Agent skill · software engineering · aiskillstore
chatbot-implementation
Details of the RAG Chatbot, including UI and backend logic.
Why this skill is useful
Provides specific implementation details for a RAG chatbot using Qdrant and Gemini, which the AI wouldn't reliably generate on its own.
What it needs
Requires qdrant installed locally. About 1k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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: textbookchunks (or similar). Fields: text, source, chunkid. 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).
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/chatbot-implementation