A comprehensive collection of examples demonstrating structured data extraction and JSON output generation using various language models and frameworks. This repository showcases different approaches to ensure LLMs return well-formatted, schema-compliant responses.

• Multiple Framework Support: Examples for LangChain, vLLM, Outlines, Ollama,OpenAI and more
• Pydantic Integration: Type-safe structured outputs with validation
• Batch Processing: Efficient handling of multiple prompts
• Vision Model Support: Structured outputs from multimodal models
• Flexible Backends: Support for local models, API services, and GGUF formats

from pydantic import BaseModel
from typing import Optional

class PersonInfo(BaseModel):
    name: str
    age: Optional[int] = None
    
# Use any of the provided scripts with this schema

• Vision Models: Outlines_for_transformers_vision.py requires PyTorch 2.4 specifically
• GGUF Models: Ensure llama_cpp is properly installed for GGUF examples
• API Keys: Set appropriate environment variables for Groq and Gemini examples

• Data Extraction: Extract structured information from unstructured text
• API Responses: Ensure consistent JSON responses from LLMs
• Batch Processing: Process large datasets with structured outputs
• Multimodal Analysis: Extract structured data from images and text
• Validation: Type-safe outputs with automatic validation

Contributions are welcome! Feel free to:

• Add examples for new frameworks
• Improve existing implementations
• Add error handling and edge cases
• Enhance documentation

This project is open source. Please check individual dependencies for their licensing terms.

• LangChain Documentation
• Outlines Library
• vLLM Documentation
• Pydantic Documentation