LlamaParse 刚把搜索工具升级了，能同时用 grep 和向量搜索，还支持文件查找和读取。做文档检索的人可以试试这新接口。
LlamaParse 推出了四个文档检索端点：混合搜索（grep + vector search）、文件 grep（支持正则）、文件 find 和文件 read。这些端点旨在提升代理在非结构化文档上的检索质量。团队持续实验不同组合，以找到最高检索效果的方案。同时强调构建生产级检索系统需要调优分块、同步、重排序、工具 API 设计等工程细节。
We've built the following document retrieval endpoints into LlamaParse: * Hybrid search (grep + vec...
We've built the following document retrieval endpoints into LlamaParse: * Hybrid search (grep + vector search) * File grep (grep incl. regex search) * File find (`find`) * File read (`sed`) We're continuing to experiment to see what combination allows agents to get the highest retrieval quality over unstructured docs. If you have thoughts let us know! Jerry Liu @jerryjliu0 I'm glad people still understand the importance of building high-quality retrieval systems in 2026, especially as the outer models/harnesses are getting better every day. Making agentic retrieval work in production doesn't necessarily require groundbreaking new techniques around retrieval or planning. This article shows that you do need to spend engineering time tuning chunking, synchronization, reranking, tool API design, permissioning, and more. Some interesting tidbits: * Concatenating onto existing Slack threads as one contiguous chunk with heuristics for determining additional relevant context * Real-time updates for Slack * Separate chunking/updates for codebases * The discovery that hybrid search works well (not like this was a super new realization, but always good to validate and understand the specific parameters they tweaked) * Having natural guardrails on which data sources are relevant for which projects Building simple retrieval is easy, building production retrieval is hard. We had to deal with a lot of these challenges when productionizing our own Index feature within LlamaParse. 🔗 View Quoted Tweet 💬 10 🔄 2 ❤️ 5 👀 959 📊 10 ⚡