Chat

Overview

The Chat component creates an AI-powered assistant that can answer questions based on your study materials. It supports web search, RAG (Retrieval Augmented Generation), and multi-step reasoning.

Creating a Chat Component

import StudyfetchSDK from '@studyfetch/sdk';

const client = new StudyfetchSDK({
  apiKey: 'your-api-key',
  baseURL: 'https://studyfetchapi.com',
});

const chatComponent = await client.v1.components.create({
  name: 'Biology Study Assistant',
  type: 'chat',
  config: {
    materials: ['mat-123', 'mat-456'],
    folders: ['folder-789'],
    model: 'gpt-4o-mini-2024-07-18',
    systemPrompt: 'You are a helpful biology tutor. Answer questions based on the provided materials.',
    temperature: 0.7,
    maxTokens: 2048,
    enableWebSearch: true,
    enableRAGSearch: true,
    maxSteps: 5,
    enableHistory: false,
    enableVoice: false,
    enableFollowUps: false,
    enableComponentCreation: false,
    enableMessageGrading: false,
    enableReferenceMode: false,
    enableFeedback: true,
    // UI Customization (optional)
    hideTitle: false,
    hideEmptyState: false,
    emptyStateHTML: '<div style="text-align: center; padding: 2rem;"><h3>Welcome to Biology Assistant!</h3><p>Ask me anything about your course materials.</p></div>'
  }
});

console.log('Chat component created:', chatComponent._id);

from studyfetch_sdk import StudyfetchSDK

client = StudyfetchSDK(
    api_key="your-api-key",
    base_url="https://studyfetchapi.com",
)

chat_component = client.v1.components.create(
    name="Biology Study Assistant",
    type="chat",
    config={
        "materials": ["mat-123", "mat-456"],
        "folders": ["folder-789"],
        "model": "gpt-4o-mini-2024-07-18",
        "systemPrompt": "You are a helpful biology tutor. Answer questions based on the provided materials.",
        "temperature": 0.7,
        "maxTokens": 2048,
        "enableWebSearch": True,
        "enableRAGSearch": True,
        "maxSteps": 5,
        "enableHistory": False,
        "enableVoice": False,
        "enableFollowUps": False,
        "enableComponentCreation": False,
        "enableMessageGrading": False,
        "enableReferenceMode": False,
        "enableFeedback": True,
        # UI Customization (optional)
        "hideTitle": False,
        "hideEmptyState": False,
        "emptyStateHTML": '<div style="text-align: center; padding: 2rem;"><h3>Welcome to Biology Assistant!</h3><p>Ask me anything about your course materials.</p></div>'
    }
)

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

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;

public class CreateChatComponent {
    public static void main(String[] args) {
        StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
            .fromEnv()
            .baseUrl("https://studyfetchapi.com")
            .build();

        ComponentCreateParams params = ComponentCreateParams.builder()
            .name("Biology Study Assistant")
            .type(ComponentCreateParams.Type.CHAT)
            .config(ComponentCreateParams.Config.ChatConfigDto.builder()
                .materials(List.of("mat-123", "mat-456"))
                .folders(List.of("folder-789"))
                .model("gpt-4o-mini-2024-07-18")
                .systemPrompt("You are a helpful biology tutor. Answer questions based on the provided materials.")
                .temperature(0.7)
                .maxTokens(2048)
                .enableWebSearch(true)
                .enableRagSearch(true)
                .maxSteps(5)
                .enableHistory(false)
                .enableVoice(false)
                .enableFollowUps(false)
                .enableComponentCreation(false)
                .enableMessageGrading(false)
                .enableReferenceMode(false)
                .enableFeedback(true)
                // UI Customization (optional)
                .hideTitle(false)
                .hideEmptyState(false)
                .emptyStateHTML("<div style='text-align: center; padding: 2rem;'><h3>Welcome to Biology Assistant!</h3><p>Ask me anything about your course materials.</p></div>")
                .build())
            .build();

        ComponentResponse component = client.v1().components().create(params);
        System.out.println("Chat component created: " + component._id());
    }
}

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;

public class CreateChatComponent
{
    public static async Task CreateChat()
    {
        var client = new StudyfetchSDKClient()
        {
            APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
            BaseUrl = new Uri("https://studyfetchapi.com")
        };

        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 = "gemini-2.5-flash",
                Materials = new List<string> { "mat-123" },
                EnableFeedback = true,
                // UI Customization (optional)
                HideTitle = false,
                HideEmptyState = false,
                EmptyStateHTML = "<div style='text-align: center; padding: 2rem;'><h3>Welcome to Biology Assistant!</h3><p>Ask me anything about your course materials.</p></div>"
            }
        });

        Console.WriteLine($"Chat component created: {chatComponent._ID}");
    }
}

Configuration Parameters

string

required

Name of the chat component

string

required

Must be "chat"

object

required

Chat configuration object

Show Configuration Properties

array

required

Array of material IDs to use as context for the chat assistant

array

Array of folder IDs containing materials

string

default:"gpt-4o-mini-2024-07-18"

AI model to use. Options:

gpt-4o - Most capable model
gpt-4o-mini-2024-07-18 - Faster, cost-effective model

string

System prompt to guide the AI assistant’s behavior and personality

number

default:"0.7"

Temperature for response generation (0-2). Lower values make responses more focused and deterministic

integer

default:"2048"

Maximum tokens for AI responses

boolean

default:"false"

Enable web search capabilities for finding current information

boolean

default:"true"

Enable RAG (Retrieval Augmented Generation) search within materials

integer

Maximum steps for multi-step tool calls and reasoning

boolean

default:"false"

Enable conversation history to allow users to continue previous chats

boolean

default:"false"

Enable voice input for hands-free interaction

boolean

default:"false"

Enable AI-suggested follow-up questions after responses

boolean

default:"false"

Allow the AI to create study components (flashcards, tests, etc.) during conversation

boolean

default:"false"

Enable message grading to track prompting quality and responsible AI usage. When enabled, user messages are scored on a 1-4 scale for prompting effectiveness and responsible interaction.

boolean

default:"false"

Show reference titles and URLs instead of source content in citations. This provides a cleaner interface while maintaining source attribution.

boolean

default:"true"

Enable thumbs up/down feedback with reason. When enabled, users can provide feedback on AI responses with optional text explanations. This data can be retrieved using the chat.retrieveFeedback() API for quality monitoring and improvement.

boolean

default:"false"

Enable guardrails to apply server-side content policy rules to AI responses. When enabled, the AI’s responses will be evaluated against your configured rules before being returned to the user.

boolean

default:"false"

Hide the chat title and avatar in the embedded component

boolean

default:"false"

Hide the default empty state (icon and text) when no messages are present

string

Provide custom HTML to replace the default icon and message when chat is empty.

array

Array of guardrail rules to control AI behavior. Rules are evaluated on each response.

