fix clippy

python/src/config.rs

@@ -11,6 +11,7 @@ pub struct TextEmbedConfig {
     pub inner: embed_anything::config::TextEmbedConfig,
 }
 
+#[allow(clippy::too_many_arguments)]
 #[pymethods]
 impl TextEmbedConfig {
     #[new]
@@ -44,7 +45,7 @@ impl TextEmbedConfig {
                 .with_buffer_size(buffer_size.unwrap_or(100))
                 .with_splitting_strategy(strategy.unwrap_or(SplittingStrategy::Sentence))
                 .with_semantic_encoder(semantic_encoder)
-                .with_ocr(use_ocr.unwrap_or(false), tesseract_path)
+                .with_ocr(use_ocr.unwrap_or(false), tesseract_path),
         }
     }
 

python/src/lib.rs

@@ -203,9 +203,7 @@ impl EmbeddingModel {
                 let model_id = model_id.unwrap_or("jinaai/jina-embeddings-v2-small-en");
                 let model = Embedder::Text(TextEmbedder::Jina(Box::new(
                     embed_anything::embeddings::local::jina::JinaEmbedder::new(
-                        model_id,
-                        revision,
-                        token,
+                        model_id, revision, token,
                     )
                     .unwrap(),
                 )));
@@ -286,10 +284,12 @@ impl EmbeddingModel {
             Some(Dtype::F32) => Some(embed_anything::Dtype::F32),
             None => None,
         };
-        let model_name = model_name.map(|model_name| embed_anything::embeddings::local::text_embedding::ONNXModel::from_str(
-                    &model_name.to_string(),
-                )
-                .unwrap());
+        let model_name = model_name.map(|model_name| {
+            embed_anything::embeddings::local::text_embedding::ONNXModel::from_str(
+                &model_name.to_string(),
+            )
+            .unwrap()
+        });
         match model {
             WhichModel::Bert => {
                 let model = Embedder::Text(TextEmbedder::Bert(Box::new(

rust/examples/audio.rs

@@ -1,7 +1,7 @@
 use std::sync::Arc;
 
 use embed_anything::{
-    config::TextEmbedConfig, emb_audio, embeddings::embed:: EmbedderBuilder,
+    config::TextEmbedConfig, emb_audio, embeddings::embed::EmbedderBuilder,
     file_processor::audio::audio_processor::AudioDecoderModel, text_loader::SplittingStrategy,
 };
 

rust/examples/bert.rs

@@ -9,15 +9,17 @@ use std::{path::PathBuf, time::Instant};
 
 #[tokio::main]
 async fn main() {
-    let model = Arc::new(EmbedderBuilder::new()
-        .model_architecture("modernbert")
-        .model_id(Some("nomic-ai/modernbert-embed-base"))
-        .revision(None)
-        .token(None)
-        .dtype(Some(Dtype::F16))
-        .from_pretrained_hf()
-        .unwrap());
-
+    let model = Arc::new(
+        EmbedderBuilder::new()
+            .model_architecture("modernbert")
+            .model_id(Some("nomic-ai/modernbert-embed-base"))
+            .revision(None)
+            .token(None)
+            .dtype(Some(Dtype::F16))
+            .from_pretrained_hf()
+            .unwrap(),
+    );
+
     let config = TextEmbedConfig::default()
         .with_chunk_size(256, Some(0.3))
         .with_batch_size(32)

rust/examples/ort_models.rs

@@ -13,10 +13,10 @@ use std::time::Instant;
 async fn main() -> Result<(), anyhow::Error> {
     let model = Arc::new(
         EmbedderBuilder::new()
-        .model_architecture("bert")
-        .onnx_model_id(Some(ONNXModel::ModernBERTBase))
-        .from_pretrained_onnx()
-        .unwrap()
+            .model_architecture("bert")
+            .onnx_model_id(Some(ONNXModel::ModernBERTBase))
+            .from_pretrained_onnx()
+            .unwrap(),
     );
 
