This PR includes a new crate, aimed at maintaining a consistent semantic
embedding database, for any project opened with Zed. At a high level,
for each file in a project, we parse the file with treesitter, embed the
symbol "document" objects with OpenAI, and maintain a consistent
database of these embeddings and offset locations in a sqlite database.
Once stored, we have built a simple modal interface for querying on
these symbols embeddings using natural language, offering the
opportunity to navigate to the selected symbol.

This initial PR is intended to provide this functionality only in preview,
as we explore, evaluate and iterate on the vector store.

- Full task details are provided in the [Semantic Search Linear
Project](https://linear.app/zed-industries/project/semantic-search-7c787d198ebe/Z)