PageIndex: Vectorless Document Indexing for RAG

PageIndex is a document indexing system designed for vectorless, reasoning-based retrieval augmented generation (RAG). It addresses the limitations of traditional vector-based retrieval methods by focusing on relevance through reasoning rather than semantic similarity.

Key features include:

• No Vector DB - Utilizes document structure and LLM reasoning for retrieval instead of vector similarity search.
• No Chunking - Organizes documents into natural sections rather than artificial chunks.
• Better Explainability and Traceability - Retrieval is based on reasoning, allowing for traceable and interpretable results.
• Context-Aware Retrieval - Incorporates full context such as conversation history and domain knowledge.
• Human-like Retrieval - Simulates expert navigation through complex documents.

PageIndex is particularly useful for analyzing long professional documents, achieving a state-of-the-art accuracy of 98.7% on the FinanceBench benchmark, making it a powerful tool for financial document analysis and other complex document retrieval tasks.