The 'query document' is now a first-class concept in QMD: a structured
document with typed sub-queries that combine for best recall.
## Query types
- lex: BM25 keyword search with phrase and negation syntax
- vec: Semantic vector search (natural language questions)
- hyde: Hypothetical document (write the expected answer)
- expand: Auto-expand via local LLM (max 1, default for plain queries)
## Lex syntax
Full BM25 operator support:
"exact phrase" verbatim match, no prefix
-term exclude documents containing term
-"exact phrase" exclude documents containing phrase
Examples:
"C++ performance" optimization -sports -athlete
"connection pool" timeout -redis
"machine learning" -sports -athlete
## MCP tool description rewritten
The 'query' tool description now fully teaches AI agents the query
document format, lex syntax, and strategy for combining types.
Includes worked examples including intent-aware lex (C++ performance,
not sports) which is critical for disambiguation in dense corpora.
## Unit tests
11 new lex parser tests covering:
- plain terms, quoted phrases, negation, combined
- intent-aware disambiguation (performance -sports -athlete)
- only-negation returns null (FTS5 constraint)
- empty/whitespace handling
## Training data
12 new intent-aware examples for next model training round:
- Real technical topics with lex phrase+negation combinations
- Covers: C++ perf, Python memory, DB connections, rate limiting,
SQL optimization, ML overfitting, Docker, JWT, async/await,
git conflicts, Kubernetes, React state
- Each shows how context/intent shapes lex query construction
(e.g. performance with C++ context → -sports -athlete exclusions)
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