin, Document AI Editor, BigQuery Admin, Project IAM Admin
Check for the roles
1. In the Cloud de Confiance console, go to the IAM page.
  Go to IAM
2. Select the project.
3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.
4. For all rows that specify or include you, check the Role column to see whether the list of roles includes the required roles.
Grant the roles
1. In the Cloud de Confiance console, go to the IAM page.
  Go to IAM
2. Select the project.
3. Click Grant access.
4. In the New principals field, enter your user identifier. This is typically the identifier for a user in a workforce identity pool. For details, see Represent workforce pool users in IAM policies, or contact your administrator.
5. Click Select a role, then search for the role.
6. To grant additional roles, click Add another role and add each additional role.
7. Click Save.

gcloud

Install the Google Cloud CLI.
Configure the gcloud CLI to use your federated identity.

For more information, see Sign in to the gcloud CLI with your federated identity.
To initialize the gcloud CLI, run the following command:
```
gcloud init
```
Create or select a Cloud de Confiance project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
- Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.
- Create a Cloud de Confiance project:
```
gcloud projects create PROJECT_ID
```
  Replace PROJECT_ID with a name for the Cloud de Confiance project you are creating.
- Select the Cloud de Confiance project that you created:
```
gcloud config set project PROJECT_ID
```
  Replace PROJECT_ID with your Cloud de Confiance project name.
Verify that billing is enabled for your Cloud de Confiance project.
Enable the BigQuery, BigQuery Connection, Vertex AI, Document AI, and Cloud Storage APIs:
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.
```
gcloud services enable bigquery.googleapis.com bigqueryconnection.googleapis.com aiplatform.googleapis.com documentai.googleapis.com storage.googleapis.com
```

Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/storage.admin, roles/documentai.editor, roles/bigquery.admin, roles/resourcemanager.projectIamAdmin
```
gcloud projects add-iam-policy-binding PROJECT_ID --member="user:USER_IDENTIFIER" --role=ROLE
```
Replace the following:
- PROJECT_ID: Your project ID.
- USER_IDENTIFIER: The identifier for your user account. For examples, see Represent workforce pool users in IAM policies.
- ROLE: The IAM role that you grant to your user account.

Create a dataset

Create a BigQuery dataset to store your ML model.

Console

In the Cloud de Confiance console, go to the BigQuery page.

Go to the BigQuery page
In the Explorer pane, click your project name.
Click View actions > Create dataset
On the Create dataset page, do the following:
- For Dataset ID, enter bqml_tutorial.
- For Location type, select Multi-region, and then select US.
- Leave the remaining default settings as they are, and click Create dataset.

bq

To create a new dataset, use the bq mk --dataset command.

Create a dataset named bqml_tutorial with the data location set to US.

bq mk --dataset \
  --location=US \
  --description "BigQuery ML tutorial dataset." \
  bqml_tutorial

Confirm that the dataset was created:
```
bq ls
```

API

Call the datasets.insert method with a defined dataset resource.

{
  "datasetReference": {
     "datasetId": "bqml_tutorial"
  }
}

Create a connection

Create a Cloud resource connection and get the connection's service account. Create the connection in the same location.

You can skip this step if you either have a default connection configured, or you have the BigQuery Admin role.

Select one of the following options:

Console

Go to the BigQuery page.

Go to BigQuery
In the left pane, click Explorer:

If you don't see the left pane, click Expand left pane to open the pane.
In the Explorer pane, expand your project name, and then click Connections.
On the Connections page, click Create connection.
For Connection type, choose Vertex AI remote models, remote functions, BigLake and Spanner (Cloud Resource).
In the Connection ID field, enter a name for your connection.
For Location type, select a location for your connection. The connection should be colocated with your other resources such as datasets.
Click Create connection.
Click Go to connection.
In the Connection info pane, copy the service account ID for use in a later step.

SQL

Use the CREATE CONNECTION statement:

In the Cloud de Confiance console, go to the BigQuery page.

Go to BigQuery
In the query editor, enter the following statement:
```
CREATE CONNECTION [IF NOT EXISTS] `CONNECTION_NAME`
OPTIONS (
  connection_type = "CLOUD_RESOURCE",
  friendly_name = "FRIENDLY_NAME",
  description = "DESCRIPTION"
  );
```
Replace the following:
- CONNECTION_NAME: the name of the connection in either the PROJECT_ID.LOCATION.CONNECTION_ID, LOCATION.CONNECTION_ID, or CONNECTION_ID format. If the project or location are omitted, then they are inferred from the project and location where the statement is run.
- FRIENDLY_NAME (optional): a descriptive name for the connection.
- DESCRIPTION (optional): a description of the connection.
Click Run.

