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 # PageIndex: Vectorless, Reasoning-based RAG
 
-<p align="center"><b>Reasoning-based RAG&nbsp; ◦ &nbsp;No Vector DB&nbsp; ◦ &nbsp;No Chunking&nbsp; ◦ &nbsp;Human-like Retrieval</b></p>
+<p align="center"><b>Reasoning-based RAG&nbsp; ◦ &nbsp;No Vector DB or Chunking&nbsp; ◦ &nbsp;Context-Aware&nbsp; ◦ &nbsp;Human-like Retrieval</b></p>
 
 <h4 align="center">
   <a href="https://vectify.ai">🌐 Homepage</a>&nbsp; • &nbsp;
@@ -33,7 +33,7 @@
 - 🔥 [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) — A simple *agentic, vectorless RAG* [example](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) with self-hosted PageIndex, using OpenAI Agents SDK.
 - [**Scale PageIndex to Millions of Documents**](https://pageindex.ai/blog/pageindex-filesystem) — *PageIndex File System* is a file-level tree layer that lets PageIndex reason over an entire corpus, not just a single document, enabling massive-scale document search.
 - [PageIndex Chat](https://chat.pageindex.ai) — Human-like document analysis agent [platform](https://chat.pageindex.ai) for professional long documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer).
-- [PageIndex Framework](https://pageindex.ai/blog/pageindex-intro) — Deep dive into PageIndex: an *agentic, in-context tree index* that enables LLMs to perform *reasoning-based, human-like retrieval* over long documents.
+- [PageIndex Framework](https://pageindex.ai/blog/pageindex-intro) — Deep dive into PageIndex: an *agentic, in-context tree index* that enables LLMs to perform *reasoning-based, context-aware retrieval* over long documents.
 
  <!-- **🧪 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.
@@ -64,8 +64,9 @@ It simulates how *human experts* navigate and extract knowledge from complex doc
 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.
+- **Better Explainability and Traceability**: Retrieval is based on reasoning, traceable and interpretable, with page and section references. No more opaque, approximate vector search (“vibe retrieval”).
+- **Context-Aware Retrieval**: Retrieval depends on your full context (e.g., conversation history and domain knowledge), and easily incorporates new context.
 - **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents.
-- **Better Explainability and Traceability**: Retrieval is based on reasoning — traceable and interpretable, with page and section references. No more opaque, approximate vector search (“vibe retrieval”).
 
 PageIndex powers a reasoning-based RAG system that achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench, demonstrating superior performance over vector-based RAG solutions in professional document analysis. See our [blog post](https://vectify.ai/blog/Mafin2.5) for details.