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vercel: Reuse existing OpenAI code (#33362)

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Follow up to #33292

Since Vercel's API is OpenAI compatible, we can reuse a bunch of code.

Release Notes:

- N/A
This commit is contained in:
Bennet Bo Fenner 2025-06-25 15:04:43 +02:00 • committed by GitHub
parent c979452c2d
commit 18f1221a44
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GPG key ID: B5690EEEBB952194
6 changed files with 30 additions and 674 deletions
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7
crates/language_models/src/provider/cloud.rs
View file

@ -888,7 +888,12 @@ impl LanguageModel for CloudLanguageModel {
Ok(model) => model,
Err(err) => return async move { Err(anyhow!(err).into()) }.boxed(),
};
let request = into_open_ai(request, &model, None);
let request = into_open_ai(
request,
model.id(),
model.supports_parallel_tool_calls(),
None,
);
let llm_api_token = self.llm_api_token.clone();
let future = self.request_limiter.stream(async move {
let PerformLlmCompletionResponse {

16
crates/language_models/src/provider/open_ai.rs
View file

@ -344,7 +344,12 @@ impl LanguageModel for OpenAiLanguageModel {
LanguageModelCompletionError,
>,
> {
let request = into_open_ai(request, &self.model, self.max_output_tokens());
let request = into_open_ai(
request,
self.model.id(),
self.model.supports_parallel_tool_calls(),
self.max_output_tokens(),
);
let completions = self.stream_completion(request, cx);
async move {
let mapper = OpenAiEventMapper::new();
@ -356,10 +361,11 @@ impl LanguageModel for OpenAiLanguageModel {
pub fn into_open_ai(
request: LanguageModelRequest,
model: &Model,
model_id: &str,
supports_parallel_tool_calls: bool,
max_output_tokens: Option<u64>,
) -> open_ai::Request {
let stream = !model.id().starts_with("o1-");
let stream = !model_id.starts_with("o1-");
let mut messages = Vec::new();
for message in request.messages {
@ -435,13 +441,13 @@ pub fn into_open_ai(
}
open_ai::Request {
model: model.id().into(),
model: model_id.into(),
messages,
stream,
stop: request.stop,
temperature: request.temperature.unwrap_or(1.0),
max_completion_tokens: max_output_tokens,
parallel_tool_calls: if model.supports_parallel_tool_calls() && !request.tools.is_empty() {
parallel_tool_calls: if supports_parallel_tool_calls && !request.tools.is_empty() {
// Disable parallel tool calls, as the Agent currently expects a maximum of one per turn.
Some(false)
} else {

