# Grep, Embeddings, or Both?

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LlamaIndex Webinar 2026-06

Hi Llama Enthusiasts!

You have the right model, but do you have the right context? For most teams the answer is no — and the reason is in knowledge harness and retrieval. Teams are split: some threw out their vector databases entirely, grep and file skills only. Others bet everything on embeddings. So which side is right?

Turns out — both are. Semantic search gives agents a fast first pass over large corpora. Grep and file reads give them the precision to verify, dig deeper, and recover when the top-k chunks cut off mid-answer. But stitching grep and semantic search into a single harness is harder than it looks at scale: server-side search across multi-tenant document corpora, index freshness, permission boundaries, and complex file formats that agents can actually navigate (with text, layout, metadata, and page screenshots) rather than hallucinate through.

We built this harness into LlamaParse Index: semantic search, server-side grep, and file-level navigation in one reasoning loop. Join our Head of Engineering, George He for a look under the hood — the architecture decisions, the dead ends, and a live demo of an agent reasoning across multiple indexes on a real enterprise task. Join the live discussion on the learnings and ask questions in our webinar next week:

Register Now

What we'll cover:

Why the grep-vs-embeddings debate is a false binary: what benchmarking both approaches taught us, and why the answer changes with corpus size

The harness, end to end: grep, directory listings, and direct file reads as first-class agent tools, composed with hybrid search and reranking; plus multimodal file objects that give agents visual context for tables where text extraction fails

Live demo: an agent navigating multiple indexes — search, grep, read, verify, answer

If you're an AI engineer or technical founder building agents, join our next webinar with live Q&A session on Tuesday, June 30th. RSVP to save your seat.

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Thank you for being a part of the LlamaIndex community. We're here to help you unlock the power of document context for your AI agents!

Best regards,

The LlamaIndex Team

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LlamaIndex, 405 Howard Street, San Francisco, California 94105, United States

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