diff --git a/server/src/bin/ingestion-worker.rs b/server/src/bin/ingestion-worker.rs
index 05c93256c..33262e989 100644
--- a/server/src/bin/ingestion-worker.rs
+++ b/server/src/bin/ingestion-worker.rs
@@ -26,7 +26,7 @@ use trieve_server::operators::dataset_operator::get_dataset_by_id_query;
 use trieve_server::operators::event_operator::create_event_query;
 use trieve_server::operators::group_operator::get_groups_from_group_ids_query;
 use trieve_server::operators::model_operator::{
-    create_embedding, create_embeddings, get_bm25_embeddings, get_sparse_vectors,
+    get_bm25_embeddings, get_dense_vector, get_dense_vectors, get_sparse_vectors,
 };
 use trieve_server::operators::parse_operator::{
     average_embeddings, coarse_doc_chunker, convert_html_to_text,
@@ -557,7 +557,7 @@ pub async fn bulk_upload_chunks(
 
     let embedding_vectors = match dataset_config.SEMANTIC_ENABLED {
         true => {
-            let vectors = match create_embeddings(
+            let vectors = match get_dense_vectors(
                 content_and_boosts
                     .iter()
                     .map(|(content, _, distance_boost)| (content.clone(), distance_boost.clone()))
@@ -845,7 +845,7 @@ async fn upload_chunk(
                 true => {
                     let chunks = coarse_doc_chunker(content.clone(), None, false, 20);
 
-                    let embeddings = create_embeddings(
+                    let embeddings = get_dense_vectors(
                         chunks
                             .iter()
                             .map(|chunk| (chunk.clone(), payload.chunk.distance_phrase.clone()))
@@ -859,7 +859,7 @@ async fn upload_chunk(
                     average_embeddings(embeddings)?
                 }
                 false => {
-                    let embedding_vectors = create_embeddings(
+                    let embedding_vectors = get_dense_vectors(
                         vec![(content.clone(), payload.chunk.distance_phrase.clone())],
                         "doc",
                         dataset_config.clone(),
@@ -1062,7 +1062,7 @@ async fn update_chunk(
 
     let embedding_vector = match dataset_config.SEMANTIC_ENABLED {
         true => {
-            let embedding = create_embedding(
+            let embedding = get_dense_vector(
                 content.to_string(),
                 payload.distance_phrase,
                 "doc",
diff --git a/server/src/operators/model_operator.rs b/server/src/operators/model_operator.rs
index 1cb4b52f5..32aa37694 100644
--- a/server/src/operators/model_operator.rs
+++ b/server/src/operators/model_operator.rs
@@ -26,7 +26,7 @@ pub struct EmbeddingParameters {
 }
 
 #[tracing::instrument]
-pub async fn create_embedding(
+pub async fn get_dense_vector(
     message: String,
     distance_phrase: Option<DistancePhrase>,
     embed_type: &str,
@@ -35,11 +35,11 @@ pub async fn create_embedding(
     let parent_span = sentry::configure_scope(|scope| scope.get_span());
     let transaction: sentry::TransactionOrSpan = match &parent_span {
         Some(parent) => parent
-            .start_child("create_embedding", "Create semantic dense embedding")
+            .start_child("get_dense_vector", "Create semantic dense embedding")
             .into(),
         None => {
             let ctx = sentry::TransactionContext::new(
-                "create_embedding",
+                "get_dense_vector",
                 "Create semantic dense embedding",
             );
             sentry::start_transaction(ctx).into()
@@ -266,8 +266,8 @@ pub async fn get_sparse_vector(
 }
 
 #[tracing::instrument]
-pub async fn create_embeddings(
-    content_and_boosts: Vec<(String, Option<DistancePhrase>)>,
+pub async fn get_dense_vectors(
+    content_and_distances: Vec<(String, Option<DistancePhrase>)>,
     embed_type: &str,
     dataset_config: DatasetConfiguration,
     reqwest_client: reqwest::Client,
@@ -275,11 +275,11 @@ pub async fn create_embeddings(
     let parent_span = sentry::configure_scope(|scope| scope.get_span());
     let transaction: sentry::TransactionOrSpan = match &parent_span {
         Some(parent) => parent
-            .start_child("create_embedding", "Create semantic dense embedding")
+            .start_child("get_dense_vector", "Create semantic dense embedding")
             .into(),
         None => {
             let ctx = sentry::TransactionContext::new(
-                "create_embedding",
+                "get_dense_vector",
                 "Create semantic dense embedding",
             );
             sentry::start_transaction(ctx).into()
@@ -323,31 +323,31 @@ pub async fn create_embeddings(
             embedding_api_key.to_string()
         };
 