     let config = TextEmbedConfig::default()

rust/examples/reranker.rs

@@ -1,4 +1,3 @@
-
 #[cfg(feature = "ort")]
 fn main() {
     use embed_anything::Dtype;

rust/examples/web_embed.rs

@@ -4,7 +4,7 @@ use candle_core::Tensor;
 use embed_anything::{
     config::TextEmbedConfig,
     embed_query, embed_webpage,
-    embeddings::embed::{EmbedData,  EmbedderBuilder},
+    embeddings::embed::{EmbedData, EmbedderBuilder},
     text_loader::SplittingStrategy,
 };
 

rust/src/config.rs

@@ -71,6 +71,7 @@ impl Default for TextEmbedConfig {
     }
 }
 
+#[allow(clippy::too_many_arguments)]
 impl TextEmbedConfig {
     pub fn new(
         chunk_size: Option<usize>,
@@ -128,9 +129,9 @@ impl TextEmbedConfig {
         self
     }
 
-    /// Use this to do OCR on the documents to extract text. 
-    /// Set the path to None if you want to use the default path with tesseract installed on your system. 
-    /// You can check if tesseract is installed by running tesseract in your command line. 
+    /// Use this to do OCR on the documents to extract text.
+    /// Set the path to None if you want to use the default path with tesseract installed on your system.
+    /// You can check if tesseract is installed by running tesseract in your command line.
     /// If you want to use a custom path, you can set the path to the path of the tesseract executable.
     pub fn with_ocr(mut self, use_ocr: bool, tesseract_path: Option<&str>) -> Self {
         self.use_ocr = Some(use_ocr);

rust/src/embeddings/embed.rs

@@ -127,19 +127,30 @@ impl TextEmbedder {
         dtype: Option<Dtype>,
     ) -> Result<Self, anyhow::Error> {
         match model {
-            "jina" | "Jina" => Ok(Self::Jina(Box::new(JinaEmbedder::new(model_id, revision, token)?))),
+            "jina" | "Jina" => Ok(Self::Jina(Box::new(JinaEmbedder::new(
+                model_id, revision, token,
+            )?))),
 
             "Bert" | "bert" => Ok(Self::Bert(Box::new(BertEmbedder::new(
                 model_id.to_string(),
                 revision.map(|s| s.to_string()),
                 token,
             )?))),
-            "sparse-bert" | "SparseBert" | "SPARSE-BERT" => Ok(Self::Bert(Box::new(
-                SparseBertEmbedder::new(model_id.to_string(), revision.map(|s| s.to_string()), token)?,
-            ))),
-            "modernbert" | "ModernBert" | "MODERNBERT" => Ok(Self::ModernBert(Box::new(
-                ModernBertEmbedder::new(model_id.to_string(), revision.map(|s| s.to_string()), token, dtype)?,
-            ))),
+            "sparse-bert" | "SparseBert" | "SPARSE-BERT" => {
+                Ok(Self::Bert(Box::new(SparseBertEmbedder::new(
+                    model_id.to_string(),
+                    revision.map(|s| s.to_string()),
+                    token,
+                )?)))
+            }
+            "modernbert" | "ModernBert" | "MODERNBERT" => {
+                Ok(Self::ModernBert(Box::new(ModernBertEmbedder::new(
+                    model_id.to_string(),
+                    revision.map(|s| s.to_string()),
+                    token,
+                    dtype,
+                )?)))
+            }
             _ => Err(anyhow::anyhow!("Model not supported")),
         }
     }
@@ -313,7 +324,7 @@ impl VisionEmbedder {
 ///     .from_pretrained_hf()
 ///     .unwrap();
 /// ```
-/// 
+///
 /// ### Cloud Embedding Model
 /// ```rust
 /// use embed_anything::embeddings::embed::EmbedderBuilder;
@@ -324,7 +335,7 @@ impl VisionEmbedder {
 ///     .from_pretrained_cloud()
 ///     .unwrap();
 /// ```
-/// 
+///
 /// ### ONNX Embedding Model
 /// ```rust,ignore
 /// use embed_anything::embeddings::embed::EmbedderBuilder;
@@ -345,7 +356,7 @@ pub struct EmbedderBuilder {
     // Either HF Model ID or the Cloud Model that youu want to use
     model_id: Option<String>,
     revision: Option<String>,
-    // The Hugging Face token 
+    // The Hugging Face token
     token: Option<String>,
     // The API key for the cloud model
     api_key: Option<String>,
@@ -509,20 +520,24 @@ impl Embedder {
                 token,
                 dtype,
             )?)),
-            "sparse-bert" | "SparseBert" | "SPARSE-BERT" => Ok(Self::Text(TextEmbedder::from_pretrained_hf(
-                model_architecture,
-                model_id,
-                revision,
-                token,
-                dtype,
-            )?)),
-            "modernbert" | "ModernBert" | "MODERNBERT" => Ok(Self::Text(TextEmbedder::from_pretrained_hf(
-                model_architecture,
-                model_id,
-                revision,
-                token,
-                dtype,
-            )?)),
+            "sparse-bert" | "SparseBert" | "SPARSE-BERT" => {
+                Ok(Self::Text(TextEmbedder::from_pretrained_hf(
+                    model_architecture,
+                    model_id,
+                    revision,
+                    token,
+                    dtype,
+                )?))
+            }
+            "modernbert" | "ModernBert" | "MODERNBERT" => {
+                Ok(Self::Text(TextEmbedder::from_pretrained_hf(
+                    model_architecture,
+                    model_id,
+                    revision,
+                    token,
+                    dtype,
+                )?))
+            }
             _ => Err(anyhow::anyhow!("Model not supported")),
         }
     }

