Search Agents with Nandan Thakur - Weaviate Podcast #137!

<p>Dr. Nandan Thakur returns to the Weaviate Podcast fresh off defending his dissertation to discuss the evolution from neural retrieval to agentic search and his new work on Orbit, a synthetic training data pipeline for search agents. The conversation opens with reflections on his PhD journey, tracing the field&#39;s shift from ColBERT-style models and sparse retrievers through RAG and into today&#39;s agentic search paradigm where LLMs iteratively search, reason, and refine.The discussion dives deep into how Orbit generates multi-hop, riddle-style training queries using DeepSeek&#39;s API on a personal laptop over four to six months, making high-quality search agent training data accessible without massive compute budgets. Thakur draws a sharp distinction between deep research (broad, multi-tool report generation) and search agents (focused on search and browse tools to answer specific questions), then connects Orbit&#39;s multi-hop queries to BrowseComp&#39;s filter-style riddles where each clue narrows the answer space like a funnel. The conversation explores the design of deep research harnesses, chunking strategies, Anthropic&#39;s contextual retrieval for entity disambiguation, context compaction to manage bloated agent contexts, and memory services like Weaviate&#39;s Engram for compressing search results between reasoning rounds.From there, the episode tackles sequential versus parallel search trajectories, the pass@K approach to rollouts in GRPO training, and whether isolated trajectories should share progress through message passing. Thakur makes a compelling case for training search agents to produce keyword-focused queries optimized for BM25 versus semantic queries for dense retrieval: the idea that one query does not fit all search engines. The conversation closes on future directions: efficiency-focused Pareto frontiers for search agents, long-form report generation evaluation through TREC RAG, and the coming wave of multilingual and multimodal search benchmarks.</p>