This PR adds OpenAI-like API to `ChatModule`, specifically the
`chatCompletion` API. See `examples/openai-api` for example usage. See
[OpenAI reference](https://platform.openai.com/docs/api-reference/chat)
on its original usage.

Changes include:
- Implement `chatCompletion()` in `ChatModule`
- Expose conversation manipulation methods in `llm_chat.ts` so that
`request.messages` can override existing chat history or system prompt
in `chat_module.ts`
- Implement `src/openai_api_protocols` that represents all
OpenAI-related data structure; largely referred to
[openai-node](https://github.com/openai/openai-node/blob/master/src/resources/chat/completions.ts)
- Add `examples/openai-api` that demonstrates `chatCompletion()` for
both streaming and non-streaming usages, without web worker
- Support both streaming and non-streaming `chatCompletion()` in
`web_worker.ts` with example usage added to `examples/web-worker`
- For streaming with web worker, users have access to an async generator
whose `next()` sends/receives messages with the worker, which has an
underlying async generator that does the actual decodings

Existing gaps from full-OpenAI compatibility are listed in
https://github.com/mlc-ai/web-llm/issues/276, some may be unavoidable
(e.g. selecting `model` in request) while some are WIP.

Benchmarked performance via `{WebWorker, SingleThread} X {OAI-Stream,
OAI-NonStream, Generate}`, virtually no degradation, with +-1 tok/s
variation. Specifically, on M3 Max Llama 2 7B q4f32_1, decoding 128
tokens with a 12-token prompt yield:
- Prefill: 182 tok/s
- Decode: 48.3 tok/s
- End-to-end: 38.5 tok/s
- Where end-to-end is from the time we create the request to finish
everything; the time recorded on the highest level.