instructor

Structured data extraction from LLM responses is handled using pydantic validation to ensure type safety and output correctness. It automatically validates extracted JSON formats and retries failed attempts using error feedback for high accuracy.

Parsing complex JSON from LLM outputs is managed by defining pydantic models for validation. The system ensures output correctness by validating the extracted data against your schema and reattempting extraction upon validation failure.

Streaming partial structured data outputs is supported for real-time processing and interactive workflows. This allows applications to consume validated data incrementally as the language model generates the response.

Structured data extraction works with both OpenAI and Anthropic APIs, requiring these dependencies alongside pydantic to function effectively. It integrates with various LLM APIs to automate validated data extraction.

Handling LLM data extraction failures is achieved through automatic validation and retry mechanisms. Upon encountering invalid JSON or validation errors, it provides error feedback to the LLM and reattempts extraction to ensure data correctness.