Scaling to 10M Documents

In the rapidly evolving landscape of artificial intelligence, the ability to retrieve accurate and relevant information is just as critical as the generation capabilities of the models themselves. As enterprises scale their AI operations, they often hit a wall: latency and accuracy at scale.

The Challenge of Scale

When dealing with millions of documents, traditional keyword search falls short. Semantic search offers a solution, but it introduces its own set of complexities. How do you maintain sub-second response times when querying a vector database containing billions of embeddings?

"The future of enterprise AI lies not just in model size, but in the efficiency of retrieval systems. Context is king, and speed is queen."

Implementing the Solution

We found that a hybrid approach yields the best results. By combining dense vector retrieval with sparse keyword search (BM25), we can capture both semantic meaning and exact keyword matches.

• Vector Database Optimization: Using HNSW indexes for approximate nearest neighbor search.
• Semantic Caching: Storing common queries and their embeddings to bypass model inference.
• Reranking: Using a cross-encoder to re-score the top K results for higher precision.

This architecture allowed us to scale to 10 million documents while keeping P99 latency under 200ms.