Semantic Index (#10329)

This introduces semantic indexing in Zed based on chunking text from
files in the developer's workspace and creating vector embeddings using
an embedding model. As part of this, we've created an embeddings
provider trait that allows us to work with OpenAI, a local Ollama model,
or a Zed hosted embedding.

The semantic index is built by breaking down text for known
(programming) languages into manageable chunks that are smaller than the
max token size. Each chunk is then fed to a language model to create a
high dimensional vector which is then normalized to a unit vector to
allow fast comparison with other vectors with a simple dot product.
Alongside the vector, we store the path of the file and the range within
the document where the vector was sourced from.

Zed will soon grok contextual similarity across different text snippets,
allowing for natural language search beyond keyword matching. This is
being put together both for human-based search as well as providing
results to Large Language Models to allow them to refine how they help
developers.

Remaining todo:

* [x] Change `provider` to `model` within the zed hosted embeddings
database (as its currently a combo of the provider and the model in one
name)


Release Notes:

- N/A

---------

Co-authored-by: Nathan Sobo <nathan@zed.dev>
Co-authored-by: Antonio Scandurra <me@as-cii.com>
Co-authored-by: Conrad Irwin <conrad@zed.dev>
Co-authored-by: Marshall Bowers <elliott.codes@gmail.com>
Co-authored-by: Antonio <antonio@zed.dev>

crates/collab/src/db/tests/embedding_tests.rs

@@ -0,0 +1,84 @@
+use super::TestDb;
+use crate::db::embedding;
+use collections::HashMap;
+use sea_orm::{sea_query::Expr, ColumnTrait, EntityTrait, QueryFilter};
+use std::ops::Sub;
+use time::{Duration, OffsetDateTime, PrimitiveDateTime};
+
+// SQLite does not support array arguments, so we only test this against a real postgres instance
+#[gpui::test]
+async fn test_get_embeddings_postgres(cx: &mut gpui::TestAppContext) {
+    let test_db = TestDb::postgres(cx.executor().clone());
+    let db = test_db.db();
+
+    let provider = "test_model";
+    let digest1 = vec![1, 2, 3];
+    let digest2 = vec![4, 5, 6];
+    let embeddings = HashMap::from_iter([
+        (digest1.clone(), vec![0.1, 0.2, 0.3]),
+        (digest2.clone(), vec![0.4, 0.5, 0.6]),
+    ]);
+
+    // Save embeddings
+    db.save_embeddings(provider, &embeddings).await.unwrap();
+
+    // Retrieve embeddings
+    let retrieved_embeddings = db
+        .get_embeddings(provider, &[digest1.clone(), digest2.clone()])
+        .await
+        .unwrap();
+    assert_eq!(retrieved_embeddings.len(), 2);
+    assert!(retrieved_embeddings.contains_key(&digest1));
+    assert!(retrieved_embeddings.contains_key(&digest2));
+
+    // Check if the retrieved embeddings are correct
+    assert_eq!(retrieved_embeddings[&digest1], vec![0.1, 0.2, 0.3]);
+    assert_eq!(retrieved_embeddings[&digest2], vec![0.4, 0.5, 0.6]);
+}
+
+#[gpui::test]
+async fn test_purge_old_embeddings(cx: &mut gpui::TestAppContext) {
+    let test_db = TestDb::postgres(cx.executor().clone());
+    let db = test_db.db();
+
+    let model = "test_model";
+    let digest = vec![7, 8, 9];
+    let embeddings = HashMap::from_iter([(digest.clone(), vec![0.7, 0.8, 0.9])]);
+
+    // Save old embeddings
+    db.save_embeddings(model, &embeddings).await.unwrap();
+
+    // Reach into the DB and change the retrieved at to be > 60 days
+    db.weak_transaction(|tx| {
+        let digest = digest.clone();
+        async move {
+            let sixty_days_ago = OffsetDateTime::now_utc().sub(Duration::days(61));
+            let retrieved_at = PrimitiveDateTime::new(sixty_days_ago.date(), sixty_days_ago.time());
+
+            embedding::Entity::update_many()
+                .filter(
+                    embedding::Column::Model
+                        .eq(model)
+                        .and(embedding::Column::Digest.eq(digest)),
+                )
+                .col_expr(embedding::Column::RetrievedAt, Expr::value(retrieved_at))
+                .exec(&*tx)
+                .await
+                .unwrap();
+
+            Ok(())
+        }
+    })
+    .await
+    .unwrap();
+
+    // Purge old embeddings
+    db.purge_old_embeddings().await.unwrap();
+
+    // Try to retrieve the purged embeddings
+    let retrieved_embeddings = db.get_embeddings(model, &[digest.clone()]).await.unwrap();
+    assert!(
+        retrieved_embeddings.is_empty(),
+        "Old embeddings should have been purged"
+    );
+}