Updating rust edition and version

Author: deven96

.github/workflows/release.yml

@@ -56,7 +56,7 @@ jobs:
             - name: Get Cargo toolchain
               uses: actions-rs/toolchain@v1
               with:
-                toolchain: 1.87.0
+                toolchain: 1.88.0
 
             - name: Build Linux Release for ${{ needs.prebuild_preparation.outputs.bin_name }}
               working-directory: ./ahnlich

.github/workflows/rust_tag_and_deploy.yml

@@ -66,7 +66,7 @@ jobs:
       - name: Get Cargo toolchain
         uses: actions-rs/toolchain@v1
         with:
-          toolchain: 1.87.0
+          toolchain: 1.88.0
 
       - name: Install Protoc
         uses: arduino/setup-protoc@v3

ahnlich/Dockerfile

@@ -1,4 +1,4 @@
-FROM lukemathwalker/cargo-chef:latest-rust-1.87.0 AS chef
+FROM lukemathwalker/cargo-chef:latest-rust-1.88.0 AS chef
 WORKDIR /app
 RUN apt update && apt install lld clang protobuf-compiler -y 
 FROM chef AS planner

ahnlich/ai/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "ai"
 version = "0.1.0"
-edition = "2021"
+edition = "2024"
 
 # only used for rust client test and not to be released
 [lib]

ahnlich/ai/src/cli/server.rs

@@ -270,7 +270,7 @@ impl SupportedModelArgs {
         let mut output = String::new();
 
         for supported_model in SupportedModels::VARIANTS.iter() {
-            output.push_str(format!("{}, ", supported_model).as_str())
+            output.push_str(format!("{supported_model}, ").as_str())
         }
         output
     }
@@ -298,6 +298,6 @@ impl SupportedModelArgs {
             text.push_str(&self.list_supported_models());
         }
 
-        writeln!(&mut stdout, "{}", text).expect("Failed to write output");
+        writeln!(&mut stdout, "{text}").expect("Failed to write output");
     }
 }

