Get up and running with StudyFetch in just 5 minutes. You'll learn how to:
- Create or Upload Material: Create text-based materials or upload files (PDFs, documents, etc.) to build your knowledge base
- Create Component: Create an AI component (chat, flashcards, tests, etc.) that uses your materials
- Embed Component: Generate an embed URL and add it to your website with a simple iframe
Prerequisites
Before you begin, make sure you have:
- An API key (get one from your console)
- Your preferred development environment set up:
- JavaScript: Node.js 16+ installed
- Python: Python 3.7+ installed
- Java: Java 8+ and Maven/Gradle
- C#: .NET 6.0+ installed
Language-Specific Quickstart
JavaScript
Install the SDK
npm install @studyfetch/sdk
Initialize the Client
import StudyfetchSDK from '@studyfetch/sdk';
import fs from 'fs';
import path from 'path';
const client = new StudyfetchSDK({
apiKey: process.env.STUDYFETCH_API_KEY,
baseURL: 'https://studyfetchapi.com',
});
Step 1: Create or Upload Study Materials
// Option 1: Create a text-based material
const textMaterial = await client.v1.materials.create({
content: {
type: 'text',
text: 'Chapter 1: Introduction to Biology\n\nBiology is the study of life...'
},
name: 'Biology Chapter 1 - Introduction',
folderId: 'optional-folder-id', // Optional: organize in folders
references: [
{
title: 'Biology Textbook Chapter 1',
url: 'https://example.com/biology-ch1'
}
] // Optional: source references
});
// Option 2: Upload a PDF file
const fileBuffer = await fs.promises.readFile(
path.join(process.cwd(), 'biology-textbook.pdf'),
);
const file = new File([fileBuffer], 'biology-textbook.pdf', {
type: 'application/pdf',
});
const uploadedMaterial = await client.v1.materials.upload.uploadFile({
file,
name: 'Biology Textbook',
folderId: 'optional-folder-id' // Optional: organize in folders
});
// Option 3: Upload material from a URL
const urlMaterial = await client.v1.materials.upload.uploadFromURL({
url: 'https://example.com/biology-notes.pdf',
name: 'Online Biology Notes',
folderId: 'optional-folder-id' // Optional: organize in folders
});
console.log('Materials created:', {
text: textMaterial._id,
uploaded: uploadedMaterial._id,
url: urlMaterial._id
});
Step 2: Create a Chat Component
// Wait for materials to finish processing
// In production, implement proper polling with exponential backoff
let materialStatus = 'processing';
while (materialStatus !== 'active') {
const material = await client.v1.materials.retrieve(textMaterial._id);
materialStatus = material.status;
if (materialStatus === 'error') {
throw new Error('Material processing failed');
}
if (materialStatus === 'processing') {
await new Promise(resolve => setTimeout(resolve, 2000)); // Wait 2 seconds
}
}
const chatComponent = await client.v1.components.create({
name: 'Biology Study Assistant',
type: 'chat',
config: {
model: 'gpt-4o-mini-2024-07-18',
materials: [textMaterial._id, uploadedMaterial._id], // Use any material IDs
temperature: 0.7,
enableWebSearch: true
}
});
console.log('Chat component created:', chatComponent._id);
Step 3: Embed the Chat Component
// Basic embed with minimal configuration
const basicEmbed = await client.v1.components.generateEmbed(chatComponent._id, {
userId: 'student-123'
});
// Advanced embed with all available options
const advancedEmbed = await client.v1.components.generateEmbed(chatComponent._id, {
// User tracking (optional)
userId: 'student-123',
studentName: 'John Doe', // Student name for display
groupIds: ['class-101', 'biology-spring-2024'],
// Display dimensions
width: '100%',
height: '600px',
// Chat-specific features
features: {
enableWebSearch: true, // Allow users to search the web
enableHistory: true, // View and continue previous conversations
enableVoice: true, // Voice input with microphone
enableFollowUps: true, // AI-suggested follow-up questions
enableComponentCreation: true, // AI can create flashcards, tests, etc.
