# LangChain Google Classroom Integration -- Complete Implementation Plan

Author: Nish\
Project: `langchain-google-classroom`\
Purpose: Build an open-source integration that loads Google Classroom
content into LangChain pipelines.

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# 1. Project Overview

This project implements a **Google Classroom integration for LangChain**
as a standalone open-source package.

The integration allows developers to load classroom data such as:

-   assignments
-   announcements
-   course materials
-   attachments

and convert them into **LangChain `Document` objects** that can be used
in:

-   RAG pipelines
-   semantic search
-   AI teaching assistants
-   course chatbots

The integration will be distributed as:

-   a **GitHub open-source project**
-   a **PyPI package**

Developers will install it using:

    pip install langchain-google-classroom

and use it like:

``` python
from langchain_google_classroom import GoogleClassroomLoader

loader = GoogleClassroomLoader(course_ids=["123"])
docs = loader.load()
```

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# 2. Motivation

Educational institutions widely use **Google Classroom** to store course
content.

However, developers building AI systems cannot easily access this data
for:

-   AI tutoring systems
-   course search assistants
-   automated assignment helpers
-   RAG-based learning tools

This integration solves that problem by providing a **structured data
ingestion pipeline** for LangChain.

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# 3. Key Use Cases

### AI Classroom Tutor

Students can ask questions about course content and assignments.

### Course Search Engine

Students can search across all assignments and materials.

### Assignment Explanation Tool

AI explains assignments using course materials.

### Educational RAG Pipeline

Course materials become searchable knowledge bases.

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# 4. Technology Stack

Core dependencies:

-   LangChain
-   langchain-community
-   Google Classroom API
-   Google Drive API
-   Python

Optional dependencies:

-   PDF parsers
-   Vision models
-   document processing tools

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# 5. Final System Architecture

    User Application
           |
           ▼
    GoogleClassroomLoader
           |
           ├── Authentication Manager
           ├── Classroom API Fetcher
           ├── Drive Attachment Resolver
           ├── File Type Detector
           ├── Document Parser Layer
           │      ├── PDF Parser
           │      ├── Docx Parser
           │      └── Text Parser
           │
           ├── Vision Processor (Optional)
           │
           ├── Content Normalizer
           │
           └── Document Builder
                  |
                  ▼
             List[Document]

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# 6. Development Plan

The project will be implemented in **four stages**.

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# Stage 1 -- Minimal Loader (MVP)

Goal: Build a working loader.

Features:

-   Google authentication
-   fetch courses
-   fetch assignments
-   fetch announcements
-   convert to LangChain Documents

Architecture:

    GoogleClassroomLoader
          |
          ├── Auth
          ├── Classroom API Fetcher
          └── Document Builder

Estimated code size:

\~200 lines

Estimated time:

1--2 days

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# Stage 2 -- Drive Attachments

Goal: Load files attached to assignments.

Features:

-   detect Drive attachments
-   download file streams
-   attach content to documents

Architecture:

    GoogleClassroomLoader
          |
          ├── Auth
          ├── Classroom API
          ├── Drive Resolver
          └── Document Builder

Estimated code size:

+150 lines

Estimated time:

1 day

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# Stage 3 -- File Parsing

Goal: Support multiple document types.

Supported formats:

  Format   Parser
  -------- -------------
  PDF      PDF parser
  DOCX     Docx parser
  TXT      Text reader

Architecture:

    Parser Layer
       ├── PDF Parser
       ├── DOCX Parser
       └── Text Parser

Estimated code size:

+200 lines

Estimated time:

1--2 days

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# Stage 4 -- Vision Processing (Advanced)

Goal: Extract meaning from diagrams inside PDFs.

Pipeline:

    PDF
     |
    Extract Images
     |
    Vision Model
     |
    Generate Description
     |
    Merge with Text

This improves search in RAG systems.

Estimated code size:

+150 lines

Estimated time:

2 days

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# 7. Project Folder Structure

    langchain-google-classroom
    │
    ├── langchain_google_classroom
    │   ├── loader.py
    │   ├── auth.py
    │   ├── classroom_api.py
    │   ├── drive_resolver.py
    │   ├── parsers
    │   │    ├── pdf_parser.py
    │   │    ├── docx_parser.py
    │   │    └── text_parser.py
    │   │
    │   ├── vision_processor.py
    │   ├── normalizer.py
    │   └── document_builder.py
    │
    ├── examples
    │   └── basic_usage.py
    │
    ├── tests
    │   └── test_loader.py
    │
    ├── pyproject.toml
    ├── README.md
    └── LICENSE

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# 8. Integration With LangChain

The project integrates with LangChain by returning:

    List[Document]

Each document contains:

    Document(
      page_content="assignment description",
      metadata={
        "course_id": "123",
        "course_name": "Machine Learning",
        "content_type": "assignment"
      }
    )

These documents can then be used in:

-   text splitters
-   embeddings
-   vector databases
-   RAG pipelines

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# 9. PyPI Distribution

The project will be published to PyPI.

Steps:

Build package:

    python -m build

Upload:

    twine upload dist/*

Users install it with:

    pip install langchain-google-classroom

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# 10. Example Application

Example pipeline:

    Google Classroom
          |
    GoogleClassroomLoader
          |
    LangChain Documents
          |
    Text Splitter
          |
    Embeddings
          |
    Vector Database
          |
    AI Chatbot

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# 11. Estimated Project Size

  Component          Lines
  ------------------ -------
  Loader             120
  Authentication     80
  API fetcher        120
  Drive resolver     100
  Parsers            200
  Vision processor   150
  Tests              150

Total:

\~900--1000 lines

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# 12. Expected Outcome

Final result:

-   open-source GitHub project
-   PyPI package
-   LangChain ecosystem integration
-   educational AI pipeline

Developers will be able to build:

-   AI tutors
-   course search systems
-   classroom assistants
-   assignment reasoning tools

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# 13. Conclusion

This project introduces a scalable integration between Google Classroom
and LangChain.

Key strengths:

-   modular architecture
-   multimodal document support
-   RAG-ready pipeline
-   extensible open-source design
