Technical implementation of retrieval enhancement generation
TaskingAI's RAG system utilizes industry-leading vector retrieval technology that allows developers to create domain-specific collections of knowledge and build efficient indexes through text chunking and vectorization. The system supports content chunking by token or character count and allows for overlapping regions to ensure semantic coherence.
In practical applications, the program significantly improves the accuracy and professionalism of AI output. Taking the legal counseling scenario as an example, developers can import regulatory provisions into the RAG system, and when the user asks a question, the system will first retrieve the relevant legal provisions, and then allow the model to generate an answer based on the retrieval results, which is more reliable than relying solely on the model's memory. The platform also provides a complete API management interface to realize the operation of adding, deleting, changing and checking the knowledge base.
This answer comes from the articleTaskingAI: An Open Source Platform for Developing AI Native ApplicationsThe
English 
简体中文 
日本語 
Deutsch 
Português do Brasil