Retrieval-Augmented Generation (RAG) systems can experience significant latency due to several factors. The primary causes include:
- Document retrieval time: Searching large vector databases or inverted indexes takes time, especially with high-dimensional embeddings or BM25 scoring.
- Embedding generation: Converting queries into embeddings using a neural model adds latency proportional to model size and input length.
- Context window limits: Retrieved chunks may be truncated or require multiple LLM calls to fit context, increasing generation time.
- Post-processing overhead: Re-ranking, filtering, or deduplication steps add sequential delays.
To reduce latency, consider these strategies:
- Use approximate nearest neighbor (ANN) search instead of exact search; libraries like FAISS or ScaNN offer sub-linear retrieval.
- Precompute and cache embeddings for common queries or static retrieval results.
- Optimize chunk sizes to reduce the number of chunks retrieved while maintaining relevance.
- Employ a lightweight retriever (e.g., BM25 for sparse vectors) when precision loss is acceptable.
- Adopt hybrid search (sparse + dense) in a single ranking step to avoid separate re-ranking.
- Use smaller, faster LLMs for generation, such as distilled models, when answer quality allows.
- Implement streaming and early stopping so partial answers appear before full generation completes.
- Parallelize retrieval and generation where possible, e.g., retrieving while the previous query is being processed.
Mastering these latency-reduction techniques is crucial for production RAG systems and is a frequent topic in AI interview questions.