313
crates/language_models/src/provider/vercel.rs
View file

@ -1,8 +1,6 @@
use anyhow::{Context as _, Result, anyhow};
use collections::{BTreeMap, HashMap};
use collections::BTreeMap;
use credentials_provider::CredentialsProvider;
use futures::Stream;
use futures::{FutureExt, StreamExt, future::BoxFuture};
use gpui::{AnyView, App, AsyncApp, Context, Entity, Subscription, Task, Window};
use http_client::HttpClient;
@ -10,16 +8,13 @@ use language_model::{
AuthenticateError, LanguageModel, LanguageModelCompletionError, LanguageModelCompletionEvent,
LanguageModelId, LanguageModelName, LanguageModelProvider, LanguageModelProviderId,
LanguageModelProviderName, LanguageModelProviderState, LanguageModelRequest,
LanguageModelToolChoice, LanguageModelToolResultContent, LanguageModelToolUse, MessageContent,
RateLimiter, Role, StopReason,
LanguageModelToolChoice, RateLimiter, Role,
};
use menu;
use open_ai::{ImageUrl, ResponseStreamEvent, stream_completion};
use open_ai::ResponseStreamEvent;
use schemars::JsonSchema;
use serde::{Deserialize, Serialize};
use settings::{Settings, SettingsStore};
use std::pin::Pin;
use std::str::FromStr as _;
use std::sync::Arc;
use strum::IntoEnumIterator;
use vercel::Model;
@ -200,14 +195,12 @@ impl LanguageModelProvider for VercelLanguageModelProvider {
fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
let mut models = BTreeMap::default();
// Add base models from vercel::Model::iter()
for model in vercel::Model::iter() {
if !matches!(model, vercel::Model::Custom { .. }) {
models.insert(model.id().to_string(), model);
}
}
// Override with available models from settings
for model in &AllLanguageModelSettings::get_global(cx)
.vercel
.available_models
@ -278,7 +271,8 @@ impl VercelLanguageModel {
let future = self.request_limiter.stream(async move {
let api_key = api_key.context("Missing Vercel API Key")?;
let request = stream_completion(http_client.as_ref(), &api_url, &api_key, request);
let request =
open_ai::stream_completion(http_client.as_ref(), &api_url, &api_key, request);
let response = request.await?;
Ok(response)
});
@ -354,264 +348,21 @@ impl LanguageModel for VercelLanguageModel {
LanguageModelCompletionError,
>,
> {
let request = into_vercel(request, &self.model, self.max_output_tokens());
let request = crate::provider::open_ai::into_open_ai(
request,
self.model.id(),
self.model.supports_parallel_tool_calls(),
self.max_output_tokens(),
);
let completions = self.stream_completion(request, cx);
async move {
let mapper = VercelEventMapper::new();
let mapper = crate::provider::open_ai::OpenAiEventMapper::new();
Ok(mapper.map_stream(completions.await?).boxed())
}
.boxed()
}
}
pub fn into_vercel(
request: LanguageModelRequest,
model: &vercel::Model,
max_output_tokens: Option<u64>,
) -> open_ai::Request {
let stream = !model.id().starts_with("o1-");
let mut messages = Vec::new();
for message in request.messages {
for content in message.content {
match content {
MessageContent::Text(text) | MessageContent::Thinking { text, .. } => {
add_message_content_part(
open_ai::MessagePart::Text { text: text },
message.role,
&mut messages,
)
}
MessageContent::RedactedThinking(_) => {}
MessageContent::Image(image) => {
add_message_content_part(
open_ai::MessagePart::Image {
image_url: ImageUrl {
url: image.to_base64_url(),
detail: None,
},
},
message.role,
&mut messages,
);
}
MessageContent::ToolUse(tool_use) => {
let tool_call = open_ai::ToolCall {
id: tool_use.id.to_string(),
content: open_ai::ToolCallContent::Function {
function: open_ai::FunctionContent {
name: tool_use.name.to_string(),
arguments: serde_json::to_string(&tool_use.input)
.unwrap_or_default(),
},
},
};
if let Some(open_ai::RequestMessage::Assistant { tool_calls, .. }) =
messages.last_mut()
{
tool_calls.push(tool_call);
} else {
messages.push(open_ai::RequestMessage::Assistant {
content: None,
tool_calls: vec![tool_call],
});
}
}
MessageContent::ToolResult(tool_result) => {
let content = match &tool_result.content {
LanguageModelToolResultContent::Text(text) => {
vec![open_ai::MessagePart::Text {
text: text.to_string(),
}]
}
LanguageModelToolResultContent::Image(image) => {
vec![open_ai::MessagePart::Image {
image_url: ImageUrl {
url: image.to_base64_url(),
detail: None,
},
}]
}
};
messages.push(open_ai::RequestMessage::Tool {
content: content.into(),
tool_call_id: tool_result.tool_use_id.to_string(),
});
}
}
}
}
open_ai::Request {
model: model.id().into(),
messages,
stream,
stop: request.stop,
temperature: request.temperature.unwrap_or(1.0),
max_completion_tokens: max_output_tokens,
parallel_tool_calls: if model.supports_parallel_tool_calls() && !request.tools.is_empty() {
