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#### 🚨 New Releases: #### 🚨 New Releases:
- 📖 [**PageIndex Chat**](https://chat.pageindex.ai): The first human-like document analyst agent, designed for professional long documents. - 📖 [**PageIndex Chat**](https://chat.pageindex.ai): The first human-like document analyst agent, designed for professional long documents.
- 🔌 [**PageIndex MCP**](https://pageindex.ai/mcp): Bring PageIndex into Claude, Cursor, or any MCP-enabled agent. Chat with long PDFs the reasoning-based, human-like way. - 🔌 [**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.
#### 📢 Recent Updates #### 📢 Recent Updates
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* ["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*. * ["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:** **🧪 Cookbooks:**
* [**Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb): A minimal, hands-on example of reasoning-based RAG using **PageIndex** — no vectors, no chunking, and human-like retrieval. * [**Vectorless RAG notebook**](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb): A minimal, hands-on example of reasoning-based RAG using **PageIndex** — no vectors, no chunking, and human-like retrieval.
* [Vision-based Vectorless RAG](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb): Experience OCR-free document understanding through PageIndex’s visual retrieval workflow — retrieving and reasoning directly over PDF page images. * [Vision-based Vectorless RAG notebook](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb): Experience OCR-free document understanding through PageIndex’s visual retrieval workflow — retrieving and reasoning directly over PDF page images.
# 📑 Introduction to PageIndex # 📑 Introduction to PageIndex
@ -58,12 +58,12 @@ Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)**
### 🧩 Features ### 🧩 Features
Compared to traditional *vector-based RAG*, **PageIndex** features: Compared to traditional *vector-based RAG*, **PageIndex** features:
- **No Vectors Needed**: Uses document structure and LLM reasoning for retrieval. - **No Vector DB Needed**: Uses document structure and LLM reasoning for retrieval, instead of vector search.
- **No Chunking Needed**: Documents are organized into natural sections, not artificial chunks. - **No Chunking Needed**: Documents are organized into natural sections, not artificial chunks.
- **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents. - **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents.
- **Transparent Retrieval Process**: Retrieval based on reasoning — traceable and interpretable. Say goodbye to approximate vector search ("vibe retrieval"). - **Transparent Retrieval Process**: Retrieval based on reasoning — traceable and interpretable. Say goodbye to approximate vector search ("vibe retrieval").
PageIndex powers a reasoning-based RAG system that achieved [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench, showing state-of-the-art performance in professional document analysis (see our [blog post](https://vectify.ai/blog/Mafin2.5) for details). PageIndex powers a reasoning-based RAG system that achieved [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench, demonstrating **state-of-the-art** performance in professional document analysis (see our [blog post](https://vectify.ai/blog/Mafin2.5) for details).
### ⚙️ Deployment Options ### ⚙️ Deployment Options
- 🛠️ Self-host — run locally with this open-source repo. - 🛠️ Self-host — run locally with this open-source repo.
@ -73,7 +73,6 @@ PageIndex powers a reasoning-based RAG system that achieved [98.7% accuracy](htt
- Try the [_**Vectorless RAG Notebook**_](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb) — a *minimal*, hands-on example of reasoning-based RAG using **PageIndex**. - Try the [_**Vectorless RAG Notebook**_](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb) — a *minimal*, hands-on example of reasoning-based RAG using **PageIndex**.
- Experiment with the [*Vision-based Vectorless RAG*](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) — no OCR; a minimal, reasoning-native RAG pipeline that works directly over page images. - Experiment with the [*Vision-based Vectorless RAG*](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) — no OCR; a minimal, reasoning-native RAG pipeline that works directly over page images.
<p align="center">
<div align="center"> <div align="center">
<a href="https://colab.research.google.com/github/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb" target="_blank" rel="noopener"> <a href="https://colab.research.google.com/github/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb" target="_blank" rel="noopener">
@ -92,7 +91,7 @@ PageIndex can transform lengthy PDF documents into a semantic **tree structure**
Here is an example output. See more [example documents](https://github.com/VectifyAI/PageIndex/tree/main/tests/pdfs) and [generated trees](https://github.com/VectifyAI/PageIndex/tree/main/tests/results). Here is an example output. See more [example documents](https://github.com/VectifyAI/PageIndex/tree/main/tests/pdfs) and [generated trees](https://github.com/VectifyAI/PageIndex/tree/main/tests/results).
```python ```jsonc
... ...
{ {
"title": "Financial Stability", "title": "Financial Stability",
@ -167,7 +166,7 @@ You can customize the processing with additional optional arguments:
<details> <details>
<summary><strong>Markdown support</strong></summary> <summary><strong>Markdown support</strong></summary>
<br> <br>
We also provide a markdown support for PageIndex. You can use the `-md` flag to generate a tree structure for a markdown file. We also provide a markdown support for PageIndex. You can use the `-md_path` flag to generate a tree structure for a markdown file.
```bash ```bash
python3 run_pageindex.py --md_path /path/to/your/document.md python3 run_pageindex.py --md_path /path/to/your/document.md
@ -195,7 +194,7 @@ To address this, we introduced PageIndex OCR — the first long-context OCR mode
# 📈 Case Study: SOTA on Finance QA Benchmark # 📈 Case Study: SOTA on Finance QA Benchmark
[Mafin 2.5](https://vectify.ai/mafin) is a reasoing-based RAG system for financial document analysis, powered by **PageIndex**. It achieved a state-of-the-art [**98.7% accuracy**](https://vectify.ai/blog/Mafin2.5) on the [FinanceBench](https://arxiv.org/abs/2311.11944) benchmark — significantly outperforming traditional vector-based RAG systems. [Mafin 2.5](https://vectify.ai/mafin) is a reasoning-based RAG system for financial document analysis, powered by **PageIndex**. It achieved a state-of-the-art [**98.7% accuracy**](https://vectify.ai/blog/Mafin2.5) on the [FinanceBench](https://arxiv.org/abs/2311.11944) benchmark — significantly outperforming traditional vector-based RAG systems.
PageIndex's hierarchical indexing enabled precise navigation and extraction of relevant content from complex financial reports, such as SEC filings and earnings disclosures. PageIndex's hierarchical indexing enabled precise navigation and extraction of relevant content from complex financial reports, such as SEC filings and earnings disclosures.
@ -222,7 +221,7 @@ PageIndex's hierarchical indexing enabled precise navigation and extraction of r
Leave a star if you like our project. Thank you! Leave a star if you like our project. Thank you!
<p> <p>
<img src="https://github.com/user-attachments/assets/eae4ff38-48ae-4a7c-b19f-eab81201d794" width="70%"> <img src="https://github.com/user-attachments/assets/eae4ff38-48ae-4a7c-b19f-eab81201d794" width="80%">
</p> </p>
### Connect with Us ### Connect with Us