BigQuery AI.Similarity
AI.SIMILARITY computes the semantic similarity between two inputs.
Use cases include the following:
- Semantic search: Search for text or images based off a description, without having to match specific keywords.
- Recommendation: Return entities with attributes similar to a given entity.
Syntax Reference
AI.SIMILARITY(
content1 => 'CONTENT1',
content2 => 'CONTENT2'
[, endpoint => 'ENDPOINT']
[, model_params => 'MODEL_PARAMS']
[, connection_id => 'CONNECTION_ID']
)Input Arguments
| Argument | Requirement | Type | Description |
|---|---|---|---|
content1 |
Required | String or | The first text content |
| : : : ObjectRef : or image context. : | |||
content2 |
Required | String or | The second text content |
| : : : ObjectRef : or image to compare : | |||
| : : : : against. : | |||
connection_id |
Optional | String | The connection ID to use |
| : : : : for the LLM. : | |||
endpoint |
Optional | String | The model endpoint (e.g. |
: : : : 'text-embedding-005'). : |
|||
model_params |
Optional | JSON | JSON object for model |
| : : : : parameters (e.g., : | |||
: : : : temperature, : |
|||
: : : : max_output_tokens). : |
Output Schema
| Column Name | Type | Description |
|---|---|---|
| (Scalar Result) | FLOAT64 |
A similarity score (e.g., cosine |
| : : : similarity). Returns null if error. : |
Examples
Compute semantic similarity between two text inputs
SELECT AI.SIMILARITY(
content1 => 'The cat sat on the mat',
content2 => 'A feline is resting on the rug'
) as similarity_score;Compute semantic similarity between text and image
CREATE SCHEMA IF NOT EXISTS cymbal_pets;
CREATE OR REPLACE EXTERNAL TABLE cymbal_pets.product_images
WITH CONNECTION DEFAULT
OPTIONS (
object_metadata = 'SIMPLE',
uris = ['gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*.png']
);
SELECT
uri,
OBJ.GET_READ_URL(ref) AS signed_url,
ai.similarity(
"aquarium device",
ref,
endpoint => 'multimodalembedding@001') AS similarity_score
FROM cymbal_pets.product_images
ORDER BY similarity_score DESC
LIMIT 3;