c9c603b1d1
This pull request adds full integration with OpenRouter, allowing users
to access a wide variety of language models through a single API key.
**Implementation Details:**
* **Provider Registration:** Registers OpenRouter as a new language
model provider within the application's model registry. This includes UI
for API key authentication, token counting, streaming completions, and
tool-call handling.
* **Dedicated Crate:** Adds a new `open_router` crate to manage
interactions with the OpenRouter HTTP API, including model discovery and
streaming helpers.
* **UI & Configuration:** Extends workspace manifests, the settings
schema, icons, and default configurations to surface the OpenRouter
provider and its settings within the UI.
* **Readability:** Reformats JSON arrays within the settings files for
improved readability.
**Design Decisions & Discussion Points:**
* **Code Reuse:** I leveraged much of the existing logic from the
`openai` provider integration due to the significant similarities
between the OpenAI and OpenRouter API specifications.
* **Default Model:** I set the default model to `openrouter/auto`. This
model automatically routes user prompts to the most suitable underlying
model on OpenRouter, providing a convenient starting point.
* **Model Population Strategy:**
* <strike>I've implemented dynamic population of available models by
querying the OpenRouter API upon initialization.
* Currently, this involves three separate API calls: one for all models,
one for tool-use models, and one for models good at programming.
* The data from the tool-use API call sets a `tool_use` flag for
relevant models.
* The data from the programming models API call is used to sort the
list, prioritizing coding-focused models in the dropdown.</strike>
* <strike>**Feedback Welcome:** I acknowledge this multi-call approach
is API-intensive. I am open to feedback and alternative implementation
suggestions if the team believes this can be optimized.</strike>
* **Update: Now this has been simplified to one api call.**
* **UI/UX Considerations:**
* <strike>Authentication Method: Currently, I've implemented the
standard API key input in settings, similar to other providers like
OpenAI/Anthropic. However, OpenRouter also supports OAuth 2.0 with PKCE.
This could offer a potentially smoother, more integrated setup
experience for users (e.g., clicking a button to authorize instead of
copy-pasting a key). Should we prioritize implementing OAuth PKCE now,
or perhaps add it as an alternative option later?</strike>(PKCE is not
straight forward and complicated so skipping this for now. So that we
can add the support and work on this later.)
* <strike>To visually distinguish models better suited for programming,
I've considered adding a marker (e.g., `</>` or `🧠`) next to their
names. Thoughts on this proposal?</strike>. (This will require a changes
and discussion across model provider. This doesn't fall under the scope
of current PR).
* OpenRouter offers 300+ models. The current implementation loads all of
them. **Feedback Needed:** Should we refine this list or implement more
sophisticated filtering/categorization for better usability?
**Motivation:**
This integration directly addresses one of the most highly upvoted
feature requests/discussions within the Zed community. Adding OpenRouter
support significantly expands the range of AI models accessible to
users.
I welcome feedback from the Zed team on this implementation and the
design choices made. I am eager to refine this feature and make it
available to users.
ISSUES: https://github.com/zed-industries/zed/discussions/16576
Release Notes:
- Added support for OpenRouter as a language model provider.
---------
Signed-off-by: Umesh Yadav <umesh4257@gmail.com>
Co-authored-by: Marshall Bowers <git@maxdeviant.com>
65 lines
1.9 KiB
TOML
65 lines
1.9 KiB
TOML
[package]
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name = "language_models"
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version = "0.1.0"
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edition.workspace = true
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publish.workspace = true
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license = "GPL-3.0-or-later"
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[lints]
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workspace = true
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[lib]
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path = "src/language_models.rs"
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[dependencies]
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anthropic = { workspace = true, features = ["schemars"] }
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anyhow.workspace = true
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aws-config = { workspace = true, features = ["behavior-version-latest"] }
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aws-credential-types = { workspace = true, features = [
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"hardcoded-credentials",
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] }
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aws_http_client.workspace = true
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bedrock.workspace = true
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client.workspace = true
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collections.workspace = true
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credentials_provider.workspace = true
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copilot.workspace = true
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deepseek = { workspace = true, features = ["schemars"] }
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editor.workspace = true
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fs.workspace = true
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futures.workspace = true
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google_ai = { workspace = true, features = ["schemars"] }
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gpui.workspace = true
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gpui_tokio.workspace = true
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http_client.workspace = true
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language_model.workspace = true
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lmstudio = { workspace = true, features = ["schemars"] }
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log.workspace = true
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menu.workspace = true
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mistral = { workspace = true, features = ["schemars"] }
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ollama = { workspace = true, features = ["schemars"] }
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open_ai = { workspace = true, features = ["schemars"] }
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open_router = { workspace = true, features = ["schemars"] }
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partial-json-fixer.workspace = true
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project.workspace = true
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proto.workspace = true
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release_channel.workspace = true
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schemars.workspace = true
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serde.workspace = true
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serde_json.workspace = true
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settings.workspace = true
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smol.workspace = true
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strum.workspace = true
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theme.workspace = true
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thiserror.workspace = true
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tiktoken-rs.workspace = true
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tokio = { workspace = true, features = ["rt", "rt-multi-thread"] }
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ui.workspace = true
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util.workspace = true
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workspace-hack.workspace = true
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zed_llm_client.workspace = true
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[dev-dependencies]
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editor = { workspace = true, features = ["test-support"] }
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language_model = { workspace = true, features = ["test-support"] }
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project = { workspace = true, features = ["test-support"] }
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