placeholderText: 'Ask your Biology tutor anything...',
enableBadWordsFilter: true
}
});
console.log('Basic Embed URL:', basicEmbed.embedUrl);
console.log('Advanced Embed URL:', advancedEmbed.embedUrl);
// Note: Theme and branding are configured at the organization level
// StudyFetch branding is automatically hidden for all embeds
Complete Example
import StudyfetchSDK from '@studyfetch/sdk';
import fs from 'fs';
async function createStudyAssistant() {
const client = new StudyfetchSDK({
apiKey: process.env.STUDYFETCH_API_KEY,
baseURL: 'https://studyfetchapi.com',
});
// 1. Create or upload materials
const materials = [];
// Create text material
const textMaterial = await client.v1.materials.create({
content: {
type: 'text',
text: 'Course introduction and key concepts...'
},
name: 'Course Introduction',
references: [
{
title: 'Course Syllabus',
url: 'https://example.com/syllabus'
}
] // Optional: source references
});
materials.push(textMaterial._id);
// Upload PDF file
const fileBuffer = await fs.promises.readFile(
path.join(process.cwd(), 'course-content.pdf'),
);
const file = new File([fileBuffer], 'course-content.pdf', {
type: 'application/pdf',
});
const uploadedMaterial = await client.v1.materials.upload.uploadFile({
file,
name: 'Course Materials',
});
materials.push(uploadedMaterial._id);
// IMPORTANT: Wait for materials to finish processing
// Materials need time to process. Using them immediately will cause component creation to fail.
for (const materialId of materials) {
let materialStatus = 'processing';
while (materialStatus !== 'active') {
const material = await client.v1.materials.retrieve(materialId);
materialStatus = material.status;
if (materialStatus === 'error') {
throw new Error(`Material ${materialId} processing failed`);
}
if (materialStatus === 'processing') {
await new Promise(resolve => setTimeout(resolve, 2000)); // Wait 2 seconds
}
}
}
// 2. Create chat assistant
const chatComponent = await client.v1.components.create({
name: 'Course Assistant',
type: 'chat',
config: {
model: 'gpt-4o-mini-2024-07-18',
materials: materials,
temperature: 0.7,
enableWebSearch: true
}
});
// 3. Generate embed URL
const embed = await client.v1.components.generateEmbed(chatComponent._id, {
userId: 'student-123',
studentName: 'John Doe', // Student name for display
groupIds: ['course-101'],
width: '100%',
height: '600px',
features: {
enableWebSearch: true,
enableHistory: true,
enableVoice: true,
enableFollowUps: true,
enableComponentCreation: true,
placeholderText: 'Ask me anything about the course materials...',
enableBadWordsFilter: true
}
});
console.log('Study assistant created!');
console.log('Embed URL:', embed.embedUrl);
console.log('Component ID:', chatComponent._id);
return { chatComponent, embed };
}
createStudyAssistant();
Python
Install the SDK
pip install studyfetch-sdk
Initialize the Client
import os
from studyfetch_sdk import StudyfetchSDK
client = StudyfetchSDK(
api_key=os.environ.get("STUDYFETCH_API_KEY"),
base_url="https://studyfetchapi.com",
)
Step 1: Create or Upload Study Materials
# Option 1: Create a text-based material
text_material = client.v1.materials.create(
content={
"type": "text",
"text": "Chapter 1: Introduction to Biology\n\nBiology is the study of life..."
