Add LM Studio support to the Assistant (#23097)

#### Release Notes:

- Added support for [LM Studio](https://lmstudio.ai/) to the Assistant.

#### Quick demo:


https://github.com/user-attachments/assets/af58fc13-1abc-4898-9747-3511016da86a

#### Future enhancements:
- wire up tool calling (new in [LM Studio
0.3.6](https://lmstudio.ai/blog/lmstudio-v0.3.6))

---------

Co-authored-by: Marshall Bowers <elliott.codes@gmail.com>

crates/semantic_index/src/embedding.rs

@@ -1,8 +1,10 @@
 mod cloud;
+mod lmstudio;
 mod ollama;
 mod open_ai;
 
 pub use cloud::*;
+pub use lmstudio::*;
 pub use ollama::*;
 pub use open_ai::*;
 use sha2::{Digest, Sha256};

crates/semantic_index/src/embedding/lmstudio.rs

@@ -0,0 +1,70 @@
+use anyhow::{Context as _, Result};
+use futures::{future::BoxFuture, AsyncReadExt as _, FutureExt};
+use http_client::HttpClient;
+use serde::{Deserialize, Serialize};
+use std::sync::Arc;
+
+use crate::{Embedding, EmbeddingProvider, TextToEmbed};
+
+pub enum LmStudioEmbeddingModel {
+    NomicEmbedText,
+}
+
+pub struct LmStudioEmbeddingProvider {
+    client: Arc<dyn HttpClient>,
+    model: LmStudioEmbeddingModel,
+}
+
+#[derive(Serialize)]
+struct LmStudioEmbeddingRequest {
+    model: String,
+    prompt: String,
+}
+
+#[derive(Deserialize)]
+struct LmStudioEmbeddingResponse {
+    embedding: Vec<f32>,
+}
+
+impl LmStudioEmbeddingProvider {
+    pub fn new(client: Arc<dyn HttpClient>, model: LmStudioEmbeddingModel) -> Self {
+        Self { client, model }
+    }
+}
+
+impl EmbeddingProvider for LmStudioEmbeddingProvider {
+    fn embed<'a>(&'a self, texts: &'a [TextToEmbed<'a>]) -> BoxFuture<'a, Result<Vec<Embedding>>> {
+        let model = match self.model {
+            LmStudioEmbeddingModel::NomicEmbedText => "nomic-embed-text",
+        };
+
+        futures::future::try_join_all(texts.iter().map(|to_embed| {
+            let request = LmStudioEmbeddingRequest {
+                model: model.to_string(),
+                prompt: to_embed.text.to_string(),
+            };
+
+            let request = serde_json::to_string(&request).unwrap();
+
+            async {
+                let response = self
+                    .client
+                    .post_json("http://localhost:1234/api/v0/embeddings", request.into())
+                    .await?;
+
+                let mut body = String::new();
+                response.into_body().read_to_string(&mut body).await?;
+
+                let response: LmStudioEmbeddingResponse =
+                    serde_json::from_str(&body).context("Unable to parse response")?;
+
+                Ok(Embedding::new(response.embedding))
+            }
+        }))
+        .boxed()
+    }
+
+    fn batch_size(&self) -> usize {
+        256
+    }
+}