Fetcher LLM Streaming

fetcher-llm-streaming

Workflow

1. Import @ahoo-wang/fetcher-eventstream for side-effect prototype helpers when using Response extensions.
2. Use standalone conversion functions when prototype mutation is undesirable.
3. Handle stream conversion errors explicitly with EventStreamConvertError.
4. For OpenAI-style DONE termination, supply a detector function (TerminateDetector); the ready-made DoneDetector lives in @ahoo-wang/fetcher-openai.
5. Load references/api.md for pipeline details, OpenAI streaming examples, and UI update patterns.

Key Practices

• Keep parsing, termination detection, and UI state updates as separate steps.
• Use async iteration over streams to avoid buffering full responses in memory.
• When pairing with decorators, configure result extractors at the endpoint boundary.

References

• references/api.md: Detailed package API, examples, and edge-case guidance. Load it only when the task needs prototype extensions, standalone stream functions, SSE structures, termination handling, OpenAI streaming examples, and React UI update snippets.

Related Skills

• $fetcher-openai-client: Use for higher-level OpenAI chat client setup.
• $fetcher-decorator-service: Use when the streaming endpoint is declared with decorators.
• $fetcher-react-hooks: Use when streaming data drives React state.