},
name="Biology Chapter 1 - Introduction",
folder_id="optional-folder-id", # Optional: organize in folders
references=[
{
"title": "Biology Textbook Chapter 1",
"url": "https://example.com/biology-ch1"
}
] # Optional: source references
)
# Option 2: Upload a PDF file
with open('biology-textbook.pdf', 'rb') as f:
uploaded_material = client.v1.materials.upload.upload_file(
file=f.read(),
name="Biology Textbook",
folder_id="optional-folder-id" # Optional: organize in folders
)
# Option 3: Upload material from a URL
url_material = client.v1.materials.upload.upload_from_url(
url="https://example.com/biology-notes.pdf",
name="Online Biology Notes",
folder_id="optional-folder-id" # Optional: organize in folders
)
print("Materials created:", {
"text": text_material.api_id,
"uploaded": uploaded_material.api_id,
"url": url_material.api_id
})
Step 2: Create a Chat Component
import time
# Wait for materials to finish processing
# In production, implement proper polling with exponential backoff
material_status = "processing"
while material_status != "active":
material = client.v1.materials.retrieve(text_material.api_id)
material_status = material.status
if material_status == "error":
raise Exception("Material processing failed")
if material_status == "processing":
time.sleep(2) # Wait 2 seconds
chat_component = client.v1.components.create(
name="Biology Study Assistant",
type="chat",
config={
"model": "gpt-4o-mini-2024-07-18",
"materials": [text_material.api_id, uploaded_material.api_id], # Use any material IDs
"temperature": 0.7,
"enable_web_search": True
}
)
print(f"Chat component created: {chat_component.api_id}")
Step 3: Embed the Chat Component
# Basic embed with minimal configuration
basic_embed = client.v1.components.generate_embed(
chat_component.api_id,
user_id="student-123"
)
# Advanced embed with all available options
advanced_embed = client.v1.components.generate_embed(
chat_component.api_id,
# User tracking (optional)
user_id="student-123",
student_name="John Doe", # Student name for display
group_ids=["class-101", "biology-spring-2024"],
# Display dimensions
width="100%",
height="600px",
# Chat-specific features
features={
"enable_bad_words_filter": True, # Required by the SDK type
"enable_web_search": True, # Allow users to search the web
"enable_history": True, # View and continue previous conversations
"enable_voice": True, # Voice input with microphone
"enable_follow_ups": True, # AI-suggested follow-up questions
"enable_component_creation": True, # AI can create flashcards, tests, etc.
"placeholder_text": "Ask your Biology tutor anything..."
}
)
print(f"Basic Embed URL: {basic_embed.embed_url}")
print(f"Advanced Embed URL: {advanced_embed.embed_url}")
# Note: Theme and branding are configured at the organization level
# StudyFetch branding is automatically hidden for all embeds
Complete Example
import os
from studyfetch_sdk import StudyfetchSDK
def create_study_assistant():
client = StudyfetchSDK(
api_key=os.environ.get("STUDYFETCH_API_KEY"),
base_url="https://studyfetchapi.com",
)
# 1. Create or upload materials
materials = []
# Create text material
text_material = client.v1.materials.create(
content={
"type": "text",
"text": "Course introduction and key concepts..."
},
name="Course Introduction",
references=[
{
"title": "Course Syllabus",
"url": "https://example.com/syllabus"
}
] # Optional: source references
)
materials.append(text_material.api_id)
# Upload PDF file
with open('course-content.pdf', 'rb') as f:
uploaded_material = client.v1.materials.upload.upload_file(
file=f.read(),
name="Course Materials"
)
materials.append(uploaded_material.api_id)
# IMPORTANT: Wait for materials to finish processing
# Materials need time to process. Using them immediately will cause component creation to fail.
import time
for material_id in materials:
material_status = "processing"
while material_status != "active":
material = client.v1.materials.retrieve(material_id)
material_status = material.status
if material_status == "error":
raise Exception(f"Material {material_id} processing failed")
if material_status == "processing":
time.sleep(2) # Wait 2 seconds
# 2. Create chat assistant
chat_component = client.v1.components.create(
name="Course Assistant",
type="chat",
config={
"model": "gpt-4o-mini-2024-07-18",
"materials": materials,
"temperature": 0.7,
"enable_web_search": True
}
)
# 3. Generate embed URL
embed = client.v1.components.generate_embed(
chat_component.api_id,
user_id="student-123",
student_name="John Doe", # Student name for display
group_ids=["course-101"],
width="100%",
height="600px",
features={
"enable_bad_words_filter": True, # Required by the SDK type
"enable_web_search": True,
"enable_history": True,
"enable_voice": True,
"enable_follow_ups": True,
"enable_component_creation": True,
"placeholder_text": "Ask me anything about the course materials..."
