Technical Realization and Application Scenarios of Natural Language Interaction
MIRIX's Q&A engine adopts a two-tier parsing architecture: first, the intent recognition module determines the type of user query (e.g., document search, content retrieval, or process consulting), and then performs a hybrid search in the memory. For example, when a user asks for "yesterday's machine learning papers", the system first locates the time range in situational memory, then matches the keywords in semantic memory, and finally returns a structured result containing the document title, browsing duration, and storage path.
Technical indicators show that the system's query accuracy for common office scenarios reaches 92%, with time-related queries (e.g., "minutes of last week's meeting") having the highest accuracy (96%), while cross-document conceptual correlation queries (e.g., "the combination of blockchain and finance ") requires extended reasoning in conjunction with the knowledge base. For unsatisfactory results, the system provides search optimization tools that support advanced operations such as adding excluded words and adjusting time weights.
Typical use cases include: legal practitioners quickly finding reference documents for a specific case, programmers backtracking the resolution of a particular bug, academic researchers building a literature citation network, etc.
This answer comes from the articleMIRIX: A Multi-Intelligent Personal Assistant for Intelligent Tracking of Screen ActivityThe
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