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port ai to zed2 (#3186)

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port ai to zed2
This commit is contained in:
Kyle Caverly
2023-10-30 20:09:49 +01:00
committed by GitHub
parent bc4f8fbf4e c17b246bac
commit 0efbc81b0f
53 changed files with 3140 additions and 965 deletions
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Cargo.lock
Generated
+28
View File
@@ -108,6 +108,33 @@ dependencies = [
"util",
]
[[package]]
name = "ai2"
version = "0.1.0"
dependencies = [
"anyhow",
"async-trait",
"bincode",
"futures 0.3.28",
"gpui2",
"isahc",
"language2",
"lazy_static",
"log",
"matrixmultiply",
"ordered-float 2.10.0",
"parking_lot 0.11.2",
"parse_duration",
"postage",
"rand 0.8.5",
"regex",
"rusqlite",
"serde",
"serde_json",
"tiktoken-rs",
"util",
]
[[package]]
name = "alacritty_config"
version = "0.1.2-dev"
@@ -10903,6 +10930,7 @@ dependencies = [
name = "zed2"
version = "0.109.0"
dependencies = [
"ai2",
"anyhow",
"async-compression",
"async-recursion 0.3.2",
crates/Cargo.toml
+38
View File
@@ -0,0 +1,38 @@
[package]
name = "ai"
version = "0.1.0"
edition = "2021"
publish = false
[lib]
path = "src/ai.rs"
doctest = false
[features]
test-support = []
[dependencies]
gpui = { path = "../gpui" }
util = { path = "../util" }
language = { path = "../language" }
async-trait.workspace = true
anyhow.workspace = true
futures.workspace = true
lazy_static.workspace = true
ordered-float.workspace = true
parking_lot.workspace = true
isahc.workspace = true
regex.workspace = true
serde.workspace = true
serde_json.workspace = true
postage.workspace = true
rand.workspace = true
log.workspace = true
parse_duration = "2.1.1"
tiktoken-rs = "0.5.0"
matrixmultiply = "0.3.7"
rusqlite = { version = "0.29.0", features = ["blob", "array", "modern_sqlite"] }
bincode = "1.3.3"
[dev-dependencies]
gpui = { path = "../gpui", features = ["test-support"] }
crates/ai/Cargo.toml
+3
View File
@@ -8,6 +8,9 @@ publish = false
path = "src/ai.rs"
doctest = false
[features]
test-support = []
[dependencies]
gpui = { path = "../gpui" }
util = { path = "../util" }
crates/ai/src/ai.rs
+5 -1
View File
@@ -1,4 +1,8 @@
pub mod auth;
pub mod completion;
pub mod embedding;
pub mod models;
pub mod templates;
pub mod prompts;
pub mod providers;
#[cfg(any(test, feature = "test-support"))]
pub mod test;
crates/ai/src/auth.rs
+15
View File
@@ -0,0 +1,15 @@
use gpui::AppContext;
#[derive(Clone, Debug)]
pub enum ProviderCredential {
Credentials { api_key: String },
NoCredentials,
NotNeeded,
}
pub trait CredentialProvider: Send + Sync {
fn has_credentials(&self) -> bool;
fn retrieve_credentials(&self, cx: &AppContext) -> ProviderCredential;
fn save_credentials(&self, cx: &AppContext, credential: ProviderCredential);
fn delete_credentials(&self, cx: &AppContext);
}
crates/ai/src/completion.rs
+12 -203
View File
@@ -1,214 +1,23 @@
use anyhow::{anyhow, Result};
use futures::{
future::BoxFuture, io::BufReader, stream::BoxStream, AsyncBufReadExt, AsyncReadExt, FutureExt,
Stream, StreamExt,
};
use gpui::executor::Background;
use isahc::{http::StatusCode, Request, RequestExt};
use serde::{Deserialize, Serialize};
use std::{
fmt::{self, Display},
io,
sync::Arc,
};
use anyhow::Result;
use futures::{future::BoxFuture, stream::BoxStream};
pub const OPENAI_API_URL: &'static str = "https://api.openai.com/v1";
use crate::{auth::CredentialProvider, models::LanguageModel};
#[derive(Clone, Copy, Serialize, Deserialize, Debug, Eq, PartialEq)]
#[serde(rename_all = "lowercase")]
pub enum Role {
User,
Assistant,
System,
pub trait CompletionRequest: Send + Sync {
fn data(&self) -> serde_json::Result<String>;
}
impl Role {
pub fn cycle(&mut self) {
*self = match self {
Role::User => Role::Assistant,
Role::Assistant => Role::System,
Role::System => Role::User,
}
}
}
impl Display for Role {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> std::fmt::Result {
match self {
Role::User => write!(f, "User"),
Role::Assistant => write!(f, "Assistant"),
Role::System => write!(f, "System"),
}
}
}
#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
pub struct RequestMessage {
pub role: Role,
pub content: String,
}
#[derive(Debug, Default, Serialize)]
pub struct OpenAIRequest {
pub model: String,
pub messages: Vec<RequestMessage>,
pub stream: bool,
pub stop: Vec<String>,
pub temperature: f32,
}
#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
pub struct ResponseMessage {
pub role: Option<Role>,
pub content: Option<String>,
}
#[derive(Deserialize, Debug)]
pub struct OpenAIUsage {
pub prompt_tokens: u32,
pub completion_tokens: u32,
pub total_tokens: u32,
}
#[derive(Deserialize, Debug)]
pub struct ChatChoiceDelta {
pub index: u32,
pub delta: ResponseMessage,
pub finish_reason: Option<String>,
}
#[derive(Deserialize, Debug)]
pub struct OpenAIResponseStreamEvent {
pub id: Option<String>,
pub object: String,
pub created: u32,
pub model: String,
pub choices: Vec<ChatChoiceDelta>,
pub usage: Option<OpenAIUsage>,
}
pub async fn stream_completion(
api_key: String,
executor: Arc<Background>,
mut request: OpenAIRequest,
) -> Result<impl Stream<Item = Result<OpenAIResponseStreamEvent>>> {
request.stream = true;
let (tx, rx) = futures::channel::mpsc::unbounded::<Result<OpenAIResponseStreamEvent>>();
let json_data = serde_json::to_string(&request)?;
let mut response = Request::post(format!("{OPENAI_API_URL}/chat/completions"))
.header("Content-Type", "application/json")
.header("Authorization", format!("Bearer {}", api_key))
.body(json_data)?
.send_async()
.await?;
let status = response.status();
if status == StatusCode::OK {
executor
.spawn(async move {
let mut lines = BufReader::new(response.body_mut()).lines();
fn parse_line(
line: Result<String, io::Error>,
) -> Result<Option<OpenAIResponseStreamEvent>> {
if let Some(data) = line?.strip_prefix("data: ") {
let event = serde_json::from_str(&data)?;
Ok(Some(event))
} else {
Ok(None)
}
}
while let Some(line) = lines.next().await {
if let Some(event) = parse_line(line).transpose() {
let done = event.as_ref().map_or(false, |event| {
event
.choices
.last()
.map_or(false, |choice| choice.finish_reason.is_some())
});
if tx.unbounded_send(event).is_err() {
break;
}
if done {
break;
}
}
}
anyhow::Ok(())
})
.detach();
Ok(rx)
} else {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
#[derive(Deserialize)]
struct OpenAIResponse {
error: OpenAIError,
}
#[derive(Deserialize)]
struct OpenAIError {
message: String,
}
match serde_json::from_str::<OpenAIResponse>(&body) {
Ok(response) if !response.error.message.is_empty() => Err(anyhow!(
"Failed to connect to OpenAI API: {}",
response.error.message,
)),
_ => Err(anyhow!(
"Failed to connect to OpenAI API: {} {}",
response.status(),
body,
)),
}
}
}
pub trait CompletionProvider {
pub trait CompletionProvider: CredentialProvider {
fn base_model(&self) -> Box<dyn LanguageModel>;
fn complete(
&self,
prompt: OpenAIRequest,
prompt: Box<dyn CompletionRequest>,
) -> BoxFuture<'static, Result<BoxStream<'static, Result<String>>>>;
fn box_clone(&self) -> Box<dyn CompletionProvider>;
}
pub struct OpenAICompletionProvider {
api_key: String,
executor: Arc<Background>,
}
impl OpenAICompletionProvider {
pub fn new(api_key: String, executor: Arc<Background>) -> Self {
Self { api_key, executor }
}
}
impl CompletionProvider for OpenAICompletionProvider {
fn complete(
&self,
prompt: OpenAIRequest,
) -> BoxFuture<'static, Result<BoxStream<'static, Result<String>>>> {
let request = stream_completion(self.api_key.clone(), self.executor.clone(), prompt);
async move {
let response = request.await?;
let stream = response
.filter_map(|response| async move {
match response {
Ok(mut response) => Some(Ok(response.choices.pop()?.delta.content?)),
Err(error) => Some(Err(error)),
}
})
.boxed();
Ok(stream)
}
.boxed()
impl Clone for Box<dyn CompletionProvider> {
fn clone(&self) -> Box<dyn CompletionProvider> {
self.box_clone()
}
}
crates/ai/src/embedding.rs
+8 -314
View File
@@ -1,32 +1,13 @@
use anyhow::{anyhow, Result};
use std::time::Instant;
use anyhow::Result;
use async_trait::async_trait;
use futures::AsyncReadExt;
use gpui::executor::Background;
use gpui::{serde_json, AppContext};
use isahc::http::StatusCode;
use isahc::prelude::Configurable;
use isahc::{AsyncBody, Response};
use lazy_static::lazy_static;
use ordered_float::OrderedFloat;
use parking_lot::Mutex;
use parse_duration::parse;
use postage::watch;
use rusqlite::types::{FromSql, FromSqlResult, ToSqlOutput, ValueRef};
use rusqlite::ToSql;
use serde::{Deserialize, Serialize};
use std::env;
use std::ops::Add;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tiktoken_rs::{cl100k_base, CoreBPE};
use util::http::{HttpClient, Request};
use util::ResultExt;
use crate::completion::OPENAI_API_URL;
lazy_static! {
static ref OPENAI_BPE_TOKENIZER: CoreBPE = cl100k_base().unwrap();
}
use crate::auth::CredentialProvider;
use crate::models::LanguageModel;
#[derive(Debug, PartialEq, Clone)]
pub struct Embedding(pub Vec<f32>);
@@ -87,301 +68,14 @@ impl Embedding {
}
}
#[derive(Clone)]
pub struct OpenAIEmbeddings {
pub client: Arc<dyn HttpClient>,
pub executor: Arc<Background>,
rate_limit_count_rx: watch::Receiver<Option<Instant>>,
rate_limit_count_tx: Arc<Mutex<watch::Sender<Option<Instant>>>>,
}
#[derive(Serialize)]
struct OpenAIEmbeddingRequest<'a> {
model: &'static str,
input: Vec<&'a str>,
}
#[derive(Deserialize)]
struct OpenAIEmbeddingResponse {
data: Vec<OpenAIEmbedding>,
usage: OpenAIEmbeddingUsage,
}
#[derive(Debug, Deserialize)]
struct OpenAIEmbedding {
embedding: Vec<f32>,
index: usize,
object: String,
}
#[derive(Deserialize)]
struct OpenAIEmbeddingUsage {
prompt_tokens: usize,
total_tokens: usize,
}
#[async_trait]
pub trait EmbeddingProvider: Sync + Send {
fn retrieve_credentials(&self, cx: &AppContext) -> Option<String>;
async fn embed_batch(
&self,
spans: Vec<String>,
api_key: Option<String>,
) -> Result<Vec<Embedding>>;
pub trait EmbeddingProvider: CredentialProvider {
fn base_model(&self) -> Box<dyn LanguageModel>;
async fn embed_batch(&self, spans: Vec<String>) -> Result<Vec<Embedding>>;
fn max_tokens_per_batch(&self) -> usize;
fn truncate(&self, span: &str) -> (String, usize);
fn rate_limit_expiration(&self) -> Option<Instant>;
}
pub struct DummyEmbeddings {}
#[async_trait]
impl EmbeddingProvider for DummyEmbeddings {
fn retrieve_credentials(&self, _cx: &AppContext) -> Option<String> {
Some("Dummy API KEY".to_string())
}
fn rate_limit_expiration(&self) -> Option<Instant> {
None
}
async fn embed_batch(
&self,
spans: Vec<String>,
_api_key: Option<String>,
) -> Result<Vec<Embedding>> {
// 1024 is the OpenAI Embeddings size for ada models.
// the model we will likely be starting with.
let dummy_vec = Embedding::from(vec![0.32 as f32; 1536]);
return Ok(vec![dummy_vec; spans.len()]);
}
fn max_tokens_per_batch(&self) -> usize {
OPENAI_INPUT_LIMIT
}
fn truncate(&self, span: &str) -> (String, usize) {
let mut tokens = OPENAI_BPE_TOKENIZER.encode_with_special_tokens(span);
let token_count = tokens.len();
let output = if token_count > OPENAI_INPUT_LIMIT {
tokens.truncate(OPENAI_INPUT_LIMIT);
let new_input = OPENAI_BPE_TOKENIZER.decode(tokens.clone());
new_input.ok().unwrap_or_else(|| span.to_string())
} else {
span.to_string()
};
(output, tokens.len())
}
}
const OPENAI_INPUT_LIMIT: usize = 8190;
impl OpenAIEmbeddings {
pub fn new(client: Arc<dyn HttpClient>, executor: Arc<Background>) -> Self {
let (rate_limit_count_tx, rate_limit_count_rx) = watch::channel_with(None);
let rate_limit_count_tx = Arc::new(Mutex::new(rate_limit_count_tx));
OpenAIEmbeddings {
client,
executor,
rate_limit_count_rx,
rate_limit_count_tx,
}
}
fn resolve_rate_limit(&self) {
let reset_time = *self.rate_limit_count_tx.lock().borrow();
if let Some(reset_time) = reset_time {
if Instant::now() >= reset_time {
*self.rate_limit_count_tx.lock().borrow_mut() = None
}
}
log::trace!(
"resolving reset time: {:?}",
*self.rate_limit_count_tx.lock().borrow()
);
}
fn update_reset_time(&self, reset_time: Instant) {
let original_time = *self.rate_limit_count_tx.lock().borrow();
let updated_time = if let Some(original_time) = original_time {
if reset_time < original_time {
Some(reset_time)
} else {
Some(original_time)
}
} else {
Some(reset_time)
};
log::trace!("updating rate limit time: {:?}", updated_time);
*self.rate_limit_count_tx.lock().borrow_mut() = updated_time;
}
async fn send_request(
&self,
api_key: &str,
spans: Vec<&str>,
request_timeout: u64,
) -> Result<Response<AsyncBody>> {
let request = Request::post("https://api.openai.com/v1/embeddings")
.redirect_policy(isahc::config::RedirectPolicy::Follow)
.timeout(Duration::from_secs(request_timeout))
.header("Content-Type", "application/json")
.header("Authorization", format!("Bearer {}", api_key))
.body(
serde_json::to_string(&OpenAIEmbeddingRequest {
input: spans.clone(),
model: "text-embedding-ada-002",
})
.unwrap()
.into(),
)?;
Ok(self.client.send(request).await?)