}
)
print("Study assistant created!")
print(f"Embed URL: {embed.embed_url}")
print(f"Component ID: {chat_component.api_id}")
return chat_component, embed
create_study_assistant()
Java
Install the SDK
Maven:
<dependency>
<groupId>com.studyfetch.javasdk</groupId>
<artifactId>studyfetch-sdk</artifactId>
<version>0.1.0-alpha.33</version>
</dependency>
Gradle:
implementation group: 'com.studyfetch.javasdk', name: 'studyfetch-sdk', version: '0.1.0-alpha.33'
Initialize the Client
import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
.apiKey(System.getenv("STUDYFETCH_API_KEY"))
.baseUrl("https://studyfetchapi.com")
.build();
Step 1: Create or Upload Study Materials
import com.studyfetch.javasdk.models.v1.materials.MaterialResponse;
import com.studyfetch.javasdk.models.v1.materials.MaterialCreateParams;
import com.studyfetch.javasdk.models.v1.materials.Content;
import com.studyfetch.javasdk.models.v1.materials.Reference;
import com.studyfetch.javasdk.models.v1.materials.upload.UploadUploadFileParams;
import com.studyfetch.javasdk.models.v1.materials.upload.UploadUploadFromUrlParams;
import java.io.FileInputStream;
import java.util.List;
// Option 1: Create a text-based material
MaterialCreateParams createParams = MaterialCreateParams.builder()
.content(Content.builder()
.type(Content.Type.TEXT)
.text("Chapter 1: Introduction to Biology\n\nBiology is the study of life...")
.build())
.name("Biology Chapter 1 - Introduction")
.folderId("optional-folder-id") // Optional: organize in folders
.references(List.of(
Reference.builder()
.title("Biology Textbook Chapter 1")
.url("https://example.com/biology-ch1")
.build()
)) // Optional: source references
.build();
MaterialResponse textMaterial = client.v1().materials().create(createParams);
// Option 2: Upload a PDF file
FileInputStream fileStream = new FileInputStream("biology-textbook.pdf");
UploadUploadFileParams uploadParams = UploadUploadFileParams.builder()
.file(fileStream)
.name("Biology Textbook")
.folderId("optional-folder-id") // Optional: organize in folders
.build();
MaterialResponse uploadedMaterial = client.v1().materials().upload().uploadFile(uploadParams);
// Option 3: Upload material from a URL
UploadUploadFromUrlParams urlParams = UploadUploadFromUrlParams.builder()
.url("https://example.com/biology-notes.pdf")
.name("Online Biology Notes")
.folderId("optional-folder-id") // Optional: organize in folders
.build();
MaterialResponse urlMaterial = client.v1().materials().upload().uploadFromUrl(urlParams);
System.out.println("Materials created:");
System.out.println("Text: " + textMaterial._id());
System.out.println("Uploaded: " + uploadedMaterial._id());
System.out.println("URL: " + urlMaterial._id());
Step 2: Create a Chat Component
import com.studyfetch.javasdk.models.v1.components.ComponentResponse;
import com.studyfetch.javasdk.models.v1.components.ComponentCreateParams;
ComponentCreateParams chatParams = ComponentCreateParams.builder()
.name("Biology Study Assistant")
.type(ComponentCreateParams.Type.CHAT)
.config(ComponentCreateParams.Config.ChatConfigDto.builder()
.model("gpt-4o-mini-2024-07-18")
.materials(List.of(textMaterial._id(), uploadedMaterial._id())) // Use any material IDs
.temperature(0.7)
.enableWebSearch(true)
.build())
.build();
ComponentResponse chatComponent = client.v1().components().create(chatParams);
System.out.println("Chat component created: " + chatComponent._id());
Step 3: Embed the Chat Component
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedParams;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedResponse;
// Basic embed with minimal configuration
ComponentGenerateEmbedParams basicEmbedParams = ComponentGenerateEmbedParams.builder()
.id(chatComponent._id())
.userId("student-123")
.build();
ComponentGenerateEmbedResponse basicEmbed = client.v1().components()
.generateEmbed(basicEmbedParams);
// Advanced embed with all available options
ComponentGenerateEmbedParams advancedEmbedParams = ComponentGenerateEmbedParams.builder()
.id(chatComponent._id())
// User tracking (optional)
.userId("student-123")
.studentName("John Doe") // Student name for display
.groupIds(List.of("class-101", "biology-spring-2024"))
// Display dimensions
.width("100%")
.height("600px")
// Chat-specific features
.features(ComponentGenerateEmbedParams.Features.builder()
.enableBadWordsFilter(true) // Required: build() throws without it
.enableWebSearch(true) // Allow users to search the web
.enableHistory(true) // View and continue previous conversations
.enableVoice(true) // Voice input with microphone
.enableFollowUps(true) // AI-suggested follow-up questions
.enableComponentCreation(true) // AI can create flashcards, tests, etc.
