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. It is especially useful for teams working in multi. Details of the RAG Chatbot, including UI and backend logic.
Details of the RAG Chatbot, including UI and backend logic.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "chatbot-implementation" skill to help with this workflow task. Context: Details of the RAG Chatbot, including UI and backend logic.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
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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