rag_skill

Build RAG chatbots for documentation sites with OpenAI, Qdrant, and Neon Postgres.

Updated Nov 28, 2025

Search Tags:#documentation #fastapi #rag #openai #chatbot #qdrant #docusaurus

Ready to run

npx skills add https://github.com/bilalmk/ai-native-book --skill rag-skill

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag_skill
Source: https://github.com/bilalmk/ai-native-book/tree/main/.claude/skills/rag_skill
Command: npx skills add https://github.com/bilalmk/ai-native-book --skill rag-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, uvicorn, python-dotenv, openai, qdrant-client, psycopg2-binary, sqlalchemy, pydantic, pydantic-settings, python-multipart, markdown, beautifulsoup4, tiktoken, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of making documentation sites interactive and searchable by providing an AI-powered chatbot that can answer user questions based on the site's content.

Core Features & Use Cases

RAG Chatbot Implementation: Build and integrate production-ready Retrieval-Augmented Generation (RAG) chatbots.
Full Stack Solution: Covers backend API (FastAPI, OpenAI, Qdrant, Neon Postgres) to frontend UI integration (React/TypeScript for Docusaurus).
Use Case: Integrate a chatbot into your project's documentation to allow users to ask natural language questions about features, setup, and usage, receiving answers directly sourced from the documentation.

Quick Start

Use the rag_skill to integrate a RAG chatbot into your Docusaurus documentation site.

Frequently Asked Questions about rag_skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I integrate a RAG chatbot into Docusaurus documentation?▼

You can integrate a RAG chatbot into Docusaurus by building a FastAPI backend for document indexing and LLM responses, connecting it to Qdrant Cloud and Neon Postgres, and embedding the React UI component into your site.

What is retrieval-augmented generation for documentation sites?▼

Retrieval-augmented generation for documentation sites uses vector search to find relevant content chunks and an LLM to generate natural language answers, enabling users to query docs conversationally instead of browsing manually.

Can I use FastAPI and Qdrant Cloud to build a documentation chatbot?▼

Yes, you can use FastAPI to build the backend API and Qdrant Cloud as the vector database for storing embeddings, enabling semantic search across your documentation content to retrieve relevant context for LLM responses.

How do I generate and store OpenAI embeddings for documentation content?▼

You generate OpenAI embeddings by processing your documentation content into chunks, creating vector representations using the OpenAI API, and storing them in Qdrant Cloud for similarity-based retrieval during chatbot conversations.

What's the best way to manage conversation history in a RAG chatbot?▼

The best way to manage conversation history in a RAG chatbot is using Neon Postgres with SQLAlchemy to persist past user queries and responses, allowing the chatbot to maintain context across multi-turn conversations.

Does this RAG chatbot approach work with existing Docusaurus documentation?▼

Yes, the RAG chatbot approach works with existing Docusaurus documentation by indexing your current markdown content, generating embeddings for semantic search, and integrating a React-based chatbot component into the frontend.