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
Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E