Rekayasa Data & Pipeline

langextract

google/langextract

LLM-based structured extraction with source-text alignment and visualization for reviewable document processing.

★ 38,6KBintang
⑂ 2,7KFork
135Isu terbuka
PythonBahasa
Apache-2.0Lisensi
Q90Skor editorial

Ringkasan

LangExtract turns text into entities and attributes while preserving where extracted spans came from. It fits after OCR or parsing and before business validation. Your team still defines schemas, examples, entity merging, and acceptable error rates.

Fitur utama

  • Few-shot extraction
  • Source alignment
  • Long-document processing
  • JSONL and visualization
  • Provider flexibility

Persyaratan, instalasi, dan mulai cepat

Install langextract, configure a documented model provider, and run the short-text extraction and visualization examples. Local Ollama has a separate example.

Penggunaan

Extract equipment, reported fault, and corrective action from maintenance notes. Check negation, dates, source alignment, and cross-paragraph links against labeled samples.

How it works
Instructions and examples guide extraction. Outputs can be saved as JSONL and visualized against source spans. An unaligned extraction may have no char_interval and should be reviewed or filtered.

Audience and requirements
Document and data engineering teams. Python, text inputs, labeled examples, and a supported model.

Practical use cases
Report structuring; entity extraction; annotation assistance.

Limitations and selection
Alignment does not guarantee semantic correctness or completeness. OCR remains a separate step. Chunking and provider limits affect quality and cost.

Related projects and selection
JaidedAI/EasyOCR:Complement: OCR supplies text; recognition errors propagate downstream.

explosion/spaCy:Comparison: spaCy serves fixed-label NLP pipelines; LangExtract uses task-specific LLM instructions.

Source review
Editorial analysis of upstream sources, without runtime or benchmark testing. Proposed workflows are editorial suggestions.

Kompatibilitas model dan kasus penggunaan

README documents Gemini, other providers, and local Ollama; schema capabilities depend on the provider.

Catatan lisensi dan risiko

The repository page identifies Apache-2.0. Read LICENSE; model weights and datasets may have separate terms.

Editorial source review 2026-09-09T05:00:00.950Z. README and live repository page verified; current stars/forks from GitHub HTML. Last-push metadata retained from 2026-09-05 discovery snapshot. No runtime benchmark. Integration proposals are editorial analysis.

Rilis dan pemeliharaan

Reviewed 2026-09-09. Counters come from repository pages; features are based on upstream documentation. See Releases in the source links. Editorial analysis of upstream sources, without runtime or benchmark testing. Proposed workflows are editorial suggestions.

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