Retrieval-Augmented Generation
Retrieval-Augmented Generation
📖 定义
RAG is a technique that combines information retrieval with text generation. It first retrieves relevant documents from a knowledge base, then uses the retrieval results as context input to the LLM to generate answers, effectively reducing model hallucinations and providing up-to-date information.
🔗 在 Higress 中的应用
Higress AI Gateway can integrate with vector databases, supporting knowledge retrieval routing and cache optimization in RAG scenarios to improve retrieval-augmented generation efficiency.
💡 示例
- 1 Enterprise knowledge base Q&A systems use RAG to ensure answer accuracy
- 2 Customer service systems retrieve product documents through RAG to answer user questions
- 3 RAG can combine real-time data sources to provide the latest information
🔄 相关术语
❓ 常见问题
Retrieval-Augmented Generation 是什么？
RAG is a technique that combines information retrieval with text generation. It first retrieves relevant documents from a knowledge base, then uses the retrieval results as context input to the LLM to generate answers, effectively reducing model hallucinations and providing up-to-date information.
Higress 如何支持 Retrieval-Augmented Generation？
Higress AI Gateway can integrate with vector databases, supporting knowledge retrieval routing and cache optimization in RAG scenarios to improve retrieval-augmented generation efficiency.
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