Introducing Rag.pro's Domain-Specific Web Retriever: A New Era for RAG

Setting up a custom RAG pipeline is no longer necessary. With RAG.pro, we've simplified the process by introducing a new domain-specific web retriever that allows you to filter your searches to specific domains. This innovation empowers developers to focus on relevant content without the hassle of scraping, vectorizing, and maintaining their own RAG pipelines.

Our retriever is the first of its kind to allow filtering down to root domains, such as https://python.langchain.com/docs/introduction/, ensuring that all your searches are restricted to trusted and relevant sources within a specific site or domain. You can specify up to three root domains per query, tailoring results to only the web content that matters most for your application.

Why This Matters

Traditional web search APIs cast a wide net, pulling results from across the entire web. This can flood your app with irrelevant or unreliable information, forcing many developers to build RAG pipelines from scratch. With RAG.pro, that’s no longer necessary. You simply specify the domains you want to search, and the retriever does the rest—no need for additional infrastructure or data maintenance.

This capability eliminates the need for:

• Web scrapers to gather data from specific websites.
• Vector databases to store and retrieve embeddings.
• Constant re-scraping to keep data up-to-date.

High-Quality Context for LLMs

What sets RAG.pro apart from traditional retrievers is the built-in RAG system that processes the results. Instead of just feeding large chunks of text back to your app, our retriever returns high-quality context tokens. This improves the accuracy of LLM responses by ensuring that the most relevant information is passed into the model. While our approach might take a few seconds longer than instant web search APIs, the trade-off is well worth it—better performance and higher-quality responses.

Test It Out

We’ve made it easy for you to see the power of RAG.pro for yourself:

• API Documentation: https://docs.RAG.pro/documentation
• Chat UI: Test out the retriever directly on our live chat UI: https://www.docu-help.com/

Whether you're building an AI assistant, research tool, or specialized search engine, RAG.pro streamlines the process of finding the most relevant, high-quality information—without the need for a complex RAG setup.

Get started with RAG.pro today and take your LLM-powered applications to the next level with domain-specific retrieval.