Show Guardrail Rule Properties

string

required

Unique identifier for the rule

string

required

Action to take when rule is triggered:

BLOCK - Prevents the response and shows your custom message
WARN - Allows the response but adds a warning message
MODIFY - Allows the assistant to revise its response to comply with the rule

string

required

Natural language description of when the rule should trigger. The AI evaluates if the response matches this condition.

string

required

Brief description of the rule’s purpose

string

Custom message shown to the user when the rule triggers

Common Guardrail Use Cases:

Academic Integrity: Prevent direct homework/test answers
Safety: Block harmful, medical, or legal advice
Age-Appropriate Content: Modify responses for younger audiences
Brand Guidelines: Ensure responses align with your organization’s values
Topic Boundaries: Keep conversations focused on intended subject matter

Response

{
  "_id": "comp_123abc",
  "name": "Biology Study Assistant",
  "type": "chat",
  "status": "active",
  "config": {
    "materials": ["mat-123", "mat-456"],
    "folders": ["folder-789"],
    "model": "gpt-4o-mini-2024-07-18",
    "systemPrompt": "You are a helpful biology tutor...",
    "temperature": 0.7,
    "maxTokens": 2048,
    "enableWebSearch": true,
    "enableRAGSearch": true,
    "maxSteps": 5
  },
  "createdAt": "2024-01-15T10:00:00Z",
  "updatedAt": "2024-01-15T10:00:00Z",
  "organizationId": "org_456def",
  "usage": {
    "interactions": 0,
    "lastUsed": null
  }
}

Adding Guardrails

Guardrails allow you to apply server-side content policy rules to AI responses. When enabled, the AI’s responses are evaluated against your configured rules before being returned to the user.

Example: Academic Integrity Guardrails

const chatWithGuardrails = await client.v1.components.create({
  name: 'Math Tutor with Guardrails',
  type: 'chat',
  config: {
    materials: ['mat-789'],
    model: 'gpt-4o-mini-2024-07-18',
    enableFeedback: true,
    enableGuardrails: true,
    guardrailRules: [
      {
        id: 'no-direct-answers',
        action: 'modify',
        condition: 'Response provides direct answers to homework problems without explanation',
        description: 'Guide students to solve problems themselves',
        message: 'Modified to provide guidance instead of direct answers'
      },
      {
        id: 'academic-honesty',
        action: 'warn',
        condition: 'User asks for test or exam answers',
        description: 'Warn about academic integrity',
        message: 'Remember: This tool is for learning, not for completing graded assignments'
      }
    ]
  }
});

chat_with_guardrails = client.v1.components.create(
    name="Math Tutor with Guardrails",
    type="chat",
    config={
        "materials": ["mat-789"],
        "model": "gpt-4o-mini-2024-07-18",
        "enableFeedback": True,
        "enableGuardrails": True,
        "guardrailRules": [
            {
                "id": "no-direct-answers",
                "action": "MODIFY",
                "condition": "Response provides direct answers to homework problems without explanation",
                "description": "Guide students to solve problems themselves",
                "message": "Modified to provide guidance instead of direct answers"
            },
            {
                "id": "academic-honesty",
                "action": "WARN",
                "condition": "User asks for test or exam answers",
                "description": "Warn about academic integrity",
                "message": "Remember: This tool is for learning, not for completing graded assignments"
            }
        ]
    }
)

ComponentCreateParams guardrailParams = ComponentCreateParams.builder()
    .name("Math Tutor with Guardrails")
    .type(ComponentCreateParams.Type.CHAT)
    .config(ComponentCreateParams.Config.ChatConfigDto.builder()
        .materials(List.of("mat-789"))
        .model("gpt-4o-mini-2024-07-18")
        .enableFeedback(true)
        .enableGuardrails(true)
        .guardrailRules(List.of(
            ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.builder()
                .id("no-direct-answers")
                .action(ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.Action.MODIFY)
                .condition("Response provides direct answers to homework problems without explanation")
                .description("Guide students to solve problems themselves")
                .message("Modified to provide guidance instead of direct answers")
                .build(),
            ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.builder()
                .id("academic-honesty")
                .action(ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.Action.WARN)
                .condition("User asks for test or exam answers")
                .description("Warn about academic integrity")
                .message("Remember: This tool is for learning, not for completing graded assignments")
                .build()
        ))
        .build())
    .build();

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using GuardrailRule = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDtoProperties.GuardrailRule;
using GuardrailAction = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDtoProperties.GuardrailRuleProperties.Action;

public class AcademicIntegrityGuardrails
{
    public static async Task CreateChatWithGuardrails()
    {
        var client = new StudyfetchSDKClient()
        {
            APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
            BaseUrl = new Uri("https://studyfetchapi.com")
        };

        var guardrailComponent = await client.V1.Components.Create(new()
        {
            Name = "Math Tutor with Guardrails",
            Type = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.Type.Chat,
            Config = new StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDto()
            {
                Model = "gemini-2.5-flash",
                Materials = new List<string> { "mat-789" },
                EnableFeedback = true,
                EnableGuardrails = true,
                GuardrailRules = new List<GuardrailRule>
                {
                    new GuardrailRule()
                    {
                        ID = "academic-honesty",
                        Action = GuardrailAction.Warn,
                        Condition = "User asks for test or exam answers",
                        Description = "Warn about academic integrity",
                        Message = "Remember: This tool is for learning, not for completing graded assignments"
                    }
                }
            }
        });

        Console.WriteLine($"Guardrail component created: {guardrailComponent._ID}");
    }
}

