Quickstart Guide

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

Material Processing Time: When you upload or create materials, they need time to process before they can be used in components. Attempting to create a component immediately after uploading materials will fail. Always check that materials have status: 'active' before using them. The examples below include proper waiting logic.

Language-Specific Quickstart

JavaScript
Python
Java
C#

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

Important: Materials need time to process after upload. If you create a material and immediately try to use it in a component, the component creation will fail. Always ensure materials have status: 'active' before using them. For production applications, implement a polling mechanism to check material status.

// 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();

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",
    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
with open('biology-textbook.pdf', 'rb') as f:
    uploaded_material = client.v1.materials.upload.upload_file(
        file=f.read(),
        name="Biology Textbook",
        folderId="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",
    folderId="optional-folder-id"  # Optional: organize in folders
)

print("Materials created:", {
    "text": text_material._id,
    "uploaded": uploaded_material._id,
    "url": url_material._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._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._id, uploaded_material._id],  # Use any material IDs
        "temperature": 0.7,
        "enableWebSearch": True
    }
)

print(f"Chat component created: {chat_component._id}")

Step 3: Embed the Chat Component

# Basic embed with minimal configuration
basic_embed = client.v1.components.generateEmbed(
    component_id=chat_component._id,
    userId="student-123"
)

# Advanced embed with all available options
advanced_embed = client.v1.components.generateEmbed(
    component_id=chat_component._id,
    # User tracking (optional)
    userId="student-123",
    student_name="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..."
    }
)

print(f"Basic Embed URL: {basic_embed.embedUrl}")
print(f"Advanced Embed URL: {advanced_embed.embedUrl}")

# 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._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._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,
            "enableWebSearch": True
        }
    )

    # 3. Generate embed URL
    embed = client.v1.components.generateEmbed(
        component_id=chat_component._id,
        userId="student-123",
        student_name="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..."
        }
    )

    print("Study assistant created!")
    print(f"Embed URL: {embed.embedUrl}")
    print(f"Component ID: {chat_component._id}")
    
    return chat_component, embed

create_study_assistant()

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()
        .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()
                .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());
    }
}

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

​Quickstart Guide

​Prerequisites

​Language-Specific Quickstart

​Install the SDK

​Initialize the Client

​Step 1: Create or Upload Study Materials

​Step 2: Create a Chat Component

​Step 3: Embed the Chat Component

​Complete Example

​Install the SDK

​Initialize the Client

​Step 1: Create or Upload Study Materials

​Step 2: Create a Chat Component

​Step 3: Embed the Chat Component

​Complete Example

​Install the SDK

​Initialize the Client

​Step 1: Create or Upload Study Materials

​Step 2: Create a Chat Component

​Step 3: Embed the Chat Component

​Complete Example

​Install the SDK

​Initialize the Client

​Step 1: Create or Upload Study Materials

​Step 2: Create a Chat Component

​Step 3: Embed the Chat Component

​Complete Example

​Embedding Components

​What’s Next?

​Learn More About Components

​Advanced Features

​Resources

​Need Help?

Quickstart Guide

Prerequisites

Language-Specific Quickstart

Install the SDK

Initialize the Client

Step 1: Create or Upload Study Materials

Step 2: Create a Chat Component

Step 3: Embed the Chat Component

Complete Example

Install the SDK

Initialize the Client

Step 1: Create or Upload Study Materials

Step 2: Create a Chat Component

Step 3: Embed the Chat Component

Complete Example

Install the SDK

Initialize the Client

Step 1: Create or Upload Study Materials

Step 2: Create a Chat Component

Step 3: Embed the Chat Component

Complete Example

Install the SDK

Initialize the Client

Step 1: Create or Upload Study Materials

Step 2: Create a Chat Component

Step 3: Embed the Chat Component

Complete Example

Embedding Components

What’s Next?

Learn More About Components

Advanced Features

Resources

Need Help?