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chatbot-implementation

Details of the RAG Chatbot, including UI and backend logic.

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Atualizado há 4 meses

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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:
  • Receives query and history.
  • Embeds query using Gemini or OpenAI embedding model.
  • Searches Qdrant (vector DB) for relevant textbook chunks.
  • Constructs context from matches.
  • 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).
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