-    let thirty_message_groups = content_and_boosts.chunks(30);
+    let (contents, distance_phrases): (Vec<_>, Vec<_>) =
+        content_and_distances.clone().into_iter().unzip();
+    let thirty_content_groups = contents.chunks(30);
 
-    let vec_futures: Vec<_> = thirty_message_groups
+    let filtered_distances_with_index = distance_phrases
+        .clone()
+        .iter()
         .enumerate()
-        .map(|(i, combined_messages)| {
-            let messages = combined_messages
-                .iter()
-                .map(|(x, _)| x)
-                .cloned()
-                .collect::<Vec<String>>();
-
-            let boost_phrase_and_index = combined_messages
-                .iter()
-                .enumerate()
-                .filter_map(|(i, (_, y))| y.clone().map(|phrase| (i, phrase)))
-                .collect::<Vec<(usize, DistancePhrase)>>();
+        .filter_map(|(index, distance_phrase)| {
+            distance_phrase
+                .clone()
+                .map(|distance_phrase| (index, distance_phrase))
+        })
+        .collect::<Vec<(usize, DistancePhrase)>>();
+    let thirty_filterted_distances_with_indices = filtered_distances_with_index.chunks(30);
 
-            let boost_phrases = combined_messages
+    let vec_distance_futures: Vec<_> = thirty_filterted_distances_with_indices
+        .map(|thirty_distances| {
+            let distance_phrases = thirty_distances
                 .iter()
-                .filter_map(|(_, y)| y.clone().map(|x| x.phrase.clone()))
+                .map(|(_, x)| x.phrase.clone())
                 .collect::<Vec<String>>();
 
-            let clipped_messages = messages
+            let clipped_messages = distance_phrases
                 .iter()
-                .chain(boost_phrases.iter())
                 .map(|message| {
                     if message.len() > 5000 {
                         message.chars().take(12000).collect()
@@ -406,65 +406,162 @@ pub async fn create_embeddings(
                         )
                     })?;
 
-            let mut vectors: Vec<Vec<f32>> = embeddings
-                .data
-                .into_iter()
-                .map(|x| match x.embedding {
-                    EmbeddingOutput::Float(v) => v.iter().map(|x| *x as f32).collect(),
-                    EmbeddingOutput::Base64(_) => {
-                        log::error!("Embedding server responded with Base64 and that is not currently supported for embeddings");
-                        vec![]
+                let vectors_and_boosts: Vec<(Vec<f32>, &(usize, DistancePhrase))> = embeddings
+                    .data
+                    .into_iter()
+                    .map(|x| match x.embedding {
+                        EmbeddingOutput::Float(v) => v.iter().map(|x| *x as f32).collect(),
+                        EmbeddingOutput::Base64(_) => {
+                            log::error!("Embedding server responded with Base64 and that is not currently supported for embeddings");
+                            vec![]
+                        }
+                    })
+                    .zip(thirty_distances)
+                    .collect();
+
+                    if vectors_and_boosts.iter().any(|x| x.0.is_empty()) {
+                        return Err(ServiceError::InternalServerError(
+                            "Embedding server responded with Base64 and that is not currently supported for embeddings".to_owned(),
+                        ));
                     }
-                })
-                .collect();
 
-                if vectors.iter().any(|x| x.is_empty()) {
-                    return Err(ServiceError::InternalServerError(
-                        "Embedding server responded with Base64 and that is not currently supported for embeddings".to_owned(),
-                    ));
-                }
+                Ok(vectors_and_boosts)
+            };
 
-            if !boost_phrase_and_index.is_empty() {
-                let boost_vectors = vectors
-                    .split_off(messages.len()).to_vec();
+            vectors_resp
+        })
+        .collect();
 
-                let mut vectors_sorted = vectors.clone();
-                for ((og_index, phrase), boost_vector) in boost_phrase_and_index.iter().zip(boost_vectors) {
-                    vectors_sorted[*og_index] = vectors_sorted[*og_index]
-                        .iter()
-                        .zip(boost_vector)
-                        .map(|(vector_elem, boost_vec_elem)| vector_elem + phrase.distance_factor * boost_vec_elem)
-                        .collect();
-                }
+    let vec_content_futures: Vec<_> = thirty_content_groups
+        .map(|messages| {
+            let clipped_messages = messages
+                .iter()
+                .map(|message| {
+                    if message.len() > 5000 {
+                        message.chars().take(12000).collect()
+                    } else {
+                        message.clone()
+                    }
+                })
+                .collect::<Vec<String>>();
 
