Build an AI Documentation Expert Chatbot with Gemini RAG
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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 Linksnode's URL to point to your desired documentation site. - Adjust Content Extraction: If your documentation structure is unique, adapt the
Extract Documentation Contentnode's CSS selectors to ensure precise content capture. - Refine Text Splitting: Experiment with the
Recursive Character Text Splitterparameters (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 Memorynode and integrate a Gemini Chat node (or similar LLM) with your vector store for retrieval to complete the full chatbot interaction flow.
Apps Used
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.
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ID: c2ad5172-715d...
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