Perform semantic search and retrieval-augmented generation

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This tutorial guides you through the end-to-end process of creating and using text embeddings, including using vector indexes to improve search performance.

This tutorial covers the following tasks:

• Creating a BigQuery ML remote model over a Vertex AI large language model (LLM).
• Using the remote model with the ML.GENERATE_EMBEDDING function to generate embeddings from text in a BigQuery table.
• Creating a vector index to index the embeddings.
• Using the VECTOR_SEARCH function with the embeddings to search for similar text.
• Perform retrieval-augmented generation (RAG) by generating text with the ML.GENERATE_TEXT function, and using vector search results to augment the prompt input and improve results.

This tutorial uses the BigQuery public table patents-public-data.google_patents_research.publications.

Required roles and permissions

• To create a connection, you need membership in the following Identity and Access Management (IAM) role:

  • roles/bigquery.connectionAdmin

• To grant permissions to the connection's service account, you need the following permission:

  • resourcemanager.projects.setIamPolicy

• The IAM permissions needed in this tutorial for the remaining BigQuery operations are included in the following two roles:

  • BigQuery Data Editor (roles/bigquery.dataEditor) to create models, tables, and indexes.
  • BigQuery User (roles/bigquery.user) to run BigQuery jobs.

Costs

In this document, you use the following billable components of Google Cloud:

• BigQuery ML: You incur costs for the data that you process in BigQuery.
• Vertex AI: You incur costs for calls to the Vertex AI service that's represented by the remote model.

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

For more information about BigQuery pricing, see BigQuery pricing in the BigQuery documentation.

For more information about Vertex AI pricing, see the Vertex AI pricing page.

Before you begin

1. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Go to project selector

2. Make sure that billing is enabled for your Google Cloud project.

3. Enable the BigQuery, BigQuery Connection, and Vertex AI APIs.

   Enable the APIs

Create a dataset

Create a BigQuery dataset to store your ML model:

1. In the Google Cloud console, go to the BigQuery page.

   Go to the BigQuery page

2. In the Explorer pane, click your project name.

3. Click more_vert View actions > Create dataset.

   

4. On the Create dataset page, do the following:

   • For Dataset ID, enter bqml_tutorial.

   • For Location type, select Multi-region, and then select US (multiple regions in United States).

     The public datasets are stored in the US multi-region. For simplicity, store your dataset in the same location.

   • Leave the remaining default settings as they are, and click Create dataset.

     

Create a connection

Create a Cloud resource connection and get the connection's service account. Create the connection in the same location as the dataset you created in the previous step.

Select one of the following options:

 ## This creates a cloud resource connection.
 ## Note: The cloud resource nested object has only one output only field - serviceAccountId.
 resource "google_bigquery_connection" "connection" {
    connection_id = "CONNECTION_ID"
    project = "PROJECT_ID"
    location = "REGION"
    cloud_resource {}
}        

Grant the service account access

To grant the connection's service account an appropriate role to access the Vertex AI service, follow these steps:

1. Go to the IAM & Admin page.

   Go to IAM & Admin

2. Click person_add Grant Access.

3. In the New principals field, enter the service account ID that you copied earlier.

4. In the Select a role field, choose Vertex AI, and then select Vertex AI User role.

5. Click Save.

Create the remote model for text embedding generation

Create a remote model that represents a hosted Vertex AI text embedding generation model:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, run the following statement:

   CREATE OR REPLACE MODEL `bqml_tutorial.embedding_model`
     REMOTE WITH CONNECTION `LOCATION.CONNECTION_ID`
     OPTIONS (ENDPOINT = 'textembedding-gecko@003');
   

   Replace the following:

   • LOCATION: the connection location
   • CONNECTION_ID: the ID of your BigQuery connection

     When you view the connection details in the Google Cloud 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

   The query takes several seconds to complete, after which the model embedding_model appears in the bqml_tutorial dataset in the Explorer pane. Because the query uses a CREATE MODEL statement to create a model, there are no query results.

Generate text embeddings

Generate text embeddings from patent abstracts using the ML.GENERATE_EMBEDDING function, and then write them to a BigQuery table so that they can be searched.

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, run the following statement:

   CREATE OR REPLACE TABLE `bqml_tutorial.embeddings` AS
   SELECT * FROM ML.GENERATE_EMBEDDING(
     MODEL `bqml_tutorial.embedding_model`,
     (
       SELECT *, abstract AS content
       FROM `patents-public-data.google_patents_research.publications`
       WHERE LENGTH(abstract) > 0 AND LENGTH(title) > 0 AND country = 'Singapore'
     )
   )
   WHERE LENGTH(ml_generate_embedding_status) = 0;
   

Embedding generation using the ML.GENERATE_EMBEDDING function might fail due to Vertex AI LLM quotas or service unavailability. Error details are returned in the ml_generate_embedding_status column. An empty ml_generate_embedding_status column indicates successful embedding generation.

For alternative text embedding generation methods in BigQuery, see the Embed text with pretrained TensorFlow models tutorial.

Create a vector index

To create a vector index, use the CREATE VECTOR INDEX data definition language (DDL) statement:

1. Go to the BigQuery page.

   Go to BigQuery

2. In the query editor, run the following SQL statement:

   CREATE OR REPLACE VECTOR INDEX my_index
   ON `bqml_tutorial.embeddings`(ml_generate_embedding_result)
   OPTIONS(index_type = 'IVF',
     distance_type = 'COSINE',
     ivf_options = '{"num_lists":500}')
   

Verify vector index creation

The vector index is populated asynchronously. You can check whether the index is ready to be used by querying the INFORMATION_SCHEMA.VECTOR_INDEXES view and verifying that the coverage_percentage column value is greater than 0 and the last_refresh_time column value isn't NULL.

