b96f76f377
Some APIs fail when they get this parameter Closes #36215 Release Notes: - Fixed OpenAI-compatible providers that don't support prompt caching and/or reasoning
960 lines
32 KiB
Rust
960 lines
32 KiB
Rust
use anyhow::{Context as _, Result, anyhow};
|
|
use collections::{BTreeMap, HashMap};
|
|
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;
|
|
use language_model::{
|
|
AuthenticateError, LanguageModel, LanguageModelCompletionError, LanguageModelCompletionEvent,
|
|
LanguageModelId, LanguageModelName, LanguageModelProvider, LanguageModelProviderId,
|
|
LanguageModelProviderName, LanguageModelProviderState, LanguageModelRequest,
|
|
LanguageModelToolChoice, LanguageModelToolResultContent, LanguageModelToolUse, MessageContent,
|
|
RateLimiter, Role, StopReason, TokenUsage,
|
|
};
|
|
use menu;
|
|
use open_ai::{ImageUrl, Model, ReasoningEffort, ResponseStreamEvent, stream_completion};
|
|
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 ui::{ElevationIndex, List, Tooltip, prelude::*};
|
|
use ui_input::SingleLineInput;
|
|
use util::ResultExt;
|
|
|
|
use crate::{AllLanguageModelSettings, ui::InstructionListItem};
|
|
|
|
const PROVIDER_ID: LanguageModelProviderId = language_model::OPEN_AI_PROVIDER_ID;
|
|
const PROVIDER_NAME: LanguageModelProviderName = language_model::OPEN_AI_PROVIDER_NAME;
|
|
|
|
#[derive(Default, Clone, Debug, PartialEq)]
|
|
pub struct OpenAiSettings {
|
|
pub api_url: String,
|
|
pub available_models: Vec<AvailableModel>,
|
|
}
|
|
|
|
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize, JsonSchema)]
|
|
pub struct AvailableModel {
|
|
pub name: String,
|
|
pub display_name: Option<String>,
|
|
pub max_tokens: u64,
|
|
pub max_output_tokens: Option<u64>,
|
|
pub max_completion_tokens: Option<u64>,
|
|
pub reasoning_effort: Option<ReasoningEffort>,
|
|
}
|
|
|
|
pub struct OpenAiLanguageModelProvider {
|
|
http_client: Arc<dyn HttpClient>,
|
|
state: gpui::Entity<State>,
|
|
}
|
|
|
|
pub struct State {
|
|
api_key: Option<String>,
|
|
api_key_from_env: bool,
|
|
_subscription: Subscription,
|
|
}
|
|
|
|
const OPENAI_API_KEY_VAR: &str = "OPENAI_API_KEY";
|
|
|
|
impl State {
|
|
//
|
|
fn is_authenticated(&self) -> bool {
|
|
self.api_key.is_some()
|
|
}
|
|
|
|
fn reset_api_key(&self, cx: &mut Context<Self>) -> Task<Result<()>> {
|
|
let credentials_provider = <dyn CredentialsProvider>::global(cx);
|
|
let api_url = AllLanguageModelSettings::get_global(cx)
|
|
.openai
|
|
.api_url
|
|
.clone();
|
|
cx.spawn(async move |this, cx| {
|
|
credentials_provider
|
|
.delete_credentials(&api_url, &cx)
|
|
.await
|
|
.log_err();
|
|
this.update(cx, |this, cx| {
|
|
this.api_key = None;
|
|
this.api_key_from_env = false;
|
|
cx.notify();
|
|
})
|
|
})
|
|
}
|
|
|
|
fn set_api_key(&mut self, api_key: String, cx: &mut Context<Self>) -> Task<Result<()>> {
|
|
let credentials_provider = <dyn CredentialsProvider>::global(cx);
|
|
let api_url = AllLanguageModelSettings::get_global(cx)
|
|
.openai
|
|
.api_url
|
|
.clone();
|
|
cx.spawn(async move |this, cx| {
|
|
credentials_provider
|
|
.write_credentials(&api_url, "Bearer", api_key.as_bytes(), &cx)
|
|
.await
|
|
.log_err();
|
|
this.update(cx, |this, cx| {
|
|
this.api_key = Some(api_key);
|
|
cx.notify();
|
|
})
|
|
})
|
|
}
|
|
|
|
fn authenticate(&self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
|
|
if self.is_authenticated() {
|
|
return Task::ready(Ok(()));
|
|
}
|
|
|
|
let credentials_provider = <dyn CredentialsProvider>::global(cx);
|
|
let api_url = AllLanguageModelSettings::get_global(cx)
|
|
.openai
|
|
.api_url
|
|
.clone();
|
|
cx.spawn(async move |this, cx| {
|
|
let (api_key, from_env) = if let Ok(api_key) = std::env::var(OPENAI_API_KEY_VAR) {
|
|
(api_key, true)
|
|
} else {
|
|
let (_, api_key) = credentials_provider
|
|
.read_credentials(&api_url, &cx)
|
|
.await?
