RAG Article in Web with Agent
Build a RAG system in Langflow that extracts content from web articles and RSS feeds, stores it in vector databases, and provides grounded answers to questions about the content using a visual drag-and-drop interface with minimal coding required.
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This Langflow flow creates a RAG (Retrieval-Augmented Generation) system that can extract content from web articles, store it in a vector database, and answer questions about the content. The system allows you to process URLs or RSS feeds, clean and chunk the text, embed it into a vector store, then provide grounded answers based on retrieved passages. Langflow's visual interface makes building this pipeline fast and requires minimal coding, letting you connect components through drag-and-drop operations.
How it works
This Langflow flow creates a RAG system that extracts content from web articles, stores it in a vector database, and answers questions about the content. The flow consists of several connected components that work together to process URLs and respond to user queries about the extracted content.
The system uses input components like Webhook to start runs from external systems, API Request for arbitrary HTTP calls, URL for crawling pages, and RSS Reader to pull article feeds. These components output Data/DataFrame or Message objects for downstream processing steps.
For article parsing, the flow normalizes raw HTML to text or Markdown using Parser components for templated extraction, or Docling components for robust document-to-Markdown conversion when handling complex content structures.
The indexing process splits text into manageable chunks, generates embeddings using Embedding Model components or provider-specific embeddings, and writes the results to a vector store such as pgvector, Pinecone, Chroma, Astra DB, or Weaviate.
At query time, the system embeds the user question, runs similarity search on the vector store, optionally applies reranking, builds a Prompt Template that inserts retrieved context, calls a Language Model, and returns a grounded answer via Chat Output. The Vector Store RAG template separates ingestion from retrieval, making it ideal for repeatable indexing plus a chat interface.
The flow operates by taking user input containing both a URL and a question about that URL's content. The system includes guardrails to ensure content extraction succeeds and that responses are grounded in the actual document content rather than external knowledge. You can run flows via API endpoints or trigger them via webhooks, making the same pipeline work for web apps, webhooks, or embedded widgets without additional backend code.
Example use cases
• Editorial research assistants that summarize multiple articles and pull quotes with citations using the Vector Store RAG template
• Compliance and news monitoring systems that flag policy changes across sources by processing RSS feeds
• Competitive and SEO briefing tools that aggregate and compare posts from multiple URLs
• Academic and market research reviewers providing grounded Q&A over reading lists
• Customer education portals that answer how-to questions from published documentation
The flow can be extended using other Langflow nodes for enhanced functionality. You can prepend web search using SearchApi components to discover relevant URLs before ingestion, use Apify Actors for structured site-wide crawling, add NVIDIA Rerank components to improve context ordering, switch among different vector stores like pgvector depending on hosting requirements, integrate LangWatch for observability, and test iterations in the Playground environment.
What you'll do
1.
Run the workflow to process your data
2.
See how data flows through each node
3.
Review and validate the results
What you'll learn
• How to build AI workflows with Langflow
• How to process and analyze data
• How to integrate with external services
Why it matters
Build a RAG system in Langflow that extracts content from web articles and RSS feeds, stores it in vector databases, and provides grounded answers to questions about the content using a visual drag-and-drop interface with minimal coding required.
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