knowhere

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Overview

Knowhere is the memory layer between complex, dirty documents and AI agents.

It ingests unstructured documents and produces persistent, navigable memory: parsing, hierarchy extraction, multi-modal structuring, and graph construction in a single pipeline. Every chunk retains full semantic context, making the output a natural fit for Agentic RAG, vector-based RAG, or any LLM workflow.

[!NOTE] Get started in seconds with Knowhere Cloud. Avoid the complexity of self-deployment. Use our managed API at knowhereto.ai and enjoy $5 in free credits upon registration.

📢 News

• June 1, 2026: 📚 Knowhere now supports ultra-long PDFs and atlas-style documents. The parsing pipeline can process long-form PDFs with hundreds of pages (for example, 300, 500, or more) and route technical atlases or drawing collections through a dedicated layout-aware parser.
• May 7, 2026: 🚀 Knowhere is now Open Source! We have open-sourced our entire stack for document ingestion, parsing, and agentic RAG. You can now self-host the full platform using knowhere-self-hosted. Check out our Contribution Guide to get involved!

How it Works

Knowhere runs in two steps: build memory from documents, then let agents retrieve from it.

Step 1: Parse and Build Memory

• Parse: Route PDFs, Office files, images, tables, Markdown, and text to specialized parsers.
• Structure: Our proprietary Tree-like algorithm reconstructs the full document hierarchy instead of flattening it into a sequence, preventing semantic fragmentation across chunks.
• Build Memory: Store chunks, navigation trees, summaries, and graph links as agent-ready context.

Step 2: Agentic Retrieval

• Discover: Fuse keyword, path, content, and semantic signals for broad first-pass coverage.
• Navigate: Walk section trees and graph links to drill into the most relevant document regions.
• Cite Evidence: Return traceable results with source document, section, chunk, and linked assets.

FAQ

Q: What is Knowhere's relationship with MinerU?

A: Knowhere uses MinerU as its default parser because it performs best in our tests. Any parser only gets you raw Markdown. Knowhere's value is what comes after: hierarchy reconstruction, multi-modal normalization, and cross-document graph construction. Any Markdown-outputting tool works.

Q: What LLM / VLM dependencies does Knowhere have?

A: By default, DeepSeek (deepseek-chat) handles text and table summarization, and Qwen-VL (qwen3.6-flash) handles image OCR and descriptions. Knowhere is model-agnostic. Swap in OpenAI, DashScope, Zhipu, or Volcengine via environment variables.

Q: How is Agentic Retrieval different from traditional RAG?

A: Traditional RAG does a flat vector lookup and returns isolated snippets. Knowhere's agents navigate the document's section tree and cross-document graph, drilling into the most relevant regions the way a human reader would, returning traceable, well-contextualized evidence.

Q: Does it handle images and tables?

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