Google BigQuery Vector Search

google bigquery

machine learning

Exploring native Vector Search capabilities of Google BigQuery

Published

February 8, 2024

Intro

Google BigQuery Vector Search

Authorise Google Colab with GCP access

First user needs to be authenticated, the project, dataset, and location IDs set, and a dataset for vector search resources created in Google BigQuery.

Adjust the following parameters in the code below: - project_id: The ID of the Google Cloud project. - dataset_id: The ID of the dataset to be created. - location_id: The location where the dataset will be created (‘us’, ‘eu’, etc.).

Show code

from google.colab import auth
auth.authenticate_user()

project_id = 'max-ostapenko'
dataset_id = 'vector_search'
location_id = 'us'

# Create a dataset for vector search resources
!bq mk --project_id={project_id} --location {location_id} --dataset {dataset_id}

Connect Vertex AI model via BigQuery connections

Show code

# Create BigQuery connection to Google cloud resources
connection_id = "vertex_ai-remote_functions-big_lake"
!bq mk --project_id={project_id} --location={location_id} \
    --connection_type=CLOUD_RESOURCE --connection {connection_id}

# Extract service account ID from the connection details
import subprocess, json
connection_details = json.loads(
    subprocess.check_output("bq show --project_id={project_id} --location={location_id} --format=json --connection {connection_id}".format(
        project_id=project_id,
        location_id=location_id,
        connection_id=connection_id
        ).split(" ")
    ).decode('utf-8')
)
service_account = connection_details["cloudResource"]["serviceAccountId"]

# Authorise 'Vertex AI User' role for connection service account
!gcloud projects add-iam-policy-binding {project_id} \
    --member='serviceAccount:{service_account}' --role='roles/aiplatform.user' > /dev/null

# Create BQ ML model for multimodal embeddings
create_model_query = """
CREATE OR REPLACE MODEL `{project_id}.{dataset_id}.multimodalembedding`
REMOTE WITH CONNECTION `{project_id}.{location_id}.vertex_ai-remote_functions-big_lake`
OPTIONS (ENDPOINT = "textembedding-gecko@latest");
""".format(dataset_id=dataset_id, project_id=project_id, location_id=location_id)
!bq query --project_id={project_id} --use_legacy_sql=false '{create_model_query}'

Generate embeddings table

ML.GENERATE_TEXT_EMBEDDING function converts strings to a vector within semantic space.

Cost of multimodal embeddings generation via Vertex AI endpoint.

Generate embeddings from URLs list

Show code

get_product_names_query = """
CREATE OR REPLACE TABLE
    `{dataset_id}.url_embedded` AS (
    SELECT
        *
    FROM
        ML.GENERATE_TEXT_EMBEDDING( MODEL `{dataset_id}.multimodalembedding`,
            (
            SELECT
                url AS content
            FROM `httparchive.urls.latest_crux_desktop`
            WHERE rank = 1000
            LIMIT 1000 ),
            STRUCT(TRUE AS flatten_json_output) ) );
""".format(dataset_id=dataset_id)
!bq query --project_id={project_id} --use_legacy_sql=false '{get_product_names_query}'

# Preview the table
query_product_names = """
SELECT
    content, statistics, ml_embed_text_status, text_embedding
FROM
`{dataset_id}.url_embedded`
LIMIT 5
""".format(dataset_id=dataset_id)
!bq query --project_id={project_id} --use_legacy_sql=false '{query_product_names}'

Waiting on bqjob_r7789bb4637c6f417_0000018dc1c3c75d_1 ... (11s) Current status: DONE   
Replaced max-ostapenko.vector_search.url_embedded

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|       content        |             statistics              | ml_embed_text_status |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    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| https://people.com/  | {"token_count":6,"truncated":false} |                      |                                                        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Generate embeddings from IMDB catalogue

