Optimizing Retrieval Augmented Generation with Low Latency Graph Embeddings and Hybrid Search Architectures
Introduction Retrieval‑Augmented Generation (RAG) has emerged as a powerful paradigm for combining the factual grounding of external knowledge bases with the expressive creativity of large language models (LLMs). In a typical RAG pipeline, a retriever fetches relevant documents (or passages) from a corpus, and a generator conditions on those documents to produce answers that are both accurate and fluent. While the conceptual simplicity of this two‑step process is appealing, real‑world deployments quickly run into a latency bottleneck: the retrieval stage must surface the most relevant pieces of information within milliseconds, otherwise the end‑user experience suffers. ...