Example: Safety and Content Moderation

const safeChat = await client.v1.components.create({
  name: 'K-12 Science Assistant',
  type: 'chat',
  config: {
    materials: ['mat-science-k12'],
    enableFeedback: true,
    enableGuardrails: true,
    guardrailRules: [
      {
        id: 'no-medical-advice',
        action: 'block',
        condition: 'Response contains medical diagnosis or treatment advice',
        description: 'Prevent medical advice',
        message: 'I cannot provide medical advice. Please consult a healthcare professional.'
      },
      {
        id: 'age-appropriate',
        action: 'modify',
        condition: 'Response contains content not suitable for K-12 students',
        description: 'Ensure age-appropriate content',
        message: 'Content adjusted for educational purposes'
      },
      {
        id: 'no-dangerous-experiments',
        action: 'block',
        condition: 'Response describes dangerous chemical reactions or experiments',
        description: 'Prevent dangerous activities',
        message: 'For safety reasons, I cannot provide instructions for this type of experiment.'
      }
    ]
  }
});

safe_chat = client.v1.components.create(
    name="K-12 Science Assistant",
    type="chat",
    config={
        "materials": ["mat-science-k12"],
        "enableFeedback": True,
        "enableGuardrails": True,
        "guardrailRules": [
            {
                "id": "no-medical-advice",
                "action": "BLOCK",
                "condition": "Response contains medical diagnosis or treatment advice",
                "description": "Prevent medical advice",
                "message": "I cannot provide medical advice. Please consult a healthcare professional."
            },
            {
                "id": "age-appropriate",
                "action": "MODIFY",
                "condition": "Response contains content not suitable for K-12 students",
                "description": "Ensure age-appropriate content",
                "message": "Content adjusted for educational purposes"
            },
            {
                "id": "no-dangerous-experiments",
                "action": "BLOCK",
                "condition": "Response describes dangerous chemical reactions or experiments",
                "description": "Prevent dangerous activities",
                "message": "For safety reasons, I cannot provide instructions for this type of experiment."
            }
        ]
    }
)

ComponentCreateParams safeParams = ComponentCreateParams.builder()
    .name("K-12 Science Assistant")
    .type(ComponentCreateParams.Type.CHAT)
    .config(ComponentCreateParams.Config.ChatConfigDto.builder()
        .materials(List.of("mat-science-k12"))
        .enableFeedback(true)
        .enableGuardrails(true)
        .guardrailRules(List.of(
            ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.builder()
                .id("no-medical-advice")
                .action(ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.Action.BLOCK)
                .condition("Response contains medical diagnosis or treatment advice")
                .description("Prevent medical advice")
                .message("I cannot provide medical advice. Please consult a healthcare professional.")
                .build(),
            ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.builder()
                .id("age-appropriate")
                .action(ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.Action.MODIFY)
                .condition("Response contains content not suitable for K-12 students")
                .description("Ensure age-appropriate content")
                .message("Content adjusted for educational purposes")
                .build(),
            ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.builder()
                .id("no-dangerous-experiments")
                .action(ComponentCreateParams.Config.ChatConfigDto.GuardrailRule.Action.BLOCK)
                .condition("Response describes dangerous chemical reactions or experiments")
                .description("Prevent dangerous activities")
                .message("For safety reasons, I cannot provide instructions for this type of experiment.")
                .build()
        ))
        .build())
    .build();

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using GuardrailRule = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDtoProperties.GuardrailRule;
using GuardrailAction = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDtoProperties.GuardrailRuleProperties.Action;

public class SafetyAndContentModeration
{
    public static async Task CreateSafeChatComponent()
    {
        var client = new StudyfetchSDKClient()
        {
            APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
            BaseUrl = new Uri("https://studyfetchapi.com")
        };

        var safeChat = await client.V1.Components.Create(new()
        {
            Name = "K-12 Science Assistant",
            Type = StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.Type.Chat,
            Config = new StudyfetchSDK.Models.V1.Components.ComponentCreateParamsProperties.ConfigProperties.ChatConfigDto()
            {
                Model = "gemini-2.5-flash",
                Materials = new List<string> { "mat-science-k12" },
                EnableFeedback = true,
                EnableGuardrails = true,
                GuardrailRules = new List<GuardrailRule>
                {
                    new GuardrailRule()
                    {
                        ID = "no-medical-advice",
                        Action = GuardrailAction.Block,
                        Condition = "Response contains medical diagnosis or treatment advice",
                        Description = "Prevent medical advice",
                        Message = "I cannot provide medical advice. Please consult a healthcare professional."
                    },
                    new GuardrailRule()
                    {
                        ID = "age-appropriate",
                        Action = GuardrailAction.Modify,
                        Condition = "Response contains content not suitable for K-12 students",
                        Description = "Ensure age-appropriate content",
                        Message = "Content adjusted for educational purposes"
                    },
                    new GuardrailRule()
                    {
                        ID = "no-dangerous-experiments",
                        Action = GuardrailAction.Block,
                        Condition = "Response describes dangerous chemical reactions or experiments",
                        Description = "Prevent dangerous activities",
                        Message = "For safety reasons, I cannot provide instructions for this type of experiment."
                    }
                }
            }
        });

        Console.WriteLine($"Safe chat component created: {safeChat._ID}");
    }
}

Guardrail Actions Explained

BLOCK: Completely prevents the response and shows your custom message to the user
WARN: Allows the response but adds a warning message before or after it
MODIFY: Instructs the AI to revise its response to comply with the rule

Embedding This Component

Once you’ve created a Chat component, you can embed it on your website using the embedding API.

Generate Embed URL

const embedResponse = await client.v1.components.generateEmbed(chatComponent._id, {
  // User tracking
  userId: 'user-456',
  studentName: 'Jane Smith',  // Student name for display
  groupIds: ['class-101', 'class-102'],
  sessionId: 'session-789',
  
  // Chat-specific features
  features: {
    enableWebSearch: true,
    enableHistory: true,
    enableVoice: true,
    enableFollowUps: true,
    enableComponentCreation: false,
    placeholderText: 'Ask me anything about biology...',
    enableWebSearchSources: true,
    enableImageSources: true,
    enableBadWordsFilter: true,
    enablePromptingScore: true,
    enableResponsibilityScore: true,
    enableReferenceMode: false,
    enableGuardrails: true,
    enableOutline: false,
    enableTranscript: false
  },
  
