# Build a Textbook RAG Chatbot

Textbook chatbots need a clear connection between the chat interface, retrieval flow, and source content. This guide outlines a RAG design using an API route, embeddings, Qdrant search, and a responsive chat component.

## Install

```bash
npx skillstore add abdulsamad94/chatbot-implementation
```

## Metadata

- Status: approved
- Slug: abdulsamad94-chatbot-implementation
- Skillstore revision: r2
- Version status: missing
- Tree hash: 342997ad65c70629366bd0d6a0e12ec2af9c3ad17b46264f52e3e0c0cd45f4eb
- Author: AbdulSamad94
- GitHub username: AbdulSamad94
- License: MIT
- Repository: https://github.com/AbdulSamad94/Hackhaton-SpecsKitPlus/tree/master/.claude/skills/chatbot
- Ref: 80999bf530a7874d7bedf8ce202001ecb4c4f5e0
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands
- Quality score: 76
- Public page: https://skillstore.pages.dev/skills/abdulsamad94-chatbot-implementation
- Manifest: https://skillstore.pages.dev/api/skills/abdulsamad94-chatbot-implementation/manifest

## Capabilities

- Describe an API route that receives a user query and conversation history.
- Outline embedding-based retrieval of relevant textbook chunks from Qdrant.
- Describe building model context from retrieved matches before response generation.
- Identify a floating chatbot component with size controls and Markdown responses.
- Document contextual questions from highlighted text and responsive mobile behavior.
- Describe dark and light theme compatibility for the chatbot UI.

## Use Cases

- Plan a textbook assistant: Map the request, retrieval, context-building, and response flow for a learning chatbot.
- Design a chat interface: Use the documented UI features to plan a floating, responsive chatbot component.
- Review a RAG architecture: Check whether an existing textbook chatbot includes retrieval, source context, and interface requirements.

## Prompt Templates

### Explain the chatbot flow

```
Use this skill to explain the basic request flow for a textbook RAG chatbot.
```

### Plan Qdrant retrieval

```
Use this skill to outline how a query is embedded and matched against textbook chunks in Qdrant.
```

### Specify the chat component

```
Use this skill to create a feature checklist for a responsive textbook chatbot UI with Markdown responses and context selection.
```

### Review the full design

```
Use this skill to review my textbook chatbot architecture. Identify missing pieces across API handling, vector retrieval, response context, UI behavior, and themes.
```

## Limitations

- It is a design reference and does not include implementation code.
- It does not configure Qdrant, embeddings, model credentials, or deployment.
- It does not define retrieval quality metrics, access control, or evaluation procedures.
- It does not provide data ingestion or textbook chunking instructions.

## Best Practices

- Keep retrieved textbook content separate from conversation history when building model context.
- Show source information for retrieved content when the product requires traceable answers.
- Test the chat component on mobile screens and in both supported themes.

## Anti Patterns

- Do not treat this reference as a complete production implementation.
- Do not send unbounded conversation history or retrieval results to the response model.
- Do not personalize answers without defining appropriate user data and access controls.

## Security Audit

- Audited at: 2026-07-18T09:42:16.522\+00:00
- Summary: All seven static detections are false positives. They identify Markdown inline code spans that name routes, fields, files, and CSS variables, not executable shell or Ruby commands. No prompt injection, command execution instruction, or data-exfiltration intent was found in the reviewed skill.

## Stats

- Views: 171
- Downloads: 8
- Favorites: 0
- Popularity score: 0