.placeholderText("Ask your Biology tutor anything...")
.build())
.build();
ComponentGenerateEmbedResponse advancedEmbed = client.v1().components()
.generateEmbed(advancedEmbedParams);
System.out.println("Basic Embed URL: " + basicEmbed.embedUrl());
System.out.println("Advanced Embed URL: " + advancedEmbed.embedUrl());
// Note: Theme and branding are configured at the organization level
// StudyFetch branding is automatically hidden for all embeds
Complete Example
package com.example;
import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.materials.MaterialResponse;
import com.studyfetch.javasdk.models.v1.materials.MaterialCreateParams;
import com.studyfetch.javasdk.models.v1.materials.MaterialRetrieveParams;
import com.studyfetch.javasdk.models.v1.materials.Content;
import com.studyfetch.javasdk.models.v1.materials.Reference;
import com.studyfetch.javasdk.models.v1.materials.upload.UploadUploadFileParams;
import com.studyfetch.javasdk.models.v1.components.ComponentResponse;
import com.studyfetch.javasdk.models.v1.components.ComponentCreateParams;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedParams;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedResponse;
import java.io.FileInputStream;
import java.util.List;
import java.util.ArrayList;
public class StudyAssistant {
public static void main(String[] args) throws Exception {
StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
.apiKey(System.getenv("STUDYFETCH_API_KEY"))
.baseUrl("https://studyfetchapi.com")
.build();
// 1. Create or upload materials
List<String> materials = new ArrayList<>();
// Create text material
MaterialCreateParams createParams = MaterialCreateParams.builder()
.content(Content.builder()
.type(Content.Type.TEXT)
.text("Course introduction and key concepts...")
.build())
.name("Course Introduction")
.references(List.of(
Reference.builder()
.title("Course Syllabus")
.url("https://example.com/syllabus")
.build()
)) // Optional: source references
.build();
MaterialResponse textMaterial = client.v1().materials().create(createParams);
materials.add(textMaterial._id());
// Upload PDF file
FileInputStream fileStream = new FileInputStream("course-content.pdf");
UploadUploadFileParams uploadParams = UploadUploadFileParams.builder()
.file(fileStream)
.name("Course Materials")
.build();
MaterialResponse uploadedMaterial = client.v1().materials().upload().uploadFile(uploadParams);
materials.add(uploadedMaterial._id());
// IMPORTANT: Wait for materials to finish processing
// Materials need time to process. Using them immediately will cause component creation to fail.
for (String materialId : materials) {
MaterialResponse.Status status = MaterialResponse.Status.PROCESSING;
while (status.equals(MaterialResponse.Status.PROCESSING)) {
Thread.sleep(2000); // Wait 2 seconds
MaterialResponse material = client.v1().materials().retrieve(
MaterialRetrieveParams.builder()
.id(materialId)
.build()
);
status = material.status();
if (status.equals(MaterialResponse.Status.ERROR)) {
throw new Exception("Material processing failed");
}
}
}
// 2. Create chat assistant
ComponentCreateParams chatParams = ComponentCreateParams.builder()
.name("Course Assistant")
.type(ComponentCreateParams.Type.CHAT)
.config(ComponentCreateParams.Config.ChatConfigDto.builder()
.model("gpt-4o-mini-2024-07-18")
.materials(materials)
.temperature(0.7)
.enableWebSearch(true)
.build())
.build();
ComponentResponse chatComponent = client.v1().components().create(chatParams);
// 3. Generate embed URL
ComponentGenerateEmbedParams embedParams = ComponentGenerateEmbedParams.builder()
.id(chatComponent._id())
.userId("student-123")
.studentName("John Doe") // Student name for display
.groupIds(List.of("course-101"))
.width("100%")
.height("600px")
.features(ComponentGenerateEmbedParams.Features.builder()
.enableBadWordsFilter(true) // Required: build() throws without it
.enableWebSearch(true)
.enableHistory(true)
.enableVoice(true)
.enableFollowUps(true)
.enableComponentCreation(true)
.placeholderText("Ask me anything about the course materials...")
