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Learn how to build a production-ready semantic search architecture that transforms massive streams of unstructured text into searchable knowledge using Apache Kafka and Qdrant vector database. Discover bakdata's 24/7 pipeline system that processes publications, news articles, and product descriptions through preprocessing and custom chunking techniques for complex documents. Explore scalable embedding generation using Hugging Face's Text Embeddings Inference (TEI) and streaming ingestion into Qdrant via Kafka sink connectors. Understand the strategic decision to decouple embedding and ingestion processes to enable independent scaling, insert aggregation or filtering steps, and maximize GPU resource utilization through batched consumers. Examine how overlapping chunks preserve context and learn schema design principles in Qdrant for accommodating complex document structures. Review performance results, operational lessons learned, and adaptable templates for implementing similar solutions across heterogeneous data landscapes to convert raw text streams into query-ready knowledge systems efficiently and reliably.
