An AI Agent That Interprets Papers So You Don’t Have To: Full Build Guide

Build a real-time AI research agent using Superlinked's vector search. It skips complex RAG pipelines by embedding and querying documents directly—making research faster, simpler, and smarter.

Learn how to build an AI agent for Research Paper Retrieval, Search, and Summarization

For researchers, staying updated with the latest findings is akin to finding a needle in a haystack. Imagine an AI-powered assistant that not only retrieves the most relevant papers but also summarizes key insights and answers your specific questions, all in real-time.

This article delves into constructing such an AI research agent using Superlinked's complex document embedding capabilities. By integrating semantic and temporal relevance, we eliminate the need for complex reranking, ensuring efficient and accurate retrieval of information.

TL;DR:

Build a real-time AI research agent using Superlinked's vector search. It skips complex RAG pipelines by embedding and querying documents directly ...