Kyle Caverly created
Parallelize Vector Database calls for project semantic search.
Release Notes: (Preview-only)
- Parallelize Vector database calls for project semantic search. Cuts
query time by 2/3rds.
- Removed default keymap for old semantic search modal.assets/keymaps/default.json | 1
crates/semantic_index/src/db.rs | 48 ++++++-------
crates/semantic_index/src/semantic_index.rs | 81 +++++++++++++++++-----
3 files changed, 84 insertions(+), 46 deletions(-)
@@ -411,7 +411,6 @@
"cmd-k cmd-t": "theme_selector::Toggle",
"cmd-k cmd-s": "zed::OpenKeymap",
"cmd-t": "project_symbols::Toggle",
- "cmd-ctrl-t": "semantic_search::Toggle",
"cmd-p": "file_finder::Toggle",
"cmd-shift-p": "command_palette::Toggle",
"cmd-shift-m": "diagnostics::Deploy",
@@ -267,41 +267,32 @@ impl VectorDatabase {
pub fn top_k_search(
&self,
- worktree_ids: &[i64],
query_embedding: &Vec<f32>,
limit: usize,
- include_globs: Vec<GlobMatcher>,
- exclude_globs: Vec<GlobMatcher>,
- ) -> Result<Vec<(i64, PathBuf, Range<usize>)>> {
+ file_ids: &[i64],
+ ) -> Result<Vec<(i64, f32)>> {
let mut results = Vec::<(i64, f32)>::with_capacity(limit + 1);
- self.for_each_document(
- &worktree_ids,
- include_globs,
- exclude_globs,
- |id, embedding| {
- let similarity = dot(&embedding, &query_embedding);
- let ix = match results.binary_search_by(|(_, s)| {
- similarity.partial_cmp(&s).unwrap_or(Ordering::Equal)
- }) {
- Ok(ix) => ix,
- Err(ix) => ix,
- };
- results.insert(ix, (id, similarity));
- results.truncate(limit);
- },
- )?;
+ self.for_each_document(file_ids, |id, embedding| {
+ let similarity = dot(&embedding, &query_embedding);
+ let ix = match results
+ .binary_search_by(|(_, s)| similarity.partial_cmp(&s).unwrap_or(Ordering::Equal))
+ {
+ Ok(ix) => ix,
+ Err(ix) => ix,
+ };
+ results.insert(ix, (id, similarity));
+ results.truncate(limit);
+ })?;
- let ids = results.into_iter().map(|(id, _)| id).collect::<Vec<_>>();
- self.get_documents_by_ids(&ids)
+ Ok(results)
}
- fn for_each_document(
+ pub fn retrieve_included_file_ids(
&self,
worktree_ids: &[i64],
include_globs: Vec<GlobMatcher>,
exclude_globs: Vec<GlobMatcher>,
- mut f: impl FnMut(i64, Vec<f32>),
- ) -> Result<()> {
+ ) -> Result<Vec<i64>> {
let mut file_query = self.db.prepare(
"
SELECT
@@ -315,6 +306,7 @@ impl VectorDatabase {
let mut file_ids = Vec::<i64>::new();
let mut rows = file_query.query([ids_to_sql(worktree_ids)])?;
+
while let Some(row) = rows.next()? {
let file_id = row.get(0)?;
let relative_path = row.get_ref(1)?.as_str()?;
@@ -330,6 +322,10 @@ impl VectorDatabase {
}
}
+ Ok(file_ids)
+ }
+
+ fn for_each_document(&self, file_ids: &[i64], mut f: impl FnMut(i64, Vec<f32>)) -> Result<()> {
let mut query_statement = self.db.prepare(
"
SELECT
@@ -350,7 +346,7 @@ impl VectorDatabase {
Ok(())
}
- fn get_documents_by_ids(&self, ids: &[i64]) -> Result<Vec<(i64, PathBuf, Range<usize>)>> {
+ pub fn get_documents_by_ids(&self, ids: &[i64]) -> Result<Vec<(i64, PathBuf, Range<usize>)>> {
let mut statement = self.db.prepare(
"
SELECT
@@ -20,6 +20,7 @@ use postage::watch;
use project::{Fs, Project, WorktreeId};
use smol::channel;
use std::{
+ cmp::Ordering,
collections::HashMap,
mem,
ops::Range,
@@ -704,27 +705,69 @@ impl SemanticIndex {
let database_url = self.database_url.clone();
let fs = self.fs.clone();
cx.spawn(|this, mut cx| async move {
- let documents = cx
- .background()
- .spawn(async move {
- let database = VectorDatabase::new(fs, database_url).await?;
+ let database = VectorDatabase::new(fs.clone(), database_url.clone()).await?;
- let phrase_embedding = embedding_provider
- .embed_batch(vec![&phrase])
- .await?
- .into_iter()
- .next()
- .unwrap();
+ let phrase_embedding = embedding_provider
+ .embed_batch(vec![&phrase])
+ .await?
+ .into_iter()
+ .next()
+ .unwrap();
- database.top_k_search(
- &worktree_db_ids,
- &phrase_embedding,
- limit,
- include_globs,
- exclude_globs,
- )
- })
- .await?;
+ let file_ids = database.retrieve_included_file_ids(
+ &worktree_db_ids,
+ include_globs,
+ exclude_globs,
+ )?;
+
+ let batch_n = cx.background().num_cpus();
+ let ids_len = file_ids.clone().len();
+ let batch_size = if ids_len <= batch_n {
+ ids_len
+ } else {
+ ids_len / batch_n
+ };
+
+ let mut result_tasks = Vec::new();
+ for batch in file_ids.chunks(batch_size) {
+ let batch = batch.into_iter().map(|v| *v).collect::<Vec<i64>>();
+ let limit = limit.clone();
+ let fs = fs.clone();
+ let database_url = database_url.clone();
+ let phrase_embedding = phrase_embedding.clone();
+ let task = cx.background().spawn(async move {
+ let database = VectorDatabase::new(fs, database_url).await.log_err();
+ if database.is_none() {
+ return Err(anyhow!("failed to acquire database connection"));
+ } else {
+ database
+ .unwrap()
+ .top_k_search(&phrase_embedding, limit, batch.as_slice())
+ }
+ });
+ result_tasks.push(task);
+ }
+
+ let batch_results = futures::future::join_all(result_tasks).await;
+
+ let mut results = Vec::new();
+ for batch_result in batch_results {
+ if batch_result.is_ok() {
+ for (id, similarity) in batch_result.unwrap() {
+ let ix = match results.binary_search_by(|(_, s)| {
+ similarity.partial_cmp(&s).unwrap_or(Ordering::Equal)
+ }) {
+ Ok(ix) => ix,
+ Err(ix) => ix,
+ };
+ results.insert(ix, (id, similarity));
+ results.truncate(limit);
+ }
+ }
+ }
+
+ let ids = results.into_iter().map(|(id, _)| id).collect::<Vec<i64>>();
+ let documents = database.get_documents_by_ids(ids.as_slice())?;
let mut tasks = Vec::new();
let mut ranges = Vec::new();