chatbot-implementation
Details of the RAG Chatbot, including UI and backend logic.
Best use case
chatbot-implementation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Details of the RAG Chatbot, including UI and backend logic.
Teams using chatbot-implementation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/chatbot-implementation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How chatbot-implementation Compares
| Feature / Agent | chatbot-implementation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Details of the RAG Chatbot, including UI and backend logic.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Chatbot Logic
## Overview
A specialized RAG (Retrieval Augmented Generation) chatbot that helps users learn from the textbook content.
## Backend
- **Route**: `app/api/chat/route.ts`
- **Logic**:
1. Receives `query` and `history`.
2. Embeds query using Gemini or OpenAI embedding model.
3. Searches Qdrant (vector DB) for relevant textbook chunks.
4. Constructs context from matches.
5. Generates response using Gemini Flash/Pro.
## Vector Search (Qdrant)
We use Qdrant for storing embeddings of the textbook.
- Collection: `textbook_chunks` (or similar).
- Fields: `text`, `source`, `chunk_id`.
## UI Component
- **Location**: `textbook/src/components/Chatbot/index.tsx`.
- **Features**:
- Floating chat window.
- Size controls (Small, Medium, Large).
- Markdown rendering of responses.
- Context selection (highlight text to ask about it).
- Mobile responsive design.
- Auth awareness (personalizes answer based on user profile).
## Styling
- **CSS**: `styles.module.css` (Premium animations, shadow effects).
- **Themes**: Dark/Light mode compatible (using `--ifm` variables).Related Skills
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