  // Dimensions
  width: '100%',
  height: '600px',
  
  // Token expiry
  expiryHours: 24
});

embed_response = client.v1.components.generateEmbed(
    component_id=chat_component._id,
    userId="user-456",
    student_name="Jane Smith",  # Student name for display
    groupIds=["class-101", "class-102"],
    sessionId="session-789",
    features={
        "enableWebSearch": True,
        "enableHistory": True,
        "enableVoice": True,
        "enableFollowUps": True,
        "enableComponentCreation": False,
        "placeholderText": "Ask me anything about biology...",
        "enableWebSearchSources": True,
        "enableImageSources": True,
        "enableBadWordsFilter": True,
        "enablePromptingScore": True,
        "enableResponsibilityScore": True,
        "enableReferenceMode": False,
        "enableGuardrails": True,
        "enableOutline": False,
        "enableTranscript": False
    },
    width="100%",
    height="600px",
    expiryHours=24
)

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedParams;
import com.studyfetch.javasdk.models.v1.components.ComponentGenerateEmbedResponse;

StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
    .fromEnv()
    .baseUrl("https://studyfetchapi.com")
    .build();

ComponentGenerateEmbedParams params = ComponentGenerateEmbedParams.builder()
    .id(chatComponent._id())
    // User tracking
    .userId("user-456")
    .studentName("Jane Smith")  // Student name for display
    .groupIds(List.of("class-101", "class-102"))
    .sessionId("session-789")
    
    // Chat-specific features
    .features(ComponentGenerateEmbedParams.Features.builder()
        .enableWebSearch(true)
        .enableHistory(true)
        .enableVoice(true)
        .enableFollowUps(true)
        .enableComponentCreation(false)
        .placeholderText("Ask me anything about biology...")
        .enableWebSearchSources(true)
        .enableImageSources(true)
        .enablePromptingScore(true)
        .enableResponsibilityScore(true)
        .enableReferenceMode(false)
        .enableGuardrails(true)
        .enableOutline(false)
        .enableTranscript(false)
        .build())
    
    // Dimensions
    .width("100%")
    .height("600px")
    
    // Token expiry
    .expiryHours(24)
    .build();

ComponentGenerateEmbedResponse embedResponse = client.v1().components()
    .generateEmbed(params);

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Components;
using StudyfetchSDK.Models.V1.Components.ComponentGenerateEmbedParamsProperties;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;

public class GenerateChatEmbedAdvanced
{
    public static async Task GenerateEmbed(string chatComponentId)
    {
        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 = chatComponentId,
            
            // User tracking
            UserID = "user-456",
            StudentName = "Jane Smith",  // Student name for display
            GroupIDs = new List<string> { "class-101", "class-102" },
            SessionID = "session-789",
            
            // Chat-specific features
            Features = new Features(enableBadWordsFilter: true)
            {
                EnableWebSearch = true,
                EnableHistory = true,
                EnableVoice = true,
                EnableFollowUps = true,
                EnableComponentCreation = false,
                PlaceholderText = "Ask me anything about biology...",
                EnableWebSearchSources = true,
                EnableImageSources = true,
                EnablePromptingScore = true,
                EnableResponsibilityScore = true,
                EnableReferenceMode = false,
                EnableGuardrails = true,
                EnableOutline = false,
                EnableTranscript = false
            },
            
            // Dimensions
            Width = "100%",
            Height = "600px",
            
            // Token expiry
            ExpiryHours = 24
        });

        Console.WriteLine($"Embed URL: {embedResponse.EmbedURL}");
        Console.WriteLine($"Token: {embedResponse.Token}");
    }
}

Chat-Specific Embedding Features

boolean

default:"false"

Allow the chat to search the web for current information

boolean

default:"true"

Show conversation history and allow users to continue previous chats

boolean

default:"false"

Enable voice input for asking questions

boolean

default:"true"

Show suggested follow-up questions after responses

boolean

default:"false"

Allow users to create other components (flashcards, tests) from chat

string

default:"Ask a question..."

Custom placeholder text for the chat input

boolean

default:"true"

Show web search sources when web search is used

boolean

default:"true"

Display image sources in responses when relevant

boolean

default:"true"

required

Enable filtering of inappropriate language (required)

boolean

default:"false"

Enable prompting quality scoring for user messages (1-4 scale)

boolean

default:"false"

Enable responsibility scoring for user messages (1-4 scale)

boolean

default:"false"

Show reference titles and URLs instead of source content in citations

boolean

default:"false"

Apply guardrail rules configured on the component to embedded chat

boolean

default:"false"

Enable document outline navigation (for document-based components)

boolean

default:"false"

Enable transcript view (for video/audio-based components)

Embed in Your HTML

<iframe 
  src="https://embed.studyfetch.com/component/comp_123abc?token=..."
  width="100%"
  height="600px"
  frameborder="0"
  allow="microphone; clipboard-write"
  style="border: 1px solid #e5e5e5; border-radius: 8px;">
</iframe>

Streaming Chat Responses

The Chat API supports real-time streaming responses using Server-Sent Events (SSE). This allows you to display responses as they’re generated, providing a more interactive experience.

Stream Chat Response

// AI SDK format with messages array
const streamResponse = await client.v1.chat.stream({
  componentId: 'comp_123abc',
  sessionId: 'session-789',
  userId: 'user-456',
  groupIds: ['class-101', 'class-102'],
  messages: [
    { role: 'system', content: 'You are a helpful biology tutor.' },
    { role: 'user', content: 'Explain photosynthesis in simple terms' }
  ]
});

// Process the stream
for await (const chunk of streamResponse) {
  console.log(chunk.content);
}

// Custom format with single message
const customStream = await client.v1.chat.stream({
  componentId: 'comp_123abc',
  sessionId: 'session-789',
  userId: 'user-456',
  message: {
    text: 'What is cellular respiration?',
    images: [
      {
        url: 'https://example.com/cell-diagram.png',
        caption: 'Cell structure diagram',
        mimeType: 'image/png'
      }
    ]
  }
});

# AI SDK format with messages array
stream_response = client.v1.chat.stream(
    component_id="comp_123abc",
    session_id="session-789",
    user_id="user-456",
    group_ids=["class-101", "class-102"],
    messages=[
        {"role": "system", "content": "You are a helpful biology tutor."},
        {"role": "user", "content": "Explain photosynthesis in simple terms"}
    ]
)

# Process the stream
for chunk in stream_response:
    print(chunk.content)

# Custom format with single message
custom_stream = client.v1.chat.stream(
    component_id="comp_123abc",
    session_id="session-789",
    user_id="user-456",
    message={
        "text": "What is cellular respiration?",
        "images": [
            {
                "url": "https://example.com/cell-diagram.png",
                "caption": "Cell structure diagram",
                "mimeType": "image/png"
            }
        ]
    }
)

