mirror of
https://github.com/zed-industries/zed.git
synced 2024-11-08 07:35:01 +03:00
532 lines
16 KiB
Rust
532 lines
16 KiB
Rust
use ai::embedding::OpenAIEmbeddings;
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use anyhow::{anyhow, Result};
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use client::{self, UserStore};
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use gpui::{AsyncAppContext, ModelHandle, Task};
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use language::LanguageRegistry;
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use node_runtime::RealNodeRuntime;
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use project::{Project, RealFs};
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use semantic_index::semantic_index_settings::SemanticIndexSettings;
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use semantic_index::{SearchResult, SemanticIndex};
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use serde::{Deserialize, Serialize};
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use settings::{default_settings, SettingsStore};
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use std::path::{Path, PathBuf};
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use std::process::Command;
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use std::sync::Arc;
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use std::time::{Duration, Instant};
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use std::{cmp, env, fs};
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use util::channel::{RELEASE_CHANNEL, RELEASE_CHANNEL_NAME};
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use util::http::{self};
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use util::paths::EMBEDDINGS_DIR;
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use zed::languages;
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#[derive(Deserialize, Clone, Serialize)]
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struct EvaluationQuery {
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query: String,
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matches: Vec<String>,
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}
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impl EvaluationQuery {
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fn match_pairs(&self) -> Vec<(PathBuf, u32)> {
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let mut pairs = Vec::new();
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for match_identifier in self.matches.iter() {
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let mut match_parts = match_identifier.split(":");
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if let Some(file_path) = match_parts.next() {
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if let Some(row_number) = match_parts.next() {
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pairs.push((PathBuf::from(file_path), row_number.parse::<u32>().unwrap()));
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}
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}
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}
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pairs
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}
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}
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#[derive(Deserialize, Clone)]
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struct RepoEval {
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repo: String,
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commit: String,
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assertions: Vec<EvaluationQuery>,
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}
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const TMP_REPO_PATH: &str = "eval_repos";
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fn parse_eval() -> anyhow::Result<Vec<RepoEval>> {
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let eval_folder = env::current_dir()?
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.as_path()
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.parent()
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.unwrap()
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.join("crates/semantic_index/eval");
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let mut repo_evals: Vec<RepoEval> = Vec::new();
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for entry in fs::read_dir(eval_folder)? {
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let file_path = entry.unwrap().path();
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if let Some(extension) = file_path.extension() {
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if extension == "json" {
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if let Ok(file) = fs::read_to_string(file_path) {
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let repo_eval = serde_json::from_str(file.as_str());
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match repo_eval {
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Ok(repo_eval) => {
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repo_evals.push(repo_eval);
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}
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Err(err) => {
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println!("Err: {:?}", err);
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}
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}
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}
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}
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}
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}
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Ok(repo_evals)
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}
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fn clone_repo(repo_eval: RepoEval) -> anyhow::Result<(String, PathBuf)> {
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let repo_name = Path::new(repo_eval.repo.as_str())
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.file_name()
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.unwrap()
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.to_str()
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.unwrap()
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.to_owned()
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.replace(".git", "");
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let clone_path = fs::canonicalize(env::current_dir()?)?
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.parent()
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.ok_or(anyhow!("path canonicalization failed"))?
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.parent()
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.unwrap()
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.join(TMP_REPO_PATH);
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// Delete Clone Path if already exists
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let _ = fs::remove_dir_all(&clone_path);
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let _ = fs::create_dir(&clone_path);
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let _ = Command::new("git")
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.args(["clone", repo_eval.repo.as_str()])
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.current_dir(clone_path.clone())
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.output()?;
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// Update clone path to be new directory housing the repo.
