Remove unwraps

milli/src/update/index_documents/extract/mod.rs

@@ -242,11 +242,9 @@ fn send_original_documents_data(
     let request_threads = rayon::ThreadPoolBuilder::new()
         .num_threads(crate::vector::REQUEST_PARALLELISM)
         .thread_name(|index| format!("embedding-request-{index}"))
-        .build()
+        .build()?;
-        .unwrap();
 
     rayon::spawn(move || {
-        /// FIXME: unwrap
         for (name, (embedder, prompt)) in embedders {
             let result = extract_vector_points(
                 documents_chunk_cloned.clone(),

milli/src/vector/error.rs

@@ -52,8 +52,6 @@ pub enum EmbedErrorKind {
     ModelForward(candle_core::Error),
     #[error("attempt to embed the following text in a configuration where embeddings must be user provided: {0:?}")]
     ManualEmbed(String),
-    #[error("could not initialize asynchronous runtime: {0}")]
-    OpenAiRuntimeInit(std::io::Error),
     #[error("model not found. Meilisearch will not automatically download models from the Ollama library, please pull the model manually: {0:?}")]
     OllamaModelNotFoundError(Option<String>),
     #[error("error deserialization the response body as JSON: {0}")]
@@ -76,6 +74,10 @@ pub enum EmbedErrorKind {
     RestOtherStatusCode(u16, Option<String>),
     #[error("could not reach embedding server: {0}")]
     RestNetwork(ureq::Transport),
+    #[error("was expected '{}' to be an object in query '{0}'", .1.join("."))]
+    RestNotAnObject(serde_json::Value, Vec<String>),
+    #[error("while embedding tokenized, was expecting embeddings of dimension `{0}`, got embeddings of dimensions `{1}`")]
+    OpenAiUnexpectedDimension(usize, usize),
 }
 
 impl EmbedError {
@@ -174,6 +176,20 @@ impl EmbedError {
     pub(crate) fn rest_network(transport: ureq::Transport) -> EmbedError {
         Self { kind: EmbedErrorKind::RestNetwork(transport), fault: FaultSource::Runtime }
     }
+
+    pub(crate) fn rest_not_an_object(
+        query: serde_json::Value,
+        input_path: Vec<String>,
+    ) -> EmbedError {
+        Self { kind: EmbedErrorKind::RestNotAnObject(query, input_path), fault: FaultSource::User }
+    }
+
+    pub(crate) fn openai_unexpected_dimension(expected: usize, got: usize) -> EmbedError {
+        Self {
+            kind: EmbedErrorKind::OpenAiUnexpectedDimension(expected, got),
+            fault: FaultSource::Runtime,
+        }
+    }
 }
 
 #[derive(Debug, thiserror::Error)]

milli/src/vector/openai.rs

@@ -210,16 +210,19 @@ impl Embedder {
             while tokens.len() > max_token_count {
                 let window = &tokens[..max_token_count];
                 let embedding = self.rest_embedder.embed_tokens(window)?;
-                /// FIXME: unwrap
+                embeddings_for_prompt.append(embedding.into_inner()).map_err(|got| {
-                embeddings_for_prompt.append(embedding.into_inner()).unwrap();
+                    EmbedError::openai_unexpected_dimension(self.dimensions(), got.len())
+                })?;
 
                 tokens = &tokens[max_token_count - OVERLAP_SIZE..];
             }
 
             // end of text
             let embedding = self.rest_embedder.embed_tokens(tokens)?;
-            /// FIXME: unwrap
+
-            embeddings_for_prompt.append(embedding.into_inner()).unwrap();
+            embeddings_for_prompt.append(embedding.into_inner()).map_err(|got| {
+                EmbedError::openai_unexpected_dimension(self.dimensions(), got.len())
+            })?;
 
             all_embeddings.push(embeddings_for_prompt);
         }

milli/src/vector/rest.rs

@@ -189,19 +189,29 @@ where
         [input] => {
             let mut body = options.query.clone();
 
-            /// FIXME unwrap
+            body.as_object_mut()
-            body.as_object_mut().unwrap().insert(input.clone(), input_value);
+                .ok_or_else(|| {
+                    EmbedError::rest_not_an_object(
+                        options.query.clone(),
+                        options.input_field.clone(),
+                    )
+                })?
+                .insert(input.clone(), input_value);
             body
         }
         [path @ .., input] => {
             let mut body = options.query.clone();
 
-            /// FIXME unwrap
             let mut current_value = &mut body;
             for component in path {
                 current_value = current_value
                     .as_object_mut()
-                    .unwrap()
+                    .ok_or_else(|| {
+                        EmbedError::rest_not_an_object(
+                            options.query.clone(),
+                            options.input_field.clone(),
+                        )
+                    })?
                     .entry(component.clone())
                     .or_insert(serde_json::json!({}));
             }