rust/src/embeddings/local/bert.rs

@@ -65,11 +65,7 @@ impl Default for BertEmbedder {
     }
 }
 impl BertEmbedder {
-    pub fn new(
-        model_id: String,
-        revision: Option<String>,
-        token: Option<&str>,
-    ) -> Result<Self, E> {
+    pub fn new(model_id: String, revision: Option<String>, token: Option<&str>) -> Result<Self, E> {
         let model_info = get_model_info_by_hf_id(&model_id);
         let pooling = match model_info {
             Some(info) => info

rust/src/embeddings/local/colbert.rs

@@ -18,9 +18,9 @@ use rayon::{iter::ParallelIterator, slice::ParallelSlice};
 use tokenizers::{PaddingParams, Tokenizer, TruncationParams};
 
 use crate::embeddings::{
-        embed::EmbeddingResult,
-        utils::{get_attention_mask_ndarray, tokenize_batch_ndarray},
-    };
+    embed::EmbeddingResult,
+    utils::{get_attention_mask_ndarray, tokenize_batch_ndarray},
+};
 
 use super::bert::{BertEmbed, TokenizerConfig};
 

rust/src/embeddings/local/mod.rs

@@ -7,10 +7,10 @@ pub mod colpali;
 pub mod colpali_ort;
 pub mod jina;
 pub mod model_info;
-pub mod pooling;
-pub mod text_embedding;
-#[cfg(feature = "ort")]
-pub mod ort_jina;
+pub mod modernbert;
 #[cfg(feature = "ort")]
 pub mod ort_bert;
-pub mod modernbert;
+#[cfg(feature = "ort")]
+pub mod ort_jina;
+pub mod pooling;
+pub mod text_embedding;

rust/src/embeddings/local/modernbert.rs

@@ -1,19 +1,23 @@
 use crate::{
-    embeddings::{normalize_l2, utils::{get_attention_mask, tokenize_batch}},
-    models::modernbert::{Config, ModernBert}, Dtype,
+    embeddings::{
+        normalize_l2,
+        utils::{get_attention_mask, tokenize_batch},
+    },
+    models::modernbert::{Config, ModernBert},
+    Dtype,
 };
 use anyhow::Error as E;
 use candle_core::{DType, Device, Tensor};
 use candle_nn::VarBuilder;
 use hf_hub::{api::sync::ApiBuilder, Repo};
 use tokenizers::{PaddingParams, Tokenizer, TruncationParams};
 