1. Go to the BigQuery page.

   Go to BigQuery

2. In the query editor, run the following SQL statement:

   SELECT table_name, index_name, index_status,
   coverage_percentage, last_refresh_time, disable_reason
   FROM `PROJECT_ID.bqml_tutorial.INFORMATION_SCHEMA.VECTOR_INDEXES`
   

   Replace PROJECT_ID with your project ID.

Perform a text similarity search using the vector index

Use the VECTOR_SEARCH function to search for the top 5 relevant patents that match embeddings generated from a text query. The model you use to generate the embeddings in this query must be the same as the one you use to generate the embeddings in the table you are comparing against, otherwise the search results won't be accurate.

1. Go to the BigQuery page.

   Go to BigQuery

2. In the query editor, run the following SQL statement:

   SELECT query.query, base.publication_number, base.title, base.abstract
   FROM VECTOR_SEARCH(
     TABLE `bqml_tutorial.embeddings`, 'ml_generate_embedding_result',
     (
     SELECT ml_generate_embedding_result, content AS query
     FROM ML.GENERATE_EMBEDDING(
     MODEL `bqml_tutorial.embedding_model`,
     (SELECT 'improving password security' AS content))
     ),
     top_k => 5, options => '{"fraction_lists_to_search": 0.01}')
   

   The output is similar to the following:

   +-----------------------------+--------------------+-------------------------------------------------+-------------------------------------------------+
   |            query            | publication_number |                       title                     |                      abstract                   |
   +-----------------------------+--------------------+-------------------------------------------------+-------------------------------------------------+
   | improving password security | SG-120868-A1       | Data storage device security method and a...    | Methods for improving security in data stora... |
   | improving password security | SG-10201610585W-A  | Passsword management system and process...      | PASSSWORD MANAGEMENT SYSTEM AND PROCESS ...     |
   | improving password security | SG-10201901821S-A  | Method and apparatus for unlocking user...      | METHOD AND APPARATUS FOR UNLOCKING USER...      |
   | improving password security | SG-10201902412Q-A  | Password protection question setting method...  | PASSWORD PROTECTION QUESTION SETTING METHOD...  |
   | improving password security | SG-194509-A1       | System and method for web-based...              | A security authentication method comprises...   |
   +-----------------------------+--------------------+-------------------------------------------------+-------------------------------------------------+
   

Create the remote model for text generation

Create a remote model that represents a hosted Vertex AI text generation model:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, run the following statement:

   CREATE OR REPLACE MODEL `bqml_tutorial.text_model`
     REMOTE WITH CONNECTION `LOCATION.CONNECTION_ID`
     OPTIONS (ENDPOINT = 'text-bison-32k');
   

   Replace the following:

   • LOCATION: the connection location
   • CONNECTION_ID: the ID of your BigQuery connection

     When you view the connection details in the Google Cloud 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

   The query takes several seconds to complete, after which the model text_model appears in the bqml_tutorial dataset in the Explorer pane. Because the query uses a CREATE MODEL statement to create a model, there are no query results.

Generate text augmented by vector search results

Feed the search results as prompts to generate text with the ML.GENERATE_TEXT function

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, run the following statement:

   SELECT ml_generate_text_llm_result AS generated, prompt
   FROM ML.GENERATE_TEXT(
     MODEL `bqml_tutorial.text_model`,
     (
       SELECT CONCAT(
         'Propose some project ideas to improve user password security using the context below: ',
         STRING_AGG(
           FORMAT("patent title: %s, patent abstract: %s", base.title, base.abstract),
           ',\n')
         ) AS prompt,
       FROM VECTOR_SEARCH(
         TABLE `bqml_tutorial.embeddings`, 'text_embedding',
         (
           SELECT ml_generate_embedding_result, content AS query
           FROM ML.GENERATE_EMBEDDING(
             MODEL `bqml_tutorial.embedding_model`,
            (SELECT 'improving password security' AS content)
           )
         ),
       top_k => 5, options => '{"fraction_lists_to_search": 0.01}')
     ),
     STRUCT(600 AS max_output_tokens, TRUE AS flatten_json_output));
   

   The output is similar to the following:

   +------------------------------------------------+------------------------------------------------------------+
   |            generated                           | prompt                                                     |
   +------------------------------------------------+------------------------------------------------------------+
   | **Project Ideas to Improve User Password       | Propose some project ideas to improve user password        |
   | Security**                                     | security using the context below: patent title: Data       |
   |                                                | storage device security method and apparatus, patent       |
   | 1. **Develop a password manager that uses a    | abstract: Methods for improving security in data storage   |
   | synchronization method to keep encrypted       | devices are disclosed. The methods include a ...,          |
   | passwords changing at each transmission...     | patent title: Active new password entry dialog with        |
   | 2. **Create a new password entry dialog that   | compact visual indication of adherence to password policy, |
   | provides a compact visual indication of        | patent abstract: An active new password entry dialog...,   |
   | adherence to password policies.** ...          | patent title: Method and system for protecting a password  |
   | 3. **Develop a system for protecting a         | during an authentication process, patent abstract: A system|
   | password during an authentication process by   | for providing security for a personal password during an   |
   | using representative characters to disguise... | authenticationprocess. The system combines the use of...   |
   +------------------------------------------------+------------------------------------------------------------+
    

Clean up

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.