Remove Asunc

Cargo.toml

@@ -5,27 +5,59 @@ version = "0.1.14"
 edition = "2021"
 
 [dependencies]
+# Data Serialization
+serde = { version = "1.0.196", features = ["derive"] }
 serde_json = "1.0.112"
+
+# HTTP Client
 reqwest = { version = "0.12.2", features = ["json", "blocking"] }
-serde = {version = "1.0.196", features = ["derive"]}
-pdf-extract = "0.7.4"
+
+# Filesystem
 walkdir = "2.4.0"
+
+# Regular Expressions
 regex = "1.10.3"
+
+# Parallelism
 rayon = "1.8.1"
+
+# Image Processing
 image = "0.25.1"
-hf-hub = "0.3.2"
+
+# Natural Language Processing
 tokenizers = "0.15.2"
+
+# PDF Processing
+pdf-extract = "0.7.4"
+
+# Hugging Face Libraries
+hf-hub = "0.3.2"
 candle-nn = { git = "https://github.com/huggingface/candle.git", version = "0.5.0" }
 candle-transformers = { git = "https://github.com/huggingface/candle.git", version = "0.5.0" }
 candle-core = { git = "https://github.com/huggingface/candle.git", version = "0.5.0" }
+
+# Error Handling
 anyhow = "1.0.81"
-tokio = {version = "1.37.0", features=["rt-multi-thread", "macros"]}
+
+# Asynchronous Programming
+tokio = { version = "1.37.0", features = ["macros", "rt-multi-thread"] }
+
+# Python Interoperability
 pyo3 = { version = "0.21" }
-intel-mkl-src = {version = "0.8.1", optional = true }
+
+# Optional Dependency
+intel-mkl-src = { version = "0.8.1", optional = true }
+
+# Markdown Processing
 markdown-parser = "0.1.2"
 markdown_to_text = "1.0.0"
+
+# Web Scraping
 scraper = "0.19.0"
+
+# Text Processing
 text-cleaner = "0.1.0"
+url = "2.5.0"
 
 [dev-dependencies]
 tempdir = "0.3.7"

examples/bert.rs

@@ -3,7 +3,7 @@ use std::{path::PathBuf, time::Instant};
 
 fn main() {
     let now = Instant::now();
-    let out = embed_directory(PathBuf::from("test_files"), "Bert", Some(vec!["md".to_string()])).unwrap();
+    let out = embed_directory(PathBuf::from("test_files"), "Bert", Some(vec!["pdf".to_string()])).unwrap();
     println!("{:?}", out);
     let elapsed_time = now.elapsed();
     println!("Elapsed Time: {}", elapsed_time.as_secs_f32());

examples/web_embed.rs

@@ -3,12 +3,14 @@ use candle_core::Tensor;
 
 #[tokio::main]
 async fn main() {
-    let url = "https://en.wikipedia.org/wiki/Long_short-term_memory";
 
-    let website_processor = website_processor::WebsiteProcesor;
+    let start_time = std::time::Instant::now();
+    let url = "https://www.scrapingbee.com/blog/web-scraping-rust/";
+
+    let website_processor = website_processor::WebsiteProcessor;
     let webpage = website_processor.process_website(url).await.unwrap();
     let embeder = embed_anything::embedding_model::bert::BertEmbeder::default();
-    let embed_data = webpage.embed_webpage(&embeder).await.unwrap();
+    let embed_data = webpage.embed_webpage(&embeder).unwrap();
     let embeddings: Vec<Vec<f32>> = embed_data.iter().map(|data| data.embedding.clone()).collect();
 
     let embeddings = Tensor::from_vec(
@@ -17,8 +19,8 @@ async fn main() {
         &candle_core::Device::Cpu,
     ).unwrap();
 
-    let query = vec!["how to use lstm for nlp".to_string()];
-    let query_embedding: Vec<f32> = embeder.embed(&query, None).await.unwrap().iter().map(|data| data.embedding.clone()).flatten().collect();
+    let query = vec!["Rust for web scraping".to_string()];
+    let query_embedding: Vec<f32> = embeder.embed(&query, None).unwrap().iter().map(|data| data.embedding.clone()).flatten().collect();
 
     let query_embedding_tensor = Tensor::from_vec(
         query_embedding.clone(),
@@ -40,5 +42,6 @@ async fn main() {
     let data = &embed_data[max_similarity_index];
 
     println!("{:?}", data);
+    println!("Time taken: {:?}", start_time.elapsed());
 
 }

src/embedding_model/bert.rs

@@ -68,7 +68,7 @@ impl BertEmbeder {
         Ok(Tensor::stack(&token_ids, 0)?)
     }
 
