collab: Remove code for embeddings (#29310)

This PR removes the embeddings-related code from collab and the
protocol, as we weren't using it anywhere.

Release Notes:

- N/A

crates/semantic_index/src/embedding.rs

@@ -1,9 +1,7 @@
-mod cloud;
 mod lmstudio;
 mod ollama;
 mod open_ai;
 
-pub use cloud::*;
 pub use lmstudio::*;
 pub use ollama::*;
 pub use open_ai::*;

crates/semantic_index/src/embedding/cloud.rs

@@ -1,93 +0,0 @@
-use crate::{Embedding, EmbeddingProvider, TextToEmbed};
-use anyhow::{Context as _, Result, anyhow};
-use client::{Client, proto};
-use collections::HashMap;
-use futures::{FutureExt, future::BoxFuture};
-use std::sync::Arc;
-
-pub struct CloudEmbeddingProvider {
-    model: String,
-    client: Arc<Client>,
-}
-
-impl CloudEmbeddingProvider {
-    pub fn new(client: Arc<Client>) -> Self {
-        Self {
-            model: "openai/text-embedding-3-small".into(),
-            client,
-        }
-    }
-}
-
-impl EmbeddingProvider for CloudEmbeddingProvider {
-    fn embed<'a>(&'a self, texts: &'a [TextToEmbed<'a>]) -> BoxFuture<'a, Result<Vec<Embedding>>> {
-        // First, fetch any embeddings that are cached based on the requested texts' digests
-        // Then compute any embeddings that are missing.
-        async move {
-            if !self.client.status().borrow().is_connected() {
-                return Err(anyhow!("sign in required"));
-            }
-
-            let cached_embeddings = self.client.request(proto::GetCachedEmbeddings {
-                model: self.model.clone(),
-                digests: texts
-                    .iter()
-                    .map(|to_embed| to_embed.digest.to_vec())
-                    .collect(),
-            });
-            let mut embeddings = cached_embeddings
-                .await
-                .context("failed to fetch cached embeddings via cloud model")?
-                .embeddings
-                .into_iter()
-                .map(|embedding| {
-                    let digest: [u8; 32] = embedding
-                        .digest
-                        .try_into()
-                        .map_err(|_| anyhow!("invalid digest for cached embedding"))?;
-                    Ok((digest, embedding.dimensions))
-                })
-                .collect::<Result<HashMap<_, _>>>()?;
-
-            let compute_embeddings_request = proto::ComputeEmbeddings {
-                model: self.model.clone(),
-                texts: texts
-                    .iter()
-                    .filter_map(|to_embed| {
-                        if embeddings.contains_key(&to_embed.digest) {
-                            None
-                        } else {
-                            Some(to_embed.text.to_string())
-                        }
-                    })
-                    .collect(),
-            };
-            if !compute_embeddings_request.texts.is_empty() {
-                let missing_embeddings = self.client.request(compute_embeddings_request).await?;
-                for embedding in missing_embeddings.embeddings {
-                    let digest: [u8; 32] = embedding
-                        .digest
-                        .try_into()
-                        .map_err(|_| anyhow!("invalid digest for cached embedding"))?;
-                    embeddings.insert(digest, embedding.dimensions);
-                }
-            }
-
-            texts
-                .iter()
-                .map(|to_embed| {
-                    let embedding =
-                        embeddings.get(&to_embed.digest).cloned().with_context(|| {
-                            format!("server did not return an embedding for {:?}", to_embed)
-                        })?;
-                    Ok(Embedding::new(embedding))
-                })
-                .collect()
-        }
-        .boxed()
-    }
-
-    fn batch_size(&self) -> usize {
-        2048
-    }
-}