ahnlich/ai/src/engine/ai/models.rs

@@ -1,21 +1,21 @@
 use crate::cli::server::SupportedModels;
-use crate::engine::ai::providers::ort::ORTProvider;
 use crate::engine::ai::providers::ModelProviders;
 use crate::engine::ai::providers::ProviderTrait;
+use crate::engine::ai::providers::ort::ORTProvider;
 use crate::error::AIProxyError;
 use ahnlich_types::ai::execution_provider::ExecutionProvider;
 use ahnlich_types::ai::models::AiStoreInputType;
 use ahnlich_types::keyval::StoreKey;
-use fast_image_resize::images::Image;
-use fast_image_resize::images::ImageRef;
 use fast_image_resize::FilterType;
 use fast_image_resize::PixelType;
 use fast_image_resize::ResizeAlg;
 use fast_image_resize::ResizeOptions;
 use fast_image_resize::Resizer;
-use image::imageops;
+use fast_image_resize::images::Image;
+use fast_image_resize::images::ImageRef;
 use image::ImageReader;
 use image::RgbImage;
+use image::imageops;
 use ndarray::{Array, Ix3};
 use ndarray::{ArrayView, Ix4};
 use nonzero_ext::nonzero;
@@ -66,65 +66,65 @@ impl SupportedModels {
                 embedding_size: nonzero!(384usize),
             },
             SupportedModels::AllMiniLML12V2 => ModelDetails {
-                    model_type: ModelType::Text {
-                        // Token size source: https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2#intended-uses
-                        max_input_tokens: nonzero!(256usize),
-                    },
-                    supported_model: SupportedModels::AllMiniLML12V2,
-                    description: String::from("Sentence Transformer model, with 12 layers, version 2."),
-                    embedding_size: nonzero!(384usize),
+                model_type: ModelType::Text {
+                    // Token size source: https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2#intended-uses
+                    max_input_tokens: nonzero!(256usize),
+                },
+                supported_model: SupportedModels::AllMiniLML12V2,
+                description: String::from("Sentence Transformer model, with 12 layers, version 2."),
+                embedding_size: nonzero!(384usize),
             },
             SupportedModels::BGEBaseEnV15 => ModelDetails {
-                    model_type: ModelType::Text {
-                        // Token size source: https://huggingface.co/BAAI/bge-large-en/discussions/11#64e44de1623074ac850aa1ae
-                        max_input_tokens: nonzero!(512usize),
-                    },
-                    supported_model: SupportedModels::BGEBaseEnV15,
-                    description: String::from(
-                        "BAAI General Embedding model with English support, base scale, version 1.5.",
-                    ),
-                    embedding_size: nonzero!(768usize),
+                model_type: ModelType::Text {
+                    // Token size source: https://huggingface.co/BAAI/bge-large-en/discussions/11#64e44de1623074ac850aa1ae
+                    max_input_tokens: nonzero!(512usize),
+                },
+                supported_model: SupportedModels::BGEBaseEnV15,
+                description: String::from(
+                    "BAAI General Embedding model with English support, base scale, version 1.5.",
+                ),
+                embedding_size: nonzero!(768usize),
             },
             SupportedModels::BGELargeEnV15 => ModelDetails {
-                    model_type: ModelType::Text {
-                        max_input_tokens: nonzero!(512usize),
-                    },
-                    supported_model: SupportedModels::BGELargeEnV15,
-                    description: String::from(
-                        "BAAI General Embedding model with English support, large scale, version 1.5.",
-                    ),
-                    embedding_size: nonzero!(1024usize),
+                model_type: ModelType::Text {
+                    max_input_tokens: nonzero!(512usize),
+                },
+                supported_model: SupportedModels::BGELargeEnV15,
+                description: String::from(
+                    "BAAI General Embedding model with English support, large scale, version 1.5.",
+                ),
+                embedding_size: nonzero!(1024usize),
             },
             SupportedModels::Resnet50 => ModelDetails {
-                    model_type: ModelType::Image {
-                        expected_image_dimensions: (nonzero!(224usize), nonzero!(224usize)),
-                    },
-                    supported_model: SupportedModels::Resnet50,
-                    description: String::from("Residual Networks model, with 50 layers."),
-                    embedding_size: nonzero!(2048usize),
+                model_type: ModelType::Image {
+                    expected_image_dimensions: (nonzero!(224usize), nonzero!(224usize)),
+                },
+                supported_model: SupportedModels::Resnet50,
+                description: String::from("Residual Networks model, with 50 layers."),
+                embedding_size: nonzero!(2048usize),
             },
             SupportedModels::ClipVitB32Image => ModelDetails {
-                    model_type: ModelType::Image {
-                        expected_image_dimensions: (nonzero!(224usize), nonzero!(224usize)),
-                    },
-                    supported_model: SupportedModels::ClipVitB32Image,
-                    description: String::from(
-                        "Contrastive Language-Image Pre-Training Vision transformer model, base scale.",
-                    ),
-                    embedding_size: nonzero!(512usize),
+                model_type: ModelType::Image {
+                    expected_image_dimensions: (nonzero!(224usize), nonzero!(224usize)),
+                },
+                supported_model: SupportedModels::ClipVitB32Image,
+                description: String::from(
+                    "Contrastive Language-Image Pre-Training Vision transformer model, base scale.",
+                ),
+                embedding_size: nonzero!(512usize),
             },
             SupportedModels::ClipVitB32Text => ModelDetails {
-                    supported_model: SupportedModels::ClipVitB32Text,
-                    description: String::from(
-                        "Contrastive Language-Image Pre-Training Text transformer model, base scale. \
+                supported_model: SupportedModels::ClipVitB32Text,
+                description: String::from(
+                    "Contrastive Language-Image Pre-Training Text transformer model, base scale. \
                             Ideal for embedding very short text and using in combination with ClipVitB32Image",
-                    ),
-                    embedding_size: nonzero!(512usize),
-                    model_type: ModelType::Text {
-                        // Token size source: https://github.com/UKPLab/sentence-transformers/issues/1269
-                        max_input_tokens: nonzero!(77usize),
-                    },
+                ),
+                embedding_size: nonzero!(512usize),
+                model_type: ModelType::Text {
+                    // Token size source: https://github.com/UKPLab/sentence-transformers/issues/1269
+                    max_input_tokens: nonzero!(77usize),
                 },
+            },
         }
     }
 

ahnlich/ai/src/engine/ai/providers/ort/executor.rs

@@ -4,7 +4,7 @@ use ort::Session;
 use crate::error::AIProxyError;
 use strum::IntoEnumIterator;
 