-    pub async fn embed(&self, text_batch: &[String],metadata:Option<HashMap<String,String>>) -> Result<Vec<EmbedData>, reqwest::Error> {
+    pub fn embed(&self, text_batch: &[String],metadata:Option<HashMap<String,String>>) -> Result<Vec<EmbedData>, anyhow::Error> {
         let token_ids = self.tokenize_batch(text_batch, &self.model.device).unwrap();
         let token_type_ids = token_ids.zeros_like().unwrap();
         let embeddings = self.model.forward(&token_ids, &token_type_ids).unwrap();
@@ -89,7 +89,7 @@ impl Embed for BertEmbeder {
     fn embed(
         &self,
         text_batch: &[String],metadata: Option<HashMap<String,String>>
-    ) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>> {
+    ) -> Result<Vec<EmbedData>, anyhow::Error> {
         self.embed(text_batch, metadata)
     }
 }
@@ -99,7 +99,7 @@ impl TextEmbed for BertEmbeder {
         &self,
         text_batch: &[String],
         metadata: Option<HashMap<String,String>>
-    ) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>> {
+    ) -> Result<Vec<EmbedData>, anyhow::Error> {
         self.embed(text_batch, metadata)
     }
 }

src/embedding_model/clip.rs

@@ -191,7 +191,7 @@ impl EmbedImage for ClipEmbeder {
 }
 
 impl Embed for ClipEmbeder {
-    async fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) -> Result<Vec<EmbedData>, reqwest::Error> {
+    fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) -> Result<Vec<EmbedData>, anyhow::Error> {
         let (input_ids, _vec_seq) = ClipEmbeder::tokenize_sequences(
             Some(text_batch.to_vec()),
             &self.tokenizer,

src/embedding_model/embed.rs

@@ -60,12 +60,12 @@ pub enum Embeder {
 }
 
 impl Embeder {
-    pub async fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) -> Result<Vec<EmbedData>, reqwest::Error> {
+    pub fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) -> Result<Vec<EmbedData>, anyhow::Error> {
         match self {
-            Embeder::OpenAI(embeder) => TextEmbed::embed(embeder, text_batch, metadata).await,
-            Embeder::Jina(embeder) => TextEmbed::embed(embeder, text_batch, metadata).await,
-            Embeder::Clip(embeder) => Embed::embed(embeder, text_batch, metadata).await,
-            Embeder::Bert(embeder) => TextEmbed::embed(embeder, text_batch, metadata).await,
+            Embeder::OpenAI(embeder) => TextEmbed::embed(embeder, text_batch, metadata),
+            Embeder::Jina(embeder) => TextEmbed::embed(embeder, text_batch, metadata),
+            Embeder::Clip(embeder) => Embed::embed(embeder, text_batch, metadata),
+            Embeder::Bert(embeder) => TextEmbed::embed(embeder, text_batch, metadata),
         }
     }
 }
@@ -76,12 +76,12 @@ pub trait Embed {
         &self,
         text_batch: &[String],
         metadata: Option<HashMap<String, String>>,
-    ) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>>;
+    ) ->Result<Vec<EmbedData>, anyhow::Error>;
 
 }
 
 pub trait TextEmbed {
-    fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>>;
+    fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) ->  Result<Vec<EmbedData>, anyhow::Error>;
 }
 
 pub trait EmbedImage {

src/embedding_model/jina.rs

@@ -71,7 +71,7 @@ impl JinaEmbeder {
         Ok(Tensor::stack(&token_ids, 0)?)
     }
 
-    async fn embed(&self, text_batch: &[String], metadata:Option<HashMap<String, String>>) -> Result<Vec<EmbedData>, reqwest::Error> {
+    fn embed(&self, text_batch: &[String], metadata:Option<HashMap<String, String>>) -> Result<Vec<EmbedData>, anyhow::Error> {
         let token_ids = self.tokenize_batch(text_batch, &self.model.device).unwrap();
         let embeddings = self.model.forward(&token_ids).unwrap();
 