// Disable parallel tool calls, as the Agent currently expects a maximum of one per turn.
Some(false)
} else {
None
},
tools: request
.tools
.into_iter()
.map(|tool| open_ai::ToolDefinition::Function {
function: open_ai::FunctionDefinition {
name: tool.name,
description: Some(tool.description),
parameters: Some(tool.input_schema),
},
})
.collect(),
tool_choice: request.tool_choice.map(|choice| match choice {
LanguageModelToolChoice::Auto => open_ai::ToolChoice::Auto,
LanguageModelToolChoice::Any => open_ai::ToolChoice::Required,
LanguageModelToolChoice::None => open_ai::ToolChoice::None,
}),
}
}
fn add_message_content_part(
new_part: open_ai::MessagePart,
role: Role,
messages: &mut Vec<open_ai::RequestMessage>,
) {
match (role, messages.last_mut()) {
(Role::User, Some(open_ai::RequestMessage::User { content }))
| (
Role::Assistant,
Some(open_ai::RequestMessage::Assistant {
content: Some(content),
..
}),
)
| (Role::System, Some(open_ai::RequestMessage::System { content, .. })) => {
content.push_part(new_part);
}
_ => {
messages.push(match role {
Role::User => open_ai::RequestMessage::User {
content: open_ai::MessageContent::from(vec![new_part]),
},
Role::Assistant => open_ai::RequestMessage::Assistant {
content: Some(open_ai::MessageContent::from(vec![new_part])),
tool_calls: Vec::new(),
},
Role::System => open_ai::RequestMessage::System {
content: open_ai::MessageContent::from(vec![new_part]),
},
});
}
}
}
pub struct VercelEventMapper {
tool_calls_by_index: HashMap<usize, RawToolCall>,
}
impl VercelEventMapper {
pub fn new() -> Self {
Self {
tool_calls_by_index: HashMap::default(),
}
}
pub fn map_stream(
mut self,
events: Pin<Box<dyn Send + Stream<Item = Result<ResponseStreamEvent>>>>,
) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
{
events.flat_map(move |event| {
futures::stream::iter(match event {
Ok(event) => self.map_event(event),
Err(error) => vec![Err(LanguageModelCompletionError::Other(anyhow!(error)))],
})
})
}
pub fn map_event(
&mut self,
event: ResponseStreamEvent,
) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
let Some(choice) = event.choices.first() else {
return Vec::new();
};
let mut events = Vec::new();
if let Some(content) = choice.delta.content.clone() {
events.push(Ok(LanguageModelCompletionEvent::Text(content)));
}
if let Some(tool_calls) = choice.delta.tool_calls.as_ref() {
for tool_call in tool_calls {
let entry = self.tool_calls_by_index.entry(tool_call.index).or_default();
if let Some(tool_id) = tool_call.id.clone() {
entry.id = tool_id;
}
if let Some(function) = tool_call.function.as_ref() {
if let Some(name) = function.name.clone() {
entry.name = name;
}
if let Some(arguments) = function.arguments.clone() {
entry.arguments.push_str(&arguments);
}
}
}
}
match choice.finish_reason.as_deref() {
Some("stop") => {
events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
}
Some("tool_calls") => {
events.extend(self.tool_calls_by_index.drain().map(|(_, tool_call)| {
match serde_json::Value::from_str(&tool_call.arguments) {
Ok(input) => Ok(LanguageModelCompletionEvent::ToolUse(
LanguageModelToolUse {
id: tool_call.id.clone().into(),
name: tool_call.name.as_str().into(),
is_input_complete: true,
input,
raw_input: tool_call.arguments.clone(),
},
)),
Err(error) => Err(LanguageModelCompletionError::BadInputJson {
id: tool_call.id.into(),
tool_name: tool_call.name.as_str().into(),
raw_input: tool_call.arguments.into(),
json_parse_error: error.to_string(),
}),
}
}));
events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
}
Some(stop_reason) => {
log::error!("Unexpected Vercel stop_reason: {stop_reason:?}",);
events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
}
None => {}
}
events
}
}
#[derive(Default)]
struct RawToolCall {
id: String,
name: String,
arguments: String,
}
pub fn count_vercel_tokens(
request: LanguageModelRequest,
model: Model,
@ -825,43 +576,3 @@ impl Render for ConfigurationView {
}
}
}
#[cfg(test)]
mod tests {
use gpui::TestAppContext;
use language_model::LanguageModelRequestMessage;
use super::*;
#[gpui::test]
fn tiktoken_rs_support(cx: &TestAppContext) {
let request = LanguageModelRequest {
thread_id: None,
prompt_id: None,
intent: None,
mode: None,
messages: vec![LanguageModelRequestMessage {
role: Role::User,
content: vec![MessageContent::Text("message".into())],
cache: false,
}],
tools: vec![],
tool_choice: None,
stop: vec![],
temperature: None,
};
// Validate that all models are supported by tiktoken-rs
for model in Model::iter() {
let count = cx
.executor()
.block(count_vercel_tokens(
request.clone(),
model,
&cx.app.borrow(),
))
.unwrap();
assert!(count > 0);
}
}
}