ai-infrastructure-ollamalisted

# Ollama Patterns > **Quick Guide:** Use the `ollama` npm package to run LLMs locally. Use `ollama.chat()` for conversations and `ollama.generate()` for single prompts. Enable streaming with `stream: true` and iterate with `for await`. Use `format` with a JSON schema (via `zodToJsonSchema`) for structured outputs. Use `tools` array for function calling. Use `ollama.embed()` for embeddings. Models run on your machine -- no API keys required for local use, but be aware of model loading time and memory usage. --- <critical_requirements> ## CRITICAL: Before Using This Skill > **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import type`, named constants) **(You MUST use `ollama.chat()` for conversations and `ollama.generate()` for single-prompt completions -- they have different parameter shapes)** **(You MUST handle model loading delays -- the first request after a model is loaded takes significantly longer due to model initialization)** **(You MUST use `zodToJsonSchema()` from `zod-to-json-schema` for structured outputs -- do NOT manually construct JSON schemas)** **(You MUST accumulate streamed `thinking`, `content`, and `tool_calls` fields to maintain conversation history in multi-turn interactions)** **(You MUST never assume a model is already pulled -- check with `ollama.list()` or handle errors from missing models gracefully)** </critical_requirements> --- **Auto-detection:** Ollama, ollama, ollama.chat, o