Extract and validate structured data from LLM responses using Pydantic schemas.
npx skills add https://github.com/CUexter/hermes-agent --skill instructor-cuexter
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/CUexter/hermes-agent/tree/main/skills/mlops/inference/instructor Command: npx skills add https://github.com/CUexter/hermes-agent --skill instructor-cuexter
Extract, validate, and stream structured data from LLM responses using Pydantic schemas, ensuring reliable, type-safe outputs with automatic retries on validation failures.
Extract a validated User model from an LLM reply and stream the result in real time
High-intent search queries and answers about installing and using this skill.
You can validate structured LLM outputs by defining a Pydantic schema and passing it to the extraction function. The system automatically validates the LLM response against your schema to ensure type-safe data extraction.
To handle LLM validation errors, the system applies automatic retries on validation failures. When the LLM response does not match your Pydantic schema, it automatically retries the request to improve reliability.
Yes, you can stream partial structured data results for real-time processing. The system supports streaming partial results as they are generated by the LLM, allowing immediate downstream data consumption.
Yes, structured data extraction works with Anthropic and Ollama. The system features multi-provider support, including OpenAI, Anthropic, and Ollama, ensuring flexible deployments across different LLM platforms.
The best way to extract type-safe data is using Pydantic schemas to validate LLM responses. This approach combines automatic validation with retry mechanisms to ensure robust, reliable data pipelines without manual parsing.
Using Pydantic for structured LLM outputs ensures type safety and automatic validation, overcoming the fragility of manual parsing. It provides automatic retries on failures and native streaming support for robust data extraction.
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