|
|
.ok_or(AuthenticateError::CredentialsNotFound)?;
|
|
(
|
|
String::from_utf8(api_key).context("invalid {PROVIDER_NAME} API key")?,
|
|
false,
|
|
)
|
|
};
|
|
this.update(cx, |this, cx| {
|
|
this.api_key = Some(api_key);
|
|
this.api_key_from_env = from_env;
|
|
cx.notify();
|
|
})?;
|
|
|
|
Ok(())
|
|
})
|
|
}
|
|
}
|
|
|
|
impl OpenAiLanguageModelProvider {
|
|
pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
|
|
let state = cx.new(|cx| State {
|
|
api_key: None,
|
|
api_key_from_env: false,
|
|
_subscription: cx.observe_global::<SettingsStore>(|_this: &mut State, cx| {
|
|
cx.notify();
|
|
}),
|
|
});
|
|
|
|
Self { http_client, state }
|
|
}
|
|
|
|
fn create_language_model(&self, model: open_ai::Model) -> Arc<dyn LanguageModel> {
|
|
Arc::new(OpenAiLanguageModel {
|
|
id: LanguageModelId::from(model.id().to_string()),
|
|
model,
|
|
state: self.state.clone(),
|
|
http_client: self.http_client.clone(),
|
|
request_limiter: RateLimiter::new(4),
|
|
})
|
|
}
|
|
}
|
|
|
|
impl LanguageModelProviderState for OpenAiLanguageModelProvider {
|
|
type ObservableEntity = State;
|
|
|
|
fn observable_entity(&self) -> Option<gpui::Entity<Self::ObservableEntity>> {
|
|
Some(self.state.clone())
|
|
}
|
|
}
|
|
|
|
impl LanguageModelProvider for OpenAiLanguageModelProvider {
|
|
fn id(&self) -> LanguageModelProviderId {
|
|
PROVIDER_ID
|
|
}
|
|
|
|
fn name(&self) -> LanguageModelProviderName {
|
|
PROVIDER_NAME
|
|
}
|
|
|
|
fn icon(&self) -> IconName {
|
|
IconName::AiOpenAi
|
|
}
|
|
|
|
fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
|
|
Some(self.create_language_model(open_ai::Model::default()))
|
|
}
|
|
|
|
fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
|
|
Some(self.create_language_model(open_ai::Model::default_fast()))
|
|
}
|
|
|
|
fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
|
|
let mut models = BTreeMap::default();
|
|
|
|
// Add base models from open_ai::Model::iter()
|
|
for model in open_ai::Model::iter() {
|
|
if !matches!(model, open_ai::Model::Custom { .. }) {
|
|
models.insert(model.id().to_string(), model);
|
|
}
|
|
}
|
|
|
|
// Override with available models from settings
|
|
for model in &AllLanguageModelSettings::get_global(cx)
|
|
.openai
|
|
.available_models
|
|
{
|
|
models.insert(
|
|
model.name.clone(),
|
|
open_ai::Model::Custom {
|
|
name: model.name.clone(),
|
|
display_name: model.display_name.clone(),
|
|
max_tokens: model.max_tokens,
|
|
max_output_tokens: model.max_output_tokens,
|
|
max_completion_tokens: model.max_completion_tokens,
|
|
reasoning_effort: model.reasoning_effort.clone(),
|
|
},
|
|
);
|
|
}
|
|
|
|
models
|
|
.into_values()
|
|
.map(|model| self.create_language_model(model))
|
|
.collect()
|
|
}
|
|
|
|
fn is_authenticated(&self, cx: &App) -> bool {
|
|
self.state.read(cx).is_authenticated()
|
|
}
|
|
|
|
fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
|
|
self.state.update(cx, |state, cx| state.authenticate(cx))
|
|
}
|
|
|
|
fn configuration_view(&self, window: &mut Window, cx: &mut App) -> AnyView {
|
|
cx.new(|cx| ConfigurationView::new(self.state.clone(), window, cx))
|
|
.into()
|
|
}
|
|
|
|
fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
|
|
self.state.update(cx, |state, cx| state.reset_api_key(cx))
|
|
}
|
|
}
|
|
|
|
pub struct OpenAiLanguageModel {
|
|
id: LanguageModelId,
|
|
model: open_ai::Model,
|
|
state: gpui::Entity<State>,
|
|
http_client: Arc<dyn HttpClient>,
|
|
