VectifyAI/PageIndex
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
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README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
Reasoning-based RAG ◦ No Vector DB, No Chunking ◦ Context-Aware Retrieval ◦ Reads Like a Human 🌐 Website • ☁️ Cloud • 📖 Docs • 📝 Blog • ✉️ Contact Updates 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. 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: PageIndex is a vectorless, reasoning-based RAG engine that mirrors…
Read the full README ↗ · Preview checked 2026-11-11T01:55:41.983Z
Inside the original README — Document outline
- PageIndex: Vectorless, Reasoning-based RAG
- What is PageIndex?
- TL;DR
- Compare with Vector RAG
- Quickstart
- Model Recommendations
- Use PageIndex through the SDK client →
- Integrate PageIndex with your own agent →
- Benchmarks
- Local indexing cost and time
- Query cost and accuracy
- Cost per query vs. native PDF input
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What this repository does
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
Repository facts
- Owner
- VectifyAI
- Primary language
- Python
- Stars
- 39,757
- Forks
- 3,430
- Open issues + pull requests
- 108
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2025-04-01T10:53:54.000Z
- Last push
- 2026-09-28T14:01:31.000Z
Topics and intended use
Owner-supplied topics: agentic-ai, agents, ai, ai-agents, context-engineering, information-retrieval, llm, rag, reasoning, retrieval, retrieval-augmented-generation, vector-database
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Source and freshness
Source: GitHub. Metadata observed 2026-10-26T12:12:12.159Z. Daily imports are snapshots, not real-time monitoring.
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