CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models

TL;DR

CoSearchAgent is a Slack plugin powered by large language models that facilitates lightweight, collaborative multi-user search by understanding conversation context, querying the web, and engaging users with clarifying questions.

Contribution

It introduces a novel LLM-based collaborative search agent integrated into Slack, enabling natural multi-user interactions and web searches in a lightweight manner.

Findings

Supports multi-party conversations with context understanding

Performs web searches via APIs for relevant info

Engages users with clarifying questions

Abstract

Collaborative search supports multiple users working together to accomplish a specific search task. Research has found that designing lightweight collaborative search plugins within instant messaging platforms aligns better with users' collaborative habits. However, due to the complexity of multi-user interaction scenarios, it is challenging to implement a fully functioning lightweight collaborative search system. Therefore, previous studies on lightweight collaborative search had to rely on the Wizard of Oz paradigm. In recent years, large language models (LLMs) have been demonstrated to interact naturally with users and achieve complex information-seeking tasks through LLM-based agents. Hence, to better support the research in collaborative search, in this demo, we propose CoSearchAgent, a lightweight collaborative search agent powered by LLMs. CoSearchAgent is designed as a Slack plugin that can support collaborative search during multi-party conversations on this platform. Equipped with the capacity to understand the queries and context in multi-user conversations and the ability to search the Web for relevant information via APIs, CoSearchAgent can respond to user queries with answers grounded on the relevant search results. It can also ask clarifying questions when the information needs are unclear. The proposed CoSearchAgent is highly flexible and would be useful for supporting further research on collaborative search. The code and demo video are accessible.