educhain 0.3.13

A Python package for generating educational content using Generative AI

Educhain 🎓🔗

Website | Documentation

Educhain is a powerful Python package that leverages Generative AI to create engaging and personalized educational content. From generating multiple-choice questions to crafting comprehensive lesson plans, Educhain makes it easy to apply AI in various educational scenarios.

🚀 Features

📝 Generate Multiple Choice Questions (MCQs)

from educhain import Educhain

client = Educhain()

# Basic MCQ generation
mcq = client.qna_engine.generate_questions(
    topic="Solar System",
    num=3,
    question_type="Multiple Choice"
)

# Advanced MCQ with custom parameters
advanced_mcq = client.qna_engine.generate_questions(
    topic="Solar System",
    num=3,
    question_type="Multiple Choice",
    difficulty_level="Hard",
    custom_instructions="Include recent discoveries"
)

print(mcq.model_dump_json())  # View in JSON format , For Dictionary format use mcq.model_dump()

📊 Create Lesson Plans

from educhain import Educhain

client = Educhain()

# Basic lesson plan
lesson = client.content_engine.generate_lesson_plan(
    topic="Photosynthesis"
)

# Advanced lesson plan with specific parameters
detailed_lesson = client.content_engine.generate_lesson_plan(
    topic="Photosynthesis",
    duration="60 minutes",
    grade_level="High School",
    learning_objectives=["Understanding the process", "Identifying key components"]
)

print(lesson.model_dump_json())  # View in JSON format , For Dictionary format use lesson.model_dump()

🔄 Support for Various LLM Models

from educhain import Educhain, LLMConfig
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import ChatOpenAI

# Using Gemini
gemini_model = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash",
    google_api_key="YOUR_GOOGLE_API_KEY"
)
gemini_config = LLMConfig(custom_model=gemini_model)
gemini_client = Educhain(gemini_config)

# Using GPT-4
gpt4_model = ChatOpenAI(
    model_name="gpt-4.1",
    openai_api_key="YOUR_OPENAI_API_KEY"
)
gpt4_config = LLMConfig(custom_model=gpt4_model)
gpt4_client = Educhain(gpt4_config)

📁 Export Questions to Different Formats

from educhain import Educhain

client = Educhain()
questions = client.qna_engine.generate_questions(topic="Climate Change", num=5)

# Export to JSON
questions.json("climate_questions.json")

# Export to PDF
questions.to_pdf("climate_questions.pdf")

# Export to CSV
questions.to_csv("climate_questions.csv")

🎨 Customizable Prompt Templates

from educhain import Educhain

client = Educhain()

# Custom template for questions
custom_template = """
Generate {num} {question_type} questions about {topic}.
Ensure the questions are:
- At {difficulty_level} level
- Focus on {learning_objective}
- Include practical examples
- {custom_instructions}
"""

questions = client.qna_engine.generate_questions(
    topic="Machine Learning",
    num=3,
    question_type="Multiple Choice",
    difficulty_level="Intermediate",
    learning_objective="Understanding Neural Networks",
    custom_instructions="Include recent developments",
    prompt_template=custom_template
)

📚 Generate Questions from Files

from educhain import Educhain

client = Educhain()

# From URL
url_questions = client.qna_engine.generate_questions_from_data(
    source="https://example.com/article",
    source_type="url",
    num=3
)

# From PDF
pdf_questions = client.qna_engine.generate_questions_from_data(
    source="path/to/document.pdf",
    source_type="pdf",
    num=3
)

# From Text File
text_questions = client.qna_engine.generate_questions_from_data(
    source="path/to/content.txt",
    source_type="text",
    num=3
)

📹 Generate Questions from YouTube Videos

from educhain import Educhain

client = Educhain()

# Basic usage - Generate 3 MCQs from a YouTube video
questions = client.qna_engine.generate_questions_from_youtube(
    url="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    num=3
)
print(questions.model_dump_json())

# Generate questions preserving original language
preserved_questions = client.qna_engine.generate_questions_from_youtube(
    url="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    num=2,
    target_language='hi',
    preserve_original_language=True  # Keeps original language
)

🥽 Generate Questions from Images

from educhain import Educhain

client = Educhain() #Default is 4o-mini (make sure to use a multimodal LLM!)

question = client.qna_engine.solve_doubt(
    image_source="path-to-your-image",
    prompt="Explain the diagram in detail",
    detail_level = "High" 
    )

print(question)

🥽 Generate Visual Questions

from langchain_google_genai import ChatGoogleGenerativeAI
from educhain import Educhain, LLMConfig

gemini_flash = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=GOOGLE_API_KEY)

flash_config = LLMConfig(custom_model=gemini_flash)

client = Educhain(flash_config)

ques = client.qna_engine.generate_visual_questions(
        topic="GMAT Statistics", num=10 )

print(ques.model_dump_json())

📈 Workflow

Reimagining Education with AI 🤖

• 📜 QnA Engine: Generates an infinte variety of Questions
• 📰 Content Engine: One-stop content generation - lesson plans, flashcards, notes etc
• 📌 Personalization Engine: Adapts to your individual level of understanding for a tailored experience.

🛠 Installation

pip install educhain

🎮 Usage

📚 Starter Guide

Quick Start

Get started with content generation in < 3 lines!

from educhain import Educhain

client = Educhain()

ques = client.qna_engine.generate_questions(topic="Newton's Law of Motion",
                                            num=5)
print(ques.model_dump_json())
ques.model_dump_json() # ques.model_dump()

Supports Different Question Types

Generates different types of questions. See the advanced guide to create a custom question type.

