> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Google Classroom integration > Integrate with the Google Classroom document loader using LangChain Python. > [Google Classroom](https://edu.google.com/workspace-for-education/classroom/) is a free learning management system developed by Google as part of Google Workspace for Education. It helps educators manage coursework, assignments, and communication. This page covers how to load data from Google Classroom using the `GoogleClassroomLoader`. The loader fetches courses, assignments (courseWork), announcements, course materials, student submissions, rubrics, topics, and class rosters from the [Classroom API](https://developers.google.com/classroom) and converts each item into a LangChain `Document`. When file attachments are present on classroom items, the loader automatically downloads and parses them from Google Drive—supporting PDF, DOCX, CSV, plain text, Google Docs, Sheets, Slides, and images. Learn more about the package on [GitHub](https://github.com/ayanokojix21/langchain-google-classroom). ## Overview | Class | Package | Serializable | | :---------------------- | :----------------------------------------------------------------------------------- | :----------- | | `GoogleClassroomLoader` | [`langchain-google-classroom`](https://pypi.org/project/langchain-google-classroom/) | ✅ | ### Integration details | Source | Document Lazy Loading | Async Support | | :---------------------- | :-------------------: | :-----------: | | `GoogleClassroomLoader` | ✅ | ✅ | ## Prerequisites To use this loader, you will need: 1. A [Google Cloud project](https://developers.google.com/workspace/guides/create-project) 2. The [Classroom API](https://console.cloud.google.com/apis/library/classroom.googleapis.com) enabled 3. The [Drive API](https://console.cloud.google.com/apis/library/drive.googleapis.com) enabled (for file attachments) 4. One of the following authentication methods: * **OAuth 2.0 credentials**—for personal Google accounts or testing * **Service Account credentials**—for Google Workspace domains (production) ## Installation Install the integration package: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU langchain-google-classroom ``` To enable parsing of PDF and DOCX attachments, install with the optional `parsers` extra: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU "langchain-google-classroom[parsers]" ``` ## Credentials ### Option A: OAuth 2.0 (personal accounts) 1. In the [Google Cloud Console](https://console.cloud.google.com/apis/credentials), create an **OAuth 2.0 Client ID** (Desktop application). 2. Download the client secrets file and save it as `credentials.json` in your working directory. 3. On first run, a browser window will open for user consent. The resulting token is cached to `token.json` for subsequent runs. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_google_classroom import GoogleClassroomLoader loader = GoogleClassroomLoader() # Opens browser for OAuth consent on first run docs = loader.load() ``` ### Option B: Service Account (Google Workspace) 1. In the [Google Cloud Console](https://console.cloud.google.com/iam-admin/serviceaccounts), create a Service Account. 2. Enable [Domain-Wide Delegation](https://developers.google.com/identity/protocols/oauth2/service-account#delegatingauthority) for the service account. 3. Download the key file and save it as `service_account.json`. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_google_classroom import GoogleClassroomLoader loader = GoogleClassroomLoader( service_account_file="service_account.json", ) docs = loader.load() ``` ## Instantiation ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_google_classroom import GoogleClassroomLoader loader = GoogleClassroomLoader( course_ids=["123456789"], # Optional: specific courses. Loads all if omitted. load_assignments=True, # courseWork items (default: True) load_announcements=True, # announcements (default: True) load_materials=True, # courseWorkMaterials (default: True) load_attachments=True, # resolve Drive file attachments (default: True) ) ``` ### Constructor parameters | Parameter | Type | Default | Description | | :--------------------- | :------------------------------- | :--------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `course_ids` | `list[str]` or `None` | `None` | Course IDs to load. Loads all accessible courses when `None`. | | `load_assignments` | `bool` | `True` | Load courseWork items. | | `load_announcements` | `bool` | `True` | Load announcements. | | `load_materials` | `bool` | `True` | Load courseWork materials. | | `load_attachments` | `bool` | `True` | Download and yield Drive file attachments. | | `parse_attachments` | `bool` | `True` | Parse attachment content using built-in parsers (install `langchain-google-classroom[parsers]` for PDF/DOCX support, or set `parse_attachments=False` to skip parsing). | | `file_parser_cls` | `type[BaseBlobParser]` or `None` | `None` | Custom parser class to use for attachments (replaces built-in parsers). | | `load_submissions` | `bool` | `False` | Load student submissions. Adds the required scope