Overview
The Data Analyst component is an AI-powered chatbot designed specifically for educators. It provides insights into student engagement, performance metrics, and learning patterns. Educators can ask natural-language questions about their students' study habits, identify areas where students may need additional support, and receive recommendations to improve learning outcomes.
The analyst answers from your organization's StudyFetch usage data: the usage events your learners generate in embedded components. It writes and runs analysis code against that data, so answers can include calculations and charts. Materials are optional and only serve as background reference.
Use config.groupIds to limit the analyst to specific classes or cohorts. The groupIds you pass when generating an embed don't restrict what the analyst can see.
Creating a Data Analyst Component
JavaScript
import StudyfetchSDK from '@studyfetch/sdk';
const client = new StudyfetchSDK({
apiKey: 'your-api-key',
baseURL: 'https://studyfetchapi.com',
});
const dataAnalystComponent = await client.v1.components.create({
name: 'Student Performance Analyst',
type: 'data_analyst',
config: {
model: 'gpt-4.1-2025-04-14',
groupIds: ['class-101', 'class-102'], // Only analyze these classes
materials: ['mat-123'], // Optional reference material
enableWebSearch: false,
maxSteps: 5,
},
});
console.log('Data Analyst component created:', dataAnalystComponent._id, dataAnalystComponent.componentId);
Python
from studyfetch_sdk import StudyfetchSDK
client = StudyfetchSDK(
api_key="your-api-key",
base_url="https://studyfetchapi.com",
)
data_analyst_component = client.v1.components.create(
name="Student Performance Analyst",
type="data_analyst",
config={
"model": "gpt-4.1-2025-04-14",
"group_ids": ["class-101", "class-102"], # Only analyze these classes
"materials": ["mat-123"], # Optional reference material
"enable_web_search": False,
"max_steps": 5,
},
)
print(f"Data Analyst component created: {data_analyst_component.api_id} {data_analyst_component.component_id}")
Java
import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.components.ComponentResponse;
import com.studyfetch.javasdk.models.v1.components.ComponentCreateParams;
import java.util.List;
public class CreateDataAnalystComponent {
public static void main(String[] args) {
StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
.fromEnv()
.baseUrl("https://studyfetchapi.com")
.build();
ComponentCreateParams params = ComponentCreateParams.builder()
.name("Student Performance Analyst")
.type(ComponentCreateParams.Type.DATA_ANALYST)
.config(ComponentCreateParams.Config.DataAnalystConfigDto.builder()
.model("gpt-4.1-2025-04-14")
.groupIds(List.of("class-101", "class-102")) // Only analyze these classes
.materials(List.of("mat-123")) // Optional reference material
.enableWebSearch(false)
.maxSteps(5)
.build())
.build();
ComponentResponse component = client.v1().components().create(params);
System.out.println("Data Analyst component created: " + component._id());
}
}
C#
using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
public class CreateDataAnalystComponent
{
public static async Task CreateDataAnalyst()
{
var client = new StudyfetchSDKClient()
{
APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
BaseUrl = new Uri("https://studyfetchapi.com")
};
var dataAnalystComponent = await client.V1.Components.Create(new()
{
Name = "Student Performance Analyst",
Type = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.Type.DataAnalyst,
Config = new StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.DataAnalystConfigDto()
{
Model = "gpt-4.1-2025-04-14",
GroupIDs = new List<string> { "class-101", "class-102" }, // Only analyze these classes
Materials = new List<string> { "mat-123" }, // Optional reference material
EnableWebSearch = false,
MaxSteps = 5,
}
});
Console.WriteLine($"Data Analyst component created: {dataAnalystComponent._ID}");
}
}
Configuration Parameters
name
string · required
Name of the data analyst component
type
string · required
Must be "data_analyst" (with an underscore)
config
object · required
Data analyst configuration object. Unknown keys are rejected with 400.
Configuration Properties
These properties go inside config.
model
string · required
Model key for analysis, such as gpt-4.1-2025-04-14 or gemini-2.5-flash. The key is checked when a question is asked, not when the component is created, so an unrecognized key makes requests fail.
groupIds
array
Group or class IDs to analyze. When set, the analyst only sees usage from learners in these groups. When omitted, it analyzes your whole organization.
materials
array
Optional material IDs the analyst can search for background, such as a syllabus or course guide
folders
array
Optional folder IDs the analyst can search for background
enableRAGSearch
boolean · default: true
Search materials and folders when answering
enableWebSearch
boolean · default: true
Allow web searches. In the embed, the educator's web search toggle decides per message; its starting state comes from features.enableWebSearch, which defaults to off.
enableComponentCreation
boolean · default: true
Allow the analyst to create study components during the conversation
maxSteps
number · default: 3
Maximum tool-call steps per answer
systemPrompt
string
Accepted and stored, but the analyst currently always uses its own built-in instructions.
temperature
number
Accepted and stored, but the analyst currently uses a fixed temperature.
maxTokens
number
Accepted and stored, but the analyst currently uses a fixed output limit.
enableHistory
boolean
Stored on the component. The embed reads this setting from the embed token; use features.enableHistory.
enableVoice
boolean
Stored on the component. Use features.enableVoice to control the embed.
enableFollowUps
boolean
Stored on the component. Use features.enableFollowUps to control the embed.