request_limiter: RateLimiter,
|
|
}
|
|
|
|
impl OpenAiLanguageModel {
|
|
fn stream_completion(
|
|
&self,
|
|
request: open_ai::Request,
|
|
cx: &AsyncApp,
|
|
) -> BoxFuture<'static, Result<futures::stream::BoxStream<'static, Result<ResponseStreamEvent>>>>
|
|
{
|
|
let http_client = self.http_client.clone();
|
|
let Ok((api_key, api_url)) = cx.read_entity(&self.state, |state, cx| {
|
|
let settings = &AllLanguageModelSettings::get_global(cx).openai;
|
|
(state.api_key.clone(), settings.api_url.clone())
|
|
}) else {
|
|
return futures::future::ready(Err(anyhow!("App state dropped"))).boxed();
|
|
};
|
|
|
|
let future = self.request_limiter.stream(async move {
|
|
let Some(api_key) = api_key else {
|
|
return Err(LanguageModelCompletionError::NoApiKey {
|
|
provider: PROVIDER_NAME,
|
|
});
|
|
};
|
|
let request = stream_completion(http_client.as_ref(), &api_url, &api_key, request);
|
|
let response = request.await?;
|
|
Ok(response)
|
|
});
|
|
|
|
async move { Ok(future.await?.boxed()) }.boxed()
|
|
}
|
|
}
|
|
|
|
impl LanguageModel for OpenAiLanguageModel {
|
|
fn id(&self) -> LanguageModelId {
|
|
self.id.clone()
|
|
}
|
|
|
|
fn name(&self) -> LanguageModelName {
|
|
LanguageModelName::from(self.model.display_name().to_string())
|
|
}
|
|
|
|
fn provider_id(&self) -> LanguageModelProviderId {
|
|
PROVIDER_ID
|
|
}
|
|
|
|
fn provider_name(&self) -> LanguageModelProviderName {
|
|
PROVIDER_NAME
|
|
}
|
|
|
|
fn supports_tools(&self) -> bool {
|
|
true
|
|
}
|
|
|
|
fn supports_images(&self) -> bool {
|
|
use open_ai::Model;
|
|
match &self.model {
|
|
Model::FourOmni
|
|
| Model::FourOmniMini
|
|
| Model::FourPointOne
|
|
| Model::FourPointOneMini
|
|
| Model::FourPointOneNano
|
|
| Model::Five
|
|
| Model::FiveMini
|
|
| Model::FiveNano
|
|
| Model::O1
|
|
| Model::O3
|
|
| Model::O4Mini => true,
|
|
Model::ThreePointFiveTurbo
|
|
| Model::Four
|
|
| Model::FourTurbo
|
|
| Model::O3Mini
|
|
| Model::Custom { .. } => false,
|
|
}
|
|
}
|
|
|
|
fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
|
|
match choice {
|
|
LanguageModelToolChoice::Auto => true,
|
|
LanguageModelToolChoice::Any => true,
|
|
LanguageModelToolChoice::None => true,
|
|
}
|
|
}
|
|
|
|
fn telemetry_id(&self) -> String {
|
|
format!("openai/{}", self.model.id())
|
|
}
|
|
|
|
fn max_token_count(&self) -> u64 {
|
|
self.model.max_token_count()
|
|
}
|
|
|
|
fn max_output_tokens(&self) -> Option<u64> {
|
|
self.model.max_output_tokens()
|
|
}
|
|
|
|
fn count_tokens(
|
|
&self,
|
|
request: LanguageModelRequest,
|
|
cx: &App,
|
|
) -> BoxFuture<'static, Result<u64>> {
|
|
count_open_ai_tokens(request, self.model.clone(), cx)
|
|
}
|
|
|
|
fn stream_completion(
|
|
&self,
|
|
request: LanguageModelRequest,
|
|
cx: &AsyncApp,
|
|
) -> BoxFuture<
|
|
'static,
|
|
Result<
|
|
futures::stream::BoxStream<
|
|
'static,
|
|
Result<LanguageModelCompletionEvent, LanguageModelCompletionError>,
|
|
>,
|
|
LanguageModelCompletionError,
|
|
>,
|
|
> {
|
|
let request = into_open_ai(
|
|
request,
|
|
self.model.id(),
|
|
self.model.supports_parallel_tool_calls(),
|
|
self.model.supports_prompt_cache_key(),
|
|
self.max_output_tokens(),
|
|
self.model.reasoning_effort(),
|
|
);
|
|
let completions = self.stream_completion(request, cx);
|
|
async move {
|
|
let mapper = OpenAiEventMapper::new();
|
|
Ok(mapper.map_stream(completions.await?).boxed())
|
|
}
|
|
.boxed()
|
|
}
|
|
}
|
|
|
|
pub fn into_open_ai(
|
|
request: LanguageModelRequest,
