In an interactive setting like a chatbot, an acceptable latency for a RAG (Retrieval-Augmented Generation) system is typically 1-2 seconds total response time, with retrieval and generation phases each taking no more than 500-1000 milliseconds. Users expect near-instantaneous interactions, and delays beyond 2-3 seconds can disrupt conversational flow. For example, if a user asks a question, the system should retrieve relevant documents and generate a response quickly enough to mimic human-like turn-taking. This target balances computational complexity with user experience, as slower responses risk disengagement or frustration.
To meet latency targets, optimize the retrieval phase by using efficient indexing and query strategies. Vector databases like FAISS or Annoy enable fast approximate nearest neighbor searches, reducing retrieval time from seconds to milliseconds. For instance, pre-indexing document chunks into smaller, searchable units (e.g., 256-token segments) speeds up matching. Caching frequent or similar queries (e.g., using Redis) can bypass full retrieval for repeated requests, such as common FAQs about business hours. Additionally, limiting the number of retrieved documents (e.g., top 3-5 results) prevents unnecessary processing overhead while maintaining relevance.
For the generation phase, prioritize model efficiency. Smaller language models (e.g., 7B-13B parameter models) often provide sufficient quality with faster inference compared to larger models like GPT-4. Techniques like quantization (reducing model precision to 8-bit or 4-bit) or hardware acceleration (GPUs/TPUs) can cut generation time by 30-50%. For example, using NVIDIA’s TensorRT-LLM optimizations or frameworks like vLLM can reduce latency by parallelizing token generation. To coordinate both phases, implement asynchronous pipelines: start generating a response as soon as the first relevant document is retrieved, rather than waiting for all retrieval results. Monitoring tools like Prometheus can track per-phase latency, allowing adjustments (e.g., scaling resources or tuning model size) to stay within targets.
