Building an AI Database with an AWS Scalable Vector Database

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Getting to Know a Vector Database

A vector database stores high-dimensional vector embeddings that represent unstructured data such as text, images, and audio. These embeddings are generated using machine learning algorithms and are necessary for similarity searches in AI applications like recommendation systems (e.g., Netflix, Spotify, Amazon). Examples of search applications include Google Images, Pinterest, and AI-powered apps (e.g., ChatGPT, Google Assistant) that use NLP.

What is Vector Search?

Vector search uses closest neighbor search (NNS) to search through a set of items for ones that are similar. Techniques used most often are:

• Brute-force search is accurate but computationally expensive
• Scanning methods like HNSW (Hierarchical Navigable Small World), FAISS (Facebook AI Similarity Search), or Annoy (Approximate Nearest Neighbors)

How a Vector Search Process Works

1. Convert data into embeddings using a machine learning model (e.g., ResNet, BERT, or OpenAI’s CLIP)
2. Save embeddings in a collection of vectors
3. Index vectors using ANN methods
4. Perform search to find relevant matches

Important Problems with Making Vector Databases Bigger

1. Computing costs: searching millions of high-dimensional vectors is expensive
2. Indexing performance and adding data
3. Storage and ability to grow
4. Query performance and latency

Building an AWS Scalable Vector Database

Choose the Right AWS Services:
Amazon OpenSearch, Amazon Aurora, and Amazon DynamoDB

Building a Vector Search System That Can Grow

1. Vector embeddings: convert data into embeddings using a machine learning model
2. Indexing: use HNSW, FAISS, or Annoy to index vectors
3. Query: perform search to find relevant matches

Getting the Best Performance

1. Use HNSW to speed up results
2. Save information separately
3. Fine-tune indexing parameters (M and ef_search)
4. Use parallelism and batch processing (e.g., AWS Lambda or Amazon SageMaker)

Cost Considerations

• AWS prices are based on storage, computing, and data processing
• Live indexing: use OpenSearch, cold storage: use Amazon S3
• Use AWS Lambda for group processing instead of always-on EC2
• Choose instance types wisely (e.g., Graviton-powered EC2 instances)

Finally

• To set up scalable vector databases for AI on AWS, choose the right services, optimize indexing, and balance speed against cost
• Amazon OpenSearch is best for real-time vector search
• Amazon Aurora with pgvector for SQL-based vector storage
• Amazon DynamoDB for information storage
• Adding vector search to your AI system on AWS enables more NLP applications, recommendation engines, and strong similarity search