Structured Output with Language Models

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.

🚀 Features

• Multiple Framework Support: Examples for LangChain, vLLM, Outlines, Ollama, 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

📁 File Structure

API-Based Solutions

File	Description	Framework
Groq_Langchain.py	Groq API integration with LangChain	LangChain + Groq
Gemini_langchain.py	Google Gemini API with guided decoding	LangChain + Gemini

Local Model Solutions

File	Description	Framework
vLLM.py	Local vLLM server with JSON schema validation	vLLM
vLLM_openai_client.py	vLLM server via OpenAI-compatible client	vLLM + OpenAI Client

Ollama Integration

File	Description	Use Case
ollama.py	Direct Ollama chat API usage	Simple structured outputs
OllamaLLM.py	Single prompt processing	Individual requests
OllamaLLM_MultiModel.py	MultiModel with structured outputs	Individual requests with Image
OllamaLLM_Batch_Processing.py	Batch processing with Pydantic validation	High-throughput scenarios
chatOllama.py	Chat-based interface	Conversational structured outputs

Outlines Library

File	Description	Model Type
Outlines_for_transformers.py	Transformer models with JSON generation	HuggingFace Transformers
Outlines_for_GGUF.py	GGUF models via llama_cpp backend	Quantized models
Outlines_for_transformers_vision.py	Vision-language models	Multimodal inputs
Outlines_for_transformers_vision_batch.py	Batch vision processing	High-volume multimodal

🔧 Quick Start

Basic Name and Age Extraction

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

⚠️ Important Notes

• 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

🎯 Use Cases

• 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

🤝 Contributing

Contributions are welcome! Feel free to:

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

📄 License

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

🔗 Related Resources

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