Build an AI Documentation Expert Chatbot with Gemini RAG

Name: Build an AI Documentation Expert Chatbot with Gemini RAG
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Author: Free N8N

Intermediate

18 nodes connected

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AIAIAutomationChatbotDocumentationGeminiKnowledge BaseLangchainRAG

This n8n workflow automates the creation of an AI-powered expert chatbot capable of answering questions based on your documentation. It intelligently ingests, cleans, and processes your knowledge base, preparing it for a Retrieval Augmented Generation (RAG) pipeline with Google Gemini.

About This Workflow

Unlock the true potential of your existing documentation by transforming it into an interactive AI expert. This n8n workflow provides a robust pipeline for building a sophisticated documentation chatbot leveraging Google's Gemini LLM and the Langchain framework for Retrieval Augmented Generation (RAG). It systematically scrapes specified documentation sites, extracts relevant content, cleans and chunks the text, and prepares it for embedding into a vector store. By automating this complex data preparation, you can ensure your chatbot has access to the most accurate and up-to-date information, delivering precise answers to user queries without hallucination. Empower your team or customers with instant access to comprehensive knowledge, reducing support load and improving overall user experience.

Key Features

Automated Documentation Scraping: Automatically fetches and processes content from specified documentation URLs, ensuring your knowledge base is always current.
Intelligent Content Cleaning & Splitting: Cleans extracted HTML, removes irrelevant elements, and splits text into optimal chunks for efficient RAG processing.
Duplicate Content Prevention: Efficiently identifies and removes duplicate links and documentation snippets, optimizing data processing and vector store integrity.
Langchain Integration for RAG: Leverages Langchain's robust tools for data loading, text splitting, and conversational memory management for advanced AI applications.
Scalable Knowledge Base Ingestion: Designed to efficiently update and expand your documentation expert's knowledge base over time with minimal manual intervention.

How To Use

Configure Documentation Source: Modify the Get All n8n Documentation Links node's URL to point to your desired documentation site.
Adjust Content Extraction: If your documentation structure is unique, adapt the Extract Documentation Content node's CSS selectors to ensure precise content capture.
Refine Text Splitting: Experiment with the Recursive Character Text Splitter parameters (e.g., chunkSize, chunkOverlap) to optimize how your documentation is broken down for embedding efficiency.
Integrate Vector Store & LLM: This workflow primarily handles content ingestion. Connect the processed output to your preferred embedding model (e.g., Gemini Embeddings) and a vector database for storage.
Build Chat Interface: Utilize the Simple Memory node and integrate a Gemini Chat node (or similar LLM) with your vector store for retrieval to complete the full chatbot interaction flow.

Apps Used

Automation

Chatbot

Documentation

Gemini

Knowledge Base

Langchain

RAG

Workflow JSON

{
  "id": "c2ad5172-715d-4c9a-ba00-f4539d4d8c54",
  "name": "Build an AI Documentation Expert Chatbot with Gemini RAG",
  "nodes": 18,
  "category": "AI",
  "status": "active",
  "version": "1.0.0"
}

Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.