For more information about how to run queries, see Run an interactive query.

bq

In a command-line environment, create a connection:
```
bq mk --connection --location=REGION --project_id=PROJECT_ID \
    --connection_type=CLOUD_RESOURCE CONNECTION_ID
```
The --project_id parameter overrides the default project.

Replace the following:
- REGION: your connection region
- PROJECT_ID: your Cloud de Confiance project ID
- CONNECTION_ID: an ID for your connection
When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

Troubleshooting: If you get the following connection error, update the Google Cloud SDK:
```
Flags parsing error: flag --connection_type=CLOUD_RESOURCE: value should be one of...
```

Retrieve and copy the service account ID for use in a later step:

bq show --connection PROJECT_ID.REGION.CONNECTION_ID

The output is similar to the following:

name                          properties
1234.REGION.CONNECTION_ID     {"serviceAccountId": "connection-1234-9u56h9@gcp-sa-bigquery-condel.s3ns-system.iam.gserviceaccount.com"}

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

Before running code samples, set the GOOGLE_CLOUD_UNIVERSE_DOMAIN environment variable to s3nsapis.fr.

import google.api_core.exceptions
from google.cloud import bigquery_connection_v1

client = bigquery_connection_v1.ConnectionServiceClient()


def create_connection(
    project_id: str,
    location: str,
    connection_id: str,
):
    """Creates a BigQuery connection to a Cloud Resource.

    Cloud Resource connection creates a service account which can then be
    granted access to other Google Cloud resources for federated queries.

    Args:
        project_id: The Google Cloud project ID.
        location: The location of the connection (for example, "us-central1").
        connection_id: The ID of the connection to create.
    """

    parent = client.common_location_path(project_id, location)

    connection = bigquery_connection_v1.Connection(
        friendly_name="Example Connection",
        description="A sample connection for a Cloud Resource.",
        cloud_resource=bigquery_connection_v1.CloudResourceProperties(),
    )

    try:
        created_connection = client.create_connection(
            parent=parent, connection_id=connection_id, connection=connection
        )
        print(f"Successfully created connection: {created_connection.name}")
        print(f"Friendly name: {created_connection.friendly_name}")
        print(
            f"Service Account: {created_connection.cloud_resource.service_account_id}"
        )

    except google.api_core.exceptions.AlreadyExists:
        print(f"Connection with ID '{connection_id}' already exists.")
        print("Please use a different connection ID.")
    except Exception as e:
        print(f"An unexpected error occurred while creating the connection: {e}")

Node.js

Before trying this sample, follow the Node.js setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Node.js API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

Before running code samples, set the GOOGLE_CLOUD_UNIVERSE_DOMAIN environment variable to s3nsapis.fr.

const {ConnectionServiceClient} =
  require('@google-cloud/bigquery-connection').v1;
const {status} = require('@grpc/grpc-js');

const client = new ConnectionServiceClient();

/**
 * Creates a new BigQuery connection to a Cloud Resource.
 *
 * A Cloud Resource connection creates a service account that can be granted access
 * to other Google Cloud resources.
 *
 * @param {string} projectId The Google Cloud project ID. for example, 'example-project-id'
 * @param {string} location The location of the project to create the connection in. for example, 'us-central1'
 * @param {string} connectionId The ID of the connection to create. for example, 'example-connection-id'
 */
async function createConnection(projectId, location, connectionId) {
  const parent = client.locationPath(projectId, location);

  const connection = {
    friendlyName: 'Example Connection',
    description: 'A sample connection for a Cloud Resource',
    // The service account for this cloudResource will be created by the API.
    // Its ID will be available in the response.
    cloudResource: {},
  };

  const request = {
    parent,
    connectionId,
    connection,
  };

  try {
    const [response] = await client.createConnection(request);

    console.log(`Successfully created connection: ${response.name}`);
    console.log(`Friendly name: ${response.friendlyName}`);

    console.log(`Service Account: ${response.cloudResource.serviceAccountId}`);
  } catch (err) {
    if (err.code === status.ALREADY_EXISTS) {
      console.log(`Connection '${connectionId}' already exists.`);
    } else {
      console.error(`Error creating connection: ${err.message}`);
    }
  }
}