# Supports "Multiple Choice" (default); "True/False"; "Fill in the Blank"; "Short Answer"

from educhain import Educhain

client = Educhain()

ques = client.qna_engine.generate_questions(topic = "Psychology", 
                                            num = 10,
                                            question_type="Fill in the Blank"
                                            custom_instructions = "Only basic questions")

print(ques.model_dump_json())
ques.model_dump_json() #ques.model_dump()

Use Different LLM Models

To use a custom model, you can pass a model configuration through the LLMConfig class

Here's an example using the Gemini Model

from langchain_google_genai import ChatGoogleGenerativeAI
from educhain import Educhain, LLMConfig

gemini_flash = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash",
    google_api_key="GOOGLE_API_KEY")

flash_config = LLMConfig(custom_model=gemini_flash)

client = Educhain(flash_config) #using gemini model with educhain

ques = client.qna_engine.generate_questions(topic="Psychology",
                                            num=10)

print(ques.model_dump_json())
ques.model_dump_json() #ques.model_dump()

Customizable Prompt Templates

Configure your prompt templates for more control over input parameters and output quality.

from educhain import Educhain

client = Educhain()

custom_template = """
Generate {num} multiple-choice question (MCQ) based on the given topic and level.
Provide the question, four answer options, and the correct answer.
Topic: {topic}
Learning Objective: {learning_objective}
Difficulty Level: {difficulty_level}
"""

ques = client.qna_engine.generate_questions(
    topic="Python Programming",
    num=2,
    learning_objective="Usage of Python classes",
    difficulty_level="Hard",
    prompt_template=custom_template,
)

print(ques.model_dump_json())

Generate Questions from Data Sources

Ingest your own data to create content. Currently supports URL/PDF/TXT.

from educhain import Educhain
client = Educhain()

ques = client.qna_engine.generate_questions_from_data(
    source="https://en.wikipedia.org/wiki/Big_Mac_Index",
    source_type="url",
    num=5)

print(ques.model_dump_json())
ques.model_dump_json() # ques.model_dump()

Generate Lesson Plans

Create interactive and detailed lesson plans.

from educhain import Educhain

client = Educhain()

plan = client.content_engine.generate_lesson_plan(
                              topic = "Newton's Law of Motion")

print(plan.model_dump_json())
plan.model_dump_json()  # plan.model_dump()

📊 Supported Question Types

• Multiple Choice Questions (MCQ)
• Short Answer Questions
• True/False Questions
• Fill in the Blank Questions

🔧 Troubleshooting

Common Issues and Solutions

API Key Authentication Errors

Error: Authentication failed. Please check your API key.

Solution: Verify that your API key is correct and properly set. For OpenAI or Google API keys, ensure they are active and have sufficient quota remaining.

# Correct way to set API keys
import os
os.environ["OPENAI_API_KEY"] = "your-api-key-here"
# or
os.environ["GOOGLE_API_KEY"] = "your-api-key-here"

Model Not Generating Expected Output

Issue: The model generates content that doesn't match your expectations or requirements.

Solution: Try adjusting the parameters or providing more specific instructions:

# Be more specific with your requirements
questions = client.qna_engine.generate_questions(
    topic="Python Programming",
    num=3,
    difficulty_level="Intermediate",
    custom_instructions="Focus on object-oriented programming concepts. Include code examples in each question."
)

Package Import Errors

ModuleNotFoundError: No module named 'educhain'

Solution: Ensure you've installed the package correctly:

pip install educhain --upgrade

If you're using a virtual environment, make sure it's activated before installing.

Memory Issues with Large Outputs

Issue: Generating a large number of questions causes memory errors.

Solution: Generate questions in smaller batches:

# Instead of generating 50 questions at once
all_questions = []
for i in range(5):
    batch = client.qna_engine.generate_questions(
        topic="History",
        num=10
    )
    all_questions.extend(batch.questions)

🗺 Roadmap

• Bulk Generation
• Outputs in JSON format
• Custom Prompt Templates
• Custom Response Models using Pydantic
• Exports questions to JSON/PDF/CSV
• Support for other LLM models
• Generate questions from text/PDF file
• Integration with popular Learning Management Systems
• Mobile app for on-the-go content generation

🤝 Open Source Contributions Welcome!

We invite you to help enhance our library. If you have any ideas, improvements, or suggestions for enhancements to contribute, please open a GitHub issue or submit a pull request. Be sure to adhere to our existing project structure and include a detailed README.md for any new Contribution.

Thank you for your continued support, community!

📈 Version History

v1.2.0 (May 2025)

• ✨ Added support for generating visual questions with multimodal LLMs
• ✨ Added support for generating questions from YouTube videos
• ✨ Added support for generating questions from images
• 🐛 Fixed issue with PDF parsing for certain file formats
• ⚡️ Improved performance for large document processing

v1.1.0 (February 2025)

• ✨ Added support for custom prompt templates
• ✨ Added export functionality to PDF, CSV, and JSON
• 🔄 Enhanced compatibility with Gemini models
• 📚 Expanded documentation with more examples

v1.0.0 (December 2024)

• 🚀 Initial release
• ✅ Core question generation functionality
• ✅ Support for multiple question types
• ✅ Basic lesson plan generation
• ✅ Integration with OpenAI models

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📬 Connect With Us

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