rag-ingestion-pipeline

To ingest documents into a RAG system with incremental updates, use content hashing for change detection to process and index only modified files. This avoids full re-indexing and optimizes batch processing for retrieval quality.

Semantic chunking is a document processing strategy that splits educational or technical content into meaningful sections. You need it during vector database ingestion to preserve context and ensure high-quality semantic search capabilities in RAG systems.

Yes, you can use FastAPI to create production-grade ingestion APIs for document indexing. It supports background job processing to handle document ingestion, semantic chunking, and vector database ingestion efficiently.

Yes, this RAG ingestion pipeline processes PDF and HTML file formats, along with Markdown. It handles document processing, semantic chunking, and vector database ingestion for these supported file types.

Yes, you need qdrant-client and OpenAI to set up vector database ingestion. These dependencies are required to process documents, generate embeddings, and index payloads within the Qdrant vector database for RAG systems.

The best way to avoid full re-indexing when updating documents in a RAG system is implementing incremental updates with content hashing. This detects changes and only processes modified documents for vector database ingestion.