Terraform

Use the google_bigquery_connection resource.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

The following example creates a Cloud resource connection named my_cloud_resource_connection in the US region:


# This queries the provider for project information.
data "google_project" "default" {}

# This creates a cloud resource connection in the US region named my_cloud_resource_connection.
# Note: The cloud resource nested object has only one output field - serviceAccountId.
resource "google_bigquery_connection" "default" {
  connection_id = "my_cloud_resource_connection"
  project       = data.google_project.default.project_id
  location      = "US"
  cloud_resource {}
}

To apply your Terraform configuration in a Cloud de Confiance project, complete the steps in the following sections.

Prepare Cloud Shell

Launch Cloud Shell.
Set the default Cloud de Confiance project where you want to apply your Terraform configurations.

You only need to run this command once per project, and you can run it in any directory.
```
export GOOGLE_CLOUD_PROJECT=PROJECT_ID
```
Environment variables are overridden if you set explicit values in the Terraform configuration file.

Prepare the directory

Each Terraform configuration file must have its own directory (also called a root module).

In Cloud Shell, create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf. In this tutorial, the file is referred to as main.tf.
```
mkdir DIRECTORY && cd DIRECTORY && touch main.tf
```
If you are following a tutorial, you can copy the sample code in each section or step.

Copy the sample code into the newly created main.tf.

Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.
Review and modify the sample parameters to apply to your environment.
Save your changes.
Initialize Terraform. You only need to do this once per directory.
```
terraform init
```
Optionally, to use the latest Google provider version, include the -upgrade option:
```
terraform init -upgrade
```

Apply the changes

Review the configuration and verify that the resources that Terraform is going to create or update match your expectations:
```
terraform plan
```
Make corrections to the configuration as necessary.
Apply the Terraform configuration by running the following command and entering yes at the prompt:
```
terraform apply
```
Wait until Terraform displays the "Apply complete!" message.
Open your Cloud de Confiance project to view the results. In the Cloud de Confiance console, navigate to your resources in the UI to make sure that Terraform has created or updated them.

Grant access to the service account

Select one of the following options:

Console

Go to the IAM & Admin page.

Go to IAM & Admin
Click Grant Access.

The Add principals dialog opens.
In the New principals field, enter the service account ID that you copied earlier.
In the Select a role field, select Document AI, and then select Document AI Viewer.
Click Add another role.
In the Select a role field, select Cloud Storage, and then select Storage Object Viewer.
Click Add another role.
In the Select a role field, select Vertex AI, and then select Vertex AI User.
Click Save.

gcloud

Use the gcloud projects add-iam-policy-binding command:

gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/documentai.viewer' --condition=None
gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/storage.objectViewer' --condition=None
gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/aiplatform.user' --condition=None

Replace the following:

PROJECT_NUMBER: your project number.
MEMBER: the service account ID that you copied earlier.

Upload the sample PDF to Cloud Storage

To upload the sample PDF to Cloud Storage, follow these steps:

Download the scf23.pdf sample PDF by going to https://www.federalreserve.gov/publications/files/scf23.pdf and clicking download .
Create a Cloud Storage bucket.
Upload the scf23.pdf file to the bucket.

Create an object table

Create an object table over the PDF file in Cloud Storage:

In the Cloud de Confiance console, go to the BigQuery page.

Go to BigQuery
In the query editor, run the following statement:
```
CREATE OR REPLACE EXTERNAL TABLE `bqml_tutorial.pdf`
WITH CONNECTION `LOCATION.CONNECTION_ID`
OPTIONS(
  object_metadata = 'SIMPLE',
  uris = ['gs://BUCKET/scf23.pdf']);
```
Replace the following:
- LOCATION: the connection location.
- CONNECTION_ID: the ID of your BigQuery connection.
  When you view the connection details in the Cloud de Confiance console, the CONNECTION_ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID, for example projects/myproject/locations/connection_location/connections/myconnection.
- BUCKET: the Cloud Storage bucket containing the scf23.pdf file. The full uri option value should look similar to ['gs://mybucket/scf23.pdf'].

Create a document processor

Create a document processor based on the layout parser processor in the us multi-region. Copy the prediction endpoint from the Processor details page to use in the next section.

