In Q3 2024, 72% of production RAG pipelines failed to meet p99 latency SLAs for multimodal queries, according to a Datadog survey of 1,200 engineering teams. Most blamed fragmented toolchains for text and image retrieval—until Stable Diffusion 3.0’s embedding API and Llama 4’s 1M-token context window changed the game. This is the definitive guide to building unified multimodal RAG pipelines that c
I built a Vamana-based vector search engine in C++ called sembed-engine. Recently I made a pull request that sped up queries by 16x and builds by 9x. The algorithm stayed exactly the same. The recall stayed at 1.0. The number of visited nodes did not change. The speedup came from data layout. The original code stored vectors as separate objects pointed to by shared_ptr: struct Record { int64_t