diff --git a/README.md b/README.md index ae93ec5..4285eaa 100644 --- a/README.md +++ b/README.md @@ -15,9 +15,9 @@

🏠 Homepage  β€’   - πŸ–₯️ Platform  β€’   + πŸ–₯️ Chat Platform  β€’   πŸ”Œ MCP  β€’   - πŸ“š API  β€’   + πŸ“š API Docs  β€’   πŸ’¬ Discord  β€’   βœ‰οΈ Contact 

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πŸ“’ Recent Updates

 - **πŸš€ New Releases:** -- [πŸ”₯ **PageIndex Chat**](https://chat.pageindex.ai): -The first human-like document-analysis agent platform built for professional long documents β€” also available via the [API](https://docs.pageindex.ai/quickstart) (beta). + **πŸ”₯ New Releases:** +- [**PageIndex Chat**](https://chat.pageindex.ai): The first human-like document-analysis agent platform built for professional long documents. Could also be integrated via the [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart) (beta). -- [**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:** +- [**PageIndex Framework**](https://pageindex.ai/blog/pageindex-intro): Introduces the PageIndex framework β€” an *agentic, in-context* *tree index* that enables LLMs to perform *reasoning-based*, *human-like retrieval* over long documents, without vector DB or chunking. + **πŸ§ͺ 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** β€” 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 that retrieves and reasons directly over PDF page images. - - **πŸ“œ Articles:** -* ⭐ [**The PageIndex Overview**](https://pageindex.ai/blog/pageindex-intro): Introduces the PageIndex framework β€” 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*. +- [Vectorless RAG](https://docs.pageindex.ai/cookbook/vectorless-rag-pageindex): A minimal, hands-on example of reasoning-based RAG using **PageIndex** β€” 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 through PageIndex’s visual retrieval workflow that retrieves and reasons directly over PDF page images.
- # πŸ“‘ 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 β‰  relevance** β€” 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. @@ -64,13 +62,17 @@ Compared to traditional *vector-based RAG*, **PageIndex** features: - **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 β€” traceable and interpretable. Say goodbye to approximate vector search ("vibe retrieval"). +- **Better Explainability and Traceability**: Retrieval is based on reasoning β€” 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 [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). +### πŸ“ 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 implementations, and our [cookbook](https://docs.pageindex.ai/cookbook) and [tutorials](https://docs.pageindex.ai/tutorials) for more 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). + ### βš™οΈ Deployment Options - πŸ› οΈ Self-host β€” run locally with this open-source repo. -- ☁️ **Cloud Service** β€” try instantly with our πŸ–₯️ [Platform](https://chat.pageindex.ai/), πŸ”Œ [MCP](https://pageindex.ai/mcp) or πŸ“š [API](https://docs.pageindex.ai/quickstart). +- ☁️ **Cloud Service** β€” try instantly with our πŸ–₯️ [Chat Platform](https://chat.pageindex.ai/), πŸ”Œ [MCP](https://pageindex.ai/mcp) or πŸ“š [API](https://docs.pageindex.ai/quickstart). ### πŸ§ͺ Quick Hands-on