Create the remote model for the document processor

Create a remote model to access the Document AI processor:

In the Cloud de Confiance console, go to the BigQuery page.

Go to BigQuery
In the query editor, run the following statement:
```
CREATE OR REPLACE MODEL `bqml_tutorial.parser_model`
REMOTE WITH CONNECTION `LOCATION.CONNECTION_ID`
  OPTIONS(remote_service_type = 'CLOUD_AI_DOCUMENT_V1', document_processor = 'PROCESSOR_ID');
```
Replace the following:
- LOCATION: the connection location.
- CONNECTION_ID: the ID of your BigQuery connection.
  When you view the connection details in the Cloud de Confiance console, the CONNECTION_ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID, for example projects/myproject/locations/connection_location/connections/myconnection.
- PROCESSOR_ID: the document processor ID. To find this value, view the processor details, and then look at the ID row in the Basic Information section.

Parse the PDF file into chunks

Use the document processor with the ML.PROCESS_DOCUMENT function to parse the PDF file into chunks, and then write that content to a table. The ML.PROCESS_DOCUMENT function returns the PDF chunks in JSON format.

In the Cloud de Confiance console, go to the BigQuery page.

Go to BigQuery

In the query editor, run the following statement:

CREATE or REPLACE TABLE bqml_tutorial.chunked_pdf AS (
  SELECT * FROM ML.PROCESS_DOCUMENT(
  MODEL bqml_tutorial.parser_model,
  TABLE bqml_tutorial.pdf,
  PROCESS_OPTIONS => (JSON '{"layout_config": {"chunking_config": {"chunk_size": 250}}}')
  )
);

Parse the PDF chunk data into separate columns

Extract the PDF content and metadata information from the JSON data returned by the ML.PROCESS_DOCUMENT function, and then write that content to a table:

In the Cloud de Confiance console, go to the BigQuery page.

Go to BigQuery

In the query editor, run the following statement to parse the PDF content:

CREATE OR REPLACE TABLE bqml_tutorial.parsed_pdf AS (
SELECT
  uri,
  JSON_EXTRACT_SCALAR(json , '$.chunkId') AS id,
  JSON_EXTRACT_SCALAR(json , '$.content') AS content,
  JSON_EXTRACT_SCALAR(json , '$.pageFooters[0].text') AS page_footers_text,
  JSON_EXTRACT_SCALAR(json , '$.pageSpan.pageStart') AS page_span_start,
  JSON_EXTRACT_SCALAR(json , '$.pageSpan.pageEnd') AS page_span_end
FROM bqml_tutorial.chunked_pdf, UNNEST(JSON_EXTRACT_ARRAY(ml_process_document_result.chunkedDocument.chunks, '$')) json
);

In the query editor, run the following statement to view a subset of the parsed PDF content:

SELECT *
FROM `bqml_tutorial.parsed_pdf`
ORDER BY id
LIMIT 5;

The output is similar to the following:

+-----------------------------------+------+------------------------------------------------------------------------------------------------------+-------------------+-----------------+---------------+
|                uri                |  id  |                                                 content                                              | page_footers_text | page_span_start | page_span_end |
+-----------------------------------+------+------------------------------------------------------------------------------------------------------+-------------------+-----------------+---------------+
| gs://mybucket/scf23.pdf           | c1   | •BOARD OF OF FEDERAL GOVERN NOR RESERVE SYSTEM RESEARCH & ANALYSIS                                   | NULL              | 1               | 1             |
| gs://mybucket/scf23.pdf           | c10  | • In 2022, 20 percent of all families, 14 percent of families in the bottom half of the usual ...    | NULL              | 8               | 9             |
| gs://mybucket/scf23.pdf           | c100 | The SCF asks multiple questions intended to capture whether families are credit constrained, ...     | NULL              | 48              | 48            |
| gs://mybucket/scf23.pdf           | c101 | Bankruptcy behavior over the past five years is based on a series of retrospective questions ...     | NULL              | 48              | 48            |
| gs://mybucket/scf23.pdf           | c102 | # Percentiles of the Distributions of Income and Net Worth                                           | NULL              | 48              | 49            |
+-----------------------------------+------+------------------------------------------------------------------------------------------------------+-------------------+-----------------+---------------+

Generate embeddings

Generate embeddings for the parsed PDF content and then write them to a table:

In the Cloud de Confiance console, go to the BigQuery page.