-                return Ok((i, vectors_sorted));
-            }
+            let input = match embed_type {
+                "doc" => EmbeddingInput::StringArray(clipped_messages),
+                "query" => EmbeddingInput::String(
+                    format!(
+                        "{}{}",
+                        dataset_config.EMBEDDING_QUERY_PREFIX, &clipped_messages[0]
+                    )
+                    .to_string(),
+                ),
+                _ => EmbeddingInput::StringArray(clipped_messages),
+            };
 
-                Ok((i, vectors))
+            let parameters = EmbeddingParameters {
+                model: dataset_config.EMBEDDING_MODEL_NAME.to_string(),
+                input,
+                truncate: true,
             };
 
-            vectors_resp
-        })
+            let cur_client = reqwest_client.clone();
+            let url = embedding_base_url.clone();
+
+            let embedding_api_key = embedding_api_key.clone();
+
+                let vectors_resp = async move {
+                    let embeddings_resp = cur_client
+                    .post(&format!("{}/embeddings?api-version=2023-05-15", url))
+                    .header("Authorization", &format!("Bearer {}", &embedding_api_key.clone()))
+                    .header("api-key", &embedding_api_key.clone())
+                    .header("Content-Type", "application/json")
+                    .json(&parameters)
+                    .send()
+                    .await
+                    .map_err(|_| {
+                        ServiceError::BadRequest("Failed to send message to embedding server".to_string())
+                    })?
+                    .text()
+                    .await
+                    .map_err(|_| {
+                        ServiceError::BadRequest("Failed to get text from embeddings".to_string())
+                    })?;
+
+                    let embeddings: EmbeddingResponse = format_response(embeddings_resp.clone())
+                        .map_err(move |_e| {
+                            log::error!("Failed to format response from embeddings server {:?}", embeddings_resp);
+                            ServiceError::InternalServerError(
+                                format!("Failed to format response from embeddings server {:?}", embeddings_resp)
+                            )
+                        })?;
+
+                let vectors: Vec<Vec<f32>> = embeddings
+                    .data
+                    .into_iter()
+                    .map(|x| match x.embedding {
+                        EmbeddingOutput::Float(v) => v.iter().map(|x| *x as f32).collect(),
+                        EmbeddingOutput::Base64(_) => {
+                            log::error!("Embedding server responded with Base64 and that is not currently supported for embeddings");
+                            vec![]
+                        }
+                    })
+                    .collect();
+
+                    if vectors.iter().any(|x| x.is_empty()) {
+                        return Err(ServiceError::InternalServerError(
+                            "Embedding server responded with Base64 and that is not currently supported for embeddings".to_owned(),
+                        ));
+                    }
+                    Ok(vectors)
+                };
+
+                vectors_resp
+
+            })
         .collect();
 
-    let all_chunk_vectors: Vec<(usize, Vec<Vec<f32>>)> = futures::future::join_all(vec_futures)
+    let mut content_vectors: Vec<_> = futures::future::join_all(vec_content_futures)
         .await
         .into_iter()
-        .collect::<Result<Vec<(usize, Vec<Vec<f32>>)>, ServiceError>>()?;
+        .collect::<Result<Vec<_>, ServiceError>>()?
+        .into_iter()
+        .flatten()
+        .collect();
 
-    let mut vectors_sorted = vec![];
-    for index in 0..all_chunk_vectors.len() {
-        let (_, vectors_i) = all_chunk_vectors.iter().find(|(i, _)| *i == index).ok_or(
-            ServiceError::InternalServerError(
-                "Failed to get index i (this should never happen)".to_string(),
-            ),
-        )?;
+    let distance_vectors: Vec<_> = futures::future::join_all(vec_distance_futures)
+        .await
+        .into_iter()
+        .collect::<Result<Vec<_>, ServiceError>>()?
+        .into_iter()
+        .flatten()
+        .collect();
 
-        vectors_sorted.extend(vectors_i.clone());
+    if !distance_vectors.is_empty() {
+        content_vectors = content_vectors
+            .into_iter()
+            .enumerate()
+            .map(|(i, message)| {
+                let distance_vector = distance_vectors
+                    .iter()
+                    .find(|(_, (og_index, _))| *og_index == i);
+                match distance_vector {
+                    Some((distance_vec, (_, distance_phrase))) => {
+                        let distance_factor = distance_phrase.distance_factor;
+                        message
+                            .iter()
+                            .zip(distance_vec)
+                            .map(|(vec_elem, distance_elem)| {
+                                vec_elem + distance_factor * distance_elem
+                            })
+                            .collect()
+                    }
+                    None => message,
+                }
+            })
+            .collect();
     }
 
     transaction.finish();
-    Ok(vectors_sorted)
+    Ok(content_vectors)
 }
 
 #[derive(Debug, Serialize, Deserialize)]
@@ -492,31 +589,31 @@ pub struct CustomSparseEmbedData {
 