Show code

get_product_names_query = """
CREATE OR REPLACE TABLE
    `{dataset_id}.movie_title_embedded` AS (
    SELECT
        *
    FROM
        ML.GENERATE_TEXT_EMBEDDING( MODEL `{dataset_id}.multimodalembedding`,
            (
                SELECT
                    primary_title as content
                FROM `bigquery-public-data.imdb.title_basics`
                WHERE
                    title_type = "movie"
                    AND start_year > 1970
                    AND is_adult = 0
                    AND tconst IN (
                        SELECT
                            tconst
                        FROM `bigquery-public-data.imdb.title_ratings`
                        WHERE
                            average_rating > 6.5
                            AND num_votes > 10000
                    )
                ),
            STRUCT(TRUE AS flatten_json_output) ) );
""".format(dataset_id=dataset_id)
!bq query --project_id={project_id} --use_legacy_sql=false '{get_product_names_query}'

# Preview the table
query_product_names = """
SELECT
    content, statistics, ml_embed_text_status, text_embedding
FROM
`{dataset_id}.movie_title_embedded`
LIMIT 5
""".format(dataset_id=dataset_id)
!bq query --project_id={project_id} --use_legacy_sql=false '{query_product_names}'

Waiting on bqjob_r4d8ecda036484bd8_0000018dc1c417df_1 ... (112s) Current status: DONE   
Replaced max-ostapenko.vector_search.movie_title_embedded

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| content  |             statistics              | ml_embed_text_status |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                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| Black    | {"token_count":1,"truncated":false} |                      |                                    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| Barbie   | {"token_count":1,"truncated":false} |                      |                  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| Election | {"token_count":1,"truncated":false} |                      |                                                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|
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Search for neighbours

The search cost consists of:

cost of an embedding generation for a search query (can be ignored for short queries),
cost of BigQuery processing.

Show code

select_neighbours_query = """
SELECT
    base.content,
    EUCLIDEAN_DISTANCE(search.text_embedding, base.text_embedding) AS distance
FROM ML.GENERATE_TEXT_EMBEDDING(
    MODEL `{dataset_id}.multimodalembedding`,
    (SELECT "{search_query}" AS content),
    STRUCT(TRUE AS flatten_json_output)
) AS search
CROSS JOIN `{dataset_id}.{source_table}` AS base
ORDER BY distance ASC
LIMIT {limit};
"""

Websites classification

Show code

search_query = "Social"
limit = "10"

search_urls = select_neighbours_query.format(
    source_table="url_embedded",
    dataset_id=dataset_id,
    search_query=search_query,
    limit = limit
)
!bq query --project_id={project_id} --use_legacy_sql=false '{search_urls}'

+--------------------------------+--------------------+
|            content             |      distance      |
+--------------------------------+--------------------+
| https://www.facebook.com/      | 0.6952186979498833 |
| https://web.facebook.com/      | 0.7158822720622858 |
| https://free.facebook.com/     |  0.740260932509051 |
| https://twitter.com/           |   0.75665178425304 |
| https://business.facebook.com/ | 0.7568543973313501 |
| https://apps.facebook.com/     | 0.7650217870318406 |
| https://www.instagram.com/     | 0.7678844917192112 |
| https://www.linkedin.com/      | 0.7805433740785335 |
| https://www.tiktok.com/        | 0.8015824452215249 |
| https://en.wikipedia.org/      | 0.8080364602207089 |
+--------------------------------+--------------------+

Movie title recommendation

Show code

search_query = "Francis Ford Coppola"
top_k = "10"

search_movies = select_neighbours_query.format(
    source_table="movie_title_embedded",
    dataset_id=dataset_id,
    search_query=search_query,
    limit = limit
)
!bq query --project_id={project_id} --use_legacy_sql=false '{search_movies}'

Waiting on bqjob_r26b23e8c2ce5254e_0000018dc1c64ffe_1 ... (0s) Current status: DONE   
+------------------------+--------------------+
|        content         |      distance      |
+------------------------+--------------------+
| Forrest Gump           |  0.693854122498681 |
| Fitzcarraldo           | 0.7192698337456384 |
| The Godfather          | 0.7495456552309414 |
| Pulp Fiction           | 0.7537942915567972 |
| Suspiria               | 0.7555659646216765 |
| Suspiria               | 0.7555659646216765 |
| The Godfather Part II  | 0.7644078793453681 |
| Capote                 | 0.7654285032288327 |
| Hitchcock              | 0.7744345185709752 |
| The Godfather Part III | 0.7786602943923249 |
+------------------------+--------------------+