building-rag-systems

Build a RAG ingestion pipeline by configuring a crawler, parser, chunker, embedder, and uploader to process documents into semantic chunks. The pipeline targets 400 tokens per chunk with 15% overlap to maintain context across sections before uploading to Qdrant.

Incremental indexing in RAG systems avoids full re-indexing by tracking file hashes for change detection. When source documents are updated, the system compares file hashes to identify modifications and only re-processes the changed files, ensuring reliable and efficient knowledge-base updates.

Implement filtered vector search in Qdrant by attaching payload metadata to vectors and indexing fields like book_id, module, chapter, lesson, and proficiency. This enables fast, tenant-isolated retrieval using multi-parameter filters during context-aware search queries.

Yes, the RAG pipeline supports tenant isolation by using payload-indexed filtering in Qdrant. Fields such as book_id, module, and chapter are indexed as payload metadata, allowing searches to be scoped to specific tenants or document segments securely.

The best way to maintain context during semantic chunking is to apply a 15% overlap between adjacent chunks. By targeting 400 tokens per chunk and overlapping boundaries, the RAG system preserves the narrative flow and contextual meaning across document sections.

Yes, you can use this ingestion pipeline for large-scale knowledge-base construction. The end-to-end workflow supports batched embeddings and incremental indexing via file hashes, enabling scalable and reliable updates without reprocessing the entire document repository.