.build())
.build();
ComponentGenerateEmbedResponse embed = client.v1().components()
.generateEmbed(embedParams);
System.out.println("Study assistant created!");
System.out.println("Embed URL: " + embed.embedUrl());
System.out.println("Component ID: " + chatComponent._id());
}
}
C#
Install the SDK
dotnet add package StudyfetchSDK --version 0.8.1
Initialize the Client
using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Materials;
using StudyfetchSDK.Models.V1.Materials.Upload;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.IO;
using System.Threading.Tasks;
var client = new StudyfetchSDKClient()
{
APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
BaseUrl = new Uri("https://studyfetchapi.com")
};
Step 1: Create or Upload Study Materials
// Option 1: Create a text-based material
var textMaterial = await client.V1.Materials.Create(new()
{
Content = new()
{
Type = StudyfetchSDK.Models.V1.Materials.ContentProperties.Type.Text,
Text = "Chapter 1: Introduction to Biology\n\nBiology is the study of life..."
},
Name = "Biology Chapter 1 - Introduction",
FolderID = "optional-folder-id", // Optional: organize in folders
References = new List<StudyfetchSDK.Models.V1.Materials.Reference>
{
new("Biology Textbook Chapter 1")
{
URL = "https://example.com/biology-ch1"
}
} // Optional: source references
});
// Option 2: Upload a PDF file
var fileBytes = await File.ReadAllBytesAsync("biology-textbook.pdf");
var uploadedMaterial = await client.V1.Materials.Upload.UploadFile(new()
{
File = fileBytes,
FileName = "biology-textbook.pdf",
Name = "Biology Textbook",
FolderID = "optional-folder-id" // Optional: organize in folders
});
// Option 3: Upload material from a URL
var urlMaterial = await client.V1.Materials.Upload.UploadFromURL(new()
{
URL = "https://example.com/biology-notes.pdf",
Name = "Online Biology Notes",
FolderID = "optional-folder-id" // Optional: organize in folders
});
Console.WriteLine("Materials created:");
Console.WriteLine($"Text: {textMaterial._ID}");
Console.WriteLine($"Uploaded: {uploadedMaterial._ID}");
Console.WriteLine($"URL: {urlMaterial._ID}");
Step 2: Create a Chat Component
// Wait for materials to finish processing
// In production, implement proper polling with exponential backoff
var materialStatus = "processing";
while (materialStatus != "active")
{
var material = await client.V1.Materials.Retrieve(new()
{
ID = textMaterial._ID
});
materialStatus = material.Status.Raw();
if (materialStatus == "error")
throw new Exception("Material processing failed");
if (materialStatus == "processing")
await Task.Delay(2000); // Wait 2 seconds
}
var chatComponent = await client.V1.Components.Create(new()
{
Name = "Biology Study Assistant",
Type = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.Type.Chat,
Config = new StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDto()
{
Model = "gpt-4o-mini-2024-07-18",
Materials = new List<string> { textMaterial._ID, uploadedMaterial._ID }, // Use any material IDs
Temperature = 0.7,
EnableWebSearch = true
}
});
Console.WriteLine($"Chat component created: {chatComponent._ID}");
Step 3: Embed the Chat Component
// Basic embed with minimal configuration
var basicEmbed = await client.V1.Components.GenerateEmbed(new()
{
ID = chatComponent._ID,
UserID = "student-123"
});
// Advanced embed with all available options
var advancedEmbed = await client.V1.Components.GenerateEmbed(new()
{
ID = chatComponent._ID,
// User tracking (optional)
UserID = "student-123",
StudentName = "John Doe", // Student name for display
GroupIDs = new List<string> { "class-101", "biology-spring-2024" },
// Display dimensions
Width = "100%",
Height = "600px",
// Chat-specific features
Features = new StudyfetchSDK.Models.V1.Components.ComponentGenerateEmbedParamsProperties.Features(enableBadWordsFilter: true)
{
EnableWebSearch = true, // Allow users to search the web
EnableHistory = true, // View and continue previous conversations
EnableVoice = true, // Voice input with microphone
EnableFollowUps = true, // AI-suggested follow-up questions
EnableComponentCreation = true, // AI can create flashcards, tests, etc.
