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#### 📢 Recent Updates
**Articles:**
* 🧩 [**The PageIndex Overview**](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 vectors 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*.
* ⭐ [**The PageIndex Overview**](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 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 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.
* [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 that retrieves and reasons directly over PDF page images.
# 📑 Introduction to PageIndex
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### 🧩 Features
Compared to traditional *vector-based RAG*, **PageIndex** features:
- **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 Vector DB**: Uses document structure and LLM reasoning for retrieval, instead of vector 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.
- **Transparent Retrieval Process**: Retrieval based on reasoning — traceable and interpretable. Say goodbye to approximate vector search ("vibe retrieval").