Overview
This case study outlines the transition of a naive Retrieval-Augmented Generation (RAG) prototype into a highly available, enterprise-grade search system. The architecture successfully ingests terabytes of unstructured internal documentation, mapping them into dense vector embeddings for instantaneous semantic retrieval.
Production Architecture
A distributed ingestion pipeline asynchronously processing PDFs and raw text, routing them through fine-tuned embedding models (deployed via FastAPI) into a sharded Qdrant cluster capable of sub-50ms Approximate Nearest Neighbor (ANN) search.
Technical Execution
- Retrieval Optimization: Improved raw semantic precision by implementing Hybrid Search (Dense Vectors + Sparse BM25) and applying an active Cross-Encoder re-ranking layer, drastically lifting NDCG@10 metrics.
- Evaluation Loops: Automated programmatic evaluation pipelines measuring Context Precision and Answer Relevancy across 10,000+ adversarial QA pairs to prevent silent regressions during embedding model updates.