Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making…

Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lancedb to build a structure-aware PDF QA pipeline🚀 Here’s how it works: 1. LiteParse extracts structured text and captures page screenshots📸 2. We embed the text with Gemini 2 Embedding⚙️ 3. Text, vectors, and images are stored in LanceDB🗄️ 4. A Claude agent retrieves the relevant context and, if text isn’t enough, it falls back to image-based reasoning on the screenshots🧠 In our evaluations, the agent achieved near-perfect scores across most tasks, showing how strong parsing (LiteParse) plus multimodal storage (LanceDB) can significantly improve agentic search pipelines📈 📚 Full breakdown: https://www.lancedb.com/blog/smart-parsing-meets-sharp-retrieval-combining-liteparse-and-lancedb 🦙 Learn more about LiteParse: https://developers.llamaindex.ai/liteparse/?utm_medium=li_socials&utm_source=twitter&utm_campaign=2026-apr-

Related

• How can you improve your agentic search pipeline? I just wrote a blog post with @tech_optimist from @lancedb to answer exactly that. TLDR: -…
• This is a great tutorial (credits @itsclelia + @lancedb) on how to build a practical retrieval pipeline that integrates directly with your a…
• We pit LlamaParse against frontier models (Opus 4.6, Gemini 3.1 Pro, GPT-5.4) in a live OCR arena. ICYMI: the full workshop is on Youtube! F…
• ParseBench: A Document Parsing Benchmark for AI Agents
• Trying to DIY your own document parser by screenshotting into a frontier VLM (Opus, 5.4, Gemini) carries when you try to scale it up into pr…
• LiteParse is the best document parsing library for coding agents. It's free, fast, integrates natively with the LLM's native visual understa…

Source: Jerry Liu (X) | 2026-04-07