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POST
/
v1
/
tables
/
{table_id}
/
enhance
const job = await client.enhance.cells("tbl_abc123", {
  row_ids: ["row_1", "row_2", "row_3"],
  column_key: "description",
  prompt: "Write a compelling product description based on the product name and specifications.",
  web_search: true,
});

console.log(job.id);                // "enhance_..."
console.log(job.estimated_credits); // 6

{
  "id": "enhance_abc123",
  "status": "queued",
  "estimated_credits": 6,
  "rows_queued": 3
}

​
Path parameters

​
table_id
string
required
The unique identifier of the table.

​
Request body

​
row_ids
string[]
required
Array of row IDs to enhance. Must be non-empty.
​
column_key
string
required
The column key to enhance.
​
prompt
string
required
Instructions for the AI describing what to generate or how to transform the data.
​
model
string
default:"gemini-pro"
The AI model to use. Defaults to gemini-pro.
​
web_search
boolean
default:"true"
Whether the AI can use web search to find additional information.
​
target_language
string
Target language for translation-based enhancements.
​
use_glossary
boolean
default:"false"
Whether to apply your glossary terms during enhancement.

​
Response

Enhancement runs asynchronously. The response confirms the job was queued.
​
id
string
Enhancement job identifier.
​
status
string
Always queued.
​
estimated_credits
number
Estimated credit cost.
​
rows_queued
integer
Number of rows queued for enhancement.
const job = await client.enhance.cells("tbl_abc123", {
  row_ids: ["row_1", "row_2", "row_3"],
  column_key: "description",
  prompt: "Write a compelling product description based on the product name and specifications.",
  web_search: true,
});

console.log(job.id);                // "enhance_..."
console.log(job.estimated_credits); // 6

{
  "id": "enhance_abc123",
  "status": "queued",
  "estimated_credits": 6,
  "rows_queued": 3
}