VoltAgent's native support for Retrieval Augmented Generation (RAG) greatly enhances the information processing capabilities of intelligentsia. The framework's built-in retrieval mechanism enables the intelligentsia to obtain the most up-to-date and relevant information from an external knowledge base, rather than relying solely on the internal knowledge of the LLM. This capability is particularly suitable for scenarios where time-sensitive or specialized domain content needs to be processed.
In terms of concrete implementation, developers can build a RAG process by configuring Retriever intelligences and vector search tools. The intelligent body will first retrieve relevant knowledge fragments and then generate answers based on this information. For example, in an education domain application, the intelligent body can retrieve relevant content from a course repository in real time and provide accurate tutorial answers.
Experimental data show that the intelligentsia with the RAG paradigm improves its answering accuracy by 351 TP3T over pure LLM, while significantly reducing hallucinations.VoltAgent also optimizes the collaborative efficiency of retrieval and generation to keep the RAG latency within an acceptable range.
This answer comes from the articleVoltAgent: TypeScript Open Source Framework for Rapidly Building AI IntelligentsiaThe
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