MindSearch provides a modular Retrieval-Augmented Generation architecture designed to enhance large language models with real-time knowledge access. By connecting to various data sources including local file systems, document stores, and cloud-based vector databases, MindSearch indexes and embeds documents using configurable embedding models. During runtime, it retrieves the most relevant context, re-ranks results using customizable scoring functions, and composes a comprehensive prompt for LLMs to generate accurate responses. It also supports caching, multi-modal data types, and pipelines combining multiple retrievers. MindSearch’s flexible API allows developers to tinker with embedding parameters, retrieval strategies, chunking methods, and prompt templates. Whether building conversational AI assistants, question-answering systems, or domain-specific chatbots, MindSearch simplifies the integration of external knowledge into LLM-driven applications.

Mosaic AI Agent Framework combines sophisticated retrieval techniques with generative AI to provide users with the power to access and generate content based on a rich set of data. It enhances an AI application's ability to not only generate text but also to factor in relevant data retrieved from various sources, offering improved accuracy and context in outputs. This technology facilitates more intelligent interactions and empowers developers to build AI solutions that are not only creative but backed by comprehensive data.