}
}
#[async_trait]
impl EmbeddingProvider for OpenAIEmbeddings {
fn retrieve_credentials(&self, cx: &AppContext) -> Option<String> {
if let Ok(api_key) = env::var("OPENAI_API_KEY") {
Some(api_key)
} else if let Some((_, api_key)) = cx
.platform()
.read_credentials(OPENAI_API_URL)
.log_err()
.flatten()
{
String::from_utf8(api_key).log_err()
} else {
None
}
}
fn max_tokens_per_batch(&self) -> usize {
50000
}
fn rate_limit_expiration(&self) -> Option<Instant> {
*self.rate_limit_count_rx.borrow()
}
fn truncate(&self, span: &str) -> (String, usize) {
let mut tokens = OPENAI_BPE_TOKENIZER.encode_with_special_tokens(span);
let output = if tokens.len() > OPENAI_INPUT_LIMIT {
tokens.truncate(OPENAI_INPUT_LIMIT);
OPENAI_BPE_TOKENIZER
.decode(tokens.clone())
.ok()
.unwrap_or_else(|| span.to_string())
} else {
span.to_string()
};
(output, tokens.len())
}
async fn embed_batch(
&self,
spans: Vec<String>,
api_key: Option<String>,
) -> Result<Vec<Embedding>> {
const BACKOFF_SECONDS: [usize; 4] = [3, 5, 15, 45];
const MAX_RETRIES: usize = 4;
let Some(api_key) = api_key else {
return Err(anyhow!("no open ai key provided"));
};
let mut request_number = 0;
let mut rate_limiting = false;
let mut request_timeout: u64 = 15;
let mut response: Response<AsyncBody>;
while request_number < MAX_RETRIES {
response = self
.send_request(
&api_key,
spans.iter().map(|x| &**x).collect(),
request_timeout,
)
.await?;
request_number += 1;
match response.status() {
StatusCode::REQUEST_TIMEOUT => {
request_timeout += 5;
}
StatusCode::OK => {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
let response: OpenAIEmbeddingResponse = serde_json::from_str(&body)?;
log::trace!(
"openai embedding completed. tokens: {:?}",
response.usage.total_tokens
);
// If we complete a request successfully that was previously rate_limited
// resolve the rate limit
if rate_limiting {
self.resolve_rate_limit()
}
return Ok(response
.data
.into_iter()
.map(|embedding| Embedding::from(embedding.embedding))
.collect());
}
StatusCode::TOO_MANY_REQUESTS => {
rate_limiting = true;
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
let delay_duration = {
let delay = Duration::from_secs(BACKOFF_SECONDS[request_number - 1] as u64);
if let Some(time_to_reset) =
response.headers().get("x-ratelimit-reset-tokens")
{
if let Ok(time_str) = time_to_reset.to_str() {
parse(time_str).unwrap_or(delay)
} else {
delay
}
} else {
delay
}
};
// If we've previously rate limited, increment the duration but not the count
let reset_time = Instant::now().add(delay_duration);
self.update_reset_time(reset_time);
log::trace!(
"openai rate limiting: waiting {:?} until lifted",
&delay_duration
);
self.executor.timer(delay_duration).await;
}
_ => {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
return Err(anyhow!(
"open ai bad request: {:?} {:?}",
&response.status(),
body
));
}
}
}
Err(anyhow!("openai max retries"))
}
}
#[cfg(test)]
mod tests {
use super::*;
crates/ai/src/models.rs
+10 -60
View File
@@ -1,66 +1,16 @@
use anyhow::anyhow;
use tiktoken_rs::CoreBPE;
use util::ResultExt;
pub enum TruncationDirection {
Start,
End,
}
pub trait LanguageModel {
fn name(&self) -> String;
fn count_tokens(&self, content: &str) -> anyhow::Result<usize>;
fn truncate(&self, content: &str, length: usize) -> anyhow::Result<String>;
fn truncate_start(&self, content: &str, length: usize) -> anyhow::Result<String>;
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String>;
fn capacity(&self) -> anyhow::Result<usize>;
}
pub struct OpenAILanguageModel {
name: String,
bpe: Option<CoreBPE>,
}
impl OpenAILanguageModel {
pub fn load(model_name: &str) -> Self {
let bpe = tiktoken_rs::get_bpe_from_model(model_name).log_err();
OpenAILanguageModel {
name: model_name.to_string(),
bpe,
}
}
}
impl LanguageModel for OpenAILanguageModel {
fn name(&self) -> String {
self.name.clone()
}
fn count_tokens(&self, content: &str) -> anyhow::Result<usize> {
if let Some(bpe) = &self.bpe {
anyhow::Ok(bpe.encode_with_special_tokens(content).len())
} else {
Err(anyhow!("bpe for open ai model was not retrieved"))
}
}
fn truncate(&self, content: &str, length: usize) -> anyhow::Result<String> {
if let Some(bpe) = &self.bpe {
let tokens = bpe.encode_with_special_tokens(content);
if tokens.len() > length {
bpe.decode(tokens[..length].to_vec())
} else {
bpe.decode(tokens)
}
} else {
Err(anyhow!("bpe for open ai model was not retrieved"))
}
}
fn truncate_start(&self, content: &str, length: usize) -> anyhow::Result<String> {
if let Some(bpe) = &self.bpe {
let tokens = bpe.encode_with_special_tokens(content);
if tokens.len() > length {
bpe.decode(tokens[length..].to_vec())
} else {
bpe.decode(tokens)
}
} else {
Err(anyhow!("bpe for open ai model was not retrieved"))
}
}
fn capacity(&self) -> anyhow::Result<usize> {
anyhow::Ok(tiktoken_rs::model::get_context_size(&self.name))
}
}
crates/ai/src/templates/base.rs → crates/ai/src/prompts/base.rs
+18 -38
View File
@@ -6,7 +6,7 @@ use language::BufferSnapshot;
use util::ResultExt;
use crate::models::LanguageModel;
use crate::templates::repository_context::PromptCodeSnippet;
use crate::prompts::repository_context::PromptCodeSnippet;
pub(crate) enum PromptFileType {
Text,
@@ -125,6 +125,9 @@ impl PromptChain {
#[cfg(test)]
pub(crate) mod tests {
use crate::models::TruncationDirection;
use crate::test::FakeLanguageModel;
use super::*;
#[test]
@@ -141,7 +144,11 @@ pub(crate) mod tests {
let mut token_count = args.model.count_tokens(&content)?;
if let Some(max_token_length) = max_token_length {
if token_count > max_token_length {
content = args.model.truncate(&content, max_token_length)?;
content = args.model.truncate(
&content,
max_token_length,
TruncationDirection::End,
)?;
token_count = max_token_length;
}
}
@@ -162,7 +169,11 @@ pub(crate) mod tests {
let mut token_count = args.model.count_tokens(&content)?;
if let Some(max_token_length) = max_token_length {
if token_count > max_token_length {
content = args.model.truncate(&content, max_token_length)?;
content = args.model.truncate(
&content,
max_token_length,
TruncationDirection::End,
)?;
token_count = max_token_length;
}
}
@@ -171,38 +182,7 @@ pub(crate) mod tests {
}
}
#[derive(Clone)]
struct DummyLanguageModel {
capacity: usize,
}
impl LanguageModel for DummyLanguageModel {
fn name(&self) -> String {
"dummy".to_string()
}
fn count_tokens(&self, content: &str) -> anyhow::Result<usize> {
anyhow::Ok(content.chars().collect::<Vec<char>>().len())
}
fn truncate(&self, content: &str, length: usize) -> anyhow::Result<String> {
anyhow::Ok(
content.chars().collect::<Vec<char>>()[..length]
.into_iter()
.collect::<String>(),
)
}
fn truncate_start(&self, content: &str, length: usize) -> anyhow::Result<String> {
anyhow::Ok(
content.chars().collect::<Vec<char>>()[length..]
.into_iter()
.collect::<String>(),
)
}
fn capacity(&self) -> anyhow::Result<usize> {
anyhow::Ok(self.capacity)
}
}
let model: Arc<dyn LanguageModel> = Arc::new(DummyLanguageModel { capacity: 100 });
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity: 100 });
let args = PromptArguments {
model: model.clone(),
language_name: None,
@@ -238,7 +218,7 @@ pub(crate) mod tests {
// Testing with Truncation Off
// Should ignore capacity and return all prompts
let model: Arc<dyn LanguageModel> = Arc::new(DummyLanguageModel { capacity: 20 });
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity: 20 });
let args = PromptArguments {
model: model.clone(),
language_name: None,
@@ -275,7 +255,7 @@ pub(crate) mod tests {
// Testing with Truncation Off
// Should ignore capacity and return all prompts
let capacity = 20;
let model: Arc<dyn LanguageModel> = Arc::new(DummyLanguageModel { capacity });
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity });
let args = PromptArguments {
model: model.clone(),
language_name: None,
@@ -311,7 +291,7 @@ pub(crate) mod tests {
// Change Ordering of Prompts Based on Priority
let capacity = 120;
let reserved_tokens = 10;
let model: Arc<dyn LanguageModel> = Arc::new(DummyLanguageModel { capacity });
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity });
let args = PromptArguments {
model: model.clone(),
language_name: None,
crates/ai/src/templates/file_context.rs → crates/ai/src/prompts/file_context.rs
+10 -6
View File
@@ -3,8 +3,9 @@ use language::BufferSnapshot;
use language::ToOffset;
use crate::models::LanguageModel;
use crate::templates::base::PromptArguments;
use crate::templates::base::PromptTemplate;
use crate::models::TruncationDirection;
use crate::prompts::base::PromptArguments;
use crate::prompts::base::PromptTemplate;
use std::fmt::Write;
use std::ops::Range;
use std::sync::Arc;
@@ -70,8 +71,9 @@ fn retrieve_context(
};
let truncated_start_window =
model.truncate_start(&start_window, start_goal_tokens)?;
let truncated_end_window = model.truncate(&end_window, end_goal_tokens)?;
model.truncate(&start_window, start_goal_tokens, TruncationDirection::Start)?;
let truncated_end_window =
model.truncate(&end_window, end_goal_tokens, TruncationDirection::End)?;
writeln!(
prompt,
"{truncated_start_window}{selected_window}{truncated_end_window}"
@@ -89,7 +91,7 @@ fn retrieve_context(
if let Some(max_token_count) = max_token_count {
if model.count_tokens(&prompt)? > max_token_count {
truncated = true;
prompt = model.truncate(&prompt, max_token_count)?;
prompt = model.truncate(&prompt, max_token_count, TruncationDirection::End)?;
}
}
}
@@ -148,7 +150,9 @@ impl PromptTemplate for FileContext {
// Really dumb truncation strategy
if let Some(max_tokens) = max_token_length {
prompt = args.model.truncate(&prompt, max_tokens)?;
prompt = args
.model
.truncate(&prompt, max_tokens, TruncationDirection::End)?;
}
let token_count = args.model.count_tokens(&prompt)?;
crates/ai/src/templates/generate.rs → crates/ai/src/prompts/generate.rs
+6 -2
View File
@@ -1,4 +1,4 @@
use crate::templates::base::{PromptArguments, PromptFileType, PromptTemplate};
use crate::prompts::base::{PromptArguments, PromptFileType, PromptTemplate};
use anyhow::anyhow;
use std::fmt::Write;
@@ -85,7 +85,11 @@ impl PromptTemplate for GenerateInlineContent {
// Really dumb truncation strategy
if let Some(max_tokens) = max_token_length {
prompt = args.model.truncate(&prompt, max_tokens)?;
prompt = args.model.truncate(
&prompt,
max_tokens,
crate::models::TruncationDirection::End,
)?;
}
let token_count = args.model.count_tokens(&prompt)?;
crates/ai/src/templates/mod.rs → crates/ai/src/prompts/mod.rs
View File
crates/ai/src/templates/preamble.rs → crates/ai/src/prompts/preamble.rs
+1 -1
View File
@@ -1,4 +1,4 @@
use crate::templates::base::{PromptArguments, PromptFileType, PromptTemplate};
use crate::prompts::base::{PromptArguments, PromptFileType, PromptTemplate};
use std::fmt::Write;
pub struct EngineerPreamble {}
crates/ai/src/templates/repository_context.rs → crates/ai/src/prompts/repository_context.rs
+1 -1
View File
@@ -1,4 +1,4 @@
use crate::templates::base::{PromptArguments, PromptTemplate};
use crate::prompts::base::{PromptArguments, PromptTemplate};
use std::fmt::Write;
use std::{ops::Range, path::PathBuf};
crates/ai/src/providers/mod.rs
+1
View File
@@ -0,0 +1 @@
pub mod open_ai;
crates/ai/src/providers/open_ai/completion.rs
+298
View File
@@ -0,0 +1,298 @@
use anyhow::{anyhow, Result};
use futures::{
future::BoxFuture, io::BufReader, stream::BoxStream, AsyncBufReadExt, AsyncReadExt, FutureExt,
Stream, StreamExt,
};
use gpui::{executor::Background, AppContext};
use isahc::{http::StatusCode, Request, RequestExt};
use parking_lot::RwLock;
use serde::{Deserialize, Serialize};
use std::{
env,
fmt::{self, Display},
io,
sync::Arc,
};
use util::ResultExt;
use crate::{
auth::{CredentialProvider, ProviderCredential},
completion::{CompletionProvider, CompletionRequest},
models::LanguageModel,
};
use crate::providers::open_ai::{OpenAILanguageModel, OPENAI_API_URL};
#[derive(Clone, Copy, Serialize, Deserialize, Debug, Eq, PartialEq)]
#[serde(rename_all = "lowercase")]
pub enum Role {
User,
Assistant,
System,
}
impl Role {
pub fn cycle(&mut self) {
*self = match self {
Role::User => Role::Assistant,
Role::Assistant => Role::System,
Role::System => Role::User,
}
}
}
impl Display for Role {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> std::fmt::Result {
match self {
Role::User => write!(f, "User"),
Role::Assistant => write!(f, "Assistant"),
Role::System => write!(f, "System"),
}
}
}
#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
pub struct RequestMessage {
pub role: Role,
pub content: String,
}
#[derive(Debug, Default, Serialize)]
pub struct OpenAIRequest {
pub model: String,
pub messages: Vec<RequestMessage>,
pub stream: bool,
pub stop: Vec<String>,
pub temperature: f32,
}
impl CompletionRequest for OpenAIRequest {
fn data(&self) -> serde_json::Result<String> {
serde_json::to_string(self)
}
}
#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
pub struct ResponseMessage {
pub role: Option<Role>,
pub content: Option<String>,
}
#[derive(Deserialize, Debug)]
pub struct OpenAIUsage {
pub prompt_tokens: u32,
pub completion_tokens: u32,
pub total_tokens: u32,
}
#[derive(Deserialize, Debug)]
pub struct ChatChoiceDelta {
pub index: u32,
pub delta: ResponseMessage,
pub finish_reason: Option<String>,
}
#[derive(Deserialize, Debug)]
pub struct OpenAIResponseStreamEvent {
pub id: Option<String>,
pub object: String,
pub created: u32,
pub model: String,
pub choices: Vec<ChatChoiceDelta>,
pub usage: Option<OpenAIUsage>,
}
pub async fn stream_completion(
credential: ProviderCredential,
executor: Arc<Background>,
request: Box<dyn CompletionRequest>,
) -> Result<impl Stream<Item = Result<OpenAIResponseStreamEvent>>> {
let api_key = match credential {
ProviderCredential::Credentials { api_key } => api_key,
_ => {
return Err(anyhow!("no credentials provider for completion"));
}
};
let (tx, rx) = futures::channel::mpsc::unbounded::<Result<OpenAIResponseStreamEvent>>();
let json_data = request.data()?;
let mut response = Request::post(format!("{OPENAI_API_URL}/chat/completions"))
.header("Content-Type", "application/json")
.header("Authorization", format!("Bearer {}", api_key))
.body(json_data)?
.send_async()
.await?;
let status = response.status();
if status == StatusCode::OK {
executor
.spawn(async move {
let mut lines = BufReader::new(response.body_mut()).lines();
fn parse_line(
line: Result<String, io::Error>,
) -> Result<Option<OpenAIResponseStreamEvent>> {
if let Some(data) = line?.strip_prefix("data: ") {
let event = serde_json::from_str(&data)?;
Ok(Some(event))
} else {
Ok(None)
}
}
while let Some(line) = lines.next().await {
if let Some(event) = parse_line(line).transpose() {
let done = event.as_ref().map_or(false, |event| {
event
.choices
.last()
.map_or(false, |choice| choice.finish_reason.is_some())
});
if tx.unbounded_send(event).is_err() {
break;
}
if done {
break;
}
}
}
anyhow::Ok(())
})
.detach();
Ok(rx)
} else {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
#[derive(Deserialize)]
struct OpenAIResponse {
error: OpenAIError,
}
#[derive(Deserialize)]
struct OpenAIError {
message: String,
}
match serde_json::from_str::<OpenAIResponse>(&body) {
Ok(response) if !response.error.message.is_empty() => Err(anyhow!(
"Failed to connect to OpenAI API: {}",
response.error.message,
)),
_ => Err(anyhow!(
"Failed to connect to OpenAI API: {} {}",
response.status(),
body,
)),
}
}
}
#[derive(Clone)]
pub struct OpenAICompletionProvider {
model: OpenAILanguageModel,
credential: Arc<RwLock<ProviderCredential>>,
executor: Arc<Background>,
}
impl OpenAICompletionProvider {
pub fn new(model_name: &str, executor: Arc<Background>) -> Self {
let model = OpenAILanguageModel::load(model_name);
let credential = Arc::new(RwLock::new(ProviderCredential::NoCredentials));
Self {
model,
credential,
executor,
}
}
}
impl CredentialProvider for OpenAICompletionProvider {
fn has_credentials(&self) -> bool {
match *self.credential.read() {
ProviderCredential::Credentials { .. } => true,
_ => false,
}
}
fn retrieve_credentials(&self, cx: &AppContext) -> ProviderCredential {
let mut credential = self.credential.write();
match *credential {
ProviderCredential::Credentials { .. } => {
return credential.clone();
}
_ => {
if let Ok(api_key) = env::var("OPENAI_API_KEY") {
*credential = ProviderCredential::Credentials { api_key };
} else if let Some((_, api_key)) = cx
.platform()
.read_credentials(OPENAI_API_URL)
.log_err()
.flatten()
{
if let Some(api_key) = String::from_utf8(api_key).log_err() {
*credential = ProviderCredential::Credentials { api_key };
}
} else {
};
}
}
credential.clone()
}
fn save_credentials(&self, cx: &AppContext, credential: ProviderCredential) {
match credential.clone() {
ProviderCredential::Credentials { api_key } => {
cx.platform()
.write_credentials(OPENAI_API_URL, "Bearer", api_key.as_bytes())
.log_err();
}
_ => {}
}
*self.credential.write() = credential;
}
fn delete_credentials(&self, cx: &AppContext) {
cx.platform().delete_credentials(OPENAI_API_URL).log_err();
*self.credential.write() = ProviderCredential::NoCredentials;
}
}
impl CompletionProvider for OpenAICompletionProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
let model: Box<dyn LanguageModel> = Box::new(self.model.clone());
model
}
fn complete(
&self,
prompt: Box<dyn CompletionRequest>,
) -> BoxFuture<'static, Result<BoxStream<'static, Result<String>>>> {
// Currently the CompletionRequest for OpenAI, includes a 'model' parameter
// This means that the model is determined by the CompletionRequest and not the CompletionProvider,
// which is currently model based, due to the langauge model.
