mirror of
https://github.com/VectifyAI/PageIndex.git
synced 2026-04-24 23:56:21 +02:00
Update README.md
This commit is contained in:
Ray
GitHub
parent
f869aba81c
commit
db6c58d741
1 changed files with 16 additions and 13 deletions
29
README.md
29
README.md
|
|
@ -30,16 +30,16 @@
|
|||
|
||||
**🔥 Releases:**
|
||||
- [**PageIndex Chat**](https://chat.pageindex.ai): The first human-like document-analysis agent platform built for professional long documents. Can also be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart) (beta).
|
||||
<!-- - [**PageIndex Chat API**](https://docs.pageindex.ai/quickstart): An API that brings PageIndex’s advanced long-document intelligence directly into your applications and workflows. -->
|
||||
<!-- - [**PageIndex Chat API**](https://docs.pageindex.ai/quickstart): An API that brings PageIndex's advanced long-document intelligence directly into your applications and workflows. -->
|
||||
<!-- - [PageIndex MCP](https://pageindex.ai/mcp): Bring PageIndex into Claude, Cursor, or any MCP-enabled agent. Chat with long PDFs in a reasoning-based, human-like way. -->
|
||||
|
||||
**✍️ Articles:**
|
||||
**📝 Articles:**
|
||||
- [**PageIndex Framework**](https://pageindex.ai/blog/pageindex-intro): Introduces the PageIndex framework — an *agentic, in-context* *tree index* that enables LLMs to perform *reasoning-based*, *human-like retrieval* over long documents, without vector DB or chunking.
|
||||
<!-- - [Do We Still Need OCR?](https://pageindex.ai/blog/do-we-need-ocr): Explores how vision-based, reasoning-native RAG challenges the traditional OCR pipeline, and why the future of document AI might be *vectorless* and *vision-based*. -->
|
||||
|
||||
**🧪 Cookbooks:**
|
||||
- [Vectorless RAG](https://docs.pageindex.ai/cookbook/vectorless-rag-pageindex): A minimal, hands-on example of reasoning-based RAG using PageIndex. No vectors, no chunking, and human-like retrieval.
|
||||
- [Vision-based Vectorless RAG](https://docs.pageindex.ai/cookbook/vision-rag-pageindex): Experience OCR-free document understanding with PageIndex’s visual retrieval workflow that retrieves and reasons directly over PDF page images.
|
||||
- [Vision-based Vectorless RAG](https://docs.pageindex.ai/cookbook/vision-rag-pageindex): OCR-free, vision-only RAG with PageIndex's reasoning-native retrieval workflow that works directly over PDF page images.
|
||||
</details>
|
||||
|
||||
---
|
||||
|
|
@ -48,7 +48,8 @@
|
|||
|
||||
Are you frustrated with vector database retrieval accuracy for long professional documents? Traditional vector-based RAG relies on semantic *similarity* rather than true *relevance*. But **similarity ≠ relevance** — what we truly need in retrieval is **relevance**, and that requires **reasoning**. When working with professional documents that demand domain expertise and multi-step reasoning, similarity search often falls short.
|
||||
|
||||
Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)** — a ***vectorless***, **reasoning-based RAG** system that builds a **hierarchical tree index** from long documents and uses LLMs to **reason over that index** for retrieval. It simulates how *human experts* navigate and extract knowledge from complex documents through *tree search*, enabling LLMs to *think* and *reason* their way to the most relevant document sections. PageIndex performs retrieval in two steps:
|
||||
Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)** — a **vectorless**, **reasoning-based RAG** system that builds a **hierarchical tree index** from long documents and uses LLMs to **reason** *over that index* for **agentic, context-aware retrieval**.
|
||||
It simulates how *human experts* navigate and extract knowledge from complex documents through *tree search*, enabling LLMs to *think* and *reason* their way to the most relevant document sections. PageIndex performs retrieval in two steps:
|
||||
|
||||
1. Generate a “Table-of-Contents” **tree structure index** of documents
|
||||
2. Perform reasoning-based retrieval through **tree search**
|
||||
|
|
@ -59,9 +60,9 @@ Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)**
|
|||
</a>
|
||||
</div>
|
||||
|
||||
### 🧩 Features
|
||||
### 🎯 Features
|
||||
|
||||
Compared to traditional *vector-based RAG*, **PageIndex** features:
|
||||
Compared to traditional vector-based RAG, **PageIndex** features:
|
||||
- **No Vector DB**: Uses document structure and LLM reasoning for retrieval, instead of vector similarity search.
|
||||
- **No Chunking**: Documents are organized into natural sections, not artificial chunks.
|
||||
- **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents.