PlaceholderText = "Ask your Biology tutor anything..."
}
});
Console.WriteLine($"Basic Embed URL: {basicEmbed.EmbedURL}");
Console.WriteLine($"Advanced Embed URL: {advancedEmbed.EmbedURL}");
// Note: Theme and branding are configured at the organization level
// StudyFetch branding is automatically hidden for all embeds
Complete Example
using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Materials;
using StudyfetchSDK.Models.V1.Materials.Upload;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.IO;
using System.Threading.Tasks;
public class StudyAssistant
{
public static async Task CreateStudyAssistant()
{
var client = new StudyfetchSDKClient()
{
APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
BaseUrl = new Uri("https://studyfetchapi.com")
};
// 1. Create or upload materials
var materials = new List<string>();
// Create text material
var textMaterial = await client.V1.Materials.Create(new()
{
Content = new()
{
Type = StudyfetchSDK.Models.V1.Materials.ContentProperties.Type.Text,
Text = "Course introduction and key concepts..."
},
Name = "Course Introduction",
References = new List<StudyfetchSDK.Models.V1.Materials.Reference>
{
new("Course Syllabus")
{
URL = "https://example.com/syllabus"
}
} // Optional: source references
});
materials.Add(textMaterial._ID);
// Upload PDF file
var fileBytes = await File.ReadAllBytesAsync("course-content.pdf");
var uploadedMaterial = await client.V1.Materials.Upload.UploadFile(new()
{
File = fileBytes,
FileName = "course-content.pdf",
Name = "Course Materials"
});
materials.Add(uploadedMaterial._ID);
// IMPORTANT: Wait for materials to finish processing
// Materials need time to process. Using them immediately will cause component creation to fail.
foreach (var materialId in materials)
{
var materialStatus = "processing";
while (materialStatus != "active")
{
var material = await client.V1.Materials.Retrieve(new()
{
ID = materialId
});
materialStatus = material.Status.Raw();
if (materialStatus == "error")
throw new Exception($"Material {materialId} processing failed");
if (materialStatus == "processing")
await Task.Delay(2000); // Wait 2 seconds
}
}
// 2. Create chat assistant
var chatComponent = await client.V1.Components.Create(new()
{
Name = "Course Assistant",
Type = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.Type.Chat,
Config = new StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDto()
{
Model = "gpt-4o-mini-2024-07-18",
Materials = materials,
Temperature = 0.7,
EnableWebSearch = true
}
});
// 3. Generate embed URL
var embed = await client.V1.Components.GenerateEmbed(new()
{
ID = chatComponent._ID,
UserID = "student-123",
StudentName = "John Doe", // Student name for display
GroupIDs = new List<string> { "course-101" },
Width = "100%",
Height = "600px",
Features = new StudyfetchSDK.Models.V1.Components.ComponentGenerateEmbedParamsProperties.Features(enableBadWordsFilter: true)
{
EnableWebSearch = true,
EnableHistory = true,
EnableVoice = true,
EnableFollowUps = true,
EnableComponentCreation = true,
PlaceholderText = "Ask me anything about the course materials..."
}
});
Console.WriteLine("Study assistant created!");
Console.WriteLine($"Embed URL: {embed.EmbedURL}");
Console.WriteLine($"Component ID: {chatComponent._ID}");
}
}
Embedding Components
After creating components, embed them in your application:
<iframe
src="YOUR_EMBED_URL"
width="100%"
height="600px"
frameborder="0"
allow="microphone"
></iframe>
What's Next?
Now that you've created your first components, explore more features:
Learn More About Components
- Chat Components - Build conversational AI tutors
- Flashcards - Create adaptive study cards
- Practice Tests - Generate assessments
- Audio Recaps - Convert materials to audio
- Scenarios - Design immersive learning experiences
- Explainers - Create animated video explanations
- Uploads - Manage and organize materials
Advanced Features
- Embedding Components - Integrate into your app
- Analytics API - Track usage and performance (see REST API tab)
Resources
- REST API Reference - Complete API documentation (see REST API tab)
Need Help?
- 📧 Email: [email protected]
- 💬 Discord: Join our community