 #[tracing::instrument]
 pub async fn get_sparse_vectors(
-    messages: Vec<(String, Option<BoostPhrase>)>,
+    content_and_boosts: Vec<(String, Option<BoostPhrase>)>,
     embed_type: &str,
     reqwest_client: reqwest::Client,
 ) -> Result<Vec<Vec<(u32, f32)>>, ServiceError> {
-    if messages.is_empty() {
+    if content_and_boosts.is_empty() {
         return Err(ServiceError::BadRequest(
             "No messages to encode".to_string(),
         ));
     }
 
-    let contents = messages
+    let contents = content_and_boosts
         .clone()
         .into_iter()
         .map(|(x, _)| x)
         .collect::<Vec<String>>();
     let thirty_content_groups = contents.chunks(30);
 
-    let filtered_boosts_with_index = messages
+    let filtered_boosts_with_index = content_and_boosts
         .into_iter()
         .enumerate()
         .filter_map(|(i, (_, y))| y.map(|boost_phrase| (i, boost_phrase)))
         .collect::<Vec<(usize, BoostPhrase)>>();
-    let filtered_boosts_with_index_groups = filtered_boosts_with_index.chunks(30);
+    let thirty_filtered_boosts_with_indices = filtered_boosts_with_index.chunks(30);
 
-    let vec_boost_futures: Vec<_> = filtered_boosts_with_index_groups
+    let vec_boost_futures: Vec<_> = thirty_filtered_boosts_with_indices
         .enumerate()
         .map(|(i, thirty_boosts)| {
             let cur_client = reqwest_client.clone();
diff --git a/server/src/operators/search_operator.rs b/server/src/operators/search_operator.rs
index d1d5c6658..dbcb2a01b 100644
--- a/server/src/operators/search_operator.rs
+++ b/server/src/operators/search_operator.rs
@@ -7,7 +7,7 @@ use super::group_operator::{
     get_group_ids_from_tracking_ids_query, get_groups_from_group_ids_query,
 };
 use super::model_operator::{
-    create_embedding, cross_encoder, get_bm25_embeddings, get_sparse_vector,
+    cross_encoder, get_bm25_embeddings, get_dense_vector, get_sparse_vector,
 };
 use super::qdrant_operator::{
     count_qdrant_query, search_over_groups_query, GroupSearchResults, QdrantSearchQuery, VectorType,
@@ -1451,7 +1451,7 @@ async fn get_qdrant_vector(
                 ));
             }
             let embedding_vector =
-                create_embedding(data.query.clone(), None, "query", config.clone()).await?;
+                get_dense_vector(data.query.clone(), None, "query", config.clone()).await?;
             Ok(VectorType::Dense(embedding_vector))
         }
         SearchMethod::BM25 => {
@@ -1615,7 +1615,7 @@ pub async fn search_hybrid_chunks(
     let dataset_config = DatasetConfiguration::from_json(dataset.server_configuration.clone());
 
     let dense_vector_future =
-        create_embedding(data.query.clone(), None, "query", dataset_config.clone());
+        get_dense_vector(data.query.clone(), None, "query", dataset_config.clone());
 
     let sparse_vector_future = get_sparse_vector(parsed_query.query.clone(), "query");
 
@@ -1829,7 +1829,7 @@ pub async fn search_hybrid_groups(
     let dataset_config = DatasetConfiguration::from_json(dataset.server_configuration.clone());
 
     let dense_vector_future =
-        create_embedding(data.query.clone(), None, "query", dataset_config.clone());
+        get_dense_vector(data.query.clone(), None, "query", dataset_config.clone());
 
     let sparse_vector_future = get_sparse_vector(parsed_query.query.clone(), "query");
 
@@ -1981,7 +1981,7 @@ pub async fn semantic_search_over_groups(
     timer.add("start to create dense embedding vector");
 
     let embedding_vector =
-        create_embedding(data.query.clone(), None, "query", dataset_config.clone()).await?;
+        get_dense_vector(data.query.clone(), None, "query", dataset_config.clone()).await?;
 
     timer.add("computed dense embedding");
 
@@ -2162,7 +2162,7 @@ pub async fn hybrid_search_over_groups(
     timer.add("start to create dense embedding vector and sparse vector");
 
     let dense_embedding_vectors_future =
-        create_embedding(data.query.clone(), None, "query", dataset_config.clone());
+        get_dense_vector(data.query.clone(), None, "query", dataset_config.clone());
 
     let sparse_embedding_vector_future = get_sparse_vector(data.query.clone(), "query");