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let clone_path = clone_path.join(repo_name.clone());
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let _ = Command::new("git")
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.args(["checkout", repo_eval.commit.as_str()])
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.current_dir(clone_path.clone())
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.output()?;
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Ok((repo_name, clone_path))
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}
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fn dcg(hits: Vec<usize>) -> f32 {
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let mut result = 0.0;
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for (idx, hit) in hits.iter().enumerate() {
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result += *hit as f32 / (2.0 + idx as f32).log2();
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}
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result
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}
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fn get_hits(
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eval_query: EvaluationQuery,
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search_results: Vec<SearchResult>,
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k: usize,
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cx: &AsyncAppContext,
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) -> (Vec<usize>, Vec<usize>) {
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let ideal = vec![1; cmp::min(eval_query.matches.len(), k)];
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let mut hits = Vec::new();
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for result in search_results {
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let (path, start_row, end_row) = result.buffer.read_with(cx, |buffer, _cx| {
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let path = buffer.file().unwrap().path().to_path_buf();
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let start_row = buffer.offset_to_point(result.range.start.offset).row;
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let end_row = buffer.offset_to_point(result.range.end.offset).row;
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(path, start_row, end_row)
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});
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let match_pairs = eval_query.match_pairs();
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let mut found = 0;
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for (match_path, match_row) in match_pairs {
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if match_path == path {
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if match_row >= start_row && match_row <= end_row {
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found = 1;
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break;
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}
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}
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}
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hits.push(found);
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}
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// For now, we are calculating ideal_hits a bit different, as technically
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// with overlapping ranges, one match can result in more than result.
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let mut ideal_hits = hits.clone();
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ideal_hits.retain(|x| x == &1);
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let ideal = if ideal.len() > ideal_hits.len() {
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ideal
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} else {
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ideal_hits
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};
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// Fill ideal to 10 length
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let mut filled_ideal = [0; 10];
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for (idx, i) in ideal.to_vec().into_iter().enumerate() {
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filled_ideal[idx] = i;
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}
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(filled_ideal.to_vec(), hits)
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}
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fn evaluate_ndcg(hits: Vec<usize>, ideal: Vec<usize>) -> Vec<f32> {
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// NDCG or Normalized Discounted Cumulative Gain, is determined by comparing the relevance of
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// items returned by the search engine relative to the hypothetical ideal.
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// Relevance is represented as a series of booleans, in which each search result returned
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// is identified as being inside the test set of matches (1) or not (0).
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// For example, if result 1, 3 and 5 match the 3 relevant results provided
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// actual dcg is calculated against a vector of [1, 0, 1, 0, 1]
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// whereas ideal dcg is calculated against a vector of [1, 1, 1, 0, 0]
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// as this ideal vector assumes the 3 relevant results provided were returned first
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// normalized dcg is then calculated as actual dcg / ideal dcg.
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// NDCG ranges from 0 to 1, which higher values indicating better performance
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// Commonly NDCG is expressed as NDCG@k, in which k represents the metric calculated
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// including only the top k values returned.
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// The @k metrics can help you identify, at what point does the relevant results start to fall off.
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// Ie. a NDCG@1 of 0.9 and a NDCG@3 of 0.5 may indicate that the first result returned in usually
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// very high quality, whereas rank results quickly drop off after the first result.
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let mut ndcg = Vec::new();
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for idx in 1..(hits.len() + 1) {
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let hits_at_k = hits[0..idx].to_vec();
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let ideal_at_k = ideal[0..idx].to_vec();
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let at_k = dcg(hits_at_k.clone()) / dcg(ideal_at_k.clone());
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ndcg.push(at_k);
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}
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ndcg
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}
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fn evaluate_map(hits: Vec<usize>) -> Vec<f32> {
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let mut map_at_k = Vec::new();
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let non_zero = hits.iter().sum::<usize>() as f32;
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if non_zero == 0.0 {
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return vec![0.0; hits.len()];
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}
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let mut rolling_non_zero = 0.0;
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let mut rolling_map = 0.0;
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for (idx, h) in hits.into_iter().enumerate() {
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rolling_non_zero += h as f32;
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if h == 1 {
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rolling_map += rolling_non_zero / (idx + 1) as f32;
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}
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map_at_k.push(rolling_map / non_zero);
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}
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map_at_k
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}
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fn evaluate_mrr(hits: Vec<usize>) -> f32 {
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for (idx, h) in hits.into_iter().enumerate() {
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if h == 1 {
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return 1.0 / (idx + 1) as f32;
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}
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}
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return 0.0;
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}
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fn init_logger() {
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env_logger::init();