@@ -97,7 +97,7 @@ impl Embed for JinaEmbeder {
         &self,
         text_batch: &[String],
         metadata: Option<HashMap<String, String>>,
-    ) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>> {
+    ) -> Result<Vec<EmbedData>, anyhow::Error> {
         self.embed(text_batch, metadata)
     }
 }
@@ -107,7 +107,7 @@ impl TextEmbed for JinaEmbeder {
         &self,
         text_batch: &[String],
         metadata: Option<HashMap<String, String>>,
-    ) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>> {
+    ) -> Result<Vec<EmbedData>, anyhow::Error> {
         self.embed(text_batch, metadata)
     }
 }

src/embedding_model/openai.rs

@@ -22,7 +22,11 @@ impl Default for OpenAIEmbeder {
 }
 
 impl Embed for OpenAIEmbeder {
-    fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>> {
+    fn embed(
+        &self,
+        text_batch: &[String],
+        metadata: Option<HashMap<String, String>>,
+    ) -> Result<Vec<EmbedData>, anyhow::Error> {
         self.embed(text_batch, metadata)
     }
 }
@@ -32,7 +36,7 @@ impl TextEmbed for OpenAIEmbeder {
         &self,
         text_batch: &[String],
         metadata: Option<HashMap<String, String>>,
-    ) -> impl std::future::Future<Output = Result<Vec<EmbedData>, reqwest::Error>> {
+    ) -> Result<Vec<EmbedData>, anyhow::Error> {
         self.embed(text_batch, metadata)
     }
 }
@@ -47,28 +51,41 @@ impl OpenAIEmbeder {
         }
     }
 
-    async fn embed(&self, text_batch: &[String], metadata: Option<HashMap<String, String>>) -> Result<Vec<EmbedData>, reqwest::Error> {
+    fn embed(
+        &self,
+        text_batch: &[String],
+        metadata: Option<HashMap<String, String>>,
+    ) -> Result<Vec<EmbedData>, anyhow::Error> {
         let client = Client::new();
-
-        let response = client
-            .post(&self.url)
-            .header("Content-Type", "application/json")
-            .header("Authorization", format!("Bearer {}", self.api_key))
-            .json(&json!({
-                "input": text_batch,
-                "model": "text-embedding-3-small",
-            }))
-            .send()
-            .await?;
-
-        let data = response.json::<EmbedResponse>().await?;
-        println!("{:?}", data.usage);
+        let runtime = tokio::runtime::Builder::new_current_thread().enable_io()
+            .build()
+            .unwrap();
+
+        let data = runtime.block_on(async move {
+            let response = client
+                .post(&self.url)
+                .header("Content-Type", "application/json")
+                .header("Authorization", format!("Bearer {}", self.api_key))
+                .json(&json!({
+                    "input": text_batch,
+                    "model": "text-embedding-3-small",
+                }))
+                .send()
+                .await
+                .unwrap();
+
+            let data = response.json::<EmbedResponse>().await.unwrap();
+            println!("{:?}", data.usage);
+            data
+        });
 
         let emb_data = data
             .data
             .iter()
             .zip(text_batch)
-            .map(move |(data, text)| EmbedData::new(data.embedding.clone(), Some(text.clone()), metadata.clone()))
+            .map(move |(data, text)| {
+                EmbedData::new(data.embedding.clone(), Some(text.clone()), metadata.clone())
+            })
             .collect::<Vec<_>>();
 
         Ok(emb_data)
@@ -79,15 +96,14 @@ impl OpenAIEmbeder {
 mod tests {
     use super::*;
 
-    #[tokio::test]
-    async fn test_openai_embed() {
+    fn test_openai_embed() {
         let openai = OpenAIEmbeder::default();
         let text_batch = vec![
             "Once upon a time".to_string(),
             "The quick brown fox jumps over the lazy dog".to_string(),
         ];
 
-        let embeddings = openai.embed(&text_batch, None).await.unwrap();
+        let embeddings = openai.embed(&text_batch, None).unwrap();
         assert_eq!(embeddings.len(), 2);
     }
 }

src/file_embed.rs

@@ -60,8 +60,8 @@ impl FileEmbeder {
 
     }
 
-    pub async fn embed(&mut self, embeder: &Embeder, metadata: Option<HashMap< String, String>>) -> Result<(), reqwest::Error> {
-        self.embeddings = embeder.embed(&self.chunks, metadata).await?;
+    pub fn embed(&mut self, embeder: &Embeder, metadata: Option<HashMap< String, String>>) -> Result<(), anyhow::Error> {
+        self.embeddings = embeder.embed(&self.chunks, metadata)?;
         Ok(())
     }
 
@@ -91,7 +91,7 @@ mod tests {
         let embeder = Embeder::Bert(BertEmbeder::default());
         let mut file_embeder = FileEmbeder::new(file_path.to_string_lossy().to_string());
         file_embeder.split_into_chunks(&text, 100);
-        file_embeder.embed(&embeder, None).await.unwrap();
+        file_embeder.embed(&embeder, None).unwrap();
         assert_eq!(file_embeder.chunks.len(), 5);
         assert_eq!(file_embeder.embeddings.len(), 5);
     }