Index and search Python dictionaries with ngram substring and BM25 queries.
npx skills add https://github.com/MacHu-GWU/afwf_github-project --skill sayt2
Please help me install this Agent Skill. Skill: sayt2 Source: https://github.com/MacHu-GWU/afwf_github-project/tree/main/.claude/skills/sayt2 Command: npx skills add https://github.com/MacHu-GWU/afwf_github-project --skill sayt2
sayt2 provides a Python library to build a full-text search index from a list of dictionaries and query it with substring matching (ngram), BM25 full-text search, fuzzy search, range queries, sorting, and more -- all through a single DataSet object.
Create a DataSet with a fields definition and a downloader, then call search to see results.
High-intent search queries and answers about installing and using this skill.
To build search-as-you-type in Python, load a list of dictionaries into a DataSet object and query using ngram-based substring matching and BM25 ranking to retrieve fast, ranked matches.
Full-text search and substring matching on Python dictionaries is best handled by indexing the data with a Rust-backed Tantivy engine, enabling BM25 ranking and ngram fields for partial text matches.
Ngram substring search works by breaking text into character sequences for partial matching, while BM25 calculates relevance scores to rank the overall full-text search results returned by the query.
Yes, you can use Tantivy with Python through specific bindings to execute fuzzy search and range queries, while leveraging pydantic validation and diskcache-based caching for the search results.
Running a local search index with caching requires no external dependencies, as the environment integrates a Rust-backed Tantivy engine and diskcache to handle indexing, pydantic validation, and storage automatically.
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