How FAZ unlocked 75 years of journalism with Qdrant - Qdrant

This post details FAZ's implementation of a semantic search engine using Qdrant and Azure OpenAI for their 75-year journalism archive. It highlights the use of Qdrant for handling schema-less metadata payloads (over 60 fields) associated with embedded article paragraphs, enabling filtering by publication date, author, section, and article type. The system supports real-time updates and deletions, and achieves sub-second performance at scale (14M+ vectors) with quantization. FAZ also retrieves adjacent context vectors for answer generation. The post outlines plans to evolve towards a hybrid search architecture combining dense vectors, sparse vectors (BM25-like), and structured metadata filtering, with GPT-4 for query interpretation.