// At some point in the future we should rectify this.
let credential = self.credential.read().clone();
let request = stream_completion(credential, self.executor.clone(), prompt);
async move {
let response = request.await?;
let stream = response
.filter_map(|response| async move {
match response {
Ok(mut response) => Some(Ok(response.choices.pop()?.delta.content?)),
Err(error) => Some(Err(error)),
}
})
.boxed();
Ok(stream)
}
.boxed()
}
fn box_clone(&self) -> Box<dyn CompletionProvider> {
Box::new((*self).clone())
}
}
crates/ai/src/providers/open_ai/embedding.rs
+306
View File
@@ -0,0 +1,306 @@
use anyhow::{anyhow, Result};
use async_trait::async_trait;
use futures::AsyncReadExt;
use gpui::executor::Background;
use gpui::{serde_json, AppContext};
use isahc::http::StatusCode;
use isahc::prelude::Configurable;
use isahc::{AsyncBody, Response};
use lazy_static::lazy_static;
use parking_lot::{Mutex, RwLock};
use parse_duration::parse;
use postage::watch;
use serde::{Deserialize, Serialize};
use std::env;
use std::ops::Add;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tiktoken_rs::{cl100k_base, CoreBPE};
use util::http::{HttpClient, Request};
use util::ResultExt;
use crate::auth::{CredentialProvider, ProviderCredential};
use crate::embedding::{Embedding, EmbeddingProvider};
use crate::models::LanguageModel;
use crate::providers::open_ai::OpenAILanguageModel;
use crate::providers::open_ai::OPENAI_API_URL;
lazy_static! {
static ref OPENAI_BPE_TOKENIZER: CoreBPE = cl100k_base().unwrap();
}
#[derive(Clone)]
pub struct OpenAIEmbeddingProvider {
model: OpenAILanguageModel,
credential: Arc<RwLock<ProviderCredential>>,
pub client: Arc<dyn HttpClient>,
pub executor: Arc<Background>,
rate_limit_count_rx: watch::Receiver<Option<Instant>>,
rate_limit_count_tx: Arc<Mutex<watch::Sender<Option<Instant>>>>,
}
#[derive(Serialize)]
struct OpenAIEmbeddingRequest<'a> {
model: &'static str,
input: Vec<&'a str>,
}
#[derive(Deserialize)]
struct OpenAIEmbeddingResponse {
data: Vec<OpenAIEmbedding>,
usage: OpenAIEmbeddingUsage,
}
#[derive(Debug, Deserialize)]
struct OpenAIEmbedding {
embedding: Vec<f32>,
index: usize,
object: String,
}
#[derive(Deserialize)]
struct OpenAIEmbeddingUsage {
prompt_tokens: usize,
total_tokens: usize,
}
impl OpenAIEmbeddingProvider {
pub fn new(client: Arc<dyn HttpClient>, executor: Arc<Background>) -> Self {
let (rate_limit_count_tx, rate_limit_count_rx) = watch::channel_with(None);
let rate_limit_count_tx = Arc::new(Mutex::new(rate_limit_count_tx));
let model = OpenAILanguageModel::load("text-embedding-ada-002");
let credential = Arc::new(RwLock::new(ProviderCredential::NoCredentials));
OpenAIEmbeddingProvider {
model,
credential,
client,
executor,
rate_limit_count_rx,
rate_limit_count_tx,
}
}
fn get_api_key(&self) -> Result<String> {
match self.credential.read().clone() {
ProviderCredential::Credentials { api_key } => Ok(api_key),
_ => Err(anyhow!("api credentials not provided")),
}
}
fn resolve_rate_limit(&self) {
let reset_time = *self.rate_limit_count_tx.lock().borrow();
if let Some(reset_time) = reset_time {
if Instant::now() >= reset_time {
*self.rate_limit_count_tx.lock().borrow_mut() = None
}
}
log::trace!(
"resolving reset time: {:?}",
*self.rate_limit_count_tx.lock().borrow()
);
}
fn update_reset_time(&self, reset_time: Instant) {
let original_time = *self.rate_limit_count_tx.lock().borrow();
let updated_time = if let Some(original_time) = original_time {
if reset_time < original_time {
Some(reset_time)
} else {
Some(original_time)
}
} else {
Some(reset_time)
};
log::trace!("updating rate limit time: {:?}", updated_time);
*self.rate_limit_count_tx.lock().borrow_mut() = updated_time;
}
async fn send_request(
&self,
api_key: &str,
spans: Vec<&str>,
request_timeout: u64,
) -> Result<Response<AsyncBody>> {
let request = Request::post("https://api.openai.com/v1/embeddings")
.redirect_policy(isahc::config::RedirectPolicy::Follow)
.timeout(Duration::from_secs(request_timeout))
.header("Content-Type", "application/json")
.header("Authorization", format!("Bearer {}", api_key))
.body(
serde_json::to_string(&OpenAIEmbeddingRequest {
input: spans.clone(),
model: "text-embedding-ada-002",
})
.unwrap()
.into(),
)?;
Ok(self.client.send(request).await?)
}
}
impl CredentialProvider for OpenAIEmbeddingProvider {
fn has_credentials(&self) -> bool {
match *self.credential.read() {
ProviderCredential::Credentials { .. } => true,
_ => false,
}
}
fn retrieve_credentials(&self, cx: &AppContext) -> ProviderCredential {
let mut credential = self.credential.write();
match *credential {
ProviderCredential::Credentials { .. } => {
return credential.clone();
}
_ => {
if let Ok(api_key) = env::var("OPENAI_API_KEY") {
*credential = ProviderCredential::Credentials { api_key };
} else if let Some((_, api_key)) = cx
.platform()
.read_credentials(OPENAI_API_URL)
.log_err()
.flatten()
{
if let Some(api_key) = String::from_utf8(api_key).log_err() {
*credential = ProviderCredential::Credentials { api_key };
}
} else {
};
}
}
credential.clone()
}
fn save_credentials(&self, cx: &AppContext, credential: ProviderCredential) {
match credential.clone() {
ProviderCredential::Credentials { api_key } => {
cx.platform()
.write_credentials(OPENAI_API_URL, "Bearer", api_key.as_bytes())
.log_err();
}
_ => {}
}
*self.credential.write() = credential;
}
fn delete_credentials(&self, cx: &AppContext) {
cx.platform().delete_credentials(OPENAI_API_URL).log_err();
*self.credential.write() = ProviderCredential::NoCredentials;
}
}
#[async_trait]
impl EmbeddingProvider for OpenAIEmbeddingProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
let model: Box<dyn LanguageModel> = Box::new(self.model.clone());
model
}
fn max_tokens_per_batch(&self) -> usize {
50000
}
fn rate_limit_expiration(&self) -> Option<Instant> {
*self.rate_limit_count_rx.borrow()
}
async fn embed_batch(&self, spans: Vec<String>) -> Result<Vec<Embedding>> {
const BACKOFF_SECONDS: [usize; 4] = [3, 5, 15, 45];
const MAX_RETRIES: usize = 4;
let api_key = self.get_api_key()?;
let mut request_number = 0;
let mut rate_limiting = false;
let mut request_timeout: u64 = 15;
let mut response: Response<AsyncBody>;
while request_number < MAX_RETRIES {
response = self
.send_request(
&api_key,
spans.iter().map(|x| &**x).collect(),
request_timeout,
)
.await?;
request_number += 1;
match response.status() {
StatusCode::REQUEST_TIMEOUT => {
request_timeout += 5;
}
StatusCode::OK => {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
let response: OpenAIEmbeddingResponse = serde_json::from_str(&body)?;
log::trace!(
"openai embedding completed. tokens: {:?}",
response.usage.total_tokens
);
// If we complete a request successfully that was previously rate_limited
// resolve the rate limit
if rate_limiting {
self.resolve_rate_limit()
}
return Ok(response
.data
.into_iter()
.map(|embedding| Embedding::from(embedding.embedding))
.collect());
}
StatusCode::TOO_MANY_REQUESTS => {
rate_limiting = true;
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
let delay_duration = {
let delay = Duration::from_secs(BACKOFF_SECONDS[request_number - 1] as u64);
if let Some(time_to_reset) =
response.headers().get("x-ratelimit-reset-tokens")
{
if let Ok(time_str) = time_to_reset.to_str() {
parse(time_str).unwrap_or(delay)
} else {
delay
}
} else {
delay
}
};
// If we've previously rate limited, increment the duration but not the count
let reset_time = Instant::now().add(delay_duration);
self.update_reset_time(reset_time);
log::trace!(
"openai rate limiting: waiting {:?} until lifted",
&delay_duration
);
self.executor.timer(delay_duration).await;
}
_ => {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
return Err(anyhow!(
"open ai bad request: {:?} {:?}",
&response.status(),
body
));
}
}
}
Err(anyhow!("openai max retries"))
}
}
crates/ai/src/providers/open_ai/mod.rs
+9
View File
@@ -0,0 +1,9 @@
pub mod completion;
pub mod embedding;
pub mod model;
pub use completion::*;
pub use embedding::*;
pub use model::OpenAILanguageModel;
pub const OPENAI_API_URL: &'static str = "https://api.openai.com/v1";
crates/ai/src/providers/open_ai/model.rs
+57
View File
@@ -0,0 +1,57 @@
use anyhow::anyhow;
use tiktoken_rs::CoreBPE;
use util::ResultExt;
use crate::models::{LanguageModel, TruncationDirection};
#[derive(Clone)]
pub struct OpenAILanguageModel {
name: String,
bpe: Option<CoreBPE>,
}
impl OpenAILanguageModel {
pub fn load(model_name: &str) -> Self {
let bpe = tiktoken_rs::get_bpe_from_model(model_name).log_err();
OpenAILanguageModel {
name: model_name.to_string(),
bpe,
}
}
}
impl LanguageModel for OpenAILanguageModel {
fn name(&self) -> String {
self.name.clone()
}
fn count_tokens(&self, content: &str) -> anyhow::Result<usize> {
if let Some(bpe) = &self.bpe {
anyhow::Ok(bpe.encode_with_special_tokens(content).len())
} else {
Err(anyhow!("bpe for open ai model was not retrieved"))
}
}
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String> {
if let Some(bpe) = &self.bpe {
let tokens = bpe.encode_with_special_tokens(content);
if tokens.len() > length {
match direction {
TruncationDirection::End => bpe.decode(tokens[..length].to_vec()),
TruncationDirection::Start => bpe.decode(tokens[length..].to_vec()),
}
} else {
bpe.decode(tokens)
}
} else {
Err(anyhow!("bpe for open ai model was not retrieved"))
}
}
fn capacity(&self) -> anyhow::Result<usize> {
anyhow::Ok(tiktoken_rs::model::get_context_size(&self.name))
}
}
crates/ai/src/providers/open_ai/new.rs
+11
View File
@@ -0,0 +1,11 @@
pub trait LanguageModel {
fn name(&self) -> String;
fn count_tokens(&self, content: &str) -> anyhow::Result<usize>;
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String>;
fn capacity(&self) -> anyhow::Result<usize>;
}
crates/ai/src/test.rs
+191
View File
@@ -0,0 +1,191 @@
use std::{
sync::atomic::{self, AtomicUsize, Ordering},
time::Instant,
};
use async_trait::async_trait;
use futures::{channel::mpsc, future::BoxFuture, stream::BoxStream, FutureExt, StreamExt};
use gpui::AppContext;
use parking_lot::Mutex;
use crate::{
auth::{CredentialProvider, ProviderCredential},
completion::{CompletionProvider, CompletionRequest},
embedding::{Embedding, EmbeddingProvider},
models::{LanguageModel, TruncationDirection},
};
#[derive(Clone)]
pub struct FakeLanguageModel {
pub capacity: usize,
}
impl LanguageModel for FakeLanguageModel {
fn name(&self) -> String {
"dummy".to_string()
}
fn count_tokens(&self, content: &str) -> anyhow::Result<usize> {
anyhow::Ok(content.chars().collect::<Vec<char>>().len())
}
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String> {
println!("TRYING TO TRUNCATE: {:?}", length.clone());
if length > self.count_tokens(content)? {
println!("NOT TRUNCATING");
return anyhow::Ok(content.to_string());
}
anyhow::Ok(match direction {
TruncationDirection::End => content.chars().collect::<Vec<char>>()[..length]
.into_iter()
.collect::<String>(),
TruncationDirection::Start => content.chars().collect::<Vec<char>>()[length..]
.into_iter()
.collect::<String>(),
})
}
fn capacity(&self) -> anyhow::Result<usize> {
anyhow::Ok(self.capacity)
}
}
pub struct FakeEmbeddingProvider {
pub embedding_count: AtomicUsize,
}
impl Clone for FakeEmbeddingProvider {
fn clone(&self) -> Self {
FakeEmbeddingProvider {
embedding_count: AtomicUsize::new(self.embedding_count.load(Ordering::SeqCst)),
}
}
}
impl Default for FakeEmbeddingProvider {
fn default() -> Self {
FakeEmbeddingProvider {
embedding_count: AtomicUsize::default(),
}
}
}
impl FakeEmbeddingProvider {
pub fn embedding_count(&self) -> usize {
self.embedding_count.load(atomic::Ordering::SeqCst)
}
pub fn embed_sync(&self, span: &str) -> Embedding {
let mut result = vec![1.0; 26];
for letter in span.chars() {
let letter = letter.to_ascii_lowercase();
if letter as u32 >= 'a' as u32 {
let ix = (letter as u32) - ('a' as u32);
if ix < 26 {
result[ix as usize] += 1.0;
}
}
}
let norm = result.iter().map(|x| x * x).sum::<f32>().sqrt();
for x in &mut result {
*x /= norm;
}
result.into()
}
}
impl CredentialProvider for FakeEmbeddingProvider {
fn has_credentials(&self) -> bool {
true
}
fn retrieve_credentials(&self, _cx: &AppContext) -> ProviderCredential {
ProviderCredential::NotNeeded
}
fn save_credentials(&self, _cx: &AppContext, _credential: ProviderCredential) {}
fn delete_credentials(&self, _cx: &AppContext) {}
}
#[async_trait]
impl EmbeddingProvider for FakeEmbeddingProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
Box::new(FakeLanguageModel { capacity: 1000 })
}
fn max_tokens_per_batch(&self) -> usize {
1000
}
fn rate_limit_expiration(&self) -> Option<Instant> {
None
}
async fn embed_batch(&self, spans: Vec<String>) -> anyhow::Result<Vec<Embedding>> {
self.embedding_count
.fetch_add(spans.len(), atomic::Ordering::SeqCst);
anyhow::Ok(spans.iter().map(|span| self.embed_sync(span)).collect())
}
}
pub struct FakeCompletionProvider {
last_completion_tx: Mutex<Option<mpsc::Sender<String>>>,
}
impl Clone for FakeCompletionProvider {
fn clone(&self) -> Self {
Self {
last_completion_tx: Mutex::new(None),
}
}
}
impl FakeCompletionProvider {
pub fn new() -> Self {
Self {
last_completion_tx: Mutex::new(None),
}
}
pub fn send_completion(&self, completion: impl Into<String>) {
let mut tx = self.last_completion_tx.lock();
tx.as_mut().unwrap().try_send(completion.into()).unwrap();
}
pub fn finish_completion(&self) {
self.last_completion_tx.lock().take().unwrap();
}
}
impl CredentialProvider for FakeCompletionProvider {
fn has_credentials(&self) -> bool {
true
}
fn retrieve_credentials(&self, _cx: &AppContext) -> ProviderCredential {
ProviderCredential::NotNeeded
}
fn save_credentials(&self, _cx: &AppContext, _credential: ProviderCredential) {}
fn delete_credentials(&self, _cx: &AppContext) {}
}
impl CompletionProvider for FakeCompletionProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
let model: Box<dyn LanguageModel> = Box::new(FakeLanguageModel { capacity: 8190 });
model
}
fn complete(
&self,
_prompt: Box<dyn CompletionRequest>,
) -> BoxFuture<'static, anyhow::Result<BoxStream<'static, anyhow::Result<String>>>> {
let (tx, rx) = mpsc::channel(1);
*self.last_completion_tx.lock() = Some(tx);
async move { Ok(rx.map(|rx| Ok(rx)).boxed()) }.boxed()
}
fn box_clone(&self) -> Box<dyn CompletionProvider> {
Box::new((*self).clone())
}
}
crates/ai2/Cargo.toml
+38
View File
@@ -0,0 +1,38 @@
[package]
name = "ai2"
version = "0.1.0"
edition = "2021"
publish = false
[lib]
path = "src/ai2.rs"
doctest = false
[features]
test-support = []
[dependencies]
gpui2 = { path = "../gpui2" }
util = { path = "../util" }
language2 = { path = "../language2" }
async-trait.workspace = true
anyhow.workspace = true
futures.workspace = true
lazy_static.workspace = true
ordered-float.workspace = true
parking_lot.workspace = true
isahc.workspace = true
regex.workspace = true
serde.workspace = true
serde_json.workspace = true
postage.workspace = true
rand.workspace = true
log.workspace = true
parse_duration = "2.1.1"
tiktoken-rs = "0.5.0"
matrixmultiply = "0.3.7"
rusqlite = { version = "0.29.0", features = ["blob", "array", "modern_sqlite"] }
bincode = "1.3.3"
[dev-dependencies]
gpui2 = { path = "../gpui2", features = ["test-support"] }
crates/ai2/src/ai2.rs
+8
View File
@@ -0,0 +1,8 @@
pub mod auth;
pub mod completion;
pub mod embedding;
pub mod models;
pub mod prompts;
pub mod providers;
#[cfg(any(test, feature = "test-support"))]
pub mod test;
crates/ai2/src/auth.rs
+17
View File
@@ -0,0 +1,17 @@
use async_trait::async_trait;
use gpui2::AppContext;
#[derive(Clone, Debug)]
pub enum ProviderCredential {
Credentials { api_key: String },
NoCredentials,
NotNeeded,
}
#[async_trait]
pub trait CredentialProvider: Send + Sync {
fn has_credentials(&self) -> bool;
async fn retrieve_credentials(&self, cx: &mut AppContext) -> ProviderCredential;
async fn save_credentials(&self, cx: &mut AppContext, credential: ProviderCredential);
async fn delete_credentials(&self, cx: &mut AppContext);
}
crates/ai2/src/completion.rs
+23
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@@ -0,0 +1,23 @@
use anyhow::Result;
use futures::{future::BoxFuture, stream::BoxStream};
use crate::{auth::CredentialProvider, models::LanguageModel};
pub trait CompletionRequest: Send + Sync {
fn data(&self) -> serde_json::Result<String>;
}
pub trait CompletionProvider: CredentialProvider {
fn base_model(&self) -> Box<dyn LanguageModel>;
fn complete(
&self,
prompt: Box<dyn CompletionRequest>,
) -> BoxFuture<'static, Result<BoxStream<'static, Result<String>>>>;
fn box_clone(&self) -> Box<dyn CompletionProvider>;
}
impl Clone for Box<dyn CompletionProvider> {
fn clone(&self) -> Box<dyn CompletionProvider> {
self.box_clone()
}
}
crates/ai2/src/embedding.rs
+123
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@@ -0,0 +1,123 @@
use std::time::Instant;
use anyhow::Result;
use async_trait::async_trait;
use ordered_float::OrderedFloat;
use rusqlite::types::{FromSql, FromSqlResult, ToSqlOutput, ValueRef};
use rusqlite::ToSql;
use crate::auth::CredentialProvider;
use crate::models::LanguageModel;
#[derive(Debug, PartialEq, Clone)]
pub struct Embedding(pub Vec<f32>);
// This is needed for semantic index functionality
// Unfortunately it has to live wherever the "Embedding" struct is created.
