≈467 tokens on demand. Your agent reads this file only when SKILL.md points to it.
BigQuery AI.Similarity
AI.SIMILARITY computes the cosine similarity between two inputs
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 |
The first text content. |
content2 |
Required |
String |
The second text content to |
| : : : : compare against. : |
|
|
|
connection_id |
Optional |
String |
The connection ID to use for |
| : : : : the LLM. : |
|
|
|
endpoint |
Optional |
String |
The model endpoint (e.g. |
: : : : 'multimodalembedding@001'). : |
|
|
|
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
SELECT AI.SIMILARITY(
content1 => 'The cat sat on the mat',
content2 => 'A feline is resting on the rug',
endpoint => 'text-embedding-005'
) as similarity_score;