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}
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#[derive(Serialize)]
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struct QueryMetrics {
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query: EvaluationQuery,
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millis_to_search: Duration,
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ndcg: Vec<f32>,
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map: Vec<f32>,
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mrr: f32,
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hits: Vec<usize>,
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precision: Vec<f32>,
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recall: Vec<f32>,
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}
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#[derive(Serialize)]
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struct SummaryMetrics {
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millis_to_search: f32,
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ndcg: Vec<f32>,
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map: Vec<f32>,
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mrr: f32,
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precision: Vec<f32>,
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recall: Vec<f32>,
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}
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#[derive(Serialize)]
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struct RepoEvaluationMetrics {
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millis_to_index: Duration,
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query_metrics: Vec<QueryMetrics>,
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repo_metrics: Option<SummaryMetrics>,
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}
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impl RepoEvaluationMetrics {
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fn new(millis_to_index: Duration) -> Self {
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RepoEvaluationMetrics {
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millis_to_index,
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query_metrics: Vec::new(),
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repo_metrics: None,
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}
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}
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fn save(&self, repo_name: String) -> Result<()> {
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let results_string = serde_json::to_string(&self)?;
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fs::write(format!("./{}_evaluation.json", repo_name), results_string)
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.expect("Unable to write file");
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Ok(())
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}
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fn summarize(&mut self) {
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let l = self.query_metrics.len() as f32;
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let millis_to_search: f32 = self
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.query_metrics
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.iter()
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.map(|metrics| metrics.millis_to_search.as_millis())
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.sum::<u128>() as f32
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/ l;
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let mut ndcg_sum = vec![0.0; 10];
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let mut map_sum = vec![0.0; 10];
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let mut precision_sum = vec![0.0; 10];
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let mut recall_sum = vec![0.0; 10];
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let mut mmr_sum = 0.0;
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for query_metric in self.query_metrics.iter() {
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for (ndcg, query_ndcg) in ndcg_sum.iter_mut().zip(query_metric.ndcg.clone()) {
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*ndcg += query_ndcg;
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}
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for (mapp, query_map) in map_sum.iter_mut().zip(query_metric.map.clone()) {
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*mapp += query_map;
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}
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for (pre, query_pre) in precision_sum.iter_mut().zip(query_metric.precision.clone()) {
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*pre += query_pre;
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}
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for (rec, query_rec) in recall_sum.iter_mut().zip(query_metric.recall.clone()) {
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*rec += query_rec;
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}
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mmr_sum += query_metric.mrr;
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}
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let ndcg = ndcg_sum.iter().map(|val| val / l).collect::<Vec<f32>>();
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let map = map_sum.iter().map(|val| val / l).collect::<Vec<f32>>();
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let precision = precision_sum
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.iter()
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.map(|val| val / l)
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.collect::<Vec<f32>>();
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let recall = recall_sum.iter().map(|val| val / l).collect::<Vec<f32>>();
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let mrr = mmr_sum / l;
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self.repo_metrics = Some(SummaryMetrics {
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millis_to_search,
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ndcg,
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map,
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mrr,
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precision,
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recall,
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})
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}
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}
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fn evaluate_precision(hits: Vec<usize>) -> Vec<f32> {
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let mut rolling_hit: f32 = 0.0;
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let mut precision = Vec::new();
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for (idx, hit) in hits.into_iter().enumerate() {
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rolling_hit += hit as f32;
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precision.push(rolling_hit / ((idx as f32) + 1.0));
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}
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precision
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}
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fn evaluate_recall(hits: Vec<usize>, ideal: Vec<usize>) -> Vec<f32> {
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let total_relevant = ideal.iter().sum::<usize>() as f32;
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let mut recall = Vec::new();
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let mut rolling_hit: f32 = 0.0;
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for hit in hits {
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rolling_hit += hit as f32;
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recall.push(rolling_hit / total_relevant);
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}
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recall
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}
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async fn evaluate_repo(
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repo_name: String,
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index: ModelHandle<SemanticIndex>,
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project: ModelHandle<Project>,
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query_matches: Vec<EvaluationQuery>,
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cx: &mut AsyncAppContext,
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) -> Result<RepoEvaluationMetrics> {
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// Index Project
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let index_t0 = Instant::now();
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index
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.update(cx, |index, cx| index.index_project(project.clone(), cx))
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.await?;
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let mut repo_metrics = RepoEvaluationMetrics::new(index_t0.elapsed());