// Keeping this in here though, introduces a 'rusqlite' dependency into AI
// which is less than ideal
impl FromSql for Embedding {
fn column_result(value: ValueRef) -> FromSqlResult<Self> {
let bytes = value.as_blob()?;
let embedding: Result<Vec<f32>, Box<bincode::ErrorKind>> = bincode::deserialize(bytes);
if embedding.is_err() {
return Err(rusqlite::types::FromSqlError::Other(embedding.unwrap_err()));
}
Ok(Embedding(embedding.unwrap()))
}
}
impl ToSql for Embedding {
fn to_sql(&self) -> rusqlite::Result<ToSqlOutput> {
let bytes = bincode::serialize(&self.0)
.map_err(|err| rusqlite::Error::ToSqlConversionFailure(Box::new(err)))?;
Ok(ToSqlOutput::Owned(rusqlite::types::Value::Blob(bytes)))
}
}
impl From<Vec<f32>> for Embedding {
fn from(value: Vec<f32>) -> Self {
Embedding(value)
}
}
impl Embedding {
pub fn similarity(&self, other: &Self) -> OrderedFloat<f32> {
let len = self.0.len();
assert_eq!(len, other.0.len());
let mut result = 0.0;
unsafe {
matrixmultiply::sgemm(
1,
len,
1,
1.0,
self.0.as_ptr(),
len as isize,
1,
other.0.as_ptr(),
1,
len as isize,
0.0,
&mut result as *mut f32,
1,
1,
);
}
OrderedFloat(result)
}
}
#[async_trait]
pub trait EmbeddingProvider: CredentialProvider {
fn base_model(&self) -> Box<dyn LanguageModel>;
async fn embed_batch(&self, spans: Vec<String>) -> Result<Vec<Embedding>>;
fn max_tokens_per_batch(&self) -> usize;
fn rate_limit_expiration(&self) -> Option<Instant>;
}
#[cfg(test)]
mod tests {
use super::*;
use rand::prelude::*;
#[gpui2::test]
fn test_similarity(mut rng: StdRng) {
assert_eq!(
Embedding::from(vec![1., 0., 0., 0., 0.])
.similarity(&Embedding::from(vec![0., 1., 0., 0., 0.])),
0.
);
assert_eq!(
Embedding::from(vec![2., 0., 0., 0., 0.])
.similarity(&Embedding::from(vec![3., 1., 0., 0., 0.])),
6.
);
for _ in 0..100 {
let size = 1536;
let mut a = vec![0.; size];
let mut b = vec![0.; size];
for (a, b) in a.iter_mut().zip(b.iter_mut()) {
*a = rng.gen();
*b = rng.gen();
}
let a = Embedding::from(a);
let b = Embedding::from(b);
assert_eq!(
round_to_decimals(a.similarity(&b), 1),
round_to_decimals(reference_dot(&a.0, &b.0), 1)
);
}
fn round_to_decimals(n: OrderedFloat<f32>, decimal_places: i32) -> f32 {
let factor = (10.0 as f32).powi(decimal_places);
(n * factor).round() / factor
}
fn reference_dot(a: &[f32], b: &[f32]) -> OrderedFloat<f32> {
OrderedFloat(a.iter().zip(b.iter()).map(|(a, b)| a * b).sum())
}
}
}
crates/ai2/src/models.rs
+16
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@@ -0,0 +1,16 @@
pub enum TruncationDirection {
Start,
End,
}
pub trait LanguageModel {
fn name(&self) -> String;
fn count_tokens(&self, content: &str) -> anyhow::Result<usize>;
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String>;
fn capacity(&self) -> anyhow::Result<usize>;
}
crates/ai2/src/prompts/base.rs
+330
View File
@@ -0,0 +1,330 @@
use std::cmp::Reverse;
use std::ops::Range;
use std::sync::Arc;
use language2::BufferSnapshot;
use util::ResultExt;
use crate::models::LanguageModel;
use crate::prompts::repository_context::PromptCodeSnippet;
pub(crate) enum PromptFileType {
Text,
Code,
}
// TODO: Set this up to manage for defaults well
pub struct PromptArguments {
pub model: Arc<dyn LanguageModel>,
pub user_prompt: Option<String>,
pub language_name: Option<String>,
pub project_name: Option<String>,
pub snippets: Vec<PromptCodeSnippet>,
pub reserved_tokens: usize,
pub buffer: Option<BufferSnapshot>,
pub selected_range: Option<Range<usize>>,
}
impl PromptArguments {
pub(crate) fn get_file_type(&self) -> PromptFileType {
if self
.language_name
.as_ref()
.and_then(|name| Some(!["Markdown", "Plain Text"].contains(&name.as_str())))
.unwrap_or(true)
{
PromptFileType::Code
} else {
PromptFileType::Text
}
}
}
pub trait PromptTemplate {
fn generate(
&self,
args: &PromptArguments,
max_token_length: Option<usize>,
) -> anyhow::Result<(String, usize)>;
}
#[repr(i8)]
#[derive(PartialEq, Eq, Ord)]
pub enum PromptPriority {
Mandatory, // Ignores truncation
Ordered { order: usize }, // Truncates based on priority
}
impl PartialOrd for PromptPriority {
fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
match (self, other) {
(Self::Mandatory, Self::Mandatory) => Some(std::cmp::Ordering::Equal),
(Self::Mandatory, Self::Ordered { .. }) => Some(std::cmp::Ordering::Greater),
(Self::Ordered { .. }, Self::Mandatory) => Some(std::cmp::Ordering::Less),
(Self::Ordered { order: a }, Self::Ordered { order: b }) => b.partial_cmp(a),
}
}
}
pub struct PromptChain {
args: PromptArguments,
templates: Vec<(PromptPriority, Box<dyn PromptTemplate>)>,
}
impl PromptChain {
pub fn new(
args: PromptArguments,
templates: Vec<(PromptPriority, Box<dyn PromptTemplate>)>,
) -> Self {
PromptChain { args, templates }
}
pub fn generate(&self, truncate: bool) -> anyhow::Result<(String, usize)> {
// Argsort based on Prompt Priority
let seperator = "\n";
let seperator_tokens = self.args.model.count_tokens(seperator)?;
let mut sorted_indices = (0..self.templates.len()).collect::<Vec<_>>();
sorted_indices.sort_by_key(|&i| Reverse(&self.templates[i].0));
// If Truncate
let mut tokens_outstanding = if truncate {
Some(self.args.model.capacity()? - self.args.reserved_tokens)
} else {
None
};
let mut prompts = vec!["".to_string(); sorted_indices.len()];
for idx in sorted_indices {
let (_, template) = &self.templates[idx];
if let Some((template_prompt, prompt_token_count)) =
template.generate(&self.args, tokens_outstanding).log_err()
{
if template_prompt != "" {
prompts[idx] = template_prompt;
if let Some(remaining_tokens) = tokens_outstanding {
let new_tokens = prompt_token_count + seperator_tokens;
tokens_outstanding = if remaining_tokens > new_tokens {
Some(remaining_tokens - new_tokens)
} else {
Some(0)
};
}
}
}
}
prompts.retain(|x| x != "");
let full_prompt = prompts.join(seperator);
let total_token_count = self.args.model.count_tokens(&full_prompt)?;
anyhow::Ok((prompts.join(seperator), total_token_count))
}
}
#[cfg(test)]
pub(crate) mod tests {
use crate::models::TruncationDirection;
use crate::test::FakeLanguageModel;
use super::*;
#[test]
pub fn test_prompt_chain() {
struct TestPromptTemplate {}
impl PromptTemplate for TestPromptTemplate {
fn generate(
&self,
args: &PromptArguments,
max_token_length: Option<usize>,
) -> anyhow::Result<(String, usize)> {
let mut content = "This is a test prompt template".to_string();
let mut token_count = args.model.count_tokens(&content)?;
if let Some(max_token_length) = max_token_length {
if token_count > max_token_length {
content = args.model.truncate(
&content,
max_token_length,
TruncationDirection::End,
)?;
token_count = max_token_length;
}
}
anyhow::Ok((content, token_count))
}
}
struct TestLowPriorityTemplate {}
impl PromptTemplate for TestLowPriorityTemplate {
fn generate(
&self,
args: &PromptArguments,
max_token_length: Option<usize>,
) -> anyhow::Result<(String, usize)> {
let mut content = "This is a low priority test prompt template".to_string();
let mut token_count = args.model.count_tokens(&content)?;
if let Some(max_token_length) = max_token_length {
if token_count > max_token_length {
content = args.model.truncate(
&content,
max_token_length,
TruncationDirection::End,
)?;
token_count = max_token_length;
}
}
anyhow::Ok((content, token_count))
}
}
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity: 100 });
let args = PromptArguments {
model: model.clone(),
language_name: None,
project_name: None,
snippets: Vec::new(),
reserved_tokens: 0,
buffer: None,
selected_range: None,
user_prompt: None,
};
let templates: Vec<(PromptPriority, Box<dyn PromptTemplate>)> = vec![
(
PromptPriority::Ordered { order: 0 },
Box::new(TestPromptTemplate {}),
),
(
PromptPriority::Ordered { order: 1 },
Box::new(TestLowPriorityTemplate {}),
),
];
let chain = PromptChain::new(args, templates);
let (prompt, token_count) = chain.generate(false).unwrap();
assert_eq!(
prompt,
"This is a test prompt template\nThis is a low priority test prompt template"
.to_string()
);
assert_eq!(model.count_tokens(&prompt).unwrap(), token_count);
// Testing with Truncation Off
// Should ignore capacity and return all prompts
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity: 20 });
let args = PromptArguments {
model: model.clone(),
language_name: None,
project_name: None,
snippets: Vec::new(),
reserved_tokens: 0,
buffer: None,
selected_range: None,
user_prompt: None,
};
let templates: Vec<(PromptPriority, Box<dyn PromptTemplate>)> = vec![
(
PromptPriority::Ordered { order: 0 },
Box::new(TestPromptTemplate {}),
),
(
PromptPriority::Ordered { order: 1 },
Box::new(TestLowPriorityTemplate {}),
),
];
let chain = PromptChain::new(args, templates);
let (prompt, token_count) = chain.generate(false).unwrap();
assert_eq!(
prompt,
"This is a test prompt template\nThis is a low priority test prompt template"
.to_string()
);
assert_eq!(model.count_tokens(&prompt).unwrap(), token_count);
// Testing with Truncation Off
// Should ignore capacity and return all prompts
let capacity = 20;
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity });
let args = PromptArguments {
model: model.clone(),
language_name: None,
project_name: None,
snippets: Vec::new(),
reserved_tokens: 0,
buffer: None,
selected_range: None,
user_prompt: None,
};
let templates: Vec<(PromptPriority, Box<dyn PromptTemplate>)> = vec![
(
PromptPriority::Ordered { order: 0 },
Box::new(TestPromptTemplate {}),
),
(
PromptPriority::Ordered { order: 1 },
Box::new(TestLowPriorityTemplate {}),
),
(
PromptPriority::Ordered { order: 2 },
Box::new(TestLowPriorityTemplate {}),
),
];
let chain = PromptChain::new(args, templates);
let (prompt, token_count) = chain.generate(true).unwrap();
assert_eq!(prompt, "This is a test promp".to_string());
assert_eq!(token_count, capacity);
// Change Ordering of Prompts Based on Priority
let capacity = 120;
let reserved_tokens = 10;
let model: Arc<dyn LanguageModel> = Arc::new(FakeLanguageModel { capacity });
let args = PromptArguments {
model: model.clone(),
language_name: None,
project_name: None,
snippets: Vec::new(),
reserved_tokens,
buffer: None,
selected_range: None,
user_prompt: None,
};
let templates: Vec<(PromptPriority, Box<dyn PromptTemplate>)> = vec![
(
PromptPriority::Mandatory,
Box::new(TestLowPriorityTemplate {}),
),
(
PromptPriority::Ordered { order: 0 },
Box::new(TestPromptTemplate {}),
),
(
PromptPriority::Ordered { order: 1 },
Box::new(TestLowPriorityTemplate {}),
),
];
let chain = PromptChain::new(args, templates);
let (prompt, token_count) = chain.generate(true).unwrap();
assert_eq!(
prompt,
"This is a low priority test prompt template\nThis is a test prompt template\nThis is a low priority test prompt "
.to_string()
);
assert_eq!(token_count, capacity - reserved_tokens);
}
}
crates/ai2/src/prompts/file_context.rs
+164
View File
@@ -0,0 +1,164 @@
use anyhow::anyhow;
use language2::BufferSnapshot;
use language2::ToOffset;
use crate::models::LanguageModel;
use crate::models::TruncationDirection;
use crate::prompts::base::PromptArguments;
use crate::prompts::base::PromptTemplate;
use std::fmt::Write;
use std::ops::Range;
use std::sync::Arc;
fn retrieve_context(
buffer: &BufferSnapshot,
selected_range: &Option<Range<usize>>,
model: Arc<dyn LanguageModel>,
max_token_count: Option<usize>,
) -> anyhow::Result<(String, usize, bool)> {
let mut prompt = String::new();
let mut truncated = false;
if let Some(selected_range) = selected_range {
let start = selected_range.start.to_offset(buffer);
let end = selected_range.end.to_offset(buffer);
let start_window = buffer.text_for_range(0..start).collect::<String>();
let mut selected_window = String::new();
if start == end {
write!(selected_window, "<|START|>").unwrap();
} else {
write!(selected_window, "<|START|").unwrap();
}
write!(
selected_window,
"{}",
buffer.text_for_range(start..end).collect::<String>()
)
.unwrap();
if start != end {
write!(selected_window, "|END|>").unwrap();
}
let end_window = buffer.text_for_range(end..buffer.len()).collect::<String>();
if let Some(max_token_count) = max_token_count {
let selected_tokens = model.count_tokens(&selected_window)?;
if selected_tokens > max_token_count {
return Err(anyhow!(
"selected range is greater than model context window, truncation not possible"
));
};
let mut remaining_tokens = max_token_count - selected_tokens;
let start_window_tokens = model.count_tokens(&start_window)?;
let end_window_tokens = model.count_tokens(&end_window)?;
let outside_tokens = start_window_tokens + end_window_tokens;
if outside_tokens > remaining_tokens {
let (start_goal_tokens, end_goal_tokens) =
if start_window_tokens < end_window_tokens {
let start_goal_tokens = (remaining_tokens / 2).min(start_window_tokens);
remaining_tokens -= start_goal_tokens;
let end_goal_tokens = remaining_tokens.min(end_window_tokens);
(start_goal_tokens, end_goal_tokens)
} else {
let end_goal_tokens = (remaining_tokens / 2).min(end_window_tokens);
remaining_tokens -= end_goal_tokens;
let start_goal_tokens = remaining_tokens.min(start_window_tokens);
(start_goal_tokens, end_goal_tokens)
};
let truncated_start_window =
model.truncate(&start_window, start_goal_tokens, TruncationDirection::Start)?;
let truncated_end_window =
model.truncate(&end_window, end_goal_tokens, TruncationDirection::End)?;
writeln!(
prompt,
"{truncated_start_window}{selected_window}{truncated_end_window}"
)
.unwrap();
truncated = true;
} else {
writeln!(prompt, "{start_window}{selected_window}{end_window}").unwrap();
}
} else {
// If we dont have a selected range, include entire file.
writeln!(prompt, "{}", &buffer.text()).unwrap();
// Dumb truncation strategy
if let Some(max_token_count) = max_token_count {
if model.count_tokens(&prompt)? > max_token_count {
truncated = true;
prompt = model.truncate(&prompt, max_token_count, TruncationDirection::End)?;
}
}
}
}
let token_count = model.count_tokens(&prompt)?;
anyhow::Ok((prompt, token_count, truncated))
}
pub struct FileContext {}
impl PromptTemplate for FileContext {
fn generate(
&self,
args: &PromptArguments,
max_token_length: Option<usize>,
) -> anyhow::Result<(String, usize)> {
if let Some(buffer) = &args.buffer {
let mut prompt = String::new();
// Add Initial Preamble
// TODO: Do we want to add the path in here?
writeln!(
prompt,
"The file you are currently working on has the following content:"
)
.unwrap();
let language_name = args
.language_name
.clone()
.unwrap_or("".to_string())
.to_lowercase();
let (context, _, truncated) = retrieve_context(
buffer,
&args.selected_range,
args.model.clone(),
max_token_length,
)?;
writeln!(prompt, "```{language_name}\n{context}\n```").unwrap();
if truncated {
writeln!(prompt, "Note the content has been truncated and only represents a portion of the file.").unwrap();
}
if let Some(selected_range) = &args.selected_range {
let start = selected_range.start.to_offset(buffer);
let end = selected_range.end.to_offset(buffer);
if start == end {
writeln!(prompt, "In particular, the user's cursor is currently on the '<|START|>' span in the above content, with no text selected.").unwrap();
} else {
writeln!(prompt, "In particular, the user has selected a section of the text between the '<|START|' and '|END|>' spans.").unwrap();
}
}
// Really dumb truncation strategy
if let Some(max_tokens) = max_token_length {
prompt = args
.model
.truncate(&prompt, max_tokens, TruncationDirection::End)?;
}
let token_count = args.model.count_tokens(&prompt)?;
anyhow::Ok((prompt, token_count))
} else {
Err(anyhow!("no buffer provided to retrieve file context from"))
}
}
}
crates/ai2/src/prompts/generate.rs
+99
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use crate::prompts::base::{PromptArguments, PromptFileType, PromptTemplate};
use anyhow::anyhow;
use std::fmt::Write;
pub fn capitalize(s: &str) -> String {
let mut c = s.chars();
match c.next() {
None => String::new(),
Some(f) => f.to_uppercase().collect::<String>() + c.as_str(),
}
}
pub struct GenerateInlineContent {}
impl PromptTemplate for GenerateInlineContent {
fn generate(
&self,
args: &PromptArguments,
max_token_length: Option<usize>,
) -> anyhow::Result<(String, usize)> {
let Some(user_prompt) = &args.user_prompt else {
return Err(anyhow!("user prompt not provided"));
};
let file_type = args.get_file_type();
let content_type = match &file_type {
PromptFileType::Code => "code",
PromptFileType::Text => "text",
};
let mut prompt = String::new();
if let Some(selected_range) = &args.selected_range {
if selected_range.start == selected_range.end {
writeln!(
prompt,
"Assume the cursor is located where the `<|START|>` span is."