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.chat.ChatStreamParams;
import java.util.List;

StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
    .fromEnv()
    .build();

// AI SDK format with messages array
ChatStreamParams aiSdkParams = ChatStreamParams.builder()
    .componentId("comp_123abc")
    .sessionId("session-789")
    .userId("user-456")
    .groupIds(List.of("class-101", "class-102"))
    .messages(List.of(
        "{\"role\":\"system\",\"content\":\"You are a helpful biology tutor.\"}",
        "{\"role\":\"user\",\"content\":\"Explain photosynthesis in simple terms\"}"
    ))
    .build();

client.v1().chat().stream(aiSdkParams);

// Custom format with single message
ChatStreamParams.Message message = ChatStreamParams.Message.builder()
    .text("What is cellular respiration?")
    .images(List.of(
        ChatStreamParams.Message.Image.builder()
            .url("https://example.com/cell-diagram.png")
            .caption("Cell structure diagram")
            .mimeType("image/png")
            .build()
    ))
    .build();

ChatStreamParams customParams = ChatStreamParams.builder()
    .componentId("comp_123abc")
    .sessionId("session-789")
    .userId("user-456")
    .message(message)
    .build();

client.v1().chat().stream(customParams);

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Chat;
using StudyfetchSDK.Models.V1.Chat.ChatStreamParamsProperties;
using StudyfetchSDK.Models.V1.Chat.ChatStreamParamsProperties.MessageProperties;
using System;
using System.Collections.Generic;
using System.Threading.Tasks;

public class StreamingChatExample
{
    public static async Task StreamChat()
    {
        var client = new StudyfetchSDKClient()
        {
            APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
            BaseUrl = new Uri("https://studyfetchapi.com")
        };

        // AI SDK format with messages array
        var aiSdkParams = new ChatStreamParams()
        {
            ComponentID = "comp_123abc",
            SessionID = "session-789",
            UserID = "user-456",
            GroupIDs = new List<string> { "class-101", "class-102" },
            Messages = new List<string>
            {
                "{\"role\": \"system\", \"content\": \"You are a helpful biology tutor.\"}",
                "{\"role\": \"user\", \"content\": \"Explain photosynthesis in simple terms\"}"
            }
        };

        await client.V1.Chat.Stream(aiSdkParams);

        // Custom format with single message
        var customParams = new ChatStreamParams()
        {
            ComponentID = "comp_123abc",
            SessionID = "session-789",
            UserID = "user-456",
            Message = new Message()
            {
                Text = "What is cellular respiration?",
                Images = new List<Image>
                {
                    new Image()
                    {
                        URL = "https://example.com/cell-diagram.png",
                        Caption = "Cell structure diagram",
                        MimeType = "image/png"
                    }
                }
            }
        };

        await client.V1.Chat.Stream(customParams);
        Console.WriteLine("Chat streams completed");
    }
}

Stream Parameters

string

ID of the chat component to use

string

Session ID to maintain conversation context

string

User ID for tracking and personalization

array

Array of group IDs for access control

object

Additional context to pass to the chat

array

Messages array for AI SDK format. Each message should have:

role: “system”, “user”, or “assistant”
content: The message content

object

Single message for custom format

Show Message Properties

string

required

Text content of the message

array

Array of images attached to the message

Show Image Properties

string

URL of the image

string

Base64 encoded image data (alternative to URL)

string

Caption for the image

string

MIME type of the image (e.g., “image/png”, “image/jpeg”)

Stream Response Format

The streaming endpoint returns Server-Sent Events (SSE) with the following event types:

data: {"type":"content","content":"The process of photosynthesis..."}

data: {"type":"tool_call","tool":"web_search","args":{"query":"latest photosynthesis research"}}

data: {"type":"sources","sources":[{"title":"Source Title","url":"https://..."}]}

data: {"type":"done","usage":{"tokens":150}}

Example: Building a Streaming Chat Interface

// React example with streaming
function ChatInterface({ componentId }) {
  const [messages, setMessages] = useState([]);
  const [streaming, setStreaming] = useState(false);

  const sendMessage = async (text) => {
    setStreaming(true);
    const userMessage = { role: 'user', content: text };
    setMessages(prev => [...prev, userMessage]);

    const assistantMessage = { role: 'assistant', content: '' };
    setMessages(prev => [...prev, assistantMessage]);

    try {
      const stream = await client.v1.chat.stream({
        componentId,
        sessionId: sessionStorage.getItem('chatSession'),
        messages: [...messages, userMessage]
      });

      for await (const chunk of stream) {
        if (chunk.type === 'content') {
          setMessages(prev => {
            const updated = [...prev];
            updated[updated.length - 1].content += chunk.content;
            return updated;
          });
        }
      }
    } finally {
      setStreaming(false);
    }
  };

  return (
    <div>
      {messages.map((msg, i) => (
        <div key={i} className={msg.role}>
          {msg.content}
        </div>
      ))}
      <input 
        onSubmit={(e) => sendMessage(e.target.value)}
        disabled={streaming}
      />
    </div>
  );
}

import asyncio
import aiohttp

async def stream_chat_response(client, component_id, message_text):
    """Stream chat responses with real-time display"""
    
    params = {
        "componentId": component_id,
        "sessionId": "session-789",
        "message": {
            "text": message_text
        }
    }
    
    async with client.v1.chat.stream_async(**params) as stream:
        full_response = ""
        
        async for chunk in stream:
            if chunk.type == "content":
                print(chunk.content, end="", flush=True)
                full_response += chunk.content
            elif chunk.type == "tool_call":
                print(f"\n[Using tool: {chunk.tool}]")
            elif chunk.type == "done":
                print(f"\n[Tokens used: {chunk.usage.tokens}]")
        
        return full_response

# Usage
async def main():
    response = await stream_chat_response(
        client, 
        "comp_123abc",
        "Explain the carbon cycle"
    )

import com.studyfetch.javasdk.models.v1.chat.ChatStreamParams;
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.HttpURLConnection;
import java.net.URL;

public class StreamingChatExample {
    public void streamChat(String componentId, String messageText) {
        ChatStreamParams params = ChatStreamParams.builder()
            .componentId(componentId)
            .sessionId("session-789")
            .message(ChatStreamParams.Message.builder()
                .text(messageText)
                .build())
            .build();

