Key features
- Pydantic model return — typed APAC LLM output instead of raw text
- Automatic retry with validation error feedback for APAC schema compliance
- Multi-provider — Anthropic, OpenAI, vLLM, Ollama for APAC data sovereignty
- Field-level Pydantic validators for APAC business rule enforcement
- Streaming support for partial model extraction from APAC LLM responses
- Async support for high-throughput APAC extraction pipelines
Best for
- APAC document processing extracting structured data from invoices, contracts, and regulatory filings requiring reliable schema compliance
- APAC teams using Pydantic for data validation who want LLM extraction integrated with existing APAC data models
- APAC AI applications requiring structured output for downstream classification and entity extraction
Limitations to know
- ! Python-only — APAC TypeScript or Go teams need alternative structured output approaches
- ! Retry token costs — validation failures rerun the full APAC LLM call; tune retry limits for complex APAC schemas
- ! Pydantic v1/v2 compatibility — version mismatches cause subtle APAC runtime errors
About Instructor
Instructor patches official LLM SDK clients to return typed Pydantic model instances instead of raw text — APAC engineers define output structure as Pydantic models and Instructor automatically prompts the LLM to generate matching JSON, parses and validates the response, and retries with validation error feedback if the output fails APAC schema validation. Multi-provider support covers Anthropic, OpenAI, Cohere, Gemini, and self-hosted APAC LLMs via vLLM and Ollama with the same APAC extraction code.
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