)
.unwrap();
writeln!(
prompt,
"{} can't be replaced, so assume your answer will be inserted at the cursor.",
capitalize(content_type)
)
.unwrap();
writeln!(
prompt,
"Generate {content_type} based on the users prompt: {user_prompt}",
)
.unwrap();
} else {
writeln!(prompt, "Modify the user's selected {content_type} based upon the users prompt: '{user_prompt}'").unwrap();
writeln!(prompt, "You must reply with only the adjusted {content_type} (within the '<|START|' and '|END|>' spans) not the entire file.").unwrap();
writeln!(prompt, "Double check that you only return code and not the '<|START|' and '|END|'> spans").unwrap();
}
} else {
writeln!(
prompt,
"Generate {content_type} based on the users prompt: {user_prompt}"
)
.unwrap();
}
if let Some(language_name) = &args.language_name {
writeln!(
prompt,
"Your answer MUST always and only be valid {}.",
language_name
)
.unwrap();
}
writeln!(prompt, "Never make remarks about the output.").unwrap();
writeln!(
prompt,
"Do not return anything else, except the generated {content_type}."
)
.unwrap();
match file_type {
PromptFileType::Code => {
// writeln!(prompt, "Always wrap your code in a Markdown block.").unwrap();
}
_ => {}
}
// Really dumb truncation strategy
if let Some(max_tokens) = max_token_length {
prompt = args.model.truncate(
&prompt,
max_tokens,
crate::models::TruncationDirection::End,
)?;
}
let token_count = args.model.count_tokens(&prompt)?;
anyhow::Ok((prompt, token_count))
}
}
crates/ai2/src/prompts/mod.rs
+5
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pub mod base;
pub mod file_context;
pub mod generate;
pub mod preamble;
pub mod repository_context;
crates/ai2/src/prompts/preamble.rs
+52
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@@ -0,0 +1,52 @@
use crate::prompts::base::{PromptArguments, PromptFileType, PromptTemplate};
use std::fmt::Write;
pub struct EngineerPreamble {}
impl PromptTemplate for EngineerPreamble {
fn generate(
&self,
args: &PromptArguments,
max_token_length: Option<usize>,
) -> anyhow::Result<(String, usize)> {
let mut prompts = Vec::new();
match args.get_file_type() {
PromptFileType::Code => {
prompts.push(format!(
"You are an expert {}engineer.",
args.language_name.clone().unwrap_or("".to_string()) + " "
));
}
PromptFileType::Text => {
prompts.push("You are an expert engineer.".to_string());
}
}
if let Some(project_name) = args.project_name.clone() {
prompts.push(format!(
"You are currently working inside the '{project_name}' project in code editor Zed."
));
}
if let Some(mut remaining_tokens) = max_token_length {
let mut prompt = String::new();
let mut total_count = 0;
for prompt_piece in prompts {
let prompt_token_count =
args.model.count_tokens(&prompt_piece)? + args.model.count_tokens("\n")?;
if remaining_tokens > prompt_token_count {
writeln!(prompt, "{prompt_piece}").unwrap();
remaining_tokens -= prompt_token_count;
total_count += prompt_token_count;
}
}
anyhow::Ok((prompt, total_count))
} else {
let prompt = prompts.join("\n");
let token_count = args.model.count_tokens(&prompt)?;
anyhow::Ok((prompt, token_count))
}
}
}
crates/ai2/src/prompts/repository_context.rs
+98
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use crate::prompts::base::{PromptArguments, PromptTemplate};
use std::fmt::Write;
use std::{ops::Range, path::PathBuf};
use gpui2::{AsyncAppContext, Model};
use language2::{Anchor, Buffer};
#[derive(Clone)]
pub struct PromptCodeSnippet {
path: Option<PathBuf>,
language_name: Option<String>,
content: String,
}
impl PromptCodeSnippet {
pub fn new(
buffer: Model<Buffer>,
range: Range<Anchor>,
cx: &mut AsyncAppContext,
) -> anyhow::Result<Self> {
let (content, language_name, file_path) = buffer.update(cx, |buffer, _| {
let snapshot = buffer.snapshot();
let content = snapshot.text_for_range(range.clone()).collect::<String>();
let language_name = buffer
.language()
.and_then(|language| Some(language.name().to_string().to_lowercase()));
let file_path = buffer
.file()
.and_then(|file| Some(file.path().to_path_buf()));
(content, language_name, file_path)
})?;
anyhow::Ok(PromptCodeSnippet {
path: file_path,
language_name,
content,
})
}
}
impl ToString for PromptCodeSnippet {
fn to_string(&self) -> String {
let path = self
.path
.as_ref()
.and_then(|path| Some(path.to_string_lossy().to_string()))
.unwrap_or("".to_string());
let language_name = self.language_name.clone().unwrap_or("".to_string());
let content = self.content.clone();
format!("The below code snippet may be relevant from file: {path}\n```{language_name}\n{content}\n```")
}
}
pub struct RepositoryContext {}
impl PromptTemplate for RepositoryContext {
fn generate(
&self,
args: &PromptArguments,
max_token_length: Option<usize>,
) -> anyhow::Result<(String, usize)> {
const MAXIMUM_SNIPPET_TOKEN_COUNT: usize = 500;
let template = "You are working inside a large repository, here are a few code snippets that may be useful.";
let mut prompt = String::new();
let mut remaining_tokens = max_token_length.clone();
let seperator_token_length = args.model.count_tokens("\n")?;
for snippet in &args.snippets {
let mut snippet_prompt = template.to_string();
let content = snippet.to_string();
writeln!(snippet_prompt, "{content}").unwrap();
let token_count = args.model.count_tokens(&snippet_prompt)?;
if token_count <= MAXIMUM_SNIPPET_TOKEN_COUNT {
if let Some(tokens_left) = remaining_tokens {
if tokens_left >= token_count {
writeln!(prompt, "{snippet_prompt}").unwrap();
remaining_tokens = if tokens_left >= (token_count + seperator_token_length)
{
Some(tokens_left - token_count - seperator_token_length)
} else {
Some(0)
};
}
} else {
writeln!(prompt, "{snippet_prompt}").unwrap();
}
}
}
let total_token_count = args.model.count_tokens(&prompt)?;
anyhow::Ok((prompt, total_token_count))
}
}
crates/ai2/src/providers/mod.rs
+1
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@@ -0,0 +1 @@
pub mod open_ai;
crates/ai2/src/providers/open_ai/completion.rs
+306
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@@ -0,0 +1,306 @@
use anyhow::{anyhow, Result};
use async_trait::async_trait;
use futures::{
future::BoxFuture, io::BufReader, stream::BoxStream, AsyncBufReadExt, AsyncReadExt, FutureExt,
Stream, StreamExt,
};
use gpui2::{AppContext, Executor};
use isahc::{http::StatusCode, Request, RequestExt};
use parking_lot::RwLock;
use serde::{Deserialize, Serialize};
use std::{
env,
fmt::{self, Display},
io,
sync::Arc,
};
use util::ResultExt;
use crate::{
auth::{CredentialProvider, ProviderCredential},
completion::{CompletionProvider, CompletionRequest},
models::LanguageModel,
};
use crate::providers::open_ai::{OpenAILanguageModel, OPENAI_API_URL};
#[derive(Clone, Copy, Serialize, Deserialize, Debug, Eq, PartialEq)]
#[serde(rename_all = "lowercase")]
pub enum Role {
User,
Assistant,
System,
}
impl Role {
pub fn cycle(&mut self) {
*self = match self {
Role::User => Role::Assistant,
Role::Assistant => Role::System,
Role::System => Role::User,
}
}
}
impl Display for Role {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> std::fmt::Result {
match self {
Role::User => write!(f, "User"),
Role::Assistant => write!(f, "Assistant"),
Role::System => write!(f, "System"),
}
}
}
#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
pub struct RequestMessage {
pub role: Role,
pub content: String,
}
#[derive(Debug, Default, Serialize)]
pub struct OpenAIRequest {
pub model: String,
pub messages: Vec<RequestMessage>,
pub stream: bool,
pub stop: Vec<String>,
pub temperature: f32,
}
impl CompletionRequest for OpenAIRequest {
fn data(&self) -> serde_json::Result<String> {
serde_json::to_string(self)
}
}
#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
pub struct ResponseMessage {
pub role: Option<Role>,
pub content: Option<String>,
}
#[derive(Deserialize, Debug)]
pub struct OpenAIUsage {
pub prompt_tokens: u32,
pub completion_tokens: u32,
pub total_tokens: u32,
}
#[derive(Deserialize, Debug)]
pub struct ChatChoiceDelta {
pub index: u32,
pub delta: ResponseMessage,
pub finish_reason: Option<String>,
}
#[derive(Deserialize, Debug)]
pub struct OpenAIResponseStreamEvent {
pub id: Option<String>,
pub object: String,
pub created: u32,
pub model: String,
pub choices: Vec<ChatChoiceDelta>,
pub usage: Option<OpenAIUsage>,
}
pub async fn stream_completion(
credential: ProviderCredential,
executor: Arc<Executor>,
request: Box<dyn CompletionRequest>,
) -> Result<impl Stream<Item = Result<OpenAIResponseStreamEvent>>> {
let api_key = match credential {
ProviderCredential::Credentials { api_key } => api_key,
_ => {
return Err(anyhow!("no credentials provider for completion"));
}
};
let (tx, rx) = futures::channel::mpsc::unbounded::<Result<OpenAIResponseStreamEvent>>();
let json_data = request.data()?;
let mut response = Request::post(format!("{OPENAI_API_URL}/chat/completions"))
.header("Content-Type", "application/json")
.header("Authorization", format!("Bearer {}", api_key))
.body(json_data)?
.send_async()
.await?;
let status = response.status();
if status == StatusCode::OK {
executor
.spawn(async move {
let mut lines = BufReader::new(response.body_mut()).lines();
fn parse_line(
line: Result<String, io::Error>,
) -> Result<Option<OpenAIResponseStreamEvent>> {
if let Some(data) = line?.strip_prefix("data: ") {
let event = serde_json::from_str(&data)?;
Ok(Some(event))
} else {
Ok(None)
}
}
while let Some(line) = lines.next().await {
if let Some(event) = parse_line(line).transpose() {
let done = event.as_ref().map_or(false, |event| {
event
.choices
.last()
.map_or(false, |choice| choice.finish_reason.is_some())
});
if tx.unbounded_send(event).is_err() {
break;
}
if done {
break;
}
}
}
anyhow::Ok(())
})
.detach();
Ok(rx)
} else {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
#[derive(Deserialize)]
struct OpenAIResponse {
error: OpenAIError,
}
#[derive(Deserialize)]
struct OpenAIError {
message: String,
}
match serde_json::from_str::<OpenAIResponse>(&body) {
Ok(response) if !response.error.message.is_empty() => Err(anyhow!(
"Failed to connect to OpenAI API: {}",
response.error.message,
)),
_ => Err(anyhow!(
"Failed to connect to OpenAI API: {} {}",
response.status(),
body,
)),
}
}
}
#[derive(Clone)]
pub struct OpenAICompletionProvider {
model: OpenAILanguageModel,
credential: Arc<RwLock<ProviderCredential>>,
executor: Arc<Executor>,
}
impl OpenAICompletionProvider {
pub fn new(model_name: &str, executor: Arc<Executor>) -> Self {
let model = OpenAILanguageModel::load(model_name);
let credential = Arc::new(RwLock::new(ProviderCredential::NoCredentials));
Self {
model,
credential,
executor,
}
}
}
#[async_trait]
impl CredentialProvider for OpenAICompletionProvider {
fn has_credentials(&self) -> bool {
match *self.credential.read() {
ProviderCredential::Credentials { .. } => true,
_ => false,
}
}
async fn retrieve_credentials(&self, cx: &mut AppContext) -> ProviderCredential {
let existing_credential = self.credential.read().clone();
let retrieved_credential = cx
.run_on_main(move |cx| match existing_credential {
ProviderCredential::Credentials { .. } => {
return existing_credential.clone();
}
_ => {
if let Some(api_key) = env::var("OPENAI_API_KEY").log_err() {
return ProviderCredential::Credentials { api_key };
}
if let Some(Some((_, api_key))) = cx.read_credentials(OPENAI_API_URL).log_err()
{
if let Some(api_key) = String::from_utf8(api_key).log_err() {
return ProviderCredential::Credentials { api_key };
} else {
return ProviderCredential::NoCredentials;
}
} else {
return ProviderCredential::NoCredentials;
}
}
})
.await;
*self.credential.write() = retrieved_credential.clone();
retrieved_credential
}
async fn save_credentials(&self, cx: &mut AppContext, credential: ProviderCredential) {
*self.credential.write() = credential.clone();
let credential = credential.clone();
cx.run_on_main(move |cx| match credential {
ProviderCredential::Credentials { api_key } => {
cx.write_credentials(OPENAI_API_URL, "Bearer", api_key.as_bytes())
.log_err();
}
_ => {}
})
.await;
}
async fn delete_credentials(&self, cx: &mut AppContext) {
cx.run_on_main(move |cx| cx.delete_credentials(OPENAI_API_URL).log_err())
.await;
*self.credential.write() = ProviderCredential::NoCredentials;
}
}
impl CompletionProvider for OpenAICompletionProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
let model: Box<dyn LanguageModel> = Box::new(self.model.clone());
model
}
fn complete(
&self,
prompt: Box<dyn CompletionRequest>,
) -> BoxFuture<'static, Result<BoxStream<'static, Result<String>>>> {
// Currently the CompletionRequest for OpenAI, includes a 'model' parameter
// This means that the model is determined by the CompletionRequest and not the CompletionProvider,
// which is currently model based, due to the langauge model.
// At some point in the future we should rectify this.
let credential = self.credential.read().clone();
let request = stream_completion(credential, self.executor.clone(), prompt);
async move {
let response = request.await?;
let stream = response
.filter_map(|response| async move {
match response {
Ok(mut response) => Some(Ok(response.choices.pop()?.delta.content?)),
Err(error) => Some(Err(error)),
}
})
.boxed();
Ok(stream)
}
.boxed()
}
fn box_clone(&self) -> Box<dyn CompletionProvider> {
Box::new((*self).clone())
}
}
crates/ai2/src/providers/open_ai/embedding.rs
+313
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@@ -0,0 +1,313 @@
use anyhow::{anyhow, Result};
use async_trait::async_trait;
use futures::AsyncReadExt;
use gpui2::Executor;
use gpui2::{serde_json, AppContext};
use isahc::http::StatusCode;
use isahc::prelude::Configurable;
use isahc::{AsyncBody, Response};
use lazy_static::lazy_static;
use parking_lot::{Mutex, RwLock};
use parse_duration::parse;
use postage::watch;
use serde::{Deserialize, Serialize};
use std::env;
use std::ops::Add;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tiktoken_rs::{cl100k_base, CoreBPE};
use util::http::{HttpClient, Request};
use util::ResultExt;
use crate::auth::{CredentialProvider, ProviderCredential};
use crate::embedding::{Embedding, EmbeddingProvider};
use crate::models::LanguageModel;
use crate::providers::open_ai::OpenAILanguageModel;
use crate::providers::open_ai::OPENAI_API_URL;
lazy_static! {
static ref OPENAI_BPE_TOKENIZER: CoreBPE = cl100k_base().unwrap();
}
#[derive(Clone)]
pub struct OpenAIEmbeddingProvider {
model: OpenAILanguageModel,
credential: Arc<RwLock<ProviderCredential>>,
pub client: Arc<dyn HttpClient>,
pub executor: Arc<Executor>,
rate_limit_count_rx: watch::Receiver<Option<Instant>>,
rate_limit_count_tx: Arc<Mutex<watch::Sender<Option<Instant>>>>,
}
#[derive(Serialize)]
struct OpenAIEmbeddingRequest<'a> {
model: &'static str,
input: Vec<&'a str>,
}
#[derive(Deserialize)]
struct OpenAIEmbeddingResponse {
data: Vec<OpenAIEmbedding>,
usage: OpenAIEmbeddingUsage,
}
#[derive(Debug, Deserialize)]
struct OpenAIEmbedding {
embedding: Vec<f32>,
index: usize,
object: String,
}
#[derive(Deserialize)]
struct OpenAIEmbeddingUsage {
prompt_tokens: usize,
total_tokens: usize,
}
impl OpenAIEmbeddingProvider {
pub fn new(client: Arc<dyn HttpClient>, executor: Arc<Executor>) -> Self {
let (rate_limit_count_tx, rate_limit_count_rx) = watch::channel_with(None);
let rate_limit_count_tx = Arc::new(Mutex::new(rate_limit_count_tx));
let model = OpenAILanguageModel::load("text-embedding-ada-002");
let credential = Arc::new(RwLock::new(ProviderCredential::NoCredentials));
OpenAIEmbeddingProvider {
model,
credential,
client,
executor,
rate_limit_count_rx,
rate_limit_count_tx,
}
}
fn get_api_key(&self) -> Result<String> {
match self.credential.read().clone() {
ProviderCredential::Credentials { api_key } => Ok(api_key),
_ => Err(anyhow!("api credentials not provided")),
}
}
fn resolve_rate_limit(&self) {
let reset_time = *self.rate_limit_count_tx.lock().borrow();
if let Some(reset_time) = reset_time {
if Instant::now() >= reset_time {
*self.rate_limit_count_tx.lock().borrow_mut() = None
}
}
log::trace!(
"resolving reset time: {:?}",
*self.rate_limit_count_tx.lock().borrow()
);
}
fn update_reset_time(&self, reset_time: Instant) {
let original_time = *self.rate_limit_count_tx.lock().borrow();
let updated_time = if let Some(original_time) = original_time {
if reset_time < original_time {
Some(reset_time)
} else {
Some(original_time)
}
} else {
Some(reset_time)
};
log::trace!("updating rate limit time: {:?}", updated_time);
*self.rate_limit_count_tx.lock().borrow_mut() = updated_time;
}
async fn send_request(
&self,
api_key: &str,
spans: Vec<&str>,
request_timeout: u64,
) -> Result<Response<AsyncBody>> {
let request = Request::post("https://api.openai.com/v1/embeddings")
.redirect_policy(isahc::config::RedirectPolicy::Follow)
.timeout(Duration::from_secs(request_timeout))
.header("Content-Type", "application/json")
.header("Authorization", format!("Bearer {}", api_key))
.body(
serde_json::to_string(&OpenAIEmbeddingRequest {
input: spans.clone(),
model: "text-embedding-ada-002",
})
.unwrap()
.into(),
)?;
Ok(self.client.send(request).await?)