Go to BigQuery

In the query editor, run the following statement:

CREATE OR REPLACE TABLE `bqml_tutorial.embeddings` AS (
  SELECT *, AI.EMBED(content, endpoint => 'text-embedding-005').result AS embedding
  FROM bqml_tutorial.parsed_pdf
);

Run a vector search

Run a vector search against the parsed PDF content.

The following query takes text input, creates an embedding for that input using the AI.EMBED function, and then uses the VECTOR_SEARCH function to match the input embedding with the most similar PDF content embeddings. The results are the top ten PDF chunks that are most related to changes in family net worth.

Go to the BigQuery page.

Go to BigQuery

In the query editor, run the following SQL statement:

SELECT
  distance,
  base.id AS chunk_id,
  base.page_span_start AS start_page,
  base.page_span_end AS end_page,
  base.content
FROM
  VECTOR_SEARCH(
    TABLE `bqml_tutorial.embeddings`,
    'embedding',
    query_value =>
      AI.EMBED(
        'Did the typical family net worth increase? If so, by how much?',
        endpoint => 'text-embedding-005').result,
    top_k => 3,
    OPTIONS => '{"fraction_lists_to_search": 0.01}')
ORDER BY distance DESC;

The output is similar to the following:

+----------+----------+------------+----------+-----------------------------------+
| distance | chunk_id | start_page | end_page | content                           |
+----------+----------+------------+----------+-----------------------------------+
| 0.645685 | 26       | 17         | 18       | 18 Between the first quarter of   |
|          |          |            |          | 2019 and the first quarter of...  |
+----------+----------+------------+----------+-----------------------------------+
| 0.602665 | 30       | 19         | 21       | ## Net Worth by Family            |
|          |          |            |          | Characteristics...                |
+----------+----------+------------+----------+-----------------------------------+
| 0.599438 | 24       | 17         | 21       | # Net Worth                       |
|          |          |            |          | The net improvements in...        |
+----------+----------+------------+----------+-----------------------------------+

Generate text augmented by vector search results

Perform a vector search on the embeddings to identify semantically similar PDF content, and then use the AI.GENERATE function with the vector search results to augment the prompt input and improve the text generation results. In this case, the query uses information from the PDF chunks to answer a question about the change in family net worth over the past decade.

In the Cloud de Confiance console, go to the BigQuery page.

Go to BigQuery

In the query editor, run the following statement:

SELECT
  AI.GENERATE(
    CONCAT('Did the typical family net worth change? How does this compare the SCF survey a decade earlier? Be concise and use the following context:',
            STRING_AGG(FORMAT("context: %s", base.content), ',\n')
    )
  ).result AS response
FROM
  VECTOR_SEARCH(
    TABLE `bqml_tutorial.embeddings`,
    'embedding',
    query_value =>
      AI.EMBED(
        'Did the typical family net worth increase? If so, by how much?',
        endpoint => 'text-embedding-005').result,
    top_k => 3,
    OPTIONS => '{"fraction_lists_to_search": 0.01}')

The output is similar to the following:

+-------------------------------------------------------------------------+
| response                                                                |
+-------------------------------------------------------------------------+
| Yes, the typical family net worth changed significantly.                |
|                                                                         |
| Real median net worth surged 37% between the 2019 and 2022 SCF surveys. |
| This contrasts sharply with a decade earlier (2010-2013), when real     |
| median net worth decreased 2%.                                          |
+-------------------------------------------------------------------------+

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

Delete a Cloud de Confiance project:

gcloud projects delete PROJECT_ID

What's next

Learn more about the ML.PROCESS_DOCUMENT function.
Learn more about performing semantic search and RAG.

Parse PDFs in a retrieval-augmented generation pipeline

Objectives

Costs

Before you begin

Console

Check for the roles

Grant the roles

gcloud

Create a dataset

Console

bq

API

Create a connection

Console

SQL

bq

Python

Node.js

Terraform

Prepare Cloud Shell

Prepare the directory

Apply the changes

Grant access to the service account

Console

gcloud

Upload the sample PDF to Cloud Storage

Create an object table

Create a document processor

Create the remote model for the document processor

Parse the PDF file into chunks

Parse the PDF chunk data into separate columns

Generate embeddings

Run a vector search

Generate text augmented by vector search results

Clean up

Delete the project

What's next