LlamaIndex 和 LanceDB 教你怎么用 LiteParse 搞定那些排版混乱的企业 PDF，表格、图片里的信息也能准确被 agent 找到。
LlamaIndex 联合 LanceDB 推出基于 LiteParse 的混合检索方案，专门应对企业级格式混乱的 PDF。LiteParse 将 PDF 分解为页面（含文本+截图+嵌入）、文本块和提取的视觉资产三个层次，存入 LanceDB 的多模态存储中。相比传统仅依赖文本块检索的 RAG 系统，该管道允许智能体跨页面、文本块和视觉资产进行联合检索与推理。最终显著提升了 agent 检索质量与响应准确性，解锁了传统方法遗漏的信息。
Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often di...
Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often different, consisting of messy PDFs where critical information is buried across tables, figures, and complex layouts. That’s why we teamed up with @lancedb to explore how LiteParse (our lightning-fast parser) combined with LanceDB’s native multimodal storage can improve retrieval quality and agent response accuracy. By separating PDFs into multiple information layers - pages (text + screenshots + embeddings), chunks, and extracted assets - and storing them in LanceDB for fast multimodal retrieval, we built a hybrid pipeline that unlocks information traditional RAG systems often miss. Instead of relying on chunk-level retrieval alone, agents can retrieve and reason across pages, chunks, and visual assets, making complex enterprise PDFs far more accessible. The result is a significantly stronger retrieval foundation for agentic workflows. 📖 Read the full breakdown in the blog post: lancedb.com/blog/from-mess… h 💻 Explore the full implementation on GitHub github.com/lancedb/litepa… Rx 💬 2 🔄 5 ❤️ 21 👀 2509 📊 6 ⚡