        // Stream handling would typically be done through the SDK
        // This is a conceptual example
        client.v1().chat().stream(params, response -> {
            // Handle each chunk as it arrives
            if (response.getType().equals("content")) {
                System.out.print(response.getContent());
            } else if (response.getType().equals("done")) {
                System.out.println("\nChat completed");
            }
        });
    }
}

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Chat;
using StudyfetchSDK.Models.V1.Chat.ChatStreamParamsProperties;
using System;
using System.Threading.Tasks;

public class StreamingChatExample
{
    private readonly StudyfetchSDKClient _client;
    
    public StreamingChatExample()
    {
        _client = new StudyfetchSDKClient()
        {
            APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
            BaseUrl = new Uri("https://studyfetchapi.com")
        };
    }
    
    public async Task StreamChat(string componentId, string messageText)
    {
        var parameters = new ChatStreamParams()
        {
            ComponentID = componentId,
            SessionID = "session-789",
            Message = new Message()
            {
                Text = messageText
            }
        };

        // Stream handling through the SDK
        await _client.V1.Chat.Stream(parameters);
        Console.WriteLine("Chat stream completed");
    }
}

Managing Chat Embed Context

The context API allows you to dynamically push contextual information to specific embedded chat instances. This is particularly useful for applications where the chat component remains persistent while the surrounding content changes, such as:

Practice Tests: Update context as users navigate between questions
Multi-page Tutorials: Provide page-specific context without resetting chat history
Dynamic Content: Keep the AI informed about what the user is currently viewing

Push Context

Add context information to an embedded chat component:

import StudyfetchSDK from '@studyfetch/sdk';

const client = new StudyfetchSDK({
  apiKey: 'your-api-key',
  baseURL: 'https://studyfetchapi.com',
});

// Push context to a specific embed
await client.v1.embed.context.push({
  token: 'embed-token-123',
  context: 'The user is now on Question 2 which discusses cellular respiration and the Krebs cycle.'
});

from studyfetch_sdk import StudyfetchSDK

client = StudyfetchSDK(
    api_key="your-api-key",
    base_url="https://studyfetchapi.com",
)

# Push context to a specific embed
client.v1.embed.context.push(
    token="embed-token-123",
    context="The user is now on Question 2 which discusses cellular respiration and the Krebs cycle."
)

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.embed.context.ContextPushParams;

StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
    .fromEnv()
    .baseUrl("https://studyfetchapi.com")
    .build();

// Push context to a specific embed
ContextPushParams params = ContextPushParams.builder()
    .token("embed-token-123")
    .context("The user is now on Question 2 which discusses cellular respiration and the Krebs cycle.")
    .build();

client.v1().embed().context().push(params);

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Embed.Context;
using System;
using System.Threading.Tasks;

var client = new StudyfetchSDKClient()
{
    APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
    BaseUrl = new Uri("https://studyfetchapi.com")
};

// Push context to a specific embed
await client.V1.Embed.Context.Push(new ContextPushParams()
{
    Token = "embed-token-123",
    Context = "The user is now on Question 2 which discusses cellular respiration and the Krebs cycle."
});

Retrieve Context

Get the current context for an embedded chat component:

// Retrieve current context
const currentContext = await client.v1.embed.context.retrieve({
  token: 'embed-token-123'
});

console.log('Current context:', currentContext);

# Retrieve current context
current_context = client.v1.embed.context.retrieve(
    token="embed-token-123"
)

print(f"Current context: {current_context}")

import com.studyfetch.javasdk.models.v1.embed.context.ContextRetrieveParams;

// Retrieve current context
ContextRetrieveParams retrieveParams = ContextRetrieveParams.builder()
    .token("embed-token-123")
    .build();

client.v1().embed().context().retrieve(retrieveParams);

using StudyfetchSDK.Models.V1.Embed.Context;

// Retrieve current context
await client.V1.Embed.Context.Retrieve(new ContextRetrieveParams()
{
    Token = "embed-token-123"
});

Clear Context

Clear all context from an embedded chat component:

// Clear all context
await client.v1.embed.context.clear({
  token: 'embed-token-123'
});

# Clear all context
client.v1.embed.context.clear(
    token="embed-token-123"
)

import com.studyfetch.javasdk.models.v1.embed.context.ContextClearParams;

// Clear all context
ContextClearParams clearParams = ContextClearParams.builder()
    .token("embed-token-123")
    .build();

client.v1().embed().context().clear(clearParams);

using StudyfetchSDK.Models.V1.Embed.Context;

// Clear all context
await client.V1.Embed.Context.Clear(new ContextClearParams()
{
    Token = "embed-token-123"
});

Complete Example: Practice Test with Context

Here’s a complete example of using context management in a practice test application:

class PracticeTestChat {
  constructor(client, embedToken) {
    this.client = client;
    this.embedToken = embedToken;
    this.currentQuestion = 0;
  }

  async navigateToQuestion(questionNumber, questionData) {
    // Clear previous context
    await this.client.v1.embed.context.clear({
      token: this.embedToken
    });

    // Push new context for current question
    const contextText = `The user is now on Question ${questionNumber}: ${questionData.title}. 
Topic: ${questionData.topic}
Content: ${questionData.content}`;

    await this.client.v1.embed.context.push({
      token: this.embedToken,
      context: contextText
    });

    this.currentQuestion = questionNumber;
  }

  async addSupplementalContext(additionalInfo) {
    // Add more context without clearing previous
    await this.client.v1.embed.context.push({
      token: this.embedToken,
      context: `Additional information: ${additionalInfo}`
    });
  }
}

// Usage
const chat = new PracticeTestChat(client, 'embed-token-123');

// User navigates to question 1
await chat.navigateToQuestion(1, {
  title: 'Photosynthesis Process',
  topic: 'Biology - Cellular Processes',
  content: 'Explain the light-dependent reactions of photosynthesis...'
});

// User moves to question 2
await chat.navigateToQuestion(2, {
  title: 'Cellular Respiration',
  topic: 'Biology - Cellular Processes',
  content: 'Describe the steps of the Krebs cycle...'
});

class PracticeTestChat:
    def __init__(self, client, embed_token):
        self.client = client
        self.embed_token = embed_token
        self.current_question = 0

    def navigate_to_question(self, question_number, question_data):
        # Clear previous context
        self.client.v1.embed.context.clear(token=self.embed_token)

