temporal-ray-demo

Expert guidance for integrating Azure OpenAI services into Python applications. Use when implementing LLM-powered features, chat completions, embeddings, structured output extraction, rate limiting, fallback strategies, or Azure-specific authentication. Covers both sync and async patterns.

Expert guidance for extracting text and data from PDF documents in Python. Use when implementing document ingestion pipelines, text extraction, chunking strategies, OCR for scanned documents, or metadata extraction. Covers pypdf, pdfplumber, and integration with LLM pipelines.

Expert guidance for using Pydantic V2 for data validation, serialization, and type-safe Python. Use when defining data models, validating API inputs/outputs, parsing JSON, handling LLM structured outputs, or integrating with Temporal workflows. Covers BaseModel, dataclasses, validators, and serialization.

Expert guidance for building distributed Python applications with Ray. Use when implementing parallel processing, distributed actors, remote tasks, GPU workloads, or scaling Python code across clusters. Covers Ray Core primitives, design patterns, anti-patterns, and performance optimization.

Expert guidance for combining Temporal durable workflows with Ray distributed computing. Use when building resilient parallel processing pipelines, fault-tolerant ML workflows, or systems requiring both durability AND parallelism. Covers integration patterns, error handling across both systems, and production architecture.

Expert guidance for building durable, fault-tolerant applications with Temporal. Use when implementing long-running workflows, retry logic, activity orchestration, state persistence, failure recovery, or distributed transactions. Covers Temporal Python SDK, workflow patterns, and production best practices.