VectifyAI/PageIndex

Health v2 · maintenance only

Adoption not part of the score

35333 stars · 3114 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PageIndex is a Python SDK and framework for vectorless, reasoning-based RAG that replaces vector similarity search with a hierarchical tree index over documents, which an LLM navigates agentically to retrieve relevant content. It supports local mode with your own LLM key or a hosted cloud API, plus MCP integration, with no embeddings, chunking, or vector database required.

Use cases

• build RAG over long complex PDFs without a vector database
• retrieve relevant sections from documents by reasoning instead of embeddings
• index PDF documents into a hierarchical tree structure
• add traceable document retrieval to AI agents via MCP
• chat with and analyze long professional documents
• avoid chunking and embedding pipelines for document Q&A

When to choose

• your documents are long, structured, and require contextual or multi-step reasoning to retrieve accurately
• you want explainable, traceable retrieval grounded in source sections
• you want to avoid maintaining embeddings, chunking, and vector database infrastructure
• you need MCP or API integration for agentic document retrieval

When to avoid

• you need fast, cheap retrieval over massive corpora where LLM-per-query reasoning cost is prohibitive
• your documents are short or unstructured with no useful hierarchy
• you need sub-second retrieval latency
• you want a fully offline solution without any LLM API key

Facets

library · maturity active

rag search-engine llm-inference agent-framework pdf mcp large-language-models pdf python cli self-hosted cloud vectorless-rag tree-index document-retrieval reasoning-based-retrieval no-embeddings document-analysis retrieval-augmented-generation natural-language-processing ai-agents search

Member repositories

For agents

markdown · JSON · MCP: product_card(name="VectifyAI/PageIndex")