DeepTutor: Detailed Introduction

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DeepTutor: Detailed Introduction

An open-source learning workspace combining reading, tutoring, practice, and progress.

• Audience: Students, independent learners, and teaching teams who want to study their own material and can configure models.
• Cost: Apache-2.0 software; models, embeddings, parsing, search, and hosting can cost separately.

Table of Contents

Getting started

1. What it is and who it is for
2. Access and setup: your first session

Usage and configuration

3. A concrete task and how to check it
4. Costs: software, models, and execution
5. Configuration and extensions

Limits and references

6. Permissions, data, and limitations
7. Frequently asked questions
8. Official references

Use the contents to jump to each section. If an in-page link does not respond, open it in a new tab.

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What it is and who it is for

In one sentence: An open-source learning workspace combining reading, tutoring, practice, and progress.

Who it suits: Students, independent learners, and teaching teams who want to study their own material and can configure models.

What you can do:

• Ask grounded questions about your study materials.
• Generate practice and track learning paths.
• Use knowledge bases, memory, and extensible tools.

Where to start: Choose an installation path from the repository or docs. Docker offers another installation path.

Cost: Apache-2.0 software; models, embeddings, parsing, search, and hosting can cost separately.

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Access and setup: your first session

Choose an installation path from the repository or docs. Docker provides a first trial:

docker run --rm --name deeptutor -p 127.0.0.1:3782:3782 -v deeptutor-data:/app/data ghcr.io/hkuds/deeptutor:latest

Open http://127.0.0.1:3782 and configure Settings → Providers. The named volume retains settings and data.

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A concrete task and how to check it

Import a short document first and inspect parsing and page references.

Example:

Explain gradient descent using only this lecture, cite the relevant material, and give three increasingly difficult questions. Wait for my answers.

Check citations against the source. Quiz scores are not a complete measure of mastery; formulas, tables, and scans need parsing checks.

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Costs: software, models, and execution

Local software is free, but knowledge workflows can call chat models, embeddings, parsers, and web search. Embeddings convert text into searchable numeric representations.

Start with minimal services and one short document. Rebuilding an index can repeat parsing and embedding costs.

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Configuration and extensions

Separate the private runtime home from the content workspace. Materials and outputs are workspace-organized, while some settings and memory remain account-level.

Configure knowledge engines, skills, MCP tools, and model roles. Switching retrieval engines can require rebuilding; retain original data and settings. Workspaces

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Permissions, data, and limitations

Retrieval does not guarantee a correct explanation. Missing pages, parsing errors, wrong answers, and excessive hints can distort learning. Verify important material against teaching sources.

Cloud models and parsers receive relevant content. Execution isolation depends on platform and configuration; the product distinguishes best-effort fallbacks.

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Frequently asked questions

Is a local GPU required? Not when using hosted models; local inference has separate requirements.

Why is the document ignored? Check parsing, indexing, workspace assignments, and retrieval settings.

Does stopping Docker lose data? The named volume persists; deleting it removes that persistence.

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Official references

• Repository
• Docs
• Setup guide
• License