Create New Dataset

# pip install futureagi

import os
from fi.datasets import Dataset
from fi.datasets.types import (
    Cell,
    Column,
    DatasetConfig,
    DataTypeChoices,
    ModelTypes,
    Row,
    SourceChoices,
)

# Set environment variables
os.environ["FI_API_KEY"] = "<fi_api_key>"
os.environ["FI_SECRET_KEY"] = "<fi_secret_key>"

# Get existing dataset
config = DatasetConfig(name="my-dataset", model_type= ModelTypes.GENERATIVE_LLM)
dataset = Dataset(dataset_config=config)
dataset = Dataset.get_dataset_config("my-dataset")

# Define columns
columns = [
    Column(
        name="user_query",
        data_type=DataTypeChoices.TEXT,
        source=SourceChoices.OTHERS
    ),
    Column(
        name="response_quality",
        data_type=DataTypeChoices.INTEGER,
        source=SourceChoices.OTHERS
    ),
    Column(
        name="is_helpful",
        data_type=DataTypeChoices.BOOLEAN,
        source=SourceChoices.OTHERS
    )
]

# Define rows
rows = [
    Row(
        order=1,
        cells=[
            Cell(column_name="user_query", value="What is machine learning?"),
            Cell(column_name="response_quality", value=8),
            Cell(column_name="is_helpful", value=True)
        ]
    ),
    Row(
        order=2,
        cells=[
            Cell(column_name="user_query", value="Explain quantum computing"),
            Cell(column_name="response_quality", value=9),
            Cell(column_name="is_helpful", value=True)
        ]
    )
]

try:
    # Add columns and rows to dataset
    dataset = dataset.add_columns(columns=columns)
    dataset = dataset.add_rows(rows=rows)
    print("✓ Data added successfully")

except Exception as e:
    print(f"Failed to add data: {e}")

import { Dataset, DataTypeChoices, createRow, createCell } from "@future-agi/sdk";

process.env["FI_API_KEY"] = "<fi_api_key>";
process.env["FI_SECRET_KEY"] = "<fi_secret_key>";

async function main() {
try {
    const dsName = "my-dataset";

    // 1) Open the dataset (fetch if it exists, create if not)
    const dataset = await Dataset.open(dsName);

    // 2) Define columns
    const columns = [
    { name: "user_query", dataType: DataTypeChoices.TEXT },
    { name: "response_quality", dataType: DataTypeChoices.INTEGER },
    { name: "is_helpful", dataType: DataTypeChoices.BOOLEAN },
    ];

    // 3) Define rows
    const rows = [
    createRow({
        cells: [
        createCell({ columnName: "user_query", value: "What is machine learning?" }),
        createCell({ columnName: "response_quality", value: 8 }),
        createCell({ columnName: "is_helpful", value: true }),
        ],
    }),
    createRow({
        cells: [
        createCell({ columnName: "user_query", value: "Explain quantum computing" }),
        createCell({ columnName: "response_quality", value: 9 }),
        createCell({ columnName: "is_helpful", value: true }),
        ],
    }),
    ];

    // 4) Add columns and rows
    await dataset.addColumns(columns);
    await dataset.addRows(rows);
    console.log("✓ Data added successfully");
} catch (err) {
    console.error("Failed to add data:", err);
}
}

main();

curl --request POST     --url https://api.futureagi.com/model-hub/develops/<dataset_id>/add_columns/     --header 'X-Api-Key: <fi_api_key>'     --header 'X-Secret-Key: <fi_secret_key>'     --header 'content-type: application/json'     --data '{
"new_columns_data": [
    {
    "name": "user_query",
    "data_type": "text"
    },
    {
    "name": "response_quality",
    "data_type": "integer"
    },
    {
    "name": "is_helpful",
    "data_type": "boolean"
    }
]
}'