Build AI Knowledge Base & Q&A from Web Content Automatically
Transform raw web content into an instantly searchable AI knowledge base, reducing research time by up to 90% and improving answer accuracy.
Manually curating and searching through extensive online content for specific answers is a time-consuming and error-prone process. This workflow automates the ingestion of web articles into a Milvus vector store, enabling an AI-powered Q&A system to deliver instant, context-aware responses.
Documentation
Build an AI-Powered Q&A System from Any Web Content
Effortlessly transform raw web content into a dynamic, searchable knowledge base. This powerful n8n workflow benefits researchers, content managers, and anyone needing quick, accurate answers from vast online resources by automating data ingestion and AI-driven Q&A.
Key Features
- Automated Web Scraping: Easily extract article links and content from any specified URL.
- Intelligent Text Processing: Cleans and chunks raw HTML text for optimal AI comprehension.
- OpenAI Embeddings: Converts text into semantic vectors for advanced similarity search.
- Milvus Vector Store Integration: Stores and indexes content for lightning-fast retrieval.
- AI-Powered Q&A: Leverage LangChain and OpenAI to answer questions contextually from your custom knowledge base.
- Chat Interface: Interact with your knowledge base via a simple chat trigger.
How It Works
This workflow operates in two main phases: Knowledge Base Ingestion and AI-Powered Q&A. The ingestion phase begins with a manual trigger that fetches a list of articles from a target website. It then sequentially scrapes each article's content, extracts only the relevant text, and splits it into smaller, manageable chunks. OpenAI generates vector embeddings for these text chunks, which are then stored in a Milvus vector database, forming your searchable knowledge base. The Q&A phase is activated by a chat message. Upon receiving a query, the workflow generates an embedding for it, searches the Milvus vector store for semantically similar content, and uses an OpenAI chat model (orchestrated by a LangChain Q&A chain) to synthesize a precise, context-aware answer based on the retrieved information.