|
|
model_id: &str,
|
|
supports_parallel_tool_calls: bool,
|
|
supports_prompt_cache_key: bool,
|
|
max_output_tokens: Option<u64>,
|
|
reasoning_effort: Option<ReasoningEffort>,
|
|
) -> 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 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
|
|
},
|
|
prompt_cache_key: if supports_prompt_cache_key {
|
|
request.thread_id
|
|
} 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,
|
|
}),
|
|
reasoning_effort,
|
|
}
|
|
}
|
|
|
|
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 OpenAiEventMapper {
|
|
tool_calls_by_index: HashMap<usize, RawToolCall>,
|
|
}
|
|
|
|
impl OpenAiEventMapper {
|
|
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::from(anyhow!(error)))],
|
|
})
|
|
})
|
|
}
|
|
|
|
pub fn map_event(
|
|
&mut self,
|
|
event: ResponseStreamEvent,
|
|
) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
|
|
let mut events = Vec::new();
|
|
if let Some(usage) = event.usage {
|
|
events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
|
|
input_tokens: usage.prompt_tokens,
|
|
output_tokens: usage.completion_tokens,
|
|
cache_creation_input_tokens: 0,
|
|
cache_read_input_tokens: 0,
|
|
})));
|
|
}
|
|
|
|
let Some(choice) = event.choices.first() else {
|
|
return events;
|
|
};
|
|
|
|
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) => Ok(LanguageModelCompletionEvent::ToolUseJsonParseError {
|
|
id: tool_call.id.into(),
|
|
tool_name: tool_call.name.into(),
|
|
raw_input: tool_call.arguments.clone().into(),
|
|
json_parse_error: error.to_string(),
|
|
}),
|
|
}
|
|
}));
|
|
|
|
events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
|
|
}
|
|
Some(stop_reason) => {
|
|
log::error!("Unexpected OpenAI stop_reason: {stop_reason:?}",);
|
|
events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
|
|
}
|
|
None => {}
|
|
}
|
|
|
|
events
|
|
}
|
|
}
|
|
|
|
#[derive(Default)]
|
|
struct RawToolCall {
|
|
id: String,
|
|
name: String,
|
|
arguments: String,
|
|
}
|
|
|
|
pub(crate) fn collect_tiktoken_messages(
|
|
request: LanguageModelRequest,
|
|
) -> Vec<tiktoken_rs::ChatCompletionRequestMessage> {
|
|
request
|
|
.messages
|
|
.into_iter()
|
|
.map(|message| tiktoken_rs::ChatCompletionRequestMessage {
|
|
role: match message.role {
|
|
Role::User => "user".into(),
|
|
Role::Assistant => "assistant".into(),
|
|
Role::System => "system".into(),
|
|
},
|
|
content: Some(message.string_contents()),
|
|
name: None,
|
|
function_call: None,
|
|
})
|
|
.collect::<Vec<_>>()
|
|
}
|
|
|
|
pub fn count_open_ai_tokens(
|
|
request: LanguageModelRequest,
|
|
model: Model,
|
|
cx: &App,
|
|
) -> BoxFuture<'static, Result<u64>> {
|
|
cx.background_spawn(async move {
|
|
let messages = collect_tiktoken_messages(request);
|
|
|
|
match model {
|
|
Model::Custom { max_tokens, .. } => {
|
|
let model = if max_tokens >= 100_000 {
|
|
// If the max tokens is 100k or more, it is likely the o200k_base tokenizer from gpt4o
|
|
"gpt-4o"
|
|
} else {
|
|
// Otherwise fallback to gpt-4, since only cl100k_base and o200k_base are
|
|
// supported with this tiktoken method
|
|
"gpt-4"
|
|
};
|
|
tiktoken_rs::num_tokens_from_messages(model, &messages)
|
|
}
|
|
// Currently supported by tiktoken_rs
|
|
// Sometimes tiktoken-rs is behind on model support. If that is the case, make a new branch
|
|
// arm with an override. We enumerate all supported models here so that we can check if new
|
|
// models are supported yet or not.
|
|
Model::ThreePointFiveTurbo
|
|
| Model::Four
|
|
| Model::FourTurbo
|
|
| Model::FourOmni
|
|
| Model::FourOmniMini
|
|
| Model::FourPointOne
|
|
| Model::FourPointOneMini
|
|
| Model::FourPointOneNano
|
|
| Model::O1
|
|
| Model::O3
|
|
| Model::O3Mini
|
|
| Model::O4Mini => tiktoken_rs::num_tokens_from_messages(model.id(), &messages),
|
|
// GPT-5 models don't have tiktoken support yet; fall back on gpt-4o tokenizer
|
|
Model::Five | Model::FiveMini | Model::FiveNano => {
|
|
tiktoken_rs::num_tokens_from_messages("gpt-4o", &messages)
|
|
}
|
|
}
|
|
.map(|tokens| tokens as u64)
|
|
})
|
|
.boxed()
|
|
}
|
|
|
|
struct ConfigurationView {
|
|
api_key_editor: Entity<SingleLineInput>,
|
|
state: gpui::Entity<State>,
|
|
load_credentials_task: Option<Task<()>>,
|
|
}
|
|
|
|
impl ConfigurationView {
|
|
fn new(state: gpui::Entity<State>, window: &mut Window, cx: &mut Context<Self>) -> Self {
|
|
let api_key_editor = cx.new(|cx| {
|
|
SingleLineInput::new(
|
|
window,
|
|
cx,
|
|
"sk-000000000000000000000000000000000000000000000000",
|
|
)
|
|
});
|
|
|
|
cx.observe(&state, |_, _, cx| {
|
|
cx.notify();
|
|
})
|
|
.detach();
|
|
|
|
let load_credentials_task = Some(cx.spawn_in(window, {
|
|
let state = state.clone();
|
|
async move |this, cx| {
|
|
if let Some(task) = state
|
|
.update(cx, |state, cx| state.authenticate(cx))
|
|
.log_err()
|
|
{
|
|
// We don't log an error, because "not signed in" is also an error.
|
|
let _ = task.await;
|
|
}
|
|
this.update(cx, |this, cx| {
|
|
this.load_credentials_task = None;
|
|
cx.notify();
|
|
})
|
|
.log_err();
|
|
}
|
|
}));
|
|
|
|
Self {
|
|
api_key_editor,
|
|
state,
|
|
load_credentials_task,
|
|
}
|
|
}
|
|
|
|
fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
|
|
let api_key = self
|
|
.api_key_editor
|
|
.read(cx)
|
|
.editor()
|
|
.read(cx)
|
|
.text(cx)
|
|
.trim()
|
|
.to_string();
|
|
|
|
// Don't proceed if no API key is provided and we're not authenticated
|
|
if api_key.is_empty() && !self.state.read(cx).is_authenticated() {
|
|
return;
|
|
}
|
|
|
|
let state = self.state.clone();
|
|
cx.spawn_in(window, async move |_, cx| {
|
|
state
|
|
.update(cx, |state, cx| state.set_api_key(api_key, cx))?
|
|
.await
|
|
})
|
|
.detach_and_log_err(cx);
|
|
|
|
cx.notify();
|
|
}
|
|
|
|
fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
|
|
self.api_key_editor.update(cx, |input, cx| {
|
|
input.editor.update(cx, |editor, cx| {
|
|
editor.set_text("", window, cx);
|
|
});
|
|
});
|
|
|
|
let state = self.state.clone();
|
|
cx.spawn_in(window, async move |_, cx| {
|
|
state.update(cx, |state, cx| state.reset_api_key(cx))?.await
|
|
})
|
|
.detach_and_log_err(cx);
|
|
|
|
cx.notify();
|
|
}
|
|
|
|
fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
|
|
!self.state.read(cx).is_authenticated()
|
|
}
|
|
}
|
|
|
|
impl Render for ConfigurationView {
|
|
fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
|
|
let env_var_set = self.state.read(cx).api_key_from_env;
|
|
|
|
let api_key_section = if self.should_render_editor(cx) {
|
|
v_flex()
|
|
.on_action(cx.listener(Self::save_api_key))
|
|
.child(Label::new("To use Zed's agent with OpenAI, you need to add an API key. Follow these steps:"))
|
|
.child(
|
|
List::new()
|
|
.child(InstructionListItem::new(
|
|
"Create one by visiting",
|
|
Some("OpenAI's console"),
|
|
Some("https://platform.openai.com/api-keys"),
|
|
))
|
|
.child(InstructionListItem::text_only(
|
|
"Ensure your OpenAI account has credits",
|
|
))
|
|
.child(InstructionListItem::text_only(
|
|
"Paste your API key below and hit enter to start using the assistant",
|
|
)),
|
|
)
|
|
.child(self.api_key_editor.clone())
|
|
.child(
|
|
Label::new(
|
|
format!("You can also assign the {OPENAI_API_KEY_VAR} environment variable and restart Zed."),
|
|
)
|
|
.size(LabelSize::Small).color(Color::Muted),
|
|
)
|
|
.child(
|
|
Label::new(
|
|
"Note that having a subscription for another service like GitHub Copilot won't work.",
|
|
)
|
|
.size(LabelSize::Small).color(Color::Muted),
|
|
)
|
|
.into_any()
|
|
} else {
|
|
h_flex()
|
|
.mt_1()
|
|
.p_1()
|
|
.justify_between()
|
|
.rounded_md()
|
|
.border_1()
|
|
.border_color(cx.theme().colors().border)
|
|
.bg(cx.theme().colors().background)
|
|
.child(
|
|
h_flex()
|
|
.gap_1()
|
|
.child(Icon::new(IconName::Check).color(Color::Success))
|
|
.child(Label::new(if env_var_set {
|
|
format!("API key set in {OPENAI_API_KEY_VAR} environment variable.")
|
|
} else {
|
|
"API key configured.".to_string()
|
|
})),
|
|
)
|
|
.child(
|
|
Button::new("reset-api-key", "Reset API Key")
|
|
.label_size(LabelSize::Small)
|
|
.icon(IconName::Undo)
|
|
.icon_size(IconSize::Small)
|
|
.icon_position(IconPosition::Start)
|
|
.layer(ElevationIndex::ModalSurface)
|
|
.when(env_var_set, |this| {
|
|
this.tooltip(Tooltip::text(format!("To reset your API key, unset the {OPENAI_API_KEY_VAR} environment variable.")))
|
|
})
|
|
.on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx))),
|
|
)
|
|
.into_any()
|
|
};
|
|
|
|
let compatible_api_section = h_flex()
|
|
.mt_1p5()
|
|
.gap_0p5()
|
|
.flex_wrap()
|
|
.when(self.should_render_editor(cx), |this| {
|
|
this.pt_1p5()
|
|
.border_t_1()
|
|
.border_color(cx.theme().colors().border_variant)
|
|
})
|
|
.child(
|
|
h_flex()
|
|
.gap_2()
|
|
.child(
|
|
Icon::new(IconName::Info)
|
|
.size(IconSize::XSmall)
|
|
.color(Color::Muted),
|
|
)
|
|
.child(Label::new("Zed also supports OpenAI-compatible models.")),
|
|
)
|
|
.child(
|
|
Button::new("docs", "Learn More")
|
|
.icon(IconName::ArrowUpRight)
|
|
.icon_size(IconSize::Small)
|
|
.icon_color(Color::Muted)
|
|
.on_click(move |_, _window, cx| {
|
|
cx.open_url("https://zed.dev/docs/ai/llm-providers#openai-api-compatible")
|
|
}),
|
|
);
|
|
|
|
if self.load_credentials_task.is_some() {
|
|
div().child(Label::new("Loading credentials…")).into_any()
|
|
} else {
|
|
v_flex()
|
|
.size_full()
|
|
.child(api_key_section)
|
|
.child(compatible_api_section)
|
|
.into_any()
|
|
}
|
|
}
|
|
}
|
|
|
|
#[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,
|
|
thinking_allowed: true,
|
|
};
|
|
|
|
// Validate that all models are supported by tiktoken-rs
|
|
for model in Model::iter() {
|
|
let count = cx
|
|
.executor()
|
|
.block(count_open_ai_tokens(
|
|
request.clone(),
|
|
model,
|
|
&cx.app.borrow(),
|
|
))
|
|
.unwrap();
|
|
assert!(count > 0);
|
|
}
|
|
}
|
|
}
|