-use crate::{
-    embeddings::{embed::EmbeddingResult, select_device},
-    models::bert::DTYPE,
-};
+use crate::embeddings::{embed::EmbeddingResult, select_device};
 
-use super::{bert::BertEmbed, pooling::{ModelOutput, Pooling}};
+use super::{
+    bert::BertEmbed,
+    pooling::{ModelOutput, Pooling},
+};
 pub struct ModernBertEmbedder {
     pub model: ModernBert,
     pub tokenizer: Tokenizer,
@@ -27,7 +31,12 @@ impl Default for ModernBertEmbedder {
     }
 }
 impl ModernBertEmbedder {
-    pub fn new(model_id: String, revision: Option<String>, token: Option<&str>, dtype: Option<Dtype>) -> Result<Self, E> {
+    pub fn new(
+        model_id: String,
+        revision: Option<String>,
+        token: Option<&str>,
+        dtype: Option<Dtype>,
+    ) -> Result<Self, E> {
         let (config_filename, tokenizer_filename, weights_filename) = {
             let api = ApiBuilder::new()
                 .with_token(token.map(|s| s.to_string()))
@@ -83,7 +92,6 @@ impl ModernBertEmbedder {
             _ => DType::F32,
         };
         let vb = if weights_filename.ends_with("model.safetensors") {
-
             unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], dtype, &device)? }
         } else {
             println!("Can't find model.safetensors, loading from pytorch_model.bin");
@@ -112,10 +120,15 @@ impl BertEmbed for ModernBertEmbedder {
         let mut encodings: Vec<EmbeddingResult> = Vec::new();
 
         for mini_text_batch in text_batch.chunks(batch_size) {
-            let token_ids =
-                tokenize_batch(&self.tokenizer, mini_text_batch, &self.device).unwrap();
-            let attention_mask = get_attention_mask(&self.tokenizer, mini_text_batch, &self.device).unwrap();
-            let embeddings: Tensor = self.model.forward(&token_ids, &attention_mask).unwrap().to_dtype(DType::F32).unwrap();
+            let token_ids = tokenize_batch(&self.tokenizer, mini_text_batch, &self.device).unwrap();
+            let attention_mask =
+                get_attention_mask(&self.tokenizer, mini_text_batch, &self.device).unwrap();
+            let embeddings: Tensor = self
+                .model
+                .forward(&token_ids, &attention_mask)
+                .unwrap()
+                .to_dtype(DType::F32)
+                .unwrap();
             let pooled_output = self
                 .pooling
                 .pool(&ModelOutput::Tensor(embeddings.clone()))

rust/src/embeddings/local/ort_bert.rs

@@ -2,12 +2,13 @@ use super::bert::{BertEmbed, TokenizerConfig};
 use super::pooling::{ModelOutput, Pooling};
 use super::text_embedding::ONNXModel;
 use crate::embeddings::embed::EmbeddingResult;
+use crate::embeddings::local::text_embedding::models_map;
 use crate::embeddings::utils::{
     get_attention_mask_ndarray, get_type_ids_ndarray, tokenize_batch_ndarray,
 };
-use crate::embeddings::local::text_embedding::models_map;
 
 use crate::Dtype;
+use anyhow::Error as E;
 use hf_hub::api::sync::Api;
 use hf_hub::Repo;
 use ndarray::prelude::*;
@@ -17,7 +18,6 @@ use ort::session::Session;
 use ort::value::Value;
 use rayon::prelude::*;
 use tokenizers::{PaddingParams, Tokenizer, TruncationParams};
-use anyhow::Error as E;
 
 #[derive(Debug)]
 pub struct OrtBertEmbedder {