        # Push new context for current question
        context_text = f"""The user is now on Question {question_number}: {question_data['title']}. 
Topic: {question_data['topic']}
Content: {question_data['content']}"""

        self.client.v1.embed.context.push(
            token=self.embed_token,
            context=context_text
        )

        self.current_question = question_number

    def add_supplemental_context(self, additional_info):
        # Add more context without clearing previous
        self.client.v1.embed.context.push(
            token=self.embed_token,
            context=f"Additional information: {additional_info}"
        )

# Usage
chat = PracticeTestChat(client, "embed-token-123")

# User navigates to question 1
chat.navigate_to_question(1, {
    "title": "Photosynthesis Process",
    "topic": "Biology - Cellular Processes",
    "content": "Explain the light-dependent reactions of photosynthesis..."
})

# User moves to question 2
chat.navigate_to_question(2, {
    "title": "Cellular Respiration",
    "topic": "Biology - Cellular Processes",
    "content": "Describe the steps of the Krebs cycle..."
})

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.models.v1.embed.context.*;

public class PracticeTestChat {
    private final StudyfetchSdkClient client;
    private final String embedToken;
    private int currentQuestion;

    public PracticeTestChat(StudyfetchSdkClient client, String embedToken) {
        this.client = client;
        this.embedToken = embedToken;
        this.currentQuestion = 0;
    }

    public void navigateToQuestion(int questionNumber, QuestionData questionData) {
        // Clear previous context
        ContextClearParams clearParams = ContextClearParams.builder()
            .token(embedToken)
            .build();
        client.v1().embed().context().clear(clearParams);

        // Push new context for current question
        String contextText = String.format(
            "The user is now on Question %d: %s.%nTopic: %s%nContent: %s",
            questionNumber,
            questionData.title,
            questionData.topic,
            questionData.content
        );

        ContextPushParams pushParams = ContextPushParams.builder()
            .token(embedToken)
            .context(contextText)
            .build();
        client.v1().embed().context().push(pushParams);

        this.currentQuestion = questionNumber;
    }

    public void addSupplementalContext(String additionalInfo) {
        // Add more context without clearing previous
        ContextPushParams params = ContextPushParams.builder()
            .token(embedToken)
            .context("Additional information: " + additionalInfo)
            .build();
        client.v1().embed().context().push(params);
    }

    static class QuestionData {
        String title;
        String topic;
        String content;
    }
}

// Usage
PracticeTestChat chat = new PracticeTestChat(client, "embed-token-123");

PracticeTestChat.QuestionData q1 = new PracticeTestChat.QuestionData();
q1.title = "Photosynthesis Process";
q1.topic = "Biology - Cellular Processes";
q1.content = "Explain the light-dependent reactions of photosynthesis...";

chat.navigateToQuestion(1, q1);

PracticeTestChat.QuestionData q2 = new PracticeTestChat.QuestionData();
q2.title = "Cellular Respiration";
q2.topic = "Biology - Cellular Processes";
q2.content = "Describe the steps of the Krebs cycle...";

chat.navigateToQuestion(2, q2);

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Embed.Context;
using System;
using System.Threading.Tasks;

public class PracticeTestChat
{
    private readonly StudyfetchSDKClient _client;
    private readonly string _embedToken;
    private int _currentQuestion;

    public PracticeTestChat(StudyfetchSDKClient client, string embedToken)
    {
        _client = client;
        _embedToken = embedToken;
        _currentQuestion = 0;
    }

    public async Task NavigateToQuestion(int questionNumber, QuestionData questionData)
    {
        // Clear previous context
        await _client.V1.Embed.Context.Clear(new ContextClearParams()
        {
            Token = _embedToken
        });

        // Push new context for current question
        var contextText = $@"The user is now on Question {questionNumber}: {questionData.Title}. 
Topic: {questionData.Topic}
Content: {questionData.Content}";

        await _client.V1.Embed.Context.Push(new ContextPushParams()
        {
            Token = _embedToken,
            Context = contextText
        });

        _currentQuestion = questionNumber;
    }

    public async Task AddSupplementalContext(string additionalInfo)
    {
        // Add more context without clearing previous
        await _client.V1.Embed.Context.Push(new ContextPushParams()
        {
            Token = _embedToken,
            Context = $"Additional information: {additionalInfo}"
        });
    }

    public class QuestionData
    {
        public string Title { get; set; }
        public string Topic { get; set; }
        public string Content { get; set; }
    }
}

// Usage
var client = new StudyfetchSDKClient()
{
    APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
    BaseUrl = new Uri("https://studyfetchapi.com")
};

var chat = new PracticeTestChat(client, "embed-token-123");

// User navigates to question 1
await chat.NavigateToQuestion(1, new PracticeTestChat.QuestionData
{
    Title = "Photosynthesis Process",
    Topic = "Biology - Cellular Processes",
    Content = "Explain the light-dependent reactions of photosynthesis..."
});

// User moves to question 2
await chat.NavigateToQuestion(2, new PracticeTestChat.QuestionData
{
    Title = "Cellular Respiration",
    Topic = "Biology - Cellular Processes",
    Content = "Describe the steps of the Krebs cycle..."
});

Context API Parameters

string

required

The embed token for the specific chat instance. Obtained from generateEmbed() response.

string

required

The context string to add to the chat. Can include any relevant information about what the user is currently viewing or doing.

