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[Turn 6428] User: I'm trying to implement the hybrid ranking logic for 75,000 combined results, and I've already completed 40% of it. However, I'm facing issues with the retrieval pipeline architecture, as I need to structure the hybrid pipelines for 2,000 queries/sec with 99.9% uptime. Can you help me design a ranking flow that meets these requirements? I'm using PyTorch 2.0.1 for score fusion, which has been stable across 5,000 computations with 99.8% stability. ->-> 3,4 [Turn 6429] Assistant: Certainly! Designing a robust retrieval pipeline architecture for hybrid ranking with the specified requirements involves several key components. Here's a step-by-step guide to help you structure the hybrid pipelines and ensure high performance and reliability: ### Step 1: Define the Hybrid Ranking Pipeline The hybrid ranking pipeline should consist of the following stages: 1. **Sparse Retrieval**: Use BM25 or a similar method to retrieve initial candidates. 2. **Dense Retrieval**: Use a dense vector model (e.g., FAISS) to retrieve additional candidates. 3. **Score Fusion**: Combine the scores from sparse and dense retrievals using PyTorch.