Nerif: The Python-Native Framework for Structured LLM Outputs & Real-Time Performance Metrics
1 months ago 高效码农Nerif: A Python-Native Way to Make Large Language Models Behave Like Ordinary Functions Large language models (LLMs) can feel like a gifted but unpredictable intern: brilliant one moment, rambling the next. Existing tools such as LangChain or Dify help, yet they often add layers of abstraction that hide what the model is actually doing. Nerif takes a different path—one that keeps LLMs firmly inside your Python code while still giving you exact control over prompts, outputs, and performance metrics. What Nerif Does, in Plain English ❀ Turn natural-language questions into True/False answers without writing ten-line prompts. ❀ Return LLM responses …
Unlock Structured LLM Outputs with Instructor: The Ultimate Developer’s Guide
3 months ago 高效码农Unlock Structured LLM Outputs with Instructor: The Developer’s Ultimate Guide Introduction: The Critical Need for Structured Outputs When working with large language models like ChatGPT, developers consistently face output unpredictability. Models might return JSON, XML, or plain text in inconsistent formats, complicating downstream processing. This is where Instructor solves a fundamental challenge—it acts as a precision “output controller” for language models. Comprehensive Feature Breakdown Six Core Capabilities Model Definition: Structure outputs using Pydantic class UserProfile(BaseModel): name: str = Field(description=”Full name”) age: int = Field(ge=0, description=”Age in years”) Auto-Retry: Built-in API error recovery client = instructor.from_openai(OpenAI(max_retries=3)) Real-Time Validation: Enforce business rules …