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EricNGOntos merged 1 commit into from
May 14, 2026
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docs: user refined TIP section wording #75

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5 changes: 1 addition & 4 deletions README.md
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Expand Up @@ -36,7 +36,7 @@
Knowhere turns unstructured documents into persistent, navigable memory for AI agents. It handles parsing, hierarchy extraction, multi-modal structuring, and graph construction, giving your agents structured, high-quality context for *Agentic RAG*, *traditional RAG*, or any LLM workflow.

> [!TIP]
> Knowhere builds on top of well-known parsers like MinerU, Docling, and Marker, and actively improves upon them with **memory-oriented parsing optimizations** and upcoming **agentic parsing**. On top of that, it **reconstructs hierarchical structure**, **normalizes multi-modal assets**, **builds cross-document graphs**, and **persists everything as long-term, citable memory** that agents can search, navigate, and reason over.
> Knowhere stands on the shoulders of giants like MinerU and Pymupdf. We take their output, optimize it, and then build **hierarchical structure** and **multi-modal cross-document graphs** on top. The result is a persistent, citable memory layer purpose-built for agent consumption.

> [!NOTE]
> **Get started in seconds with Knowhere Cloud.**
Expand All @@ -49,9 +49,6 @@ Knowhere turns unstructured documents into persistent, navigable memory for AI a

## How it Works

> [!TIP]
> **TL;DR**: Knowhere builds navigable memory from messy documents, then lets agents retrieve and cite evidence from that memory.

Knowhere turns raw documents into a structured memory store that AI agents can navigate and cite. The process follows two steps:

### Step 1: Parse and Build Memory
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