This PR updates the `storybook` with support for adding stories for
individual components.
### Motivation
Right now we just have one story in the storybook that renders an entire
`WorkspaceElement`.
While iterating on the various UI components, it will be helpful to be
able to create stories of those components just by themselves.
This is especially true for components that have a number of different
states, as we can render the components in all of the various states in
a single layout.
### Explanation
We achieve this by adding a simple CLI to the storybook.
The `storybook` binary now accepts an optional `[STORY]` parameter that
can be used to indicate which story should be loaded. If this parameter
is not provided, it will load the workspace story as it currently does.
Passing a story name will load the corresponding story, if it exists.
For example:
```
cargo run -- elements/avatar
```
<img width="723" alt="Screenshot 2023-09-21 at 10 29 52 PM"
src="https://github.com/zed-industries/zed/assets/1486634/5df489ed-8607-4024-9c19-c5f4541f97c9">
```
cargo run -- components/facepile
```
<img width="785" alt="Screenshot 2023-09-21 at 10 30 07 PM"
src="https://github.com/zed-industries/zed/assets/1486634/e04a4577-7403-405d-b23c-e765b7a06229">
Release Notes:
- N/A
* [x] Re-send operations that weren't sent while disconnected
* [x] Apply other clients' operations that were missed while
disconnected
* [x] Update collaborators that joined / left while disconnected
* [x] Inform current collaborators that your peer id has changed
* [x] Refresh channel buffer collaborators on server restart
* [x] randomized test
This PR ships a series of optimizations for the semantic search engine.
Mostly focused on removing invalid states, optimizing requests to
OpenAI, and reducing token usage.
Release Notes (Preview-Only):
- Added eager incremental indexing in the background on a debounce.
- Added a local embeddings cache for reducing redundant calls to OpenAI.
- Moved to an Embeddings Queue model which ensures optimal batch sizes
at the token level, and atomic file & document writes.
- Adjusted OpenAI Embedding API requests to use provided backoff delays
during Rate Limiting.
- Removed flush races between parsing files step and embedding queue
steps.
- Moved truncation to parsing step reducing the probability that OpenAI
encounters bad data.