}
}
#[async_trait]
impl CredentialProvider for OpenAIEmbeddingProvider {
fn has_credentials(&self) -> bool {
match *self.credential.read() {
ProviderCredential::Credentials { .. } => true,
_ => false,
}
}
async fn retrieve_credentials(&self, cx: &mut AppContext) -> ProviderCredential {
let existing_credential = self.credential.read().clone();
let retrieved_credential = cx
.run_on_main(move |cx| match existing_credential {
ProviderCredential::Credentials { .. } => {
return existing_credential.clone();
}
_ => {
if let Some(api_key) = env::var("OPENAI_API_KEY").log_err() {
return ProviderCredential::Credentials { api_key };
}
if let Some(Some((_, api_key))) = cx.read_credentials(OPENAI_API_URL).log_err()
{
if let Some(api_key) = String::from_utf8(api_key).log_err() {
return ProviderCredential::Credentials { api_key };
} else {
return ProviderCredential::NoCredentials;
}
} else {
return ProviderCredential::NoCredentials;
}
}
})
.await;
*self.credential.write() = retrieved_credential.clone();
retrieved_credential
}
async fn save_credentials(&self, cx: &mut AppContext, credential: ProviderCredential) {
*self.credential.write() = credential.clone();
let credential = credential.clone();
cx.run_on_main(move |cx| match credential {
ProviderCredential::Credentials { api_key } => {
cx.write_credentials(OPENAI_API_URL, "Bearer", api_key.as_bytes())
.log_err();
}
_ => {}
})
.await;
}
async fn delete_credentials(&self, cx: &mut AppContext) {
cx.run_on_main(move |cx| cx.delete_credentials(OPENAI_API_URL).log_err())
.await;
*self.credential.write() = ProviderCredential::NoCredentials;
}
}
#[async_trait]
impl EmbeddingProvider for OpenAIEmbeddingProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
let model: Box<dyn LanguageModel> = Box::new(self.model.clone());
model
}
fn max_tokens_per_batch(&self) -> usize {
50000
}
fn rate_limit_expiration(&self) -> Option<Instant> {
*self.rate_limit_count_rx.borrow()
}
async fn embed_batch(&self, spans: Vec<String>) -> Result<Vec<Embedding>> {
const BACKOFF_SECONDS: [usize; 4] = [3, 5, 15, 45];
const MAX_RETRIES: usize = 4;
let api_key = self.get_api_key()?;
let mut request_number = 0;
let mut rate_limiting = false;
let mut request_timeout: u64 = 15;
let mut response: Response<AsyncBody>;
while request_number < MAX_RETRIES {
response = self
.send_request(
&api_key,
spans.iter().map(|x| &**x).collect(),
request_timeout,
)
.await?;
request_number += 1;
match response.status() {
StatusCode::REQUEST_TIMEOUT => {
request_timeout += 5;
}
StatusCode::OK => {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
let response: OpenAIEmbeddingResponse = serde_json::from_str(&body)?;
log::trace!(
"openai embedding completed. tokens: {:?}",
response.usage.total_tokens
);
// If we complete a request successfully that was previously rate_limited
// resolve the rate limit
if rate_limiting {
self.resolve_rate_limit()
}
return Ok(response
.data
.into_iter()
.map(|embedding| Embedding::from(embedding.embedding))
.collect());
}
StatusCode::TOO_MANY_REQUESTS => {
rate_limiting = true;
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
let delay_duration = {
let delay = Duration::from_secs(BACKOFF_SECONDS[request_number - 1] as u64);
if let Some(time_to_reset) =
response.headers().get("x-ratelimit-reset-tokens")
{
if let Ok(time_str) = time_to_reset.to_str() {
parse(time_str).unwrap_or(delay)
} else {
delay
}
} else {
delay
}
};
// If we've previously rate limited, increment the duration but not the count
let reset_time = Instant::now().add(delay_duration);
self.update_reset_time(reset_time);
log::trace!(
"openai rate limiting: waiting {:?} until lifted",
&delay_duration
);
self.executor.timer(delay_duration).await;
}
_ => {
let mut body = String::new();
response.body_mut().read_to_string(&mut body).await?;
return Err(anyhow!(
"open ai bad request: {:?} {:?}",
&response.status(),
body
));
}
}
}
Err(anyhow!("openai max retries"))
}
}
crates/ai2/src/providers/open_ai/mod.rs
+9
View File
@@ -0,0 +1,9 @@
pub mod completion;
pub mod embedding;
pub mod model;
pub use completion::*;
pub use embedding::*;
pub use model::OpenAILanguageModel;
pub const OPENAI_API_URL: &'static str = "https://api.openai.com/v1";
crates/ai2/src/providers/open_ai/model.rs
+57
View File
@@ -0,0 +1,57 @@
use anyhow::anyhow;
use tiktoken_rs::CoreBPE;
use util::ResultExt;
use crate::models::{LanguageModel, TruncationDirection};
#[derive(Clone)]
pub struct OpenAILanguageModel {
name: String,
bpe: Option<CoreBPE>,
}
impl OpenAILanguageModel {
pub fn load(model_name: &str) -> Self {
let bpe = tiktoken_rs::get_bpe_from_model(model_name).log_err();
OpenAILanguageModel {
name: model_name.to_string(),
bpe,
}
}
}
impl LanguageModel for OpenAILanguageModel {
fn name(&self) -> String {
self.name.clone()
}
fn count_tokens(&self, content: &str) -> anyhow::Result<usize> {
if let Some(bpe) = &self.bpe {
anyhow::Ok(bpe.encode_with_special_tokens(content).len())
} else {
Err(anyhow!("bpe for open ai model was not retrieved"))
}
}
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String> {
if let Some(bpe) = &self.bpe {
let tokens = bpe.encode_with_special_tokens(content);
if tokens.len() > length {
match direction {
TruncationDirection::End => bpe.decode(tokens[..length].to_vec()),
TruncationDirection::Start => bpe.decode(tokens[length..].to_vec()),
}
} else {
bpe.decode(tokens)
}
} else {
Err(anyhow!("bpe for open ai model was not retrieved"))
}
}
fn capacity(&self) -> anyhow::Result<usize> {
anyhow::Ok(tiktoken_rs::model::get_context_size(&self.name))
}
}
crates/ai2/src/providers/open_ai/new.rs
+11
View File
@@ -0,0 +1,11 @@
pub trait LanguageModel {
fn name(&self) -> String;
fn count_tokens(&self, content: &str) -> anyhow::Result<usize>;
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String>;
fn capacity(&self) -> anyhow::Result<usize>;
}
crates/ai2/src/test.rs
+193
View File
@@ -0,0 +1,193 @@
use std::{
sync::atomic::{self, AtomicUsize, Ordering},
time::Instant,
};
use async_trait::async_trait;
use futures::{channel::mpsc, future::BoxFuture, stream::BoxStream, FutureExt, StreamExt};
use gpui2::AppContext;
use parking_lot::Mutex;
use crate::{
auth::{CredentialProvider, ProviderCredential},
completion::{CompletionProvider, CompletionRequest},
embedding::{Embedding, EmbeddingProvider},
models::{LanguageModel, TruncationDirection},
};
#[derive(Clone)]
pub struct FakeLanguageModel {
pub capacity: usize,
}
impl LanguageModel for FakeLanguageModel {
fn name(&self) -> String {
"dummy".to_string()
}
fn count_tokens(&self, content: &str) -> anyhow::Result<usize> {
anyhow::Ok(content.chars().collect::<Vec<char>>().len())
}
fn truncate(
&self,
content: &str,
length: usize,
direction: TruncationDirection,
) -> anyhow::Result<String> {
println!("TRYING TO TRUNCATE: {:?}", length.clone());
if length > self.count_tokens(content)? {
println!("NOT TRUNCATING");
return anyhow::Ok(content.to_string());
}
anyhow::Ok(match direction {
TruncationDirection::End => content.chars().collect::<Vec<char>>()[..length]
.into_iter()
.collect::<String>(),
TruncationDirection::Start => content.chars().collect::<Vec<char>>()[length..]
.into_iter()
.collect::<String>(),
})
}
fn capacity(&self) -> anyhow::Result<usize> {
anyhow::Ok(self.capacity)
}
}
pub struct FakeEmbeddingProvider {
pub embedding_count: AtomicUsize,
}
impl Clone for FakeEmbeddingProvider {
fn clone(&self) -> Self {
FakeEmbeddingProvider {
embedding_count: AtomicUsize::new(self.embedding_count.load(Ordering::SeqCst)),
}
}
}
impl Default for FakeEmbeddingProvider {
fn default() -> Self {
FakeEmbeddingProvider {
embedding_count: AtomicUsize::default(),
}
}
}
impl FakeEmbeddingProvider {
pub fn embedding_count(&self) -> usize {
self.embedding_count.load(atomic::Ordering::SeqCst)
}
pub fn embed_sync(&self, span: &str) -> Embedding {
let mut result = vec![1.0; 26];
for letter in span.chars() {
let letter = letter.to_ascii_lowercase();
if letter as u32 >= 'a' as u32 {
let ix = (letter as u32) - ('a' as u32);
if ix < 26 {
result[ix as usize] += 1.0;
}
}
}
let norm = result.iter().map(|x| x * x).sum::<f32>().sqrt();
for x in &mut result {
*x /= norm;
}
result.into()
}
}
#[async_trait]
impl CredentialProvider for FakeEmbeddingProvider {
fn has_credentials(&self) -> bool {
true
}
async fn retrieve_credentials(&self, _cx: &mut AppContext) -> ProviderCredential {
ProviderCredential::NotNeeded
}
async fn save_credentials(&self, _cx: &mut AppContext, _credential: ProviderCredential) {}
async fn delete_credentials(&self, _cx: &mut AppContext) {}
}
#[async_trait]
impl EmbeddingProvider for FakeEmbeddingProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
Box::new(FakeLanguageModel { capacity: 1000 })
}
fn max_tokens_per_batch(&self) -> usize {
1000
}
fn rate_limit_expiration(&self) -> Option<Instant> {
None
}
async fn embed_batch(&self, spans: Vec<String>) -> anyhow::Result<Vec<Embedding>> {
self.embedding_count
.fetch_add(spans.len(), atomic::Ordering::SeqCst);
anyhow::Ok(spans.iter().map(|span| self.embed_sync(span)).collect())
}
}
pub struct FakeCompletionProvider {
last_completion_tx: Mutex<Option<mpsc::Sender<String>>>,
}
impl Clone for FakeCompletionProvider {
fn clone(&self) -> Self {
Self {
last_completion_tx: Mutex::new(None),
}
}
}
impl FakeCompletionProvider {
pub fn new() -> Self {
Self {
last_completion_tx: Mutex::new(None),
}
}
pub fn send_completion(&self, completion: impl Into<String>) {
let mut tx = self.last_completion_tx.lock();
tx.as_mut().unwrap().try_send(completion.into()).unwrap();
}
pub fn finish_completion(&self) {
self.last_completion_tx.lock().take().unwrap();
}
}
#[async_trait]
impl CredentialProvider for FakeCompletionProvider {
fn has_credentials(&self) -> bool {
true
}
async fn retrieve_credentials(&self, _cx: &mut AppContext) -> ProviderCredential {
ProviderCredential::NotNeeded
}
async fn save_credentials(&self, _cx: &mut AppContext, _credential: ProviderCredential) {}
async fn delete_credentials(&self, _cx: &mut AppContext) {}
}
impl CompletionProvider for FakeCompletionProvider {
fn base_model(&self) -> Box<dyn LanguageModel> {
let model: Box<dyn LanguageModel> = Box::new(FakeLanguageModel { capacity: 8190 });
model
}
fn complete(
&self,
_prompt: Box<dyn CompletionRequest>,
) -> BoxFuture<'static, anyhow::Result<BoxStream<'static, anyhow::Result<String>>>> {
let (tx, rx) = mpsc::channel(1);
*self.last_completion_tx.lock() = Some(tx);
async move { Ok(rx.map(|rx| Ok(rx)).boxed()) }.boxed()
}
fn box_clone(&self) -> Box<dyn CompletionProvider> {
Box::new((*self).clone())
}
}
crates/assistant/Cargo.toml
+1
View File
@@ -45,6 +45,7 @@ tiktoken-rs = "0.5"
[dev-dependencies]
editor = { path = "../editor", features = ["test-support"] }
project = { path = "../project", features = ["test-support"] }
ai = { path = "../ai", features = ["test-support"]}
ctor.workspace = true
env_logger.workspace = true
crates/assistant/src/assistant.rs
+1 -1
View File
@@ -4,7 +4,7 @@ mod codegen;
mod prompts;
mod streaming_diff;
use ai::completion::Role;
use ai::providers::open_ai::Role;
use anyhow::Result;
pub use assistant_panel::AssistantPanel;
use assistant_settings::OpenAIModel;
crates/assistant/src/assistant_panel.rs
+136 -152
View File
@@ -5,12 +5,14 @@ use crate::{
MessageId, MessageMetadata, MessageStatus, Role, SavedConversation, SavedConversationMetadata,
SavedMessage,
};
use ai::{
completion::{
stream_completion, OpenAICompletionProvider, OpenAIRequest, RequestMessage, OPENAI_API_URL,
},
templates::repository_context::PromptCodeSnippet,
auth::ProviderCredential,
completion::{CompletionProvider, CompletionRequest},
providers::open_ai::{OpenAICompletionProvider, OpenAIRequest, RequestMessage},
};
use ai::prompts::repository_context::PromptCodeSnippet;
use anyhow::{anyhow, Result};
use chrono::{DateTime, Local};
use client::{telemetry::AssistantKind, ClickhouseEvent, TelemetrySettings};
@@ -43,8 +45,8 @@ use search::BufferSearchBar;
use semantic_index::{SemanticIndex, SemanticIndexStatus};
use settings::SettingsStore;
use std::{
cell::{Cell, RefCell},
cmp, env,
cell::Cell,
cmp,
fmt::Write,
iter,
ops::Range,
@@ -97,8 +99,8 @@ pub fn init(cx: &mut AppContext) {
cx.capture_action(ConversationEditor::copy);
cx.add_action(ConversationEditor::split);
cx.capture_action(ConversationEditor::cycle_message_role);
cx.add_action(AssistantPanel::save_api_key);
cx.add_action(AssistantPanel::reset_api_key);
cx.add_action(AssistantPanel::save_credentials);
cx.add_action(AssistantPanel::reset_credentials);
cx.add_action(AssistantPanel::toggle_zoom);
cx.add_action(AssistantPanel::deploy);
cx.add_action(AssistantPanel::select_next_match);
@@ -140,9 +142,8 @@ pub struct AssistantPanel {
zoomed: bool,
has_focus: bool,
toolbar: ViewHandle<Toolbar>,
api_key: Rc<RefCell<Option<String>>>,
completion_provider: Box<dyn CompletionProvider>,
api_key_editor: Option<ViewHandle<Editor>>,
has_read_credentials: bool,
languages: Arc<LanguageRegistry>,
fs: Arc<dyn Fs>,
subscriptions: Vec<Subscription>,
@@ -202,6 +203,11 @@ impl AssistantPanel {
});
let semantic_index = SemanticIndex::global(cx);
// Defaulting currently to GPT4, allow for this to be set via config.
