-- 🔥 [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) — A simple agentic, vectorless RAG [example](#agentic-vectorless-rag-an-example) 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.
+- 🔥 [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) — A simple agentic, vectorless RAG [example](#-agentic-vectorless-rag-an-example) 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 indexing 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, context-aware retrieval* over long documents.
@@ -45,13 +45,13 @@
# 📑 Introduction to PageIndex
-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 contextual understanding, domain expertise, and multi-step reasoning, similarity search often falls short — missing what's relevant but not similar, and returning what's similar yet not relevant.
+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 *contextual understanding*, domain expertise, and multi-step reasoning, similarity search often falls short — missing what's relevant but not similar, and returning what's similar yet not relevant.
-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**. The retrieval is *traceable* and *explainable*, with no vector DBs or chunking.
+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**. The retrieval is *traceable* and *explainable*, with no vector DBs or chunking.
PageIndex 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. It performs retrieval in two steps:
1. Generate a “Table-of-Contents” **tree structure index** of documents
-2. Perform reasoning-based retrieval through **tree search**
+2. Perform (agentic) reasoning-based retrieval through **tree search**