Challenges Faced When Scaling Semantic Search:

• Volume of Data: As data grows, the sheer volume can make it difficult to maintain real-time search capabilities.
• Complexity of Queries: Users might input complex multi-faceted queries that require more processing to understand semantically.
• Consistency Across Data: Ensuring uniformity in embeddings, especially when data is updated frequently.
• Computational Costs: Semantic search is more computationally intensive than traditional keyword search, which can increase costs as scale grows.
• Storage Overhead: Storing vector representations (embeddings) alongside raw data can be storage-intensive.

Strategies for Efficient Indexing and Retrieval:

• Batch Processing: Instead of processing one item at a time, process multiple items simultaneously to speed up indexing.
• Incremental Indexing: Only index new or changed data, rather than re-indexing everything.
• Use Specialized Databases: Employ databases designed for vector search

Using Distributed Systems for Large-Scale Search:

• Horizontal Scaling: Add more machines to your cluster to distribute the search load.
• Partitioning/Sharding: Split your dataset into smaller chunks and store them across multiple machines. Direct search queries to relevant shards.
• Replication: Duplicate data across multiple machines to ensure high availability and fault tolerance.
• Load Balancers: Distribute incoming search queries to different machines to ensure no single machine is overwhelmed.
• Asynchronous Processing: Use async operations to handle non-blocking tasks, improving throughput.