Low-Latency Retriever
A Low-Latency Retriever is a component within an AI or search system designed to fetch highly relevant information or data snippets from a large knowledge base with minimal delay. Its primary function is to bridge the gap between a user query and the necessary context required by a generative model (like an LLM) to produce an accurate and timely response.
In modern, interactive AI applications, speed is as crucial as accuracy. High latency frustrates users and degrades the perceived quality of the service. A low-latency retriever ensures that the context provided to the downstream model is delivered almost instantaneously, enabling real-time conversational AI, instant search results, and immediate decision support.
These systems typically rely on advanced indexing and vector databases. When a query arrives, the retriever converts the query into a numerical vector (embedding). It then performs a high-speed nearest-neighbor search against a pre-indexed collection of document vectors. Techniques like Approximate Nearest Neighbor (ANN) algorithms are employed to balance search speed with retrieval accuracy, ensuring the closest matches are found rapidly.
