Key features
- SQL-like syntax: APAC declarative structured generation query programs for APAC text
- Type constraints: APAC typed output variables with cardinality and value validation
- Source grounding: APAC enforce extracted values appear in APAC source document
- Multi-step: APAC chained reasoning steps with intermediate constraint validation
- HuggingFace: APAC full constraint on local APAC-language model backends
- Reproducible: APAC version-controlled extraction specifications across LLM backends
Best for
- APAC ML engineering and research teams building reproducible structured extraction pipelines from Japanese, Korean, and Chinese text — particularly APAC organizations that need type-safe, version-controlled extraction specifications that encode both extraction logic and correctness constraints in a single auditable program rather than evolving natural language prompt strings.
Limitations to know
- ! APAC new syntax learning curve vs Pydantic/JSON schema structured output approaches
- ! APAC smaller production ecosystem than Instructor or Pydantic-based structured output libraries
- ! APAC constraint validation overhead adds inference cost for high-throughput APAC extraction
About LMQL
LMQL is an open-source LLM query language from ETH Zürich that provides APAC engineering teams with a declarative programming interface for LLM generation — expressing output constraints, variable interpolation, control flow, and validation rules as type-safe query programs with SQL-inspired syntax, replacing brittle prompt strings with structured query specifications that encode both the extraction logic and its correctness constraints in a single reproducible artifact.
LMQL's query syntax enables APAC teams to express structured extraction tasks as typed programs rather than natural language instructions — a query that extracts named entities from Japanese legal text specifies the output variable names, their types (str, int, bool), cardinality constraints (at most N entities), and validation conditions (entity text must appear in source document) all within a single LMQL program that runs against any supported LLM backend. APAC information extraction teams use LMQL programs as version-controlled extraction specifications that produce deterministic output schemas regardless of which LLM backend executes them.
LMQL's constraint system enables APAC teams to express generation constraints beyond JSON schema — specifying that an extracted entity must appear verbatim in the input text, that a generated answer must not exceed a character limit, or that intermediate reasoning steps must satisfy semantic conditions before the final extraction is accepted. APAC fact extraction systems for Korean news and Japanese regulatory filings use LMQL constraint programs to enforce domain-specific validation rules that catch hallucinated extractions before they enter downstream pipelines.
LMQL integrates with HuggingFace Transformers and OpenAI APIs — APAC teams running local APAC-language models use LMQL's full constraint validation on model outputs, while APAC teams using cloud APIs benefit from LMQL's programmatic prompt construction and output validation at the application layer. APAC research and development teams use LMQL's query format to systematically compare structured extraction quality across different LLMs, enabling reproducible benchmarking of Japanese and Korean extraction accuracy across model versions.
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