[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fcfpu3jlzlbe8":3},{"_id":4,"slug":5,"title":6,"subtitle":7,"kind":8,"cards":9,"tags":58,"categories":59,"source":60,"lang":63,"author":64,"audioState":67,"stats":68,"publishedAt":71,"renderer":72},"6abbaa23ca21c797c7e9d33f","github---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7","GitHub - VectifyAI\u002FPageIndex: 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG","Reasoning-based RAG ◦ No Vector DB, No Chunking ◦ Context-Aware Retrieval ◦ Reads Like a Human","news",[10,13,18,23,28,33,38,43,48,53],{"headline":11,"body":7,"imageUrl":12,"sourceImageUrl":12},"GitHub - VectifyAI\u002FPageIndex: 📑 PageIndex: Document Index for Vectorless…","https:\u002F\u002Fopengraph.githubassets.com\u002F76f12de767c7f059654142e54e9b54b0c8630d1f90468b6e4f92a91ea9c1268d\u002FVectifyAI\u002FPageIndex",{"headline":14,"body":15,"imageUrl":16,"images":17},"[Aug '26] 🔥 PageIndex SDK: pip install -U","[Aug '26] 🔥 PageIndex SDK: pip install -U pageindex now ships local mode: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key.","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F1.webp",{"local":16},{"headline":19,"body":20,"imageUrl":21,"images":22},"[Aug '26] ⚡ PageIndex Flash: fast tree index","[Aug '26] ⚡ PageIndex Flash: fast tree index generation for text-based PDFs, now the default indexing method in PageIndex SDK local mode.","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F2.webp",{"local":21},{"headline":24,"body":25,"imageUrl":26,"images":27},"Scale PageIndex to Millions of Documents: PageIndex File","Scale PageIndex to Millions of Documents: PageIndex File System is a file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. PageIndex App: a human-like document analysis agent for long professional documents.","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F3.webp",{"local":26},{"headline":29,"body":30,"imageUrl":31,"images":32},"Are you frustrated with vector database retrieval accuracy","Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic similarity. But similarity ≠ relevance — what retrieval actually needs is relevance, and relevance requires reasoning. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant.","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F4.webp",{"local":31},{"headline":34,"body":35,"imageUrl":36,"images":37},"Inspired by AlphaGo, PageIndex replaces the vector index","Inspired by AlphaGo, PageIndex replaces the vector index with a hierarchical tree index and lets an LLM reason its way through it, the way a human expert turns to and reads the right section of a long report. Retrieval happens in two steps: Index: generate a tree-structure index for each document Retrieve: agentically search that tree with LLM reasoning","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F5.webp",{"local":36},{"headline":39,"body":40,"imageUrl":41,"images":42},"PageIndex is a vectorless, reasoning-based RAG engine that","PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read, delivering traceable, explainable, and context-aware retrieval, with no vector DBs or chunking.","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F6.webp",{"local":41},{"headline":44,"body":45,"imageUrl":46,"images":47},"It is ideal for financial reports, legal documents","It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks, and any other long, complex professional document.","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F7.webp",{"local":46},{"headline":49,"body":50,"imageUrl":51,"images":52},"index=: a basic model is sufficient. The tree","index=: a basic model is sufficient. The tree structure itself is extracted from the document layout without an LLM; the index model only summarizes and refines it, which a basic model does well.","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F8.webp",{"local":51},{"headline":54,"body":55,"imageUrl":56,"images":57},"chat=: use the best model you can afford","chat=: use the best model you can afford. The chat model searches the tree to retrieve information. See Query cost and accuracy. Configure other models, streaming, multi-document search, citations, and more. Drop PageIndex tools into the OpenAI Agents SDK, the Claude Agent SDK, or any other framework. Local indexing cost and time","\u002Fapi\u002Fmedia\u002Fposts\u002Fgithub---vectifyaipageindex-pageindex-document-index-for-vec-2732fee7\u002F9.webp",{"local":56},[],[],{"name":61,"url":62},"GitHub","https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex","en",{"handle":65,"displayName":66},"spots","Spots","queued",{"views":69,"likes":70,"saves":70,"shares":70,"completions":70,"opens":70,"skips":70,"depthSum":70},3,0,"2026-09-29T12:08:03.631Z","local"]