@llama_index
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://t.co/k3swCwPmme 🦙 Learn more about LiteParse: https://t.co/lHZWj9hhl1