Add Data Using SDK

By adding data using the SDK, you can:

• Create new datasets programmatically.
• Add structured data efficiently.
• Automate dataset updates.
• Seamlessly integrate with Future AGI’s data processing pipeline.

​

1. Accessing the SDK Integration Panel

To add data using the SDK:

1. Go to the “Datasets & Experiments” Section
   • Navigate to the Datasets page from the main dashboard.
   • Click on the “Add Dataset” button.
2. Select “Add Data Using SDK”
   • A pop-up will appear with various dataset creation methods.
   • Choose “Add Data Using SDK” to proceed.

​

2. Creating a Dataset

1. Enter a Dataset Name
   • A prompt appears asking for the dataset name.
   • Enter a clear, descriptive name
   • Click “Next” to proceed.
2. Review Dataset Creation Settings
   • The interface confirms the dataset setup before proceeding.
   • Click “Next” to generate the SDK integration code.

​

3. Generating SDK Integration Code

1. View Pre-Generated Code
   • The system generates ready-to-use SDK code in multiple languages:
     • Python
     • TypeScript
     • cURL
   • The code includes:
     • API key authentication.
     • Dataset creation command.
     • Methods for adding data.

​

Adding Data using SDK

# pip install futureagi

import os
os.environ["FI_API_KEY"] = "YOUR_FI_API_KEY"  # Replace with your actual API key
os.environ["FI_SECRET_KEY"] = "YOUR_FI_SECRET_KEY" # Replace with your actual secret key

from fi.datasets import Dataset

columns = [
    {
        "name": "text",
        "data_type": "text"
    },
    {
        "name": "label",
        "data_type": "integer"
    }
]

rows = [
    {
        "cells": [
            {
                "column_name": "text",
                "value": "Hello, world!"
            },
            {
                "column_name": "label",
                "value": 0
            }
        ]
    }
]

try:
    Dataset.add_dataset_columns(dataset_name="dataset-1", columns=columns)
    Dataset.add_dataset_rows(dataset_name="dataset-1", rows=rows)

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

# pip install futureagi

import os
os.environ["FI_API_KEY"] = "YOUR_FI_API_KEY"  # Replace with your actual API key
os.environ["FI_SECRET_KEY"] = "YOUR_FI_SECRET_KEY" # Replace with your actual secret key

from fi.datasets import Dataset

columns = [
    {
        "name": "text",
        "data_type": "text"
    },
    {
        "name": "label",
        "data_type": "integer"
    }
]

rows = [
    {
        "cells": [
            {
                "column_name": "text",
                "value": "Hello, world!"
            },
            {
                "column_name": "label",
                "value": 0
            }
        ]
    }
]

try:
    Dataset.add_dataset_columns(dataset_name="dataset-1", columns=columns)
    Dataset.add_dataset_rows(dataset_name="dataset-1", rows=rows)

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

const addColumnsRequest = async () => {
    const url = `https://api.futureagi.com/model-hub/develops/None/add_columns/`;

    const requestBody = {
      new_columns_data: [
        {
          name: "column1",
          data_type: "integer",
        },
        {
          name: "column2",
          data_type: "text",
        },
      ],
    };

    const response = await fetch(url, {
      method: "POST",
      headers: {
        Authorization: `Bearer {accessToken}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify(requestBody),
    });

    const data = await response.json();
    return data;
  };

  addColumnsRequest()
    .then((data) => console.log("Columns Added:", data))
    .catch((error) => console.error("Error Adding Columns:", error));
  
const addRowsRequest = async () => {
    const url = `https://api.futureagi.com/model-hub/develops/None/add_rows/`;

    const requestBody = {
      rows: [
        {
          cells: [
            {
              column_name: "column1",
              value: 0,
            },
            {
              column_name: "column2",
              value: 45,
            },
          ],
        },
      ],
    };

    const response = await fetch(url, {
      method: "POST",
      headers: {
        Authorization: `Bearer {accessToken}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify(requestBody),
    });

  const data = await response.json();
  return data;
};

curl --request POST \
     --url https://api.futureagi.com/model-hub/develops/None/add_columns/ \
     --header 'Authorization: Bearer {access_token}' \
     --header 'content-type: application/json' \
     --data '{
    "new_columns_data": [
        {
          "name": "column1",
          "data_type": "integer"
        },
        {
          "name": "column2",
          "data_type": "text"
        }
      ]
  }'

curl --request POST \
     --url https://api.futureagi.com/model-hub/develops/None/add_rows/ \
     --header 'Authorization: Bearer {access_token}' \
     --header 'content-type: application/json' \
     --data '{
    "rows":[
      {
      "cells":[
              {
                  "column_name": "column1",
                  "value": 0
              },
              {
                  "column_name": "column2",
                  "value": 45  
              }
      ]
      }
  ]
  }'

​

4. Adding Data to the Dataset

1. Use the SDK Code to Add Data
   • Paste the copied SDK code into your development environment.
   • Replace any placeholder values if necessary.
   • Execute the script to send data to the dataset.
2. Confirm Data Upload
   • After execution, the dataset will be populated with the provided data.
   • The dataset will appear in the Datasets & Experiments list.

​

5. Viewing the Dataset

1. Navigate to the Dataset List
   • The newly created dataset is now visible in the Datasets panel.
   • Displays dataset details such as:
     • Name
     • Type (e.g., Generative)
     • Number of data points
     • Associated experiments and evaluations
2. Verify Data Entries
   • Click on the dataset name to inspect the added rows.
   • Check for correct formatting and structure.

Using the SDK, you can efficiently create and manage datasets in Future AGI. This method provides a scalable way to ingest, update, and automate data handling, making it suitable for AI-driven workflows and large-scale applications.