|
||||
|
|
@ -71,12 +72,14 @@ PageIndex powers a reasoning-based RAG system that achieved **state-of-the-art**
|
|||
|
||||
### 📍 Explore PageIndex
|
||||
|
||||
Please see a detailed introduction of the [PageIndex framework](https://pageindex.ai/blog/pageindex-intro). Check out this GitHub repo for open-source code, and [cookbooks](https://docs.pageindex.ai/cookbook) and [tutorials](https://docs.pageindex.ai/tutorials) for additional usage guides and examples. The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or could be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart).
|
||||
To learn more, please see a detailed introduction of the [PageIndex framework](https://pageindex.ai/blog/pageindex-intro). Check out this GitHub repo for open-source code, and the [cookbooks](https://docs.pageindex.ai/cookbook), [tutorials](https://docs.pageindex.ai/tutorials), and [blog](https://pageindex.ai/blog) for additional usage guides and examples.
|
||||
|
||||
### ⚙️ Deployment Options
|
||||
The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or can be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart).
|
||||
|
||||
### 🛠️ Deployment Options
|
||||
- Self-host — run locally with this open-source repo.
|
||||
- Cloud Service — try instantly with our [Chat Platform](https://chat.pageindex.ai/), or integrate with [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart).
|
||||
- Enterprise — private or on-prem deployment. [Contact us](https://ii2abc2jejf.typeform.com/to/tK3AXl8T) or [book a demo](https://calendly.com/pageindex/meet).
|
||||
- _Enterprise_ — private or on-prem deployment. [Contact us](https://ii2abc2jejf.typeform.com/to/tK3AXl8T) or [book a demo](https://calendly.com/pageindex/meet) for more details.
|
||||
|
||||
### 🧪 Quick Hands-on
|
||||
|
||||
|
|
@ -128,11 +131,11 @@ Below is an example PageIndex tree structure. Also see more example [documents](
|
|||
...
|
||||
```
|
||||
|
||||
You can either generate the PageIndex tree structure with this open-source repo, or try our [API](https://docs.pageindex.ai/quickstart) service.
|
||||
You can generate the PageIndex tree structure with this open-source repo, or use our [API](https://docs.pageindex.ai/quickstart)
|
||||
|
||||
---
|
||||
|
||||
# 📦 Package Usage
|
||||
# ⚙️ Package Usage
|
||||
|
||||
You can follow these steps to generate a PageIndex tree from a PDF document.
|
||||
|
||||
|
|
@ -181,7 +184,7 @@ We also provide markdown support for PageIndex. You can use the `-md_path` flag
|
|||
python3 run_pageindex.py --md_path /path/to/your/document.md
|
||||
```
|
||||
|
||||
> Note: in this function, we use "#" to determine node heading and their levels. For example, "##" is level 2, "###" is level 3, etc. Make sure your markdown file is formatted correctly. If your Markdown file was converted from a PDF or HTML, we don’t recommend using this function, since most existing conversion tools cannot preserve the original hierarchy. Instead, use our [PageIndex OCR](https://pageindex.ai/blog/ocr), which is designed to preserve the original hierarchy, to convert the PDF to a markdown file and then use this function.
|
||||
> Note: in this function, we use "#" to determine node heading and their levels. For example, "##" is level 2, "###" is level 3, etc. Make sure your markdown file is formatted correctly. If your Markdown file was converted from a PDF or HTML, we don't recommend using this function, since most existing conversion tools cannot preserve the original hierarchy. Instead, use our [PageIndex OCR](https://pageindex.ai/blog/ocr), which is designed to preserve the original hierarchy, to convert the PDF to a markdown file and then use this function.
|
||||
</details>
|
||||
|
||||
<!--
|
||||
|
|
@ -222,7 +225,7 @@ Explore the full [benchmark results](https://github.com/VectifyAI/Mafin2.5-Finan
|
|||
* 🧪 [Cookbooks](https://docs.pageindex.ai/cookbook/vectorless-rag-pageindex): hands-on, runnable examples and advanced use cases.
|
||||
* 📖 [Tutorials](https://docs.pageindex.ai/doc-search): practical guides and strategies, including *Document Search* and *Tree Search*.
|
||||
* 📝 [Blog](https://pageindex.ai/blog): technical articles, research insights, and product updates.
|
||||
* ⚙️ [MCP setup](https://pageindex.ai/mcp#quick-setup) & [API docs](https://docs.pageindex.ai/quickstart): integration details and configuration options.
|
||||
* 🔌 [MCP setup](https://pageindex.ai/mcp#quick-setup) & [API docs](https://docs.pageindex.ai/quickstart): integration details and configuration options.
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue