Cargo.lock 🔗
@@ -17431,11 +17431,8 @@ name = "vercel"
version = "0.1.0"
dependencies = [
"anyhow",
- "futures 0.3.31",
- "http_client",
"schemars",
"serde",
- "serde_json",
"strum 0.27.1",
"workspace-hack",
]
vercel: Reuse existing OpenAI code (#33362)
Bennet Bo Fenner created
Follow up to #33292
Since Vercel's API is OpenAI compatible, we can reuse a bunch of code.
Release Notes:
- N/AChange summary
Cargo.lock | 3
crates/language_models/src/provider/cloud.rs | 7
crates/language_models/src/provider/open_ai.rs | 16
crates/language_models/src/provider/vercel.rs | 313 ----------------
crates/vercel/Cargo.toml | 3
crates/vercel/src/vercel.rs | 362 -------------------
6 files changed, 30 insertions(+), 674 deletions(-)
Detailed changes
crates/language_models/src/provider/cloud.rs 🔗
@@ -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 {
crates/language_models/src/provider/open_ai.rs 🔗
@@ -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 {
crates/language_models/src/provider/vercel.rs 🔗
@@ -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);
- }
- }
-}
crates/vercel/Cargo.toml 🔗
@@ -17,10 +17,7 @@ schemars = ["dep:schemars"]
[dependencies]
anyhow.workspace = true
-futures.workspace = true
-http_client.workspace = true
schemars = { workspace = true, optional = true }
serde.workspace = true
-serde_json.workspace = true
strum.workspace = true
workspace-hack.workspace = true
crates/vercel/src/vercel.rs 🔗
@@ -1,51 +1,9 @@
-use anyhow::{Context as _, Result, anyhow};
-use futures::{AsyncBufReadExt, AsyncReadExt, StreamExt, io::BufReader, stream::BoxStream};
-use http_client::{AsyncBody, HttpClient, Method, Request as HttpRequest};
+use anyhow::Result;
use serde::{Deserialize, Serialize};
-use serde_json::Value;
-use std::{convert::TryFrom, future::Future};
use strum::EnumIter;
pub const VERCEL_API_URL: &str = "https://api.v0.dev/v1";
-fn is_none_or_empty<T: AsRef<[U]>, U>(opt: &Option<T>) -> bool {
- opt.as_ref().map_or(true, |v| v.as_ref().is_empty())
-}
-
-#[derive(Clone, Copy, Serialize, Deserialize, Debug, Eq, PartialEq)]
-#[serde(rename_all = "lowercase")]
-pub enum Role {
- User,
- Assistant,
- System,
- Tool,
-}
-
-impl TryFrom<String> for Role {
- type Error = anyhow::Error;
-
- fn try_from(value: String) -> Result<Self> {
- match value.as_str() {
- "user" => Ok(Self::User),
- "assistant" => Ok(Self::Assistant),
- "system" => Ok(Self::System),
- "tool" => Ok(Self::Tool),
- _ => anyhow::bail!("invalid role '{value}'"),
- }
- }
-}
-
-impl From<Role> for String {
- fn from(val: Role) -> Self {
- match val {
- Role::User => "user".to_owned(),
- Role::Assistant => "assistant".to_owned(),
- Role::System => "system".to_owned(),
- Role::Tool => "tool".to_owned(),
- }
- }
-}
-
#[cfg_attr(feature = "schemars", derive(schemars::JsonSchema))]
#[derive(Clone, Debug, Default, Serialize, Deserialize, PartialEq, EnumIter)]
pub enum Model {
@@ -118,321 +76,3 @@ impl Model {
}
}
}
-
-#[derive(Debug, Serialize, Deserialize)]
-pub struct Request {
- pub model: String,
- pub messages: Vec<RequestMessage>,
- pub stream: bool,
- #[serde(default, skip_serializing_if = "Option::is_none")]
- pub max_completion_tokens: Option<u64>,
- #[serde(default, skip_serializing_if = "Vec::is_empty")]
- pub stop: Vec<String>,
- pub temperature: f32,
- #[serde(default, skip_serializing_if = "Option::is_none")]
- pub tool_choice: Option<ToolChoice>,
- /// Whether to enable parallel function calling during tool use.
- #[serde(default, skip_serializing_if = "Option::is_none")]
- pub parallel_tool_calls: Option<bool>,
- #[serde(default, skip_serializing_if = "Vec::is_empty")]
- pub tools: Vec<ToolDefinition>,
-}
-
-#[derive(Debug, Serialize, Deserialize)]
-#[serde(untagged)]
-pub enum ToolChoice {
- Auto,
- Required,
- None,
- Other(ToolDefinition),
-}
-
-#[derive(Clone, Deserialize, Serialize, Debug)]
-#[serde(tag = "type", rename_all = "snake_case")]
-pub enum ToolDefinition {
- #[allow(dead_code)]
- Function { function: FunctionDefinition },
-}
-
-#[derive(Clone, Debug, Serialize, Deserialize)]
-pub struct FunctionDefinition {
- pub name: String,
- pub description: Option<String>,
- pub parameters: Option<Value>,
-}
-
-#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
-#[serde(tag = "role", rename_all = "lowercase")]
-pub enum RequestMessage {
- Assistant {
- content: Option<MessageContent>,
- #[serde(default, skip_serializing_if = "Vec::is_empty")]
- tool_calls: Vec<ToolCall>,
- },
- User {
- content: MessageContent,
- },
- System {
- content: MessageContent,
- },
- Tool {
- content: MessageContent,
- tool_call_id: String,
- },
-}
-
-#[derive(Serialize, Deserialize, Clone, Debug, Eq, PartialEq)]
-#[serde(untagged)]
-pub enum MessageContent {
- Plain(String),
- Multipart(Vec<MessagePart>),
-}
-
-impl MessageContent {
- pub fn empty() -> Self {
- MessageContent::Multipart(vec![])
- }
-
- pub fn push_part(&mut self, part: MessagePart) {
- match self {
- MessageContent::Plain(text) => {
- *self =
- MessageContent::Multipart(vec![MessagePart::Text { text: text.clone() }, part]);
- }
- MessageContent::Multipart(parts) if parts.is_empty() => match part {
- MessagePart::Text { text } => *self = MessageContent::Plain(text),
- MessagePart::Image { .. } => *self = MessageContent::Multipart(vec![part]),
- },
- MessageContent::Multipart(parts) => parts.push(part),
- }
- }
-}
-
-impl From<Vec<MessagePart>> for MessageContent {
- fn from(mut parts: Vec<MessagePart>) -> Self {
- if let [MessagePart::Text { text }] = parts.as_mut_slice() {
- MessageContent::Plain(std::mem::take(text))
- } else {
- MessageContent::Multipart(parts)
- }
- }
-}
-
-#[derive(Serialize, Deserialize, Clone, Debug, Eq, PartialEq)]
-#[serde(tag = "type")]
-pub enum MessagePart {
- #[serde(rename = "text")]
- Text { text: String },
- #[serde(rename = "image_url")]
- Image { image_url: ImageUrl },
-}
-
-#[derive(Serialize, Deserialize, Clone, Debug, Eq, PartialEq)]
-pub struct ImageUrl {
- pub url: String,
- #[serde(skip_serializing_if = "Option::is_none")]
- pub detail: Option<String>,
-}
-
-#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
-pub struct ToolCall {
- pub id: String,
- #[serde(flatten)]
- pub content: ToolCallContent,
-}
-
-#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
-#[serde(tag = "type", rename_all = "lowercase")]
-pub enum ToolCallContent {
- Function { function: FunctionContent },
-}
-
-#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
-pub struct FunctionContent {
- pub name: String,
- pub arguments: String,
-}
-
-#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
-pub struct ResponseMessageDelta {
- pub role: Option<Role>,
- pub content: Option<String>,
- #[serde(default, skip_serializing_if = "is_none_or_empty")]
- pub tool_calls: Option<Vec<ToolCallChunk>>,
-}
-
-#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
-pub struct ToolCallChunk {
- pub index: usize,
- pub id: Option<String>,
-
- // There is also an optional `type` field that would determine if a
- // function is there. Sometimes this streams in with the `function` before
- // it streams in the `type`
- pub function: Option<FunctionChunk>,
-}
-
-#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
-pub struct FunctionChunk {
- pub name: Option<String>,
- pub arguments: Option<String>,
-}
-
-#[derive(Serialize, Deserialize, Debug)]
-pub struct Usage {
- pub prompt_tokens: u32,
- pub completion_tokens: u32,
- pub total_tokens: u32,
-}
-
-#[derive(Serialize, Deserialize, Debug)]
-pub struct ChoiceDelta {
- pub index: u32,
- pub delta: ResponseMessageDelta,
- pub finish_reason: Option<String>,
-}
-
-#[derive(Serialize, Deserialize, Debug)]
-#[serde(untagged)]
-pub enum ResponseStreamResult {
- Ok(ResponseStreamEvent),
- Err { error: String },
-}
-
-#[derive(Serialize, Deserialize, Debug)]
-pub struct ResponseStreamEvent {
- pub model: String,
- pub choices: Vec<ChoiceDelta>,
- pub usage: Option<Usage>,
-}
-
-pub async fn stream_completion(
- client: &dyn HttpClient,
- api_url: &str,
- api_key: &str,
- request: Request,
-) -> Result<BoxStream<'static, Result<ResponseStreamEvent>>> {
- let uri = format!("{api_url}/chat/completions");
- let request_builder = HttpRequest::builder()
- .method(Method::POST)
- .uri(uri)
- .header("Content-Type", "application/json")
- .header("Authorization", format!("Bearer {}", api_key));
-
- let request = request_builder.body(AsyncBody::from(serde_json::to_string(&request)?))?;
- let mut response = client.send(request).await?;
- if response.status().is_success() {
- let reader = BufReader::new(response.into_body());
- Ok(reader
- .lines()
- .filter_map(|line| async move {
- match line {
- Ok(line) => {
- let line = line.strip_prefix("data: ")?;
- if line == "[DONE]" {
- None
- } else {
- match serde_json::from_str(line) {
- Ok(ResponseStreamResult::Ok(response)) => Some(Ok(response)),
- Ok(ResponseStreamResult::Err { error }) => {
- Some(Err(anyhow!(error)))
- }
- Err(error) => Some(Err(anyhow!(error))),
- }
- }
- }
- Err(error) => Some(Err(anyhow!(error))),
- }
- })
- .boxed())
- } else {
- let mut body = String::new();
- response.body_mut().read_to_string(&mut body).await?;
-
- #[derive(Deserialize)]
- struct VercelResponse {
- error: VercelError,
- }
-
- #[derive(Deserialize)]
- struct VercelError {
- message: String,
- }
-
- match serde_json::from_str::<VercelResponse>(&body) {
- Ok(response) if !response.error.message.is_empty() => Err(anyhow!(
- "Failed to connect to Vercel API: {}",
- response.error.message,
- )),
-
- _ => anyhow::bail!(
- "Failed to connect to Vercel API: {} {}",
- response.status(),
- body,
- ),
- }
- }
-}
-
-#[derive(Copy, Clone, Serialize, Deserialize)]
-pub enum VercelEmbeddingModel {
- #[serde(rename = "text-embedding-3-small")]
- TextEmbedding3Small,
- #[serde(rename = "text-embedding-3-large")]
- TextEmbedding3Large,
-}
-
-#[derive(Serialize)]
-struct VercelEmbeddingRequest<'a> {
- model: VercelEmbeddingModel,
- input: Vec<&'a str>,
-}
-
-#[derive(Deserialize)]
-pub struct VercelEmbeddingResponse {
- pub data: Vec<VercelEmbedding>,
-}
-
-#[derive(Deserialize)]
-pub struct VercelEmbedding {
- pub embedding: Vec<f32>,
-}
-
-pub fn embed<'a>(
- client: &dyn HttpClient,
- api_url: &str,
- api_key: &str,
- model: VercelEmbeddingModel,
- texts: impl IntoIterator<Item = &'a str>,
-) -> impl 'static + Future<Output = Result<VercelEmbeddingResponse>> {
- let uri = format!("{api_url}/embeddings");
-
- let request = VercelEmbeddingRequest {
- model,
- input: texts.into_iter().collect(),
- };
- let body = AsyncBody::from(serde_json::to_string(&request).unwrap());
- let request = HttpRequest::builder()
- .method(Method::POST)
- .uri(uri)
- .header("Content-Type", "application/json")
- .header("Authorization", format!("Bearer {}", api_key))
- .body(body)
- .map(|request| client.send(request));
-
- async move {
- let mut response = request?.await?;
- let mut body = String::new();
- response.body_mut().read_to_string(&mut body).await?;
-
- anyhow::ensure!(
- response.status().is_success(),
- "error during embedding, status: {:?}, body: {:?}",
- response.status(),
- body
- );
- let response: VercelEmbeddingResponse =
- serde_json::from_str(&body).context("failed to parse Vercel embedding response")?;
- Ok(response)
- }
-}