How to Implement Structured Output with Local LLMs
A guide to obtaining structured data output from local large language models.
1Advantages of Local LLMs
Local large language models reduce dependency on cloud APIs and enhance data privacy, particularly crucial in sectors like healthcare and finance. Models like Gemma 4 offer high performance with low resource consumption through their 4B parameter size. Local execution significantly decreases data transfer costs while maintaining processing capabilities comparable to cloud solutions for many use cases.
2Need for Structured Output
The natural output format of LLMs is free-form text, which poses challenges for automation systems. Critical data like device scheduling in smart home applications requires structured formats. The Pydantic library enables schema definitions for Python objects, providing both data validation and automatic documentation generation. This structured approach ensures consistency in downstream processing.
3Smart Home Application Example
The article presents a system responding to user queries like 'Should the dishwasher run now or later?' It extracts device information, timing constraints, and electricity tariffs from household notes. The system records operational details for multiple devices including robot vacuums, dishwashers, and dryers - tracking their run times and energy consumption. Electricity costs vary across different time blocks, adding complexity to scheduling decisions.
4Technical Implementation Details
Ollama serves as the runtime environment for local LLM execution, installable via winget on Windows or curl on macOS/Linux. The Gemma 4 model downloads via 'ollama pull gemma4:e4b' command. Python environment setup requires Ollama client and Pydantic installation through pip. The 4B parameter model offers an optimal balance between capability and resource usage for local deployment scenarios.
5Pydantic Schema Definition
The ClockTime model enforces HH:MM time format validation. DeviceToSchedule captures device-specific details including name, runtime duration, and energy consumption. SchedulingContext defines the broader home context including current time, focus device, and concurrent operation limits. The nested model structure elegantly represents complex real-world data relationships while maintaining validation rigor.
6LLM Calling and Transformation
The call_local_llm function facilitates structured output by passing defined schemas to the LLM. System messages explicitly instruct the model to transform data without making decisions. Combined user questions and source material form the prompt input. The LLM's output undergoes automatic Pydantic validation, converting directly to Python objects for seamless integration with application logic.
7Application Scenarios
This technique finds applications in medical record anonymization, financial report processing, and customer data structuring. It proves particularly valuable for GDPR and HIPAA compliant industries requiring local data processing. Additional use cases include structuring e-commerce product reviews and generating consistent responses for customer service chatbots, demonstrating broad applicability across sectors.
8Limitations and Solutions
Local models may lack the capability of larger cloud-based counterparts, prompting the article's two-phase approach: sensitive data processing locally and complex logic offloading to cloud LLMs. Accuracy may decrease with smaller model sizes, emphasizing the need for simple, clear schema design. Implementation should include retry mechanisms for error handling and fallback procedures when structured output validation fails.
9Future Applications
Structured output techniques will streamline enterprise data integration and enable automatic processing of raw IoT device data. Low-code platforms will leverage these methods for more effective LLM utilization. As local LLMs proliferate, significant advances in edge computing applications are anticipated. This technology holds particular promise for industrial applications requiring real-time decision making, potentially revolutionizing operational efficiency across sectors.
Original article: Towards Data Science — Yerel LLM'lerle Yapılandırılmış Çıktı Nasıl Uygulanır?