let completion_provider = Box::new(OpenAICompletionProvider::new(
"gpt-4",
cx.background().clone(),
));
let mut this = Self {
workspace: workspace_handle,
@@ -213,9 +219,8 @@ impl AssistantPanel {
zoomed: false,
has_focus: false,
toolbar,
api_key: Rc::new(RefCell::new(None)),
completion_provider,
api_key_editor: None,
has_read_credentials: false,
languages: workspace.app_state().languages.clone(),
fs: workspace.app_state().fs.clone(),
width: None,
@@ -254,10 +259,7 @@ impl AssistantPanel {
cx: &mut ViewContext<Workspace>,
) {
let this = if let Some(this) = workspace.panel::<AssistantPanel>(cx) {
if this
.update(cx, |assistant, cx| assistant.load_api_key(cx))
.is_some()
{
if this.update(cx, |assistant, _| assistant.has_credentials()) {
this
} else {
workspace.focus_panel::<AssistantPanel>(cx);
@@ -289,12 +291,6 @@ impl AssistantPanel {
cx: &mut ViewContext<Self>,
project: &ModelHandle<Project>,
) {
let api_key = if let Some(api_key) = self.api_key.borrow().clone() {
api_key
} else {
return;
};
let selection = editor.read(cx).selections.newest_anchor().clone();
if selection.start.excerpt_id != selection.end.excerpt_id {
return;
@@ -325,10 +321,13 @@ impl AssistantPanel {
let inline_assist_id = post_inc(&mut self.next_inline_assist_id);
let provider = Arc::new(OpenAICompletionProvider::new(
api_key,
"gpt-4",
cx.background().clone(),
));
// Retrieve Credentials Authenticates the Provider
// provider.retrieve_credentials(cx);
let codegen = cx.add_model(|cx| {
Codegen::new(editor.read(cx).buffer().clone(), codegen_kind, provider, cx)
});
@@ -745,13 +744,14 @@ impl AssistantPanel {
content: prompt,
});
let request = OpenAIRequest {
let request = Box::new(OpenAIRequest {
model: model.full_name().into(),
messages,
stream: true,
stop: vec!["|END|>".to_string()],
temperature,
};
});
codegen.update(&mut cx, |codegen, cx| codegen.start(request, cx));
anyhow::Ok(())
})
@@ -811,7 +811,7 @@ impl AssistantPanel {
fn new_conversation(&mut self, cx: &mut ViewContext<Self>) -> ViewHandle<ConversationEditor> {
let editor = cx.add_view(|cx| {
ConversationEditor::new(
self.api_key.clone(),
self.completion_provider.clone(),
self.languages.clone(),
self.fs.clone(),
self.workspace.clone(),
@@ -870,17 +870,19 @@ impl AssistantPanel {
}
}
fn save_api_key(&mut self, _: &menu::Confirm, cx: &mut ViewContext<Self>) {
fn save_credentials(&mut self, _: &menu::Confirm, cx: &mut ViewContext<Self>) {
if let Some(api_key) = self
.api_key_editor
.as_ref()
.map(|editor| editor.read(cx).text(cx))
{
if !api_key.is_empty() {
cx.platform()
.write_credentials(OPENAI_API_URL, "Bearer", api_key.as_bytes())
.log_err();
*self.api_key.borrow_mut() = Some(api_key);
let credential = ProviderCredential::Credentials {
api_key: api_key.clone(),
};
self.completion_provider.save_credentials(cx, credential);
self.api_key_editor.take();
cx.focus_self();
cx.notify();
@@ -890,9 +892,8 @@ impl AssistantPanel {
}
}
fn reset_api_key(&mut self, _: &ResetKey, cx: &mut ViewContext<Self>) {
cx.platform().delete_credentials(OPENAI_API_URL).log_err();
self.api_key.take();
fn reset_credentials(&mut self, _: &ResetKey, cx: &mut ViewContext<Self>) {
self.completion_provider.delete_credentials(cx);
self.api_key_editor = Some(build_api_key_editor(cx));
cx.focus_self();
cx.notify();
@@ -1151,13 +1152,12 @@ impl AssistantPanel {
let fs = self.fs.clone();
let workspace = self.workspace.clone();
let api_key = self.api_key.clone();
let languages = self.languages.clone();
cx.spawn(|this, mut cx| async move {
let saved_conversation = fs.load(&path).await?;
let saved_conversation = serde_json::from_str(&saved_conversation)?;
let conversation = cx.add_model(|cx| {
Conversation::deserialize(saved_conversation, path.clone(), api_key, languages, cx)
Conversation::deserialize(saved_conversation, path.clone(), languages, cx)
});
this.update(&mut cx, |this, cx| {
// If, by the time we've loaded the conversation, the user has already opened
@@ -1181,30 +1181,12 @@ impl AssistantPanel {
.position(|editor| editor.read(cx).conversation.read(cx).path.as_deref() == Some(path))
}
fn load_api_key(&mut self, cx: &mut ViewContext<Self>) -> Option<String> {
if self.api_key.borrow().is_none() && !self.has_read_credentials {
self.has_read_credentials = true;
let api_key = if let Ok(api_key) = env::var("OPENAI_API_KEY") {
Some(api_key)
} else if let Some((_, api_key)) = cx
.platform()
.read_credentials(OPENAI_API_URL)
.log_err()
.flatten()
{
String::from_utf8(api_key).log_err()
} else {
None
};
if let Some(api_key) = api_key {
*self.api_key.borrow_mut() = Some(api_key);
} else if self.api_key_editor.is_none() {
self.api_key_editor = Some(build_api_key_editor(cx));
cx.notify();
}
}
fn has_credentials(&mut self) -> bool {
self.completion_provider.has_credentials()
}
self.api_key.borrow().clone()
fn load_credentials(&mut self, cx: &mut ViewContext<Self>) {
self.completion_provider.retrieve_credentials(cx);
}
}
@@ -1389,7 +1371,7 @@ impl Panel for AssistantPanel {
fn set_active(&mut self, active: bool, cx: &mut ViewContext<Self>) {
if active {
self.load_api_key(cx);
self.load_credentials(cx);
if self.editors.is_empty() {
self.new_conversation(cx);
@@ -1454,10 +1436,10 @@ struct Conversation {
token_count: Option<usize>,
max_token_count: usize,
pending_token_count: Task<Option<()>>,
api_key: Rc<RefCell<Option<String>>>,
pending_save: Task<Result<()>>,
path: Option<PathBuf>,
_subscriptions: Vec<Subscription>,
completion_provider: Box<dyn CompletionProvider>,
}
impl Entity for Conversation {
@@ -1466,9 +1448,9 @@ impl Entity for Conversation {
impl Conversation {
fn new(
api_key: Rc<RefCell<Option<String>>>,
language_registry: Arc<LanguageRegistry>,
cx: &mut ModelContext<Self>,
completion_provider: Box<dyn CompletionProvider>,
) -> Self {
let markdown = language_registry.language_for_name("Markdown");
let buffer = cx.add_model(|cx| {
@@ -1507,8 +1489,8 @@ impl Conversation {
_subscriptions: vec![cx.subscribe(&buffer, Self::handle_buffer_event)],
pending_save: Task::ready(Ok(())),
path: None,
api_key,
buffer,
completion_provider,
};
let message = MessageAnchor {
id: MessageId(post_inc(&mut this.next_message_id.0)),
@@ -1554,7 +1536,6 @@ impl Conversation {
fn deserialize(
saved_conversation: SavedConversation,
path: PathBuf,
api_key: Rc<RefCell<Option<String>>>,
language_registry: Arc<LanguageRegistry>,
cx: &mut ModelContext<Self>,
) -> Self {
@@ -1563,6 +1544,10 @@ impl Conversation {
None => Some(Uuid::new_v4().to_string()),
};
let model = saved_conversation.model;
let completion_provider: Box<dyn CompletionProvider> = Box::new(
OpenAICompletionProvider::new(model.full_name(), cx.background().clone()),
);
completion_provider.retrieve_credentials(cx);
let markdown = language_registry.language_for_name("Markdown");
let mut message_anchors = Vec::new();
let mut next_message_id = MessageId(0);
@@ -1609,8 +1594,8 @@ impl Conversation {
_subscriptions: vec![cx.subscribe(&buffer, Self::handle_buffer_event)],
pending_save: Task::ready(Ok(())),
path: Some(path),
api_key,
buffer,
completion_provider,
};
this.count_remaining_tokens(cx);
this
@@ -1731,11 +1716,11 @@ impl Conversation {
}
if should_assist {
let Some(api_key) = self.api_key.borrow().clone() else {
if !self.completion_provider.has_credentials() {
return Default::default();
};
}
let request = OpenAIRequest {
let request: Box<dyn CompletionRequest> = Box::new(OpenAIRequest {
model: self.model.full_name().to_string(),
messages: self
.messages(cx)
@@ -1745,9 +1730,9 @@ impl Conversation {
stream: true,
stop: vec![],
temperature: 1.0,
};
});
let stream = stream_completion(api_key, cx.background().clone(), request);
let stream = self.completion_provider.complete(request);
let assistant_message = self
.insert_message_after(last_message_id, Role::Assistant, MessageStatus::Pending, cx)
.unwrap();
@@ -1765,33 +1750,28 @@ impl Conversation {
let mut messages = stream.await?;
while let Some(message) = messages.next().await {
let mut message = message?;
if let Some(choice) = message.choices.pop() {
this.upgrade(&cx)
.ok_or_else(|| anyhow!("conversation was dropped"))?
.update(&mut cx, |this, cx| {
let text: Arc<str> = choice.delta.content?.into();
let message_ix =
this.message_anchors.iter().position(|message| {
message.id == assistant_message_id
})?;
this.buffer.update(cx, |buffer, cx| {
let offset = this.message_anchors[message_ix + 1..]
.iter()
.find(|message| message.start.is_valid(buffer))
.map_or(buffer.len(), |message| {
message
.start
.to_offset(buffer)
.saturating_sub(1)
});
buffer.edit([(offset..offset, text)], None, cx);
});
cx.emit(ConversationEvent::StreamedCompletion);
let text = message?;
Some(())
this.upgrade(&cx)
.ok_or_else(|| anyhow!("conversation was dropped"))?
.update(&mut cx, |this, cx| {
let message_ix = this
.message_anchors
.iter()
.position(|message| message.id == assistant_message_id)?;
this.buffer.update(cx, |buffer, cx| {
let offset = this.message_anchors[message_ix + 1..]
.iter()
.find(|message| message.start.is_valid(buffer))
.map_or(buffer.len(), |message| {
message.start.to_offset(buffer).saturating_sub(1)
});
buffer.edit([(offset..offset, text)], None, cx);
});
}
cx.emit(ConversationEvent::StreamedCompletion);
Some(())
});
smol::future::yield_now().await;
}
@@ -2013,57 +1993,54 @@ impl Conversation {
fn summarize(&mut self, cx: &mut ModelContext<Self>) {
if self.message_anchors.len() >= 2 && self.summary.is_none() {
let api_key = self.api_key.borrow().clone();
if let Some(api_key) = api_key {
let messages = self
.messages(cx)
.take(2)
.map(|message| message.to_open_ai_message(self.buffer.read(cx)))
.chain(Some(RequestMessage {
role: Role::User,
content:
"Summarize the conversation into a short title without punctuation"
.into(),
}));
let request = OpenAIRequest {
model: self.model.full_name().to_string(),
messages: messages.collect(),
stream: true,
stop: vec![],
temperature: 1.0,
};
let stream = stream_completion(api_key, cx.background().clone(), request);
self.pending_summary = cx.spawn(|this, mut cx| {
async move {
let mut messages = stream.await?;
while let Some(message) = messages.next().await {
let mut message = message?;
if let Some(choice) = message.choices.pop() {
let text = choice.delta.content.unwrap_or_default();
this.update(&mut cx, |this, cx| {
this.summary
.get_or_insert(Default::default())
.text
.push_str(&text);
cx.emit(ConversationEvent::SummaryChanged);
});
}
}
this.update(&mut cx, |this, cx| {
if let Some(summary) = this.summary.as_mut() {
summary.done = true;
cx.emit(ConversationEvent::SummaryChanged);
}
});
anyhow::Ok(())
}
.log_err()
});
if !self.completion_provider.has_credentials() {
return;
}
let messages = self
.messages(cx)
.take(2)
.map(|message| message.to_open_ai_message(self.buffer.read(cx)))
.chain(Some(RequestMessage {
role: Role::User,
content: "Summarize the conversation into a short title without punctuation"
.into(),
}));
let request: Box<dyn CompletionRequest> = Box::new(OpenAIRequest {
model: self.model.full_name().to_string(),
messages: messages.collect(),
stream: true,
stop: vec![],
temperature: 1.0,
});
let stream = self.completion_provider.complete(request);
self.pending_summary = cx.spawn(|this, mut cx| {
async move {
let mut messages = stream.await?;
while let Some(message) = messages.next().await {
let text = message?;
this.update(&mut cx, |this, cx| {
this.summary
.get_or_insert(Default::default())
.text
.push_str(&text);
cx.emit(ConversationEvent::SummaryChanged);
});
}
this.update(&mut cx, |this, cx| {
if let Some(summary) = this.summary.as_mut() {
summary.done = true;
cx.emit(ConversationEvent::SummaryChanged);
}
});
anyhow::Ok(())
}
.log_err()
});
}
}
@@ -2224,13 +2201,14 @@ struct ConversationEditor {
impl ConversationEditor {
fn new(
api_key: Rc<RefCell<Option<String>>>,
completion_provider: Box<dyn CompletionProvider>,
language_registry: Arc<LanguageRegistry>,
fs: Arc<dyn Fs>,
workspace: WeakViewHandle<Workspace>,
cx: &mut ViewContext<Self>,
) -> Self {
let conversation = cx.add_model(|cx| Conversation::new(api_key, language_registry, cx));
let conversation =
cx.add_model(|cx| Conversation::new(language_registry, cx, completion_provider));
Self::for_conversation(conversation, fs, workspace, cx)
}
@@ -3419,6 +3397,7 @@ fn merge_ranges(ranges: &mut Vec<Range<Anchor>>, buffer: &MultiBufferSnapshot) {
mod tests {
use super::*;
use crate::MessageId;
use ai::test::FakeCompletionProvider;
use gpui::AppContext;
#[gpui::test]
@@ -3426,7 +3405,9 @@ mod tests {
cx.set_global(SettingsStore::test(cx));
init(cx);
let registry = Arc::new(LanguageRegistry::test());
let conversation = cx.add_model(|cx| Conversation::new(Default::default(), registry, cx));
let completion_provider = Box::new(FakeCompletionProvider::new());
let conversation = cx.add_model(|cx| Conversation::new(registry, cx, completion_provider));
let buffer = conversation.read(cx).buffer.clone();
let message_1 = conversation.read(cx).message_anchors[0].clone();
@@ -3554,7 +3535,9 @@ mod tests {
cx.set_global(SettingsStore::test(cx));
init(cx);
let registry = Arc::new(LanguageRegistry::test());
let conversation = cx.add_model(|cx| Conversation::new(Default::default(), registry, cx));
let completion_provider = Box::new(FakeCompletionProvider::new());
let conversation = cx.add_model(|cx| Conversation::new(registry, cx, completion_provider));
let buffer = conversation.read(cx).buffer.clone();
let message_1 = conversation.read(cx).message_anchors[0].clone();
@@ -3650,7 +3633,8 @@ mod tests {
cx.set_global(SettingsStore::test(cx));
init(cx);
let registry = Arc::new(LanguageRegistry::test());
let conversation = cx.add_model(|cx| Conversation::new(Default::default(), registry, cx));
let completion_provider = Box::new(FakeCompletionProvider::new());
let conversation = cx.add_model(|cx| Conversation::new(registry, cx, completion_provider));
let buffer = conversation.read(cx).buffer.clone();
let message_1 = conversation.read(cx).message_anchors[0].clone();
@@ -3732,8 +3716,9 @@ mod tests {
cx.set_global(SettingsStore::test(cx));
init(cx);
let registry = Arc::new(LanguageRegistry::test());
let completion_provider = Box::new(FakeCompletionProvider::new());
let conversation =
cx.add_model(|cx| Conversation::new(Default::default(), registry.clone(), cx));
cx.add_model(|cx| Conversation::new(registry.clone(), cx, completion_provider));
let buffer = conversation.read(cx).buffer.clone();
let message_0 = conversation.read(cx).message_anchors[0].id;
let message_1 = conversation.update(cx, |conversation, cx| {
@@ -3770,7 +3755,6 @@ mod tests {
Conversation::deserialize(
conversation.read(cx).serialize(cx),
Default::default(),
Default::default(),
registry.clone(),
cx,
)
crates/assistant/src/codegen.rs
+34 -46
View File
@@ -1,5 +1,5 @@
use crate::streaming_diff::{Hunk, StreamingDiff};
use ai::completion::{CompletionProvider, OpenAIRequest};
use ai::completion::{CompletionProvider, CompletionRequest};
use anyhow::Result;
use editor::{Anchor, MultiBuffer, MultiBufferSnapshot, ToOffset, ToPoint};
use futures::{channel::mpsc, SinkExt, Stream, StreamExt};
@@ -96,7 +96,7 @@ impl Codegen {
self.error.as_ref()
}
pub fn start(&mut self, prompt: OpenAIRequest, cx: &mut ModelContext<Self>) {
pub fn start(&mut self, prompt: Box<dyn CompletionRequest>, cx: &mut ModelContext<Self>) {
let range = self.range();
let snapshot = self.snapshot.clone();
let selected_text = snapshot
@@ -336,17 +336,25 @@ fn strip_markdown_codeblock(
#[cfg(test)]
mod tests {
use super::*;
use futures::{
future::BoxFuture,
stream::{self, BoxStream},
};
use ai::test::FakeCompletionProvider;
use futures::stream::{self};
use gpui::{executor::Deterministic, TestAppContext};
use indoc::indoc;
use language::{language_settings, tree_sitter_rust, Buffer, Language, LanguageConfig, Point};
use parking_lot::Mutex;
use rand::prelude::*;
use serde::Serialize;
use settings::SettingsStore;
use smol::future::FutureExt;
#[derive(Serialize)]
pub struct DummyCompletionRequest {
pub name: String,
}
impl CompletionRequest for DummyCompletionRequest {
fn data(&self) -> serde_json::Result<String> {
serde_json::to_string(self)
}
}
#[gpui::test(iterations = 10)]
async fn test_transform_autoindent(
@@ -372,7 +380,7 @@ mod tests {
let snapshot = buffer.snapshot(cx);
snapshot.anchor_before(Point::new(1, 0))..snapshot.anchor_after(Point::new(4, 5))
});
let provider = Arc::new(TestCompletionProvider::new());
let provider = Arc::new(FakeCompletionProvider::new());
let codegen = cx.add_model(|cx| {
Codegen::new(
buffer.clone(),
@@ -381,7 +389,11 @@ mod tests {
cx,
)
});
codegen.update(cx, |codegen, cx| codegen.start(Default::default(), cx));
let request = Box::new(DummyCompletionRequest {
name: "test".to_string(),
});
codegen.update(cx, |codegen, cx| codegen.start(request, cx));
