Skill UI

Guides hooking Phoenix AI services with OpenInference tracing, covering setup, spans, instrumentation, and production deployment for LLM workflows using Python or TypeScript.

A comprehensive guide to implementing production-ready patterns for interacting with the Together AI API. This includes a singleton client for seamless OpenAI-compatible integration, robust error wrappers with retry logic, and a fluent request builder. It covers best practices for LLM inference, model setup, and handling diverse response types (chat, embedding, fine-tuning).

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.