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for query in query_matches {
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// Query each match in order
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let search_t0 = Instant::now();
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let search_results = index
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.update(cx, |index, cx| {
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index.search_project(project.clone(), query.clone().query, 10, vec![], vec![], cx)
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})
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.await?;
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let millis_to_search = search_t0.elapsed();
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// Get Hits/Ideal
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let k = 10;
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let (ideal, hits) = self::get_hits(query.clone(), search_results, k, cx);
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// Evaluate ndcg@k, for k = 1, 3, 5, 10
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let ndcg = evaluate_ndcg(hits.clone(), ideal.clone());
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// Evaluate map@k, for k = 1, 3, 5, 10
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let map = evaluate_map(hits.clone());
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// Evaluate mrr
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let mrr = evaluate_mrr(hits.clone());
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// Evaluate precision
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let precision = evaluate_precision(hits.clone());
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// Evaluate Recall
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let recall = evaluate_recall(hits.clone(), ideal);
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let query_metrics = QueryMetrics {
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query,
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millis_to_search,
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ndcg,
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map,
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mrr,
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hits,
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precision,
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recall,
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};
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repo_metrics.query_metrics.push(query_metrics);
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}
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repo_metrics.summarize();
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let _ = repo_metrics.save(repo_name);
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anyhow::Ok(repo_metrics)
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}
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fn main() {
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// Launch new repo as a new Zed workspace/project
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let app = gpui::App::new(()).unwrap();
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let fs = Arc::new(RealFs);
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let http = http::client();
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let http_client = http::client();
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init_logger();
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app.run(move |cx| {
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cx.set_global(*RELEASE_CHANNEL);
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let client = client::Client::new(http.clone(), cx);
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let user_store = cx.add_model(|cx| UserStore::new(client.clone(), http_client.clone(), cx));
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// Initialize Settings
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let mut store = SettingsStore::default();
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store
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.set_default_settings(default_settings().as_ref(), cx)
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.unwrap();
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cx.set_global(store);
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// Initialize Languages
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let login_shell_env_loaded = Task::ready(());
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let mut languages = LanguageRegistry::new(login_shell_env_loaded);
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languages.set_executor(cx.background().clone());
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let languages = Arc::new(languages);
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let node_runtime = RealNodeRuntime::new(http.clone());
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languages::init(languages.clone(), node_runtime.clone());
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language::init(cx);
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project::Project::init(&client, cx);
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semantic_index::init(fs.clone(), http.clone(), languages.clone(), cx);
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settings::register::<SemanticIndexSettings>(cx);
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let db_file_path = EMBEDDINGS_DIR
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.join(Path::new(RELEASE_CHANNEL_NAME.as_str()))
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.join("embeddings_db");
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let languages = languages.clone();
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let fs = fs.clone();
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cx.spawn(|mut cx| async move {
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let semantic_index = SemanticIndex::new(
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fs.clone(),
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db_file_path,
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Arc::new(OpenAIEmbeddings::new(http_client, cx.background())),
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languages.clone(),
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cx.clone(),
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)
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.await?;
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if let Ok(repo_evals) = parse_eval() {
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for repo in repo_evals {
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let cloned = clone_repo(repo.clone());
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match cloned {
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Ok((repo_name, clone_path)) => {
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println!(
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"Cloned {:?} @ {:?} into {:?}",
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repo.repo, repo.commit, &clone_path
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);
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// Create Project
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let project = cx.update(|cx| {
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Project::local(
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client.clone(),
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user_store.clone(),
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languages.clone(),
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fs.clone(),
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cx,
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)
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});
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// Register Worktree
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let _ = project
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.update(&mut cx, |project, cx| {
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project.find_or_create_local_worktree(clone_path, true, cx)
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})
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.await;
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let _ = evaluate_repo(
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repo_name,
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semantic_index.clone(),
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project,
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repo.assertions,
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&mut cx,
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)
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.await?;
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}
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Err(err) => {
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println!("Error cloning: {:?}", err);
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}
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}
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}
|
|
}
|
|
|
|
anyhow::Ok(())
|
|
})
|
|
.detach();
|
|
});
|
|
}
|