How does Instructor handle invalid LLM responses?

When validation fails, Instructor automatically feeds the validation error back to the LLM and requests a correction, performing multiple retries until the output is valid.

Can I use Instructor for real-time streaming?

Yes, Instructor supports streaming partial results, allowing your application to begin processing or displaying data even before the full LLM response is finished.

Is Instructor limited to simple data types?

No, Instructor handles complex data structures including nested Pydantic models, lists of objects, Enums, and custom field validators.

What is the primary benefit of using Instructor with Claude?

Instructor ensures that Claude's responses conform exactly to a predefined Pydantic schema, providing type safety and reducing the risk of runtime errors in your application.

Does this skill work with local models?

Yes, Instructor is compatible with local model providers like Ollama via its OpenAI-compatible mode, allowing for structured outputs from local LLMs.

Instructor Structured Outputs

Name: Instructor Structured Outputs
Author: zechenzhangAGI

byzechenzhangAGI

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384

数据科学与机器学习

Extracts structured, type-safe data from Large Language Models using Pydantic validation and automatic retries.

关于

Instructor is a powerful library designed to bridge the gap between unstructured LLM responses and reliable, production-grade data. By leveraging Pydantic, it allows developers to define strict schemas for AI outputs, ensuring type safety and consistency across multiple providers including Anthropic and OpenAI. The skill excels at handling complex JSON parsing, providing automatic error-correction via retries, and supporting real-time streaming of partial results, making it an essential tool for building robust AI-driven applications that require predictable data structures.

主要功能

Pydantic-based schema validation for LLM responses
Support for nested models, enums, and complex validation rules
Cross-provider compatibility with Anthropic, OpenAI, and Ollama
384 GitHub stars
Real-time streaming of partial objects and iterables
Automatic error handling and validation-driven retries

使用场景

Building automated classification systems with predefined enum constraints
Extracting specific entities and relationships from unstructured documents
Implementing real-time data pipelines that process AI outputs as they are generated

关于

主要功能

Pydantic-based schema validation for LLM responses
Support for nested models, enums, and complex validation rules
Cross-provider compatibility with Anthropic, OpenAI, and Ollama
384 GitHub stars
Real-time streaming of partial objects and iterables
Automatic error handling and validation-driven retries

使用场景

Building automated classification systems with predefined enum constraints
Extracting specific entities and relationships from unstructured documents
Implementing real-time data pipelines that process AI outputs as they are generated