Search Agents with Nandan Thakur - Weaviate Podcast #137!

About this episode

Dr. Nandan Thakur discusses the evolution of search agents and his work on Orbit, a synthetic training data pipeline for search agents.

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's shift from ColBERT-style models and sparse retrievers through RAG and into today'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'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's multi-hop queries to BrowseComp'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's contextual retrieval for entity disambiguation, context compaction to…

People in this episode

Host: Weaviate

Guest: Dr. Nandan Thakur

Topics covered

• neural retrieval
• agentic search
• synthetic training data
• multi-hop queries
• search agents
• contextual retrieval

Keywords

• search agents
• neural retrieval
• synthetic data
• multi-hop queries
• contextual retrieval
• PhD journey
• DeepSeek
• Weaviate

Mentioned in this episode

Organizations: Weaviate

Products: Orbit, DeepSeek, BrowseComp, Weaviate's Engram

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