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Introduction to Embeddings
- Embeddings are numeric representations of text that encode semantic information
- They can be used for improved search and retrieval, compared to simple lexical matching
- Key concepts:
- Tokens: Words or units of text
- Chunk size: How the text is broken into smaller pieces for embedding
- Overlap: The overlap between chunks
- Embedding models: Used to convert text into numeric embeddings (e.g. Amazon Titan)
Scaling Embeddings
- Scaling embeddings comes with challenges:
- Cost of creating embeddings for documents and queries
- Supporting multiple languages and data types (e.g. code)
- Processing speed for large document collections
- Storage cost for the embeddings
Titan Text Embeddings
- Titan text embeddings address these challenges:
- Low cost for embedding creation: $0.00002 per 1K tokens
- Support for English, multilingual, and code
- Two options for embedding speed: online inference or batch processing
- Reducing storage cost through techniques:
- Chopping: Reducing the number of dimensions in the embeddings
- Rounding: Reducing precision by rounding to integers or using binary (0/1)
- Combining chopping and rounding
Net DOS Success Story
- Net DOS is a document management company with billions of documents
- They explored using embeddings but found traditional float-based embeddings were prohibitively expensive to store
- Adopted Titan binary embeddings, achieving:
- 90%+ reduction in storage requirements
- 86% reduction in hardware costs for hosting the vector database
Architecture Approaches
- Basic approach:
- Embed documents in S3, store embeddings in OpenSearch
- Embed queries, search OpenSearch, fetch documents from S3
- Hybrid approach:
- Store both binary and float-point embeddings
- Use binary for initial search, then re-rank using float-point
- Advanced hybrid approach:
- Store binary embeddings in OpenSearch
- Store float-point embeddings and document passages in S3
- Use binary for initial search, then re-rank using float-point from S3
The choice of approach depends on the tradeoffs between storage cost, search quality, and performance requirements for the specific use case.