Vexa achieves intelligent knowledge extraction through RAG (Retrieval Augmented Generation) technology, which works and has the following practical value:
technical realization
- Text Preprocessing: The transcribed text is first cleaned and normalized.
- semantic analysis: Identify key entities (e.g., names, times, decision items) and contextual relationships.
- vectorized storage: Convert information into a retrievable vector format for deposit in the knowledge base.
- intelligent retrieval: When a user queries, the system returns the associated content in combination with semantic similarity.
applied value
- Meeting Efficiency Improvement: Automatically generate meeting minutes, extract action items, and reduce 80% manual organizing time.
- fig. repository of knowledge (e.g. scientific knowledge): Transform discrete discussions into searchable structured knowledge, for example:
- Product Requirements → Functional Knowledge Graph
- Customer feedback → service improvement points
- Technical Discussion → Solution Library
- Intelligent Assistance::
- New members get a quick overview of the background of the program through historical conference knowledge
- Solution for customer service staff to retrieve similar cases instantly
- Researchers automatically generate content analysis reports for interviews
This feature supports accessing knowledge entries through the management interface (http://localhost:8057) or API and exporting to JSON/CSV format for further analysis.
This answer comes from the articleVexa: a real-time meeting transcription and intelligent knowledge extraction toolThe
May not be reproduced without permission:AI productivity tools " How does Vexa's knowledge extraction feature work? What practical value can it bring?
