PageIndex

PageIndex is an open-source, vectorless, reasoning-based Retrieval-Augmented Generation (RAG) framework for long document analysis. It enables LLMs to perform human-like, context-aware retrieval over complex documents without relying on vector databases or chunking.

Key Features

• No Vector DB: Uses document structure and LLM reasoning for retrieval, not vector similarity search.
• No Chunking: Organizes documents into natural sections, not artificial chunks.
• Human-like Retrieval: Simulates expert navigation and extraction of knowledge from documents.
• Explainability: Traceable, interpretable retrieval with page/section references.
• High Accuracy: Achieved state-of-the-art results on FinanceBench (98.7% accuracy).
• Multiple Deployment Options: Self-host, cloud service, or enterprise/on-prem.

Use Cases

• Professional document analysis (finance, legal, research)
• Building advanced RAG systems without vector DBs
• Explainable, traceable document retrieval

References

• GitHub Repository
• Framework Introduction
• Cookbooks & Tutorials
• Chat Platform