How can you improve your agentic search pipeline? I just wrote a blog post with @tech_optimist from @lancedb to answer exactly that. TLDR: -…

How can you improve your agentic search pipeline? I just wrote a blog post with @tech_optimist from @lancedb to answer exactly that. TLDR: - Parse files and take page-level screenshots with LiteParse, the parser we just open sourced at @llama_index - Chunk and embed text, and store everything (text, image bytes, vector data) in a local LanceDB instance - Expose text and image retrieval tools to a Claude agent, and let it reason on both data types With our eval dataset, the agent got near-perfect scores on most complex QA tasks, showing how a strong parsing foundation and multimodal retrieval can really improve your search🚀 Read the full breakdown here: https://www.lancedb.com/blog/smart-parsing-meets-sharp-retrieval-combining-liteparse-and-lancedb

This is a great tutorial (credits @itsclelia + @lancedb) on how to build a practical retrieval pipeline that integrates directly with your a…
LiteParse is the best document parsing library for coding agents. It's free, fast, integrates natively with the LLM's native visual understa…
Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making…
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…
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…

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

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