-use super::{register_provider, InnerAIExecutionProvider};
+use super::{InnerAIExecutionProvider, register_provider};
 use std::path::PathBuf;
 use std::sync::Arc;
 use std::thread::available_parallelism;

ahnlich/ai/src/engine/ai/providers/ort/helper.rs

@@ -10,18 +10,18 @@ use std::path::PathBuf;
 /// To be used when loading local model files.
 pub fn read_file_to_bytes(file: &PathBuf) -> Result<Vec<u8>, AIProxyError> {
     let mut file = File::open(file).map_err(|_| AIProxyError::ModelConfigLoadError {
-        message: format!("failed to open file {:?}", file),
+        message: format!("failed to open file {file:?}"),
     })?;
     let file_size = file
         .metadata()
         .map_err(|_| AIProxyError::ModelConfigLoadError {
-            message: format!("failed to get metadata for file {:?}", file),
+            message: format!("failed to get metadata for file {file:?}"),
         })?
         .len() as usize;
     let mut buffer = Vec::with_capacity(file_size);
     file.read_to_end(&mut buffer)
         .map_err(|_| AIProxyError::ModelConfigLoadError {
-            message: format!("failed to read file {:?}", file),
+            message: format!("failed to read file {file:?}"),
         })?;
     Ok(buffer)
 }
@@ -47,15 +47,15 @@ impl HFConfigReader {
             self.model_repo
                 .get(config_name)
                 .map_err(|e| AIProxyError::ModelConfigLoadError {
-                    message: format!("failed to fetch {}, {}", config_name, e),
+                    message: format!("failed to fetch {config_name}, {e}"),
                 })?;
         let contents =
             read_file_to_bytes(&file).map_err(|e| AIProxyError::ModelConfigLoadError {
-                message: format!("failed to read {}, {}", config_name, e),
+                message: format!("failed to read {config_name}, {e}"),
             })?;
         let value: serde_json::Value =
             serde_json::from_slice(&contents).map_err(|e| AIProxyError::ModelConfigLoadError {
-                message: format!("failed to parse {}, {}", config_name, e),
+                message: format!("failed to parse {config_name}, {e}"),
             })?;
         self.cache
             .insert(config_name.to_string(), Ok(value.clone()));

ahnlich/ai/src/engine/ai/providers/ort/mod.rs

@@ -9,7 +9,7 @@ use ahnlich_types::ai::execution_provider::ExecutionProvider as AIExecutionProvi
 use ahnlich_types::keyval::StoreKey;
 use executor::ExecutorWithSessionCache;
 use fallible_collections::FallibleVec;
-use hf_hub::{api::sync::ApiBuilder, Cache};
+use hf_hub::{Cache, api::sync::ApiBuilder};
 use itertools::Itertools;
 use ort::{
     CUDAExecutionProvider, CoreMLExecutionProvider, DirectMLExecutionProvider, ExecutionProvider,
@@ -325,7 +325,7 @@ impl ORTProvider {
             _ => {
                 return Err(AIProxyError::AIModelNotSupported {
                     model_name: self.supported_models.to_string(),
-                })
+                });
             }
         };
 

ahnlich/ai/src/engine/ai/providers/processors/center_crop.rs

@@ -1,6 +1,6 @@
 use crate::engine::ai::models::ImageArray;
 use crate::engine::ai::providers::processors::{
-    Preprocessor, PreprocessorData, CONV_NEXT_FEATURE_EXTRACTOR_CENTER_CROP_THRESHOLD,
+    CONV_NEXT_FEATURE_EXTRACTOR_CENTER_CROP_THRESHOLD, Preprocessor, PreprocessorData,
 };
 use crate::error::AIProxyError;
 use rayon::iter::{IntoParallelIterator, ParallelIterator};
@@ -87,9 +87,8 @@ impl CenterCrop {
             }
             _ => Err(AIProxyError::ModelConfigLoadError {
                 message: format!(
-                    "The key 'image_processor_type' in the configuration has the wrong value: {}; \
-                it should be either 'CLIPImageProcessor' or 'ConvNextFeatureExtractor'.",
-                    image_processor_type
+                    "The key 'image_processor_type' in the configuration has the wrong value: {image_processor_type}; \
+                it should be either 'CLIPImageProcessor' or 'ConvNextFeatureExtractor'."
                 )
                 .to_string(),
             }),