Adding `proxy` keyword to configure proxy while using zed. After setting
the proxy, restart Zed to acctually use the proxy.
Example setting:
```rust
"proxy" = "socks5://localhost:10808"
"proxy" = "http://127.0.0.1:10809"
```
Closes#9424, closes#9422, closes#8650, closes#5032, closes#6701,
closes#11890
Release Notes:
- Added settings to configure proxy in Zed
---------
Co-authored-by: Jason Lee <huacnlee@gmail.com>
Previously, a failure to embed the search query (due to a rate limit
error) would appear the same as if there were no results.
* Avoid repeatedly embedding the search query for each worktree
* Unify tasks for searching all worktree
Release Notes:
- N/A
This is a crate only addition of a new version of the AssistantPanel.
We'll be putting this behind a feature flag while we iron out the new
experience.
Release Notes:
- N/A
---------
Co-authored-by: Nathan Sobo <nathan@zed.dev>
Co-authored-by: Antonio Scandurra <me@as-cii.com>
Co-authored-by: Conrad Irwin <conrad@zed.dev>
Co-authored-by: Marshall Bowers <elliott.codes@gmail.com>
Co-authored-by: Antonio Scandurra <antonio@zed.dev>
Co-authored-by: Nate Butler <nate@zed.dev>
Co-authored-by: Nate Butler <iamnbutler@gmail.com>
Co-authored-by: Max Brunsfeld <maxbrunsfeld@gmail.com>
Co-authored-by: Max <max@zed.dev>
This introduces semantic indexing in Zed based on chunking text from
files in the developer's workspace and creating vector embeddings using
an embedding model. As part of this, we've created an embeddings
provider trait that allows us to work with OpenAI, a local Ollama model,
or a Zed hosted embedding.
The semantic index is built by breaking down text for known
(programming) languages into manageable chunks that are smaller than the
max token size. Each chunk is then fed to a language model to create a
high dimensional vector which is then normalized to a unit vector to
allow fast comparison with other vectors with a simple dot product.
Alongside the vector, we store the path of the file and the range within
the document where the vector was sourced from.
Zed will soon grok contextual similarity across different text snippets,
allowing for natural language search beyond keyword matching. This is
being put together both for human-based search as well as providing
results to Large Language Models to allow them to refine how they help
developers.
Remaining todo:
* [x] Change `provider` to `model` within the zed hosted embeddings
database (as its currently a combo of the provider and the model in one
name)
Release Notes:
- N/A
---------
Co-authored-by: Nathan Sobo <nathan@zed.dev>
Co-authored-by: Antonio Scandurra <me@as-cii.com>
Co-authored-by: Conrad Irwin <conrad@zed.dev>
Co-authored-by: Marshall Bowers <elliott.codes@gmail.com>
Co-authored-by: Antonio <antonio@zed.dev>
Antonio ScandurraandNathan
Mikayla Maki
张小白andJason Lee
Conrad Irwin
Max Brunsfeld
Marshall Bowers
