indexing-pipeline

Automating PDF indexing for large document collections involves loading content, applying page-aware chunking, and generating embeddings into a vector store. This pipeline supports delta reindexing and batch checkpoints to resume failed runs robustly.

Graph-based retrieval, or GraphRAG, works with PDF embeddings by extracting entities and relationships from chunked text. This generates a knowledge graph that enables semantic search and context-rich answers beyond standard vector retrieval.

Extracting text and tables from PDFs for chunking requires hybrid loaders like PyMuPDF, Docling, and Vision OCR. These tools work together to parse complex document layouts before splitting the content into page-aware chunks.

Delta reindexing with deterministic IDs is fully supported for PDF documents. This approach enables incremental indexing by tracking specific chunks, allowing you to update only modified content without rebuilding the entire vector store.

The pipeline supports flexible loaders and splitters for different PDF formats. You can combine PyMuPDF, Docling, and Vision OCR for loading, while applying page-aware chunking strategies tailored to your document structure.

The best way to build a knowledge graph from PDF files is to automate extraction through loading, splitting, and embedding content while generating entities and relationships. This prepares data for GraphRAG workflows and advanced semantic retrieval.