--- title: Lexical, Fuzzy, and Semantic Search Compared with... description: A practical comparison of three search strategies — lexical, fuzzy, and semantic — implemented with Amazon DynamoDB and Amazon Bedrock. Lexical search uses... canonical: https://daily.dev/posts/lexical-fuzzy-and-semantic-search-compared-with-dynamodb-and-amazon-bedrock-ev1yry241 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Lexical, Fuzzy, and Semantic Search Compared with DynamoDB and Amazon Bedrock | daily.dev og:description: A practical comparison of three search strategies — lexical, fuzzy, and semantic — implemented with Amazon DynamoDB and Amazon Bedrock. Lexical search uses... og:url: https://daily.dev/posts/lexical-fuzzy-and-semantic-search-compared-with-dynamodb-and-amazon-bedrock-ev1yry241 og:image: https://api.daily.dev/og/posts/ev1YRY241.png og:image:alt: Lexical, Fuzzy, and Semantic Search Compared with DynamoDB and Amazon Bedrock og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Lexical, Fuzzy, and Semantic Search Compared with DynamoDB and Amazon Bedrock **[Medium](https://daily.dev/sources/medium_js)** · 14 min read · 0 upvotes · 0 comments ## Summary A practical comparison of three search strategies — lexical, fuzzy, and semantic — implemented with Amazon DynamoDB and Amazon Bedrock. Lexical search uses DynamoDB's native `contains` filter for exact substring matching. Fuzzy search is explored through three distinct algorithms: Levenshtein distance (best for typos in short strings), SequenceMatcher/Ratcliff-Obershelp (best for partial names and abbreviations), and regex fuzzy matching (best for finding patterns within longer text with a fixed error budget). Semantic search uses Amazon Titan Text Embeddings V2 to generate vectors and cosine similarity to find conceptually related items regardless of wording. The article includes a Jupyter notebook with side-by-side test scenarios, a comparison table, and notes on when to use each approach. Additional strategies like tag-based search with fuzzy fallback, hybrid search, phonetic search, and schema-aware prompting are also briefly covered. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Implementing Hybrid Semantic \+ Lexical Search](https://daily.dev/posts/implementing-hybrid-semantic-lexical-search-ewvlp6hhm) · Kent C. 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