automatically. | | `load_topics` | `bool` | `False` | Load course topics. Adds the required scope automatically. | | `load_roster` | `bool` | `False` | Load student and teacher roster. Adds the required scope automatically. | | `load_images` | `bool` | `False` | Process image attachments (requires a `vision_model`). | | `vision_model` | `BaseChatModel` or `None` | `None` | Vision-capable LLM for image understanding in PDFs and images. | | `max_file_size` | `int` | `50000000` | Maximum attachment file size in bytes. Files larger than this are skipped. | | `service_account_file` | `str` or `None` | `None` | Path to a service-account key JSON file. | | `token_file` | `str` or `None` | `None` | Path to a cached OAuth token JSON file. | | `client_secrets_file` | `str` or `None` | `None` | Path to an OAuth client-secrets JSON file. | ## Load documents Use `load()` to fetch all documents at once: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_google_classroom import GoogleClassroomLoader loader = GoogleClassroomLoader( course_ids=["123456789"], ) docs = loader.load() print(f"Loaded {len(docs)} documents") ``` Each `Document` has structured `page_content` and rich `metadata`: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} doc = docs[0] print(doc.page_content[:200]) print(doc.metadata) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # Example metadata { "source": "google_classroom", "content_type": "assignment", "course_id": "123456789", "course_name": "Introduction to Computer Science", "title": "Week 3: Data Structures", "created_time": "2025-03-15T10:30:00.000Z", "updated_time": "2025-03-15T10:30:00.000Z", "alternate_link": "https://classroom.google.com/...", } ``` ## Lazy loading For large courses or memory-constrained environments, use `lazy_load()` to stream documents one at a time: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_google_classroom import GoogleClassroomLoader loader = GoogleClassroomLoader( course_ids=["123456789"], ) for doc in loader.lazy_load(): print(f"[{doc.metadata['content_type']}] {doc.metadata.get('title', '')}") ``` ## Async loading The loader supports async iteration via `alazy_load()`: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import asyncio from langchain_google_classroom import GoogleClassroomLoader async def main(): loader = GoogleClassroomLoader( course_ids=["123456789"], ) docs = [] async for doc in loader.alazy_load(): docs.append(doc) return docs docs = asyncio.run(main()) ``` ## Loading additional data types ### Student submissions ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} loader = GoogleClassroomLoader( course_ids=["123456789"], load_submissions=True, ) docs = loader.load() # Includes documents with content_type="submission" ``` ### Topics ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} loader = GoogleClassroomLoader( course_ids=["123456789"], load_topics=True, ) docs = loader.load() # Includes documents with content_type="topic" ``` ### Class roster (students and teachers) ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} loader = GoogleClassroomLoader( course_ids=["123456789"], load_roster=True, ) docs = loader.load() # Includes documents with content_type="student" and content_type="teacher" ``` ## File attachment parsing When `load_attachments=True` (the default), the loader resolves Google Drive file attachments on each classroom item and parses them into additional `Document` objects. ### Supported formats | Format | Parser | Notes | | :------------------------ | :------------------- | :----------------------------------------------------- | | PDF | `PDFParser` | Extracts text; optional vision LLM for embedded images | | DOCX | `DocxParser` | Extracts text and tables | | CSV | `CSVParser` | One document per row with header-aware formatting | | Plain text | `TextParser` | UTF-8 decoding | | Images (PNG, JPEG, etc.) | `ImageParser` | Requires `vision_model` and `load_images=True` | | Google Docs/Sheets/Slides | Exported then parsed | Auto-exported to PDF/CSV/plain text via Drive API | ### Using a vision LLM for images To process image attachments and extract visual context from PDF pages, provide a vision-capable model: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_google_genai import ChatGoogleGenerativeAI from langchain_google_classroom import GoogleClassroomLoader loader = GoogleClassroomLoader( course_ids=["123456789"], load_attachments=True, load_images=True, vision_model=ChatGoogleGenerativeAI(model="gemini-2.0-flash"), ) docs = loader.load() ``` ### Using a custom file parser You can replace the built-in parsers with any `BaseBlobParser` subclass: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_community.document_loaders.parsers.pdf import PyMuPDFParser from langchain_google_classroom import GoogleClassroomLoader loader = GoogleClassroomLoader( course_ids=["123456789"], file_parser_cls=PyMuPDFParser, ) docs = loader.load() ``` ## API reference * **PyPI:** [`langchain-google-classroom`](https://pypi.org/project/langchain-google-classroom/) * **Source:** [GitHub](https://github.com/ayanokojix21/langchain-google-classroom) ***
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