Retrieving Chat Feedback

You can retrieve feedback data (thumbs up/down) from users interacting with your chat components:

import StudyfetchSDK from '@studyfetch/sdk';

const client = new StudyfetchSDK({
  apiKey: 'your-api-key',
  baseURL: 'https://studyfetchapi.com',
});

// Retrieve all feedback
await client.v1.chat.retrieveFeedback();

// Filter feedback by component
await client.v1.chat.retrieveFeedback({
  componentId: 'comp_123abc',
  startDate: '2025-01-01T00:00:00Z',
  endDate: '2025-12-31T23:59:59Z',
  feedbackType: 'thumbsUp',
  limit: '100',
  skip: '0'
});

from studyfetch_sdk import StudyfetchSDK

client = StudyfetchSDK(
    api_key="your-api-key",
    base_url="https://studyfetchapi.com",
)

# Retrieve all feedback
client.v1.chat.retrieve_feedback()

# Filter feedback by component
client.v1.chat.retrieve_feedback(
    component_id="comp_123abc",
    start_date="2025-01-01T00:00:00Z",
    end_date="2025-12-31T23:59:59Z",
    feedback_type="thumbsUp",
    limit="100",
    skip="0"
)

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.chat.ChatRetrieveFeedbackParams;

StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
    .fromEnv()
    .baseUrl("https://studyfetchapi.com")
    .build();

// Retrieve all feedback
client.v1().chat().retrieveFeedback();

// Filter feedback by component
ChatRetrieveFeedbackParams params = ChatRetrieveFeedbackParams.builder()
    .componentId("comp_123abc")
    .startDate("2025-01-01T00:00:00Z")
    .endDate("2025-12-31T23:59:59Z")
    .feedbackType(ChatRetrieveFeedbackParams.FeedbackType.THUMBS_UP)
    .limit("100")
    .skip("0")
    .build();

client.v1().chat().retrieveFeedback(params);

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Chat;
using StudyfetchSDK.Models.V1.Chat.ChatRetrieveFeedbackParamsProperties;
using System;
using System.Threading.Tasks;

var client = new StudyfetchSDKClient()
{
    APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
    BaseUrl = new Uri("https://studyfetchapi.com")
};

// Retrieve all feedback
await client.V1.Chat.RetrieveFeedback();

// Filter feedback by component
await client.V1.Chat.RetrieveFeedback(new ChatRetrieveFeedbackParams()
{
    ComponentID = "comp_123abc",
    StartDate = "2025-01-01T00:00:00Z",
    EndDate = "2025-12-31T23:59:59Z",
    FeedbackType = FeedbackType.ThumbsUp,
    Limit = "100",
    Skip = "0"
});

Feedback Parameters

string

Filter feedback by specific component ID

string

Filter feedback by specific user ID

string

Start date for feedback range (ISO 8601 format)

string

End date for feedback range (ISO 8601 format)

enum

Filter by feedback type:

thumbsUp - Positive feedback
thumbsDown - Negative feedback

string

default:"100"

Number of records to return

string

default:"0"

Number of records to skip (for pagination)

Retrieving Feedback Context

Get the specific message and full conversation for a feedback item to understand the context of user feedback:

import StudyfetchSDK from '@studyfetch/sdk';

const client = new StudyfetchSDK({
  apiKey: 'your-api-key',
  baseURL: 'https://studyfetchapi.com',
});

// Retrieve feedback context for a specific feedback ID
await client.v1.chat.retrieveFeedbackContext({
  feedbackId: 'feedback_123abc'
});

from studyfetch_sdk import StudyfetchSDK

client = StudyfetchSDK(
    api_key="your-api-key",
    base_url="https://studyfetchapi.com",
)

# Retrieve feedback context for a specific feedback ID
client.v1.chat.retrieve_feedback_context(
    feedback_id="feedback_123abc"
)

import com.studyfetch.javasdk.client.StudyfetchSdkClient;
import com.studyfetch.javasdk.client.okhttp.StudyfetchSdkOkHttpClient;
import com.studyfetch.javasdk.models.v1.chat.ChatRetrieveFeedbackContextParams;

StudyfetchSdkClient client = StudyfetchSdkOkHttpClient.builder()
    .fromEnv()
    .baseUrl("https://studyfetchapi.com")
    .build();

// Retrieve feedback context for a specific feedback ID
ChatRetrieveFeedbackContextParams params = ChatRetrieveFeedbackContextParams.builder()
    .feedbackId("feedback_123abc")
    .build();

client.v1().chat().retrieveFeedbackContext(params);

using StudyfetchSDK;
using StudyfetchSDK.Models.V1.Chat;
using System;
using System.Threading.Tasks;

var client = new StudyfetchSDKClient()
{
    APIKey = Environment.GetEnvironmentVariable("STUDYFETCH_API_KEY"),
    BaseUrl = new Uri("https://studyfetchapi.com")
};

// Retrieve feedback context for a specific feedback ID
await client.V1.Chat.RetrieveFeedbackContext(new ChatRetrieveFeedbackContextParams()
{
    FeedbackID = "feedback_123abc"
});

Feedback Context Parameters

string

required

The ID of the feedback item to retrieve context for

Component API

REST API

Overview

Creating a Chat Component

Configuration Parameters

Response

Adding Guardrails

Example: Academic Integrity Guardrails

Example: Safety and Content Moderation

Guardrail Actions Explained

Embedding This Component

Generate Embed URL

Chat-Specific Embedding Features

Embed in Your HTML

Streaming Chat Responses

Stream Chat Response

Stream Parameters

Stream Response Format

Example: Building a Streaming Chat Interface

Managing Chat Embed Context

Push Context

Retrieve Context

Clear Context

Complete Example: Practice Test with Context

Context API Parameters

Retrieving Chat Feedback

Feedback Parameters

Retrieving Feedback Context

Feedback Context Parameters

​Overview

​Creating a Chat Component

​Configuration Parameters

​Response

​Adding Guardrails

​Example: Academic Integrity Guardrails

​Example: Safety and Content Moderation

​Guardrail Actions Explained

​Embedding This Component

​Generate Embed URL

​Chat-Specific Embedding Features

​Embed in Your HTML

​Streaming Chat Responses

​Stream Chat Response

​Stream Parameters

​Stream Response Format

​Example: Building a Streaming Chat Interface

​Managing Chat Embed Context

​Push Context

​Retrieve Context

​Clear Context

​Complete Example: Practice Test with Context

​Context API Parameters

​Retrieving Chat Feedback

​Feedback Parameters

​Retrieving Feedback Context

​Feedback Context Parameters

Overview

Creating a Chat Component

Configuration Parameters

Response

Adding Guardrails

Example: Academic Integrity Guardrails

Example: Safety and Content Moderation

Guardrail Actions Explained

Embedding This Component

Generate Embed URL

Chat-Specific Embedding Features

Embed in Your HTML

Streaming Chat Responses

Stream Chat Response

Stream Parameters

Stream Response Format

Example: Building a Streaming Chat Interface

Managing Chat Embed Context

Push Context

Retrieve Context

Clear Context

Complete Example: Practice Test with Context

Context API Parameters

Retrieving Chat Feedback

Feedback Parameters

Retrieving Feedback Context

Feedback Context Parameters