Langroid's built-in DocChatAgent automates the entire RAG process: from document loading (supporting PDF/Word/URL, etc.), text chunking, vector embedding generation, to storage in a variety of vector databases such as Qdrant. When a user asks a question, the system first retrieves the relevant document fragments and then passes them to LLM to generate evidence-based answers. The design avoids the patchwork development of traditional RAG systems, and developers only need to configure the document path to quickly build an intelligent Q&A system for specialized fields, especially suitable for legal, medical, and other scenarios that need to accurately refer to the knowledge base.
This answer comes from the articleLangroid: Easily Navigating Large Language Models with Multi-Intelligent Body ProgrammingThe
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