Response
{
"_id": "6650f1c2a9b3e4d5f6a7b8c9",
"componentId": "data_analyst_V1StGXR8_Z",
"name": "Student Performance Analyst",
"type": "data_analyst",
"status": "active",
"origin": "api",
"organization": "64f0a1b2c3d4e5f6a7b8c9d0",
"materials": ["mat-123"],
"folders": [],
"config": {
"model": "gpt-4.1-2025-04-14",
"groupIds": ["class-101", "class-102"],
"materials": ["mat-123"],
"enableWebSearch": false,
"maxSteps": 5
},
"usageCount": 0,
"createdAt": "2026-10-06T10:00:00.000Z",
"updatedAt": "2026-10-06T10:00:00.000Z"
}
Embedding This Component
Once you've created a Data Analyst component, you can embed it on your website using the embedding API. The component must be active.
Generate Embed URL
JavaScript
const embedResponse = await client.v1.components.generateEmbed(dataAnalystComponent._id, {
// User tracking
userId: 'educator-456',
studentName: 'Ms. Rivera', // Name for display
sessionId: 'session-789',
// Data Analyst features
features: {
enableBadWordsFilter: true, // Required by the SDK type; no effect on Data Analyst
enableHistory: true,
enableVoice: false,
enableFollowUps: true,
placeholderText: 'Ask about your students...',
},
// Dimensions
width: '100%',
height: '700px',
});
console.log(embedResponse.embedUrl);
Python
embed_response = client.v1.components.generate_embed(
data_analyst_component.api_id,
user_id="educator-456",
student_name="Ms. Rivera", # Name for display
session_id="session-789",
features={
"enable_bad_words_filter": True, # Required by the SDK type; no effect on Data Analyst
"enable_history": True,
"enable_voice": False,
"enable_follow_ups": True,
"placeholder_text": "Ask about your students...",
},
width="100%",
height="700px",
)
print(embed_response.embed_url)
Java
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedParams;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedResponse;
ComponentGenerateEmbedParams params = ComponentGenerateEmbedParams.builder()
.id(dataAnalystComponent._id())
// User tracking
.userId("educator-456")
.studentName("Ms. Rivera") // Name for display
.sessionId("session-789")
// Data Analyst features
.features(ComponentGenerateEmbedParams.Features.builder()
.enableBadWordsFilter(true) // Required: build() throws without it
.enableHistory(true)
.enableVoice(false)
.enableFollowUps(true)
.placeholderText("Ask about your students...")
.build())
// Dimensions
.width("100%")
.height("700px")
.build();
ComponentGenerateEmbedResponse embedResponse = client.v1().components()
.generateEmbed(params);
System.out.println(embedResponse.embedUrl());
C#
using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using StudyfetchSDK.Models.V1.Components.ComponentGenerateEmbedParamsProperties;
using System;
using System.Threading.Tasks;
public class GenerateDataAnalystEmbed
{
public static async Task GenerateEmbed(string dataAnalystComponentId)
{
var client = new StudyfetchSDKClient()
{
APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
BaseUrl = new Uri("https://studyfetchapi.com")
};
var embedResponse = await client.V1.Components.GenerateEmbed(new()
{
ID = dataAnalystComponentId,
UserID = "educator-456",
StudentName = "Ms. Rivera", // Name for display
SessionID = "session-789",
Features = new Features(enableBadWordsFilter: true)
{
EnableHistory = true,
EnableVoice = false,
EnableFollowUps = true,
PlaceholderText = "Ask about your students..."
},
Width = "100%",
Height = "700px"
});
Console.WriteLine($"Embed URL: {embedResponse.EmbedURL}");
Console.WriteLine($"Token: {embedResponse.Token}");
}
}
The response contains embedUrl, token, expiresAt, componentId (the component's _id), componentType, and options (width and height, defaulting to 100% and 600px).
Data Analyst-Specific Embedding Features
features.enableHistory
boolean · default: true
Show past conversations and let educators continue them. History is stored per userId.
features.enableVoice
boolean · default: true
Enable voice input. Your iframe needs allow="microphone".
features.enableFollowUps
boolean · default: true
Show suggested follow-up questions after answers
features.enableWebSearch
boolean · default: false
Starting state of the web search toggle
features.enableComponentCreation
boolean · default: true
Allow creating study components from the conversation
features.enableReferenceMode
boolean · default: false
Show reference titles and URLs instead of source content in citations
features.placeholderText
string · default: Ask your AI Assistant about anything
Placeholder text for the input
Embed in Your HTML
Use the embedUrl from the response as the iframe source. The host can differ per organization, so don't build the URL yourself.
<iframe
src="EMBED_URL_FROM_RESPONSE"
width="100%"
height="700px"
frameborder="0"
allow="microphone; clipboard-write"
style="border: 1px solid #e5e5e5; border-radius: 8px;">
</iframe>
The React Native SDK does not include a Data Analyst component; use the iframe embed.