SlideRAG: PPT-Centric Multimodal RAG for Study Preview & Exam Review

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🆕 Updates

• [04/2026] 🔥 SlideRAG is now open-source.

🚀 SlideRAG

🎓 SlideRAG is an end-to-end assistant for understanding PPT/PPTX files as multimodal learning materials.

🧠 Unlike text-only QA systems, SlideRAG treats each slide as a structured multimodal unit and combines parsing, retrieval, and agent tool-calling.

📌 It is designed for two key learning scenarios: before-class preview and before-exam review.

🎬 Demo Showcase

This video presents SlideRAG from a product perspective and walks through the full user flow: upload slides, inspect parsing results, and run multi-turn QA over PPT content.

283d7dc3010f5cc719306119e71a9e2e.mp4

Here is an example that demonstrates the performance of SlideRAG in understanding the content and structure of multimodal slides:

QA case snapshots:

✨ Key Features

• 🖼️ PPT-first multimodal RAG pipeline: Uses a unified multimodal parser and a graph-and-vector hybrid retrieval engine to support grounded QA across text, images, tables, and equations.
• 🪄 Hidden-information expansion for concise slides: Detects high-compression pages and expands implicit content into grounded explanatory text.
• 🔗 Page-topic extraction and structural linking: Extracts per-page topics and links related slides to model section-level continuity in long decks.
• 🤝 Easy to use: One backend supports Web, QQ, and WeChat, making the assistant accessible in familiar study workflows.

🧩 Framework

SlideRAG follows a retrieval-augmented agent workflow: 1. Parse PPT/PPTX into typed multimodal items with page metadata. 2. Perform PPT-oriented enhancement (hidden-info expansion + topic extraction/linking). 3. Build unified multimodal knowledge storage for hybrid retrieval. 4. Use tool-calling agent loop to retrieve evidence and trigger optional image understanding. 5. Return grounded answers through Web/QQ/WeChat channels.

🚀 Quick Start

This section helps you run SlideRAG quickly for web usage, then optionally connect it to QQ or WeChat.

1. Clone and install

git clone https://github.com/Hitlh/SlideRAG.git
cd SlideRAG

# Core dependencies
pip install -e .

# Optional channel dependencies
pip install -e .[qq]
pip install -e .[weixin]
pip install -e .[channels]

Because this project focuses on PPT understanding, install LibreOffice as an extra system dependency:

• Ubuntu/Debian: sudo apt-get install libreoffice
• Windows: download installer from the official website: https://www.libreoffice.org/
• macOS: brew install --cask libreoffice

2. Configure environment variables

Create a .env file by copying content from env.project.example, then fill in your keys and model settings.

Note: If you want to tune advanced parser/context behavior (for example, SUMMARY_LANGUAGE, hidden-expansion options, and context window settings), edit the Advanced parser/context options (optional) section in env.project.example.

Tip for users in mainland China: MinerU uses Hugging Face by default. If Hugging Face access is unstable, switch to ModelScope before running:

export MINERU_MODEL_SOURCE=modelscope

2.1 API keys and base models

OPENAI_API_KEY=your_openai_api_key
OPENAI_BASE_URL=your_base_url

# Text and vision models used by SlideRAG pipeline
TEXT_LLM_MODEL=gpt-5.4
VLM_MODEL=gpt-5.4

2.2 Agent model provider

# Agent provider for rag_agent loop: openai | anthropic
AGENT_PROVIDER=openai
AGENT_MODEL=gpt-5.4

# Anthropic provider settings (only required when AGENT_PROVIDER=anthropic)
# If empty, runtime may fall back to OPENAI_API_KEY / OPENAI_BASE_URL.
ANTHROPIC_API_KEY=
ANTHROPIC_BASE_URL=

3. Start the web app

streamlit run client/app.py

After startup, open the Streamlit URL shown in terminal and start asking questions about your PPT files.

Chat App Integration

SlideRAG can run the same QA agent through QQ and WeChat.

QQ setup (requires QQ extras)

1. Create a QQ bot.
2. Go to the QQ Open Platform (https://q.qq.com/#/), sign in, and create your bot.
3. In your bot console, open "开发控制" and copy APPID and APPSecret.
4. Go to "沙箱配置", then in "在消息列表配置" choose "添加成员", enter your QQ number, and scan the QR code.
5. Put the files you want to chat with in a folder (default: ./uploaded_docs).
6. Configure QQ environment variables:

QQ_ENABLED=false                    # Set to true to enable QQ integration
QQ_APP_ID=                          # QQ bot APPID from the Open Platform
QQ_SECRET=                          # QQ bot APPSecret from the Open Platform
QQ_ALLOW_FROM=*                     # Allowed senders; use * to accept all
QQ_TARGET_FILE=                     # Default file to chat with; can switch via /file <filename>
QQ_UPLOADED_DOCS_DIR=./uploaded_docs # Directory that stores your source files

# Startup ready notification
QQ_STARTUP_NOTIFY_ENABLED=true      # Send a startup message when agent is ready
QQ_STARTUP_NOTIFY_MESSAGE=rag agent is ready. # Startup message content
QQ_STARTUP_NOTIFY_CHAT_ID=          # Target chat/user ID for startup notification

7. Run QQ backend:

python3 client/qq_runtime.py
# or
sliderag-qq

8. Start chatting. You can switch the active document with: /file <filename>.

WeChat setup (requires WeChat extras)

1. Put the files you want to chat with in a folder (default: ./uploaded_docs).
2. Configure WeChat environment variables:

WEIXIN_ENABLED=true                # Set to true to enable WeChat integration
WEIXIN_ALLOW_FROM=*                # Allowed senders; use * to accept all
WEIXIN_TARGET_FILE=                # Default file to chat with; can switch via /file <filename>
WEIXIN_UPLOADED_DOCS_DIR=./uploaded_docs # Directory that stores your source files
WEIXIN_STARTUP_NOTIFY_ENABLED=true # Send a startup message when agent is ready
WEIXIN_STARTUP_NOTIFY_MESSAGE=agent is ready. # Startup message content
WEIXIN_STARTUP_NOTIFY_CHAT_ID=     # Target chat/user ID for startup notification

3. Run WeChat backend:

python3 client/weixin_runtime.py
# use -r to force re-login
python3 client/weixin_runtime.py -r

4. Scan QR code on first login.
5. Start chatting. You can switch the active document with: /file <filename>.

How to set ALLOW_FROM and STARTUP_NOTIFY_CHAT_ID

After you send one message in QQ/WeChat, check runtime logs for a line like:

Inbound message: chat_id=..., sender_id=...

Use the sender_id value for your allowlist and startup notification target.

🔗 Related Projects

Project	Description	Link
RAG-Anything	All-in-One RAG Framework	GitHub
nanobot	Ultra-Lightweight Personal AI Assistant	GitHub

🤗 Citation

If you find this project useful, please cite:

@software{sliderag2026,
  title={SlideRAG: PPT-Centric Multimodal RAG for Study Preview and Exam Review},
  author={He Liu,Jiahao Zhang},
  year={2026},
  url={https://github.com/Hitlh/SlideRAG}
}