mirror of
https://github.com/zed-industries/zed.git
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181 lines
5.9 KiB
Rust
181 lines
5.9 KiB
Rust
use anyhow::{anyhow, Result};
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use async_trait::async_trait;
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use futures::AsyncReadExt;
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use gpui::executor::Background;
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use gpui::serde_json;
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use isahc::http::StatusCode;
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use isahc::prelude::Configurable;
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use isahc::{AsyncBody, Response};
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use lazy_static::lazy_static;
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use serde::{Deserialize, Serialize};
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use std::env;
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use std::sync::Arc;
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use std::time::Duration;
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use tiktoken_rs::{cl100k_base, CoreBPE};
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use util::http::{HttpClient, Request};
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lazy_static! {
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static ref OPENAI_API_KEY: Option<String> = env::var("OPENAI_API_KEY").ok();
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static ref OPENAI_BPE_TOKENIZER: CoreBPE = cl100k_base().unwrap();
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}
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#[derive(Clone)]
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pub struct OpenAIEmbeddings {
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pub client: Arc<dyn HttpClient>,
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pub executor: Arc<Background>,
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}
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#[derive(Serialize)]
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struct OpenAIEmbeddingRequest<'a> {
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model: &'static str,
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input: Vec<&'a str>,
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}
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#[derive(Deserialize)]
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struct OpenAIEmbeddingResponse {
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data: Vec<OpenAIEmbedding>,
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usage: OpenAIEmbeddingUsage,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAIEmbedding {
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embedding: Vec<f32>,
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index: usize,
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object: String,
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}
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#[derive(Deserialize)]
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struct OpenAIEmbeddingUsage {
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prompt_tokens: usize,
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total_tokens: usize,
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}
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#[async_trait]
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pub trait EmbeddingProvider: Sync + Send {
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async fn embed_batch(&self, spans: Vec<&str>) -> Result<Vec<Vec<f32>>>;
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}
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pub struct DummyEmbeddings {}
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#[async_trait]
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impl EmbeddingProvider for DummyEmbeddings {
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async fn embed_batch(&self, spans: Vec<&str>) -> Result<Vec<Vec<f32>>> {
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// 1024 is the OpenAI Embeddings size for ada models.
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// the model we will likely be starting with.
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let dummy_vec = vec![0.32 as f32; 1536];
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return Ok(vec![dummy_vec; spans.len()]);
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}
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}
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const OPENAI_INPUT_LIMIT: usize = 8190;
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impl OpenAIEmbeddings {
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fn truncate(span: String) -> String {
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let mut tokens = OPENAI_BPE_TOKENIZER.encode_with_special_tokens(span.as_ref());
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if tokens.len() > OPENAI_INPUT_LIMIT {
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tokens.truncate(OPENAI_INPUT_LIMIT);
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let result = OPENAI_BPE_TOKENIZER.decode(tokens.clone());
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if result.is_ok() {
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let transformed = result.unwrap();
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return transformed;
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}
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}
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span
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}
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async fn send_request(&self, api_key: &str, spans: Vec<&str>) -> Result<Response<AsyncBody>> {
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let request = Request::post("https://api.openai.com/v1/embeddings")
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.redirect_policy(isahc::config::RedirectPolicy::Follow)
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.timeout(Duration::from_secs(4))
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.header("Content-Type", "application/json")
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.header("Authorization", format!("Bearer {}", api_key))
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.body(
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serde_json::to_string(&OpenAIEmbeddingRequest {
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input: spans.clone(),
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model: "text-embedding-ada-002",
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})
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.unwrap()
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.into(),
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)?;
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Ok(self.client.send(request).await?)
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}
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}
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#[async_trait]
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impl EmbeddingProvider for OpenAIEmbeddings {
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async fn embed_batch(&self, spans: Vec<&str>) -> Result<Vec<Vec<f32>>> {
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const BACKOFF_SECONDS: [usize; 3] = [45, 75, 125];
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const MAX_RETRIES: usize = 3;
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let api_key = OPENAI_API_KEY
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.as_ref()
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.ok_or_else(|| anyhow!("no api key"))?;
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let mut request_number = 0;
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let mut truncated = false;
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let mut response: Response<AsyncBody>;
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let mut spans: Vec<String> = spans.iter().map(|x| x.to_string()).collect();
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while request_number < MAX_RETRIES {
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response = self
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.send_request(api_key, spans.iter().map(|x| &**x).collect())
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.await?;
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request_number += 1;
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if request_number + 1 == MAX_RETRIES && response.status() != StatusCode::OK {
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return Err(anyhow!(
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"openai max retries, error: {:?}",
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&response.status()
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));
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}
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match response.status() {
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StatusCode::TOO_MANY_REQUESTS => {
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let delay = Duration::from_secs(BACKOFF_SECONDS[request_number - 1] as u64);
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log::trace!(
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"open ai rate limiting, delaying request by {:?} seconds",
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delay.as_secs()
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);
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self.executor.timer(delay).await;
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}
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StatusCode::BAD_REQUEST => {
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// Only truncate if it hasnt been truncated before
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if !truncated {
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for span in spans.iter_mut() {
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*span = Self::truncate(span.clone());
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}
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truncated = true;
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} else {
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// If failing once already truncated, log the error and break the loop
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let mut body = String::new();
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response.body_mut().read_to_string(&mut body).await?;
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log::trace!("open ai bad request: {:?} {:?}", &response.status(), body);
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break;
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}
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}
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StatusCode::OK => {
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let mut body = String::new();
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response.body_mut().read_to_string(&mut body).await?;
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let response: OpenAIEmbeddingResponse = serde_json::from_str(&body)?;
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log::trace!(
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"openai embedding completed. tokens: {:?}",
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response.usage.total_tokens
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);
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return Ok(response
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.data
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.into_iter()
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.map(|embedding| embedding.embedding)
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.collect());
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}
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_ => {
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return Err(anyhow!("openai embedding failed {}", response.status()));
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}
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}
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}
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Err(anyhow!("openai embedding failed"))
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}
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}
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