HuggingFace: Clearer error message when a model is not supported

milli/src/vector/error.rs

@@ -217,16 +217,41 @@ impl NewEmbedderError {
     }
 
     pub fn deserialize_config(
+        model_name: String,
         config: String,
         config_filename: PathBuf,
         inner: serde_json::Error,
     ) -> NewEmbedderError {
-        let deserialize_config = DeserializeConfig { config, filename: config_filename, inner };
+        match serde_json::from_str(&config) {
+            Ok(value) => {
+                let value: serde_json::Value = value;
+                let architectures = match value.get("architectures") {
+                    Some(serde_json::Value::Array(architectures)) => architectures
+                        .iter()
+                        .filter_map(|value| match value {
+                            serde_json::Value::String(s) => Some(s.to_owned()),
+                            _ => None,
+                        })
+                        .collect(),
+                    _ => vec![],
+                };
+
+                let unsupported_model = UnsupportedModel { model_name, inner, architectures };
+                Self {
+                    kind: NewEmbedderErrorKind::UnsupportedModel(unsupported_model),
+                    fault: FaultSource::User,
+                }
+            }
+            Err(error) => {
+                let deserialize_config =
+                    DeserializeConfig { model_name, filename: config_filename, inner: error };
                 Self {
                     kind: NewEmbedderErrorKind::DeserializeConfig(deserialize_config),
                     fault: FaultSource::Runtime,
                 }
             }
+        }
+    }
 
     pub fn open_tokenizer(
         tokenizer_filename: PathBuf,
@@ -252,7 +277,7 @@ impl NewEmbedderError {
     }
 
     pub fn safetensor_weight(inner: candle_core::Error) -> Self {
-        Self { kind: NewEmbedderErrorKind::PytorchWeight(inner), fault: FaultSource::Runtime }
+        Self { kind: NewEmbedderErrorKind::SafetensorWeight(inner), fault: FaultSource::Runtime }
     }
 
     pub fn load_model(inner: candle_core::Error) -> Self {
@@ -275,13 +300,26 @@ pub struct OpenConfig {
 }
 
 #[derive(Debug, thiserror::Error)]
-#[error("could not deserialize config at {filename}: {inner}. Config follows:\n{config}")]
+#[error("for model '{model_name}', could not deserialize config at {filename} as JSON: {inner}")]
 pub struct DeserializeConfig {
-    pub config: String,
+    pub model_name: String,
     pub filename: PathBuf,
     pub inner: serde_json::Error,
 }
 
+#[derive(Debug, thiserror::Error)]
+#[error("model `{model_name}` appears to be unsupported{}\n  - inner error: {inner}",
+if architectures.is_empty() {
+    "\n  - Note: only models with architecture \"BertModel\" are supported.".to_string()
+} else {
+    format!("\n  - Note: model has declared architectures `{architectures:?}`, only models with architecture `\"BertModel\"` are supported.")
+})]
+pub struct UnsupportedModel {
+    pub model_name: String,
+    pub inner: serde_json::Error,
+    pub architectures: Vec<String>,
+}
+
 #[derive(Debug, thiserror::Error)]
 #[error("could not open tokenizer at {filename}: {inner}")]
 pub struct OpenTokenizer {
@@ -298,6 +336,8 @@ pub enum NewEmbedderErrorKind {
     #[error(transparent)]
     DeserializeConfig(DeserializeConfig),
     #[error(transparent)]
+    UnsupportedModel(UnsupportedModel),
+    #[error(transparent)]
     OpenTokenizer(OpenTokenizer),
     #[error("could not build weights from Pytorch weights: {0}")]
     PytorchWeight(candle_core::Error),

milli/src/vector/hf.rs

@@ -103,7 +103,12 @@ impl Embedder {
         let config = std::fs::read_to_string(&config_filename)
             .map_err(|inner| NewEmbedderError::open_config(config_filename.clone(), inner))?;
         let config: Config = serde_json::from_str(&config).map_err(|inner| {
-            NewEmbedderError::deserialize_config(config, config_filename, inner)
+            NewEmbedderError::deserialize_config(
+                options.model.clone(),
+                config,
+                config_filename,
+                inner,
+            )
         })?;
         let mut tokenizer = Tokenizer::from_file(&tokenizer_filename)
             .map_err(|inner| NewEmbedderError::open_tokenizer(tokenizer_filename, inner))?;