传统向量检索增强生成（RAG）流程存在结构性局限。1该方法将用户问题转换为向量嵌入，通过向量数据库搜索获取文档块，随后发送给模型生成答案，但这种固定的检索-生成管道无法适应复杂的信息获取需求。1相比之下，代理式RAG（Agentic RAG）赋予AI智能体更多自主权——智能体应当分析用户任务、自主决定需要的信息类型、灵活调用检索工具、评估结果质量，并在必要时触发其他工具完成最终操作或响应。1
传统向量RAG的主要弱点包括文档分块会破坏原始结构、相似度指标无法准确反映业务相关性，以及单次检索往往不足以满足需求。1代理式方案引入了结构化提取、直接文档查询和多文档处理等替代策略。1Claix作为一个文档处理层被推出，支持将PDF、Excel、Word、图像和音频等多种格式转换为结构化的Markdown或JSON输出，并支持多文档比较和跨文档处理能力。1该系统采用了四层架构：格式感知提取、文档操作、智能体推理和业务行动。1
向量数据库仍在特定场景下保持价值——包括需要处理数百万文档片段、维护大型永久知识库、承载高查询量、面对大量用户并发查询同一数据集、需要复杂过滤和访问策略，以及存在长期索引需求的情况。1
Traditional vector retrieval-augmented generation (RAG) follows a fixed process: user questions are embedded, sent to vector databases for similarity search, and the retrieved chunks are passed to language models for answer generation 1. However, this approach has significant limitations—document chunking destroys structural integrity, similarity scores do not necessarily indicate business relevance, and a single retrieval pass is often insufficient for complex tasks 1.
Agentic RAG proposes a fundamentally different approach in which AI agents actively control when and how to retrieve external information 1. Rather than passively receiving pre-retrieved chunks, agents analyze user tasks, determine what information is needed, invoke retrieval tools strategically, evaluate results, call additional tools as necessary, and execute operations or generate responses based on the findings 1. This architecture operates across four layers: format-aware extraction, document operations, agent reasoning, and business actions 1.
The solution presented includes Claix, a document processing layer that converts PDFs, Excel files, Word documents, images, and audio into structured Markdown or JSON formats 1. Claix also supports cross-document comparison and multi-document processing capabilities 1. Traditional vector databases remain applicable in specific scenarios—managing millions of document fragments, maintaining large permanent knowledge bases, handling high query volumes, serving many users querying the same data, implementing complex filtering and access policies, and supporting long-term indexing needs 1. For many other use cases, however, direct document querying and structured extraction offer more precise and controllable alternatives to similarity-based retrieval 1.
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