diff --git a/README.md b/README.md index e04564880..59b4097ce 100644 --- a/README.md +++ b/README.md @@ -31,11 +31,11 @@ 🖥️ Dashboard

-## What We Are +## Overview -**Knowhere is the memory layer between raw documents and AI agents.** +**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. The output is structured, high-quality context ready for *Agentic RAG*, *traditional RAG*, or any LLM workflow. +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.** @@ -44,11 +44,10 @@ It ingests unstructured documents and produces persistent, navigable memory: par ## 📢 News - **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](https://github.com/Ontos-AI/knowhere-self-hosted). Check out our [Contribution Guide](CONTRIBUTING.md) to get involved! -- **Apr 30, 2026**: 📦 **Version [2026.04.30.1](https://github.com/Ontos-AI/knowhere/releases/tag/2026.04.30.1) has been released.** This update includes several stability improvements and initial support for the agentic RAG layer. See the [full changelog](https://github.com/Ontos-AI/knowhere/commits/2026.04.30.1) for details. ## How it Works -The pipeline runs in two steps. +Knowhere runs in two steps: build memory from documents, then let agents retrieve from it. ### Step 1: Parse and Build Memory @@ -57,7 +56,7 @@ The pipeline runs in two steps.

- **Parse**: Route PDFs, Office files, images, tables, Markdown, and text to specialized parsers. -- **Structure**: Preserve headings, section paths, multi-modal assets, and chunk relationships. +- **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 @@ -70,6 +69,24 @@ The pipeline runs in two steps. - **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 (`qwen3.5-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?** + +A: Yes. Knowhere extracts them, runs them through VLMs for summarization and feature extraction, and links them back to their source chunks so agents can retrieve and cite multi-modal assets at inference time. + ## Performance Benchmark Agents using Knowhere outperform those working from raw documents or MinerU-parsed output on real-world tasks: searching, modifying, and answering questions. @@ -106,20 +123,6 @@ Agents using Knowhere outperform those working from raw documents or MinerU-pars - **Agentic RAG**: A hybrid retrieval engine combining traditional search (RRF) with autonomous agent navigation. - **Evidence-based Citations**: Every result is backed by traceable source paths, ensuring reliability for AI Agent decision-making. -## Frequently Asked Questions (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 (`qwen3.5-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?** -A: Yes. Knowhere extracts them, runs them through VLMs for summarization and feature extraction, and links them back to their source chunks so agents can retrieve and cite multi-modal assets at inference time. - ## Supported Formats **✅ Supported** @@ -246,7 +249,7 @@ If you use Knowhere in your research, please cite it as: ```bibtex @software{knowhere2026, author = {Ontos AI}, - title = {Knowhere: Build AI Agent Memory from Real-World Documents}, + title = {Knowhere: Prepare Unstructured Data for AI Agents}, year = {2026}, publisher = {GitHub}, url = {https://github.com/Ontos-AI/knowhere}, diff --git a/docs/assets/benchmark.png b/docs/assets/benchmark.png index 1ebd123dc..ed5b555f7 100644 Binary files a/docs/assets/benchmark.png and b/docs/assets/benchmark.png differ