let mut new_text = concat!(
" let mut x = 0;\n",
@@ -434,7 +446,7 @@ mod tests {
let snapshot = buffer.snapshot(cx);
snapshot.anchor_before(Point::new(1, 6))
});
let provider = Arc::new(TestCompletionProvider::new());
let provider = Arc::new(FakeCompletionProvider::new());
let codegen = cx.add_model(|cx| {
Codegen::new(
buffer.clone(),
@@ -443,7 +455,11 @@ mod tests {
cx,
)
});
codegen.update(cx, |codegen, cx| codegen.start(Default::default(), cx));
let request = Box::new(DummyCompletionRequest {
name: "test".to_string(),
});
codegen.update(cx, |codegen, cx| codegen.start(request, cx));
let mut new_text = concat!(
"t mut x = 0;\n",
@@ -496,7 +512,7 @@ mod tests {
let snapshot = buffer.snapshot(cx);
snapshot.anchor_before(Point::new(1, 2))
});
let provider = Arc::new(TestCompletionProvider::new());
let provider = Arc::new(FakeCompletionProvider::new());
let codegen = cx.add_model(|cx| {
Codegen::new(
buffer.clone(),
@@ -505,7 +521,11 @@ mod tests {
cx,
)
});
codegen.update(cx, |codegen, cx| codegen.start(Default::default(), cx));
let request = Box::new(DummyCompletionRequest {
name: "test".to_string(),
});
codegen.update(cx, |codegen, cx| codegen.start(request, cx));
let mut new_text = concat!(
"let mut x = 0;\n",
@@ -593,38 +613,6 @@ mod tests {
}
}
struct TestCompletionProvider {
last_completion_tx: Mutex<Option<mpsc::Sender<String>>>,
}
impl TestCompletionProvider {
fn new() -> Self {
Self {
last_completion_tx: Mutex::new(None),
}
}
fn send_completion(&self, completion: impl Into<String>) {
let mut tx = self.last_completion_tx.lock();
tx.as_mut().unwrap().try_send(completion.into()).unwrap();
}
fn finish_completion(&self) {
self.last_completion_tx.lock().take().unwrap();
}
}
impl CompletionProvider for TestCompletionProvider {
fn complete(
&self,
_prompt: OpenAIRequest,
) -> BoxFuture<'static, Result<BoxStream<'static, Result<String>>>> {
let (tx, rx) = mpsc::channel(1);
*self.last_completion_tx.lock() = Some(tx);
async move { Ok(rx.map(|rx| Ok(rx)).boxed()) }.boxed()
}
}
fn rust_lang() -> Language {
Language::new(
LanguageConfig {
crates/assistant/src/prompts.rs
+7 -6
View File
@@ -1,9 +1,10 @@
use ai::models::{LanguageModel, OpenAILanguageModel};
use ai::templates::base::{PromptArguments, PromptChain, PromptPriority, PromptTemplate};
use ai::templates::file_context::FileContext;
use ai::templates::generate::GenerateInlineContent;
use ai::templates::preamble::EngineerPreamble;
use ai::templates::repository_context::{PromptCodeSnippet, RepositoryContext};
use ai::models::LanguageModel;
use ai::prompts::base::{PromptArguments, PromptChain, PromptPriority, PromptTemplate};
use ai::prompts::file_context::FileContext;
use ai::prompts::generate::GenerateInlineContent;
use ai::prompts::preamble::EngineerPreamble;
use ai::prompts::repository_context::{PromptCodeSnippet, RepositoryContext};
use ai::providers::open_ai::OpenAILanguageModel;
use language::{BufferSnapshot, OffsetRangeExt, ToOffset};
use std::cmp::{self, Reverse};
use std::ops::Range;
crates/semantic_index/Cargo.toml
+1
View File
@@ -42,6 +42,7 @@ sha1 = "0.10.5"
ndarray = { version = "0.15.0" }
[dev-dependencies]
ai = { path = "../ai", features = ["test-support"] }
collections = { path = "../collections", features = ["test-support"] }
gpui = { path = "../gpui", features = ["test-support"] }
language = { path = "../language", features = ["test-support"] }
crates/semantic_index/src/embedding_queue.rs
+2 -13
View File
@@ -41,7 +41,6 @@ pub struct EmbeddingQueue {
pending_batch_token_count: usize,
finished_files_tx: channel::Sender<FileToEmbed>,
finished_files_rx: channel::Receiver<FileToEmbed>,
api_key: Option<String>,
}
#[derive(Clone)]
@@ -51,11 +50,7 @@ pub struct FileFragmentToEmbed {
}
impl EmbeddingQueue {
pub fn new(
embedding_provider: Arc<dyn EmbeddingProvider>,
executor: Arc<Background>,
api_key: Option<String>,
) -> Self {
pub fn new(embedding_provider: Arc<dyn EmbeddingProvider>, executor: Arc<Background>) -> Self {
let (finished_files_tx, finished_files_rx) = channel::unbounded();
Self {
embedding_provider,
@@ -64,14 +59,9 @@ impl EmbeddingQueue {
pending_batch_token_count: 0,
finished_files_tx,
finished_files_rx,
api_key,
}
}
pub fn set_api_key(&mut self, api_key: Option<String>) {
self.api_key = api_key
}
pub fn push(&mut self, file: FileToEmbed) {
if file.spans.is_empty() {
self.finished_files_tx.try_send(file).unwrap();
@@ -118,7 +108,6 @@ impl EmbeddingQueue {
let finished_files_tx = self.finished_files_tx.clone();
let embedding_provider = self.embedding_provider.clone();
let api_key = self.api_key.clone();
self.executor
.spawn(async move {
@@ -143,7 +132,7 @@ impl EmbeddingQueue {
return;
};
match embedding_provider.embed_batch(spans, api_key).await {
match embedding_provider.embed_batch(spans).await {
Ok(embeddings) => {
let mut embeddings = embeddings.into_iter();
for fragment in batch {
crates/semantic_index/src/parsing.rs
+28 -5
View File
@@ -1,4 +1,7 @@
use ai::embedding::{Embedding, EmbeddingProvider};
use ai::{
embedding::{Embedding, EmbeddingProvider},
models::TruncationDirection,
};
use anyhow::{anyhow, Result};
use language::{Grammar, Language};
use rusqlite::{
@@ -108,7 +111,14 @@ impl CodeContextRetriever {
.replace("<language>", language_name.as_ref())
.replace("<item>", &content);
let digest = SpanDigest::from(document_span.as_str());
let (document_span, token_count) = self.embedding_provider.truncate(&document_span);
let model = self.embedding_provider.base_model();
let document_span = model.truncate(
&document_span,
model.capacity()?,
ai::models::TruncationDirection::End,
)?;
let token_count = model.count_tokens(&document_span)?;
Ok(vec![Span {
range: 0..content.len(),
content: document_span,
@@ -131,7 +141,15 @@ impl CodeContextRetriever {
)
.replace("<item>", &content);
let digest = SpanDigest::from(document_span.as_str());
let (document_span, token_count) = self.embedding_provider.truncate(&document_span);
let model = self.embedding_provider.base_model();
let document_span = model.truncate(
&document_span,
model.capacity()?,
ai::models::TruncationDirection::End,
)?;
let token_count = model.count_tokens(&document_span)?;
Ok(vec![Span {
range: 0..content.len(),
content: document_span,
@@ -222,8 +240,13 @@ impl CodeContextRetriever {
.replace("<language>", language_name.as_ref())
.replace("item", &span.content);
let (document_content, token_count) =
self.embedding_provider.truncate(&document_content);
let model = self.embedding_provider.base_model();
let document_content = model.truncate(
&document_content,
model.capacity()?,
TruncationDirection::End,
)?;
let token_count = model.count_tokens(&document_content)?;
span.content = document_content;
span.token_count = token_count;
crates/semantic_index/src/semantic_index.rs
+22 -33
View File
@@ -7,7 +7,8 @@ pub mod semantic_index_settings;
mod semantic_index_tests;
use crate::semantic_index_settings::SemanticIndexSettings;
use ai::embedding::{Embedding, EmbeddingProvider, OpenAIEmbeddings};
use ai::embedding::{Embedding, EmbeddingProvider};
use ai::providers::open_ai::OpenAIEmbeddingProvider;
use anyhow::{anyhow, Result};
use collections::{BTreeMap, HashMap, HashSet};
use db::VectorDatabase;
@@ -88,7 +89,7 @@ pub fn init(
let semantic_index = SemanticIndex::new(
fs,
db_file_path,
Arc::new(OpenAIEmbeddings::new(http_client, cx.background())),
Arc::new(OpenAIEmbeddingProvider::new(http_client, cx.background())),
language_registry,
cx.clone(),
)
@@ -123,8 +124,6 @@ pub struct SemanticIndex {
_embedding_task: Task<()>,
_parsing_files_tasks: Vec<Task<()>>,
projects: HashMap<WeakModelHandle<Project>, ProjectState>,
api_key: Option<String>,
embedding_queue: Arc<Mutex<EmbeddingQueue>>,
}
struct ProjectState {
@@ -278,18 +277,18 @@ impl SemanticIndex {
}
}
pub fn authenticate(&mut self, cx: &AppContext) {
if self.api_key.is_none() {
self.api_key = self.embedding_provider.retrieve_credentials(cx);
self.embedding_queue
.lock()
.set_api_key(self.api_key.clone());
pub fn authenticate(&mut self, cx: &AppContext) -> bool {
if !self.embedding_provider.has_credentials() {
self.embedding_provider.retrieve_credentials(cx);
} else {
return true;
}
self.embedding_provider.has_credentials()
}
pub fn is_authenticated(&self) -> bool {
self.api_key.is_some()
self.embedding_provider.has_credentials()
}
pub fn enabled(cx: &AppContext) -> bool {
@@ -339,7 +338,7 @@ impl SemanticIndex {
Ok(cx.add_model(|cx| {
let t0 = Instant::now();
let embedding_queue =
EmbeddingQueue::new(embedding_provider.clone(), cx.background().clone(), None);
EmbeddingQueue::new(embedding_provider.clone(), cx.background().clone());
let _embedding_task = cx.background().spawn({
let embedded_files = embedding_queue.finished_files();
let db = db.clone();
@@ -404,8 +403,6 @@ impl SemanticIndex {
_embedding_task,
_parsing_files_tasks,
projects: Default::default(),
api_key: None,
embedding_queue
}
}))
}
@@ -720,13 +717,13 @@ impl SemanticIndex {
let index = self.index_project(project.clone(), cx);
let embedding_provider = self.embedding_provider.clone();
let api_key = self.api_key.clone();
cx.spawn(|this, mut cx| async move {
index.await?;
let t0 = Instant::now();
let query = embedding_provider
.embed_batch(vec![query], api_key)
.embed_batch(vec![query])
.await?
.pop()
.ok_or_else(|| anyhow!("could not embed query"))?;
@@ -944,7 +941,6 @@ impl SemanticIndex {
let fs = self.fs.clone();
let db_path = self.db.path().clone();
let background = cx.background().clone();
let api_key = self.api_key.clone();
cx.background().spawn(async move {
let db = VectorDatabase::new(fs, db_path.clone(), background).await?;
let mut results = Vec::<SearchResult>::new();
@@ -959,15 +955,10 @@ impl SemanticIndex {
.parse_file_with_template(None, &snapshot.text(), language)
.log_err()
.unwrap_or_default();
if Self::embed_spans(
&mut spans,
embedding_provider.as_ref(),
&db,
api_key.clone(),
)
.await
.log_err()
.is_some()
if Self::embed_spans(&mut spans, embedding_provider.as_ref(), &db)
.await
.log_err()
.is_some()
{
for span in spans {
let similarity = span.embedding.unwrap().similarity(&query);
@@ -1007,9 +998,8 @@ impl SemanticIndex {
project: ModelHandle<Project>,
cx: &mut ModelContext<Self>,
) -> Task<Result<()>> {
if self.api_key.is_none() {
self.authenticate(cx);
if self.api_key.is_none() {
if !self.is_authenticated() {
if !self.authenticate(cx) {
return Task::ready(Err(anyhow!("user is not authenticated")));
}
}
@@ -1192,7 +1182,6 @@ impl SemanticIndex {
spans: &mut [Span],
embedding_provider: &dyn EmbeddingProvider,
db: &VectorDatabase,
api_key: Option<String>,
) -> Result<()> {
let mut batch = Vec::new();
let mut batch_tokens = 0;
@@ -1215,7 +1204,7 @@ impl SemanticIndex {
if batch_tokens + span.token_count > embedding_provider.max_tokens_per_batch() {
let batch_embeddings = embedding_provider
.embed_batch(mem::take(&mut batch), api_key.clone())
.embed_batch(mem::take(&mut batch))
.await?;
embeddings.extend(batch_embeddings);
batch_tokens = 0;
@@ -1227,7 +1216,7 @@ impl SemanticIndex {
if !batch.is_empty() {
let batch_embeddings = embedding_provider
.embed_batch(mem::take(&mut batch), api_key)
.embed_batch(mem::take(&mut batch))
.await?;
embeddings.extend(batch_embeddings);
crates/semantic_index/src/semantic_index_tests.rs
+13 -80
View File
@@ -4,10 +4,9 @@ use crate::{
semantic_index_settings::SemanticIndexSettings,
FileToEmbed, JobHandle, SearchResult, SemanticIndex, EMBEDDING_QUEUE_FLUSH_TIMEOUT,
};
use ai::embedding::{DummyEmbeddings, Embedding, EmbeddingProvider};
use anyhow::Result;
use async_trait::async_trait;
use gpui::{executor::Deterministic, AppContext, Task, TestAppContext};
use ai::test::FakeEmbeddingProvider;
use gpui::{executor::Deterministic, Task, TestAppContext};
use language::{Language, LanguageConfig, LanguageRegistry, ToOffset};
use parking_lot::Mutex;
use pretty_assertions::assert_eq;
@@ -15,14 +14,7 @@ use project::{project_settings::ProjectSettings, search::PathMatcher, FakeFs, Fs
use rand::{rngs::StdRng, Rng};
use serde_json::json;
use settings::SettingsStore;
use std::{
path::Path,
sync::{
atomic::{self, AtomicUsize},
Arc,
},
time::{Instant, SystemTime},
};
use std::{path::Path, sync::Arc, time::SystemTime};
use unindent::Unindent;
use util::RandomCharIter;
@@ -228,7 +220,7 @@ async fn test_embedding_batching(cx: &mut TestAppContext, mut rng: StdRng) {
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut queue = EmbeddingQueue::new(embedding_provider.clone(), cx.background(), None);
let mut queue = EmbeddingQueue::new(embedding_provider.clone(), cx.background());
for file in &files {
queue.push(file.clone());
}
@@ -280,7 +272,7 @@ fn assert_search_results(
#[gpui::test]
async fn test_code_context_retrieval_rust() {
let language = rust_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = "
@@ -382,7 +374,7 @@ async fn test_code_context_retrieval_rust() {
#[gpui::test]
async fn test_code_context_retrieval_json() {
let language = json_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = r#"
@@ -466,7 +458,7 @@ fn assert_documents_eq(
#[gpui::test]
async fn test_code_context_retrieval_javascript() {
let language = js_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = "
@@ -565,7 +557,7 @@ async fn test_code_context_retrieval_javascript() {
#[gpui::test]
async fn test_code_context_retrieval_lua() {
let language = lua_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = r#"
@@ -639,7 +631,7 @@ async fn test_code_context_retrieval_lua() {
#[gpui::test]
async fn test_code_context_retrieval_elixir() {
let language = elixir_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = r#"
@@ -756,7 +748,7 @@ async fn test_code_context_retrieval_elixir() {
#[gpui::test]
async fn test_code_context_retrieval_cpp() {
let language = cpp_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = "
@@ -909,7 +901,7 @@ async fn test_code_context_retrieval_cpp() {
#[gpui::test]
async fn test_code_context_retrieval_ruby() {
let language = ruby_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = r#"
@@ -1100,7 +1092,7 @@ async fn test_code_context_retrieval_ruby() {
#[gpui::test]
async fn test_code_context_retrieval_php() {
let language = php_lang();
let embedding_provider = Arc::new(DummyEmbeddings {});
let embedding_provider = Arc::new(FakeEmbeddingProvider::default());
let mut retriever = CodeContextRetriever::new(embedding_provider);
let text = r#"
@@ -1248,65 +1240,6 @@ async fn test_code_context_retrieval_php() {
);
}
#[derive(Default)]
struct FakeEmbeddingProvider {
embedding_count: AtomicUsize,
}
impl FakeEmbeddingProvider {
fn embedding_count(&self) -> usize {
self.embedding_count.load(atomic::Ordering::SeqCst)
}
fn embed_sync(&self, span: &str) -> Embedding {
let mut result = vec![1.0; 26];
for letter in span.chars() {
let letter = letter.to_ascii_lowercase();
if letter as u32 >= 'a' as u32 {
let ix = (letter as u32) - ('a' as u32);
if ix < 26 {
result[ix as usize] += 1.0;
}
}
}
let norm = result.iter().map(|x| x * x).sum::<f32>().sqrt();
for x in &mut result {
*x /= norm;
}
result.into()
}
}
#[async_trait]
impl EmbeddingProvider for FakeEmbeddingProvider {
fn retrieve_credentials(&self, _cx: &AppContext) -> Option<String> {
Some("Fake Credentials".to_string())
}
fn truncate(&self, span: &str) -> (String, usize) {
(span.to_string(), 1)
}
fn max_tokens_per_batch(&self) -> usize {
200
}
fn rate_limit_expiration(&self) -> Option<Instant> {
None
}
async fn embed_batch(
&self,
spans: Vec<String>,
_api_key: Option<String>,
) -> Result<Vec<Embedding>> {
self.embedding_count
.fetch_add(spans.len(), atomic::Ordering::SeqCst);
Ok(spans.iter().map(|span| self.embed_sync(span)).collect())
}
}
fn js_lang() -> Arc<Language> {
Arc::new(
Language::new(
crates/ui2/src/elements/icon.rs
+1 -1
View File
@@ -36,7 +36,7 @@ impl IconColor {
IconColor::Error => gpui2::red(),
IconColor::Warning => gpui2::red(),
IconColor::Success => gpui2::red(),
IconColor::Info => gpui2::red()
IconColor::Info => gpui2::red(),
}
}
}
crates/zed/examples/semantic_index_eval.rs
+2 -2
View File
@@ -1,4 +1,4 @@
use ai::embedding::OpenAIEmbeddings;
use ai::providers::open_ai::OpenAIEmbeddingProvider;
use anyhow::{anyhow, Result};
use client::{self, UserStore};
use gpui::{AsyncAppContext, ModelHandle, Task};
@@ -475,7 +475,7 @@ fn main() {
let semantic_index = SemanticIndex::new(
fs.clone(),
db_file_path,
Arc::new(OpenAIEmbeddings::new(http_client, cx.background())),
Arc::new(OpenAIEmbeddingProvider::new(http_client, cx.background())),
languages.clone(),
cx.clone(),
)
crates/zed2/Cargo.toml
+1
View File
@@ -15,6 +15,7 @@ name = "Zed"
path = "src/main.rs"
[dependencies]
ai2 = { path = "../ai2"}
# audio = { path = "../audio" }
# activity_indicator = { path = "../activity_indicator" }
# auto_update = { path = "../auto_update" }