Architecting Sub-150ms Hybrid RAG for Voice Agents: Combining pgvector, BM25, and Async FastAPI…

1. 1Build a hybrid RAG model using pgvector and BM25
2. 2Configure Async FastAPI for low-latency API responses
3. 3Optimize Postgres database queries for fast data retrieval
4. 4Test and evaluate the performance of the hybrid RAG model
5. 5Deploy the model using a cloud-based infrastructure for scalability

This tutorial is useful for machine learning engineers, software engineers, and data scientists working on voice agent projects, especially those dealing with large catalogs and low-latency requirements

💡 Combining pgvector, BM25, and Async FastAPI can achieve sub-150ms response times for voice agents with large catalogs

🚀 Serve large industrial catalogs over voice channels in under 150ms with hybrid RAG and Async FastAPI! 📚