What is OmniSearch
OmniSearch is a multimodal retrieval-augmented generation framework launched by the Alibaba Tongyi Lab, featuring adaptive planning capabilities. OmniSearch can dynamically break down complex problems, adjust retrieval strategies based on retrieval results and problem context, simulate human behavior in solving complex problems, and improve retrieval efficiency and accuracy. OmniSearch introduces a dynamic retrieval planning framework, based on a recursive retrieval and reasoning process, gradually approaching problem solutions, and significantly improving the flexibility and effectiveness of multimodal retrieval.
Main Features of OmniSearch
- Adaptive Retrieval Planning: Plans each retrieval action in real time based on the stage of problem solving and the current retrieval content, simulating the way humans behave when solving complex problems.
- Dynamic Problem Decomposition: Decomposes complex problems into multiple sub-questions, and formulates corresponding retrieval steps and strategies for each sub-question.
- Multimodal Information Processing: Supports image retrieval, text retrieval, and cross-modal retrieval, processing multimodal information including text and images.
- Iterative Reasoning and Retrieval: Based on a recursive retrieval and reasoning process, determines whether to continue retrieval or propose new sub-questions according to the current answer status.
- Multimodal Feature Interaction: Effectively interacts with the multimodal features obtained from retrieval, and flexibly adjusts retrieval strategies according to different modal information.
Technical Principles of OmniSearch
- Planning Agent: OmniSearch's planning agent is responsible for gradually decomposing the original problem, and decides the next sub-question to solve and retrieval tool based on the feedback from each retrieval step.
- Retriever: Executes the actual retrieval operations, performs image retrieval, text retrieval, or cross-modal retrieval according to the planning agent's instructions, and returns relevant information for the planning agent to analyze.
- Sub-question Solver: Summarizes and answers the retrieved content, including any multimodal large language model, or the planning agent itself.
- Dynamic Retrieval Planning: The core innovation lies in dynamic retrieval planning, based on simulating the way humans think about problems, gradually approaching the final solution to the problem.
- Recursive Retrieval and Reasoning: Based on a recursive retrieval and reasoning process, after each sub-question is proposed and a preliminary answer is obtained, determines whether to continue retrieval according to the current answer status.
Project Address of OmniSearch
- GitHub Repository：https://github.com/Alibaba-NLP/OmniSearch
- arXiv Technical Paper：https://arxiv.org/pdf/2411.02937
- Online Experience Demo：https://modelscope.cn/studios/iic/OmniSearch/summary
Application Scenarios of OmniSearch
- Intelligent Customer Service Systems: Integrated into intelligent customer service systems to understand users' complex queries, dynamically retrieve relevant information, and provide more accurate and personalized services.
- Education and Learning Assistance: In the education field, helps students and researchers answer complex questions, and provides in-depth academic materials and explanations based on dynamic retrieval and reasoning.
- Medical Consultation and Diagnosis: In the medical field, assists doctors and patients in symptom analysis, disease diagnosis, and retrieval of treatment plans, providing the latest medical knowledge and research.
- News and Information Aggregation: Used in news aggregation platforms, based on multimodal retrieval-augmented generation, to provide users with richer and more accurate news content and background information.
- Enterprise Knowledge Management: In enterprises, it helps employees quickly retrieve internal documents, reports, and data, improving work efficiency and decision quality.