LangExtract Features

Precise Source Grounding

Every extracted value includes precise source grounding—exact character offsets and spans that show where in the original text each piece of information came from. This enables:

• Provenance tracking: Know exactly where each extracted value originated
• Verification: Review and validate extractions against source text
• Debugging: Understand model behavior and improve prompts
• Compliance: Meet requirements for explainable AI and auditability

Learn more about grounding and span data structures in the documentation.

Schema Validation & Type Safety

Define structured schemas using Pydantic models to ensure type safety and validation:

• Type checking: Automatic validation of extracted data types
• Field constraints: Enforce required fields, optional fields, and nested structures
• Error handling: Clear validation errors when extraction doesn't match schema
• IDE support: Full autocomplete and type hints in your editor

See the schemas & validation guide for detailed examples.

Multi-Provider Support

LangExtract works with multiple LLM providers, giving you flexibility and cost optimization:

• Google Gemini: Native support for Gemini models via AI Studio or Vertex AI
• OpenAI: Support for GPT-4o and other OpenAI models
• Ollama: Run local models without API keys for privacy and cost savings
• Custom providers: Extensible plugin system for adding your own providers
• Batch processing: Vertex AI Batch API support for large-scale tasks

Explore all provider options and configurations.

Interactive Visualization

Built-in visualization tools help you understand and debug extractions:

• Span highlighting: Visual overlay showing extracted spans in source text
• Structured view: Formatted display of extracted data structures
• Interactive exploration: Click through extractions to see source locations
• Export options: Save visualizations for documentation and presentations

Check out the visualization guide and see it in action in the radiology report example.

Example-Based Prompting

LangExtract uses few-shot learning with example-based prompting:

• Few-shot examples: Provide example input-output pairs to guide extraction
• Pattern learning: Models learn extraction patterns from your examples
• Domain adaptation: Easily adapt to new domains with targeted examples
• Iterative improvement: Refine prompts based on extraction results

Learn extraction design patterns and see examples in the Examples section.

Parallel Processing

Efficient processing for large-scale extraction tasks:

• Concurrent requests: Process multiple documents in parallel
• Batch API support: Use Vertex AI Batch API for cost-effective large-scale processing
• Rate limiting: Built-in handling of API rate limits
• Error recovery: Robust error handling and retry logic

Production Ready

Features designed for production deployments:

• Error handling: Comprehensive error types and recovery strategies
• Logging: Detailed logging for debugging and monitoring
• Performance: Optimized for speed and cost efficiency
• Documentation: Extensive docs, examples, and community resources
• Testing: Comprehensive test suite and benchmarks