Build your own RAG without code

2026-01-19 · Source: Abhishek Thakur · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, medium

Summary

The content introduces a no-code method for building a Retrieval-Augmented Generation (RAG) system using the NAG UI, which runs locally or on Vespa Cloud. Users can install NAG via `uv pip install NAG` or `pip install NAG` and launch the UI on port 8000. The system supports two primary templates: "web" for crawling websites and "doc" for ingesting local documents like PDFs or Markdown files, converting them to Markdown internally. Key configurations include specifying the start URL for web crawling, excluding URLs, setting RAG parameters like transformer models, chunk size, overlap, and distance metrics, and configuring LLM APIs (e.g., OpenAI GPT 5.2, OpenRouter, LM Studio). The process involves starting a local Docker daemon for data storage in Vespa, crawling the specified sources, creating chunks, calculating embeddings, and then querying the indexed data to generate grounded answers without hallucination.

Key takeaway

For AI Engineers or ML practitioners looking to quickly prototype or deploy RAG systems without extensive coding, the NAG UI offers a streamlined, no-code solution. You should consider using the local mode for initial development or smaller datasets, ensuring Docker is installed and running. For scalable, persistent storage, explore the cloud deployment option. This approach allows for rapid experimentation with different data sources and LLM configurations.

Key insights

NAG UI enables no-code RAG system creation, supporting web crawling or document ingestion with local or cloud deployment.

Principles

• RAG systems ground LLM responses in provided context.
• Local deployment is suitable for smaller datasets.
• Docker is a prerequisite for local data processing.

Method

Install NAG, launch UI, select web/doc template, configure crawling/ingestion parameters, set RAG and LLM parameters, ensure Docker is running, start crawling, and then query the indexed data.

In practice

• Use `uv pip install NAG` for quick setup.
• Configure `exclude URLs` to refine web crawls.
• Adjust chunk size and overlap for embedding quality.

Topics

• Retrieval-Augmented Generation
• No-Code AI Development
• Vespa Database
• LLM Integration
• Web Crawling

Best for: Machine Learning Engineer, AI Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Abhishek Thakur.