update line endings to LF

Author: 64bit

async-openai/src/types/audio/audio_types.rs

@@ -1,34 +1,34 @@
-use std::{collections::HashMap, path::PathBuf};
-
-use bytes::Bytes;
-use serde::{Deserialize, Serialize};
-
-#[derive(Debug, Clone, PartialEq)]
-pub enum InputSource {
-    Path { path: PathBuf },
-    Bytes { filename: String, bytes: Bytes },
-    VecU8 { filename: String, vec: Vec<u8> },
-}
-
-#[derive(Debug, Serialize, Deserialize, Clone, Copy, PartialEq)]
-#[serde(rename_all = "lowercase")]
-pub enum OrganizationRole {
-    Owner,
-    Reader,
-}
-
-/// Set of 16 key-value pairs that can be attached to an object.
-/// This can be useful for storing additional information about the
-/// object in a structured format, and querying for objects via API
-/// or the dashboard. Keys are strings with a maximum length of 64
-/// characters. Values are strings with a maximum length of 512
-/// characters.
-#[derive(Debug, Serialize, Deserialize, Clone, PartialEq, Default)]
-#[serde(transparent)]
-pub struct Metadata(HashMap<String, String>);
-
-impl From<HashMap<String, String>> for Metadata {
-    fn from(value: HashMap<String, String>) -> Self {
-        Self(value)
-    }
-}
+use std::{collections::HashMap, path::PathBuf};
+
+use bytes::Bytes;
+use serde::{Deserialize, Serialize};
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum InputSource {
+    Path { path: PathBuf },
+    Bytes { filename: String, bytes: Bytes },
+    VecU8 { filename: String, vec: Vec<u8> },
+}
+
+#[derive(Debug, Serialize, Deserialize, Clone, Copy, PartialEq)]
+#[serde(rename_all = "lowercase")]
+pub enum OrganizationRole {
+    Owner,
+    Reader,
+}
+
+/// Set of 16 key-value pairs that can be attached to an object.
+/// This can be useful for storing additional information about the
+/// object in a structured format, and querying for objects via API
+/// or the dashboard. Keys are strings with a maximum length of 64
+/// characters. Values are strings with a maximum length of 512
+/// characters.
+#[derive(Debug, Serialize, Deserialize, Clone, PartialEq, Default)]
+#[serde(transparent)]
+pub struct Metadata(HashMap<String, String>);
+
+impl From<HashMap<String, String>> for Metadata {
+    fn from(value: HashMap<String, String>) -> Self {
+        Self(value)
+    }
+}

async-openai/src/types/common.rs

@@ -1,122 +1,122 @@
-use base64::engine::{general_purpose, Engine};
-use derive_builder::Builder;
-use serde::{Deserialize, Serialize};
-
-use crate::error::OpenAIError;
-
-#[derive(Debug, Serialize, Clone, PartialEq, Deserialize)]
-#[serde(untagged)]
-pub enum EmbeddingInput {
-    String(String),
-    StringArray(Vec<String>),
-    // Minimum value is 0, maximum value is 100257 (inclusive).
-    IntegerArray(Vec<u32>),
-    ArrayOfIntegerArray(Vec<Vec<u32>>),
-}
-
-#[derive(Debug, Serialize, Default, Clone, PartialEq, Deserialize)]
-#[serde(rename_all = "lowercase")]
-pub enum EncodingFormat {
-    #[default]
-    Float,
-    Base64,
-}
-
-#[derive(Debug, Serialize, Default, Clone, Builder, PartialEq, Deserialize)]
-#[builder(name = "CreateEmbeddingRequestArgs")]
-#[builder(pattern = "mutable")]
-#[builder(setter(into, strip_option), default)]
-#[builder(derive(Debug))]
-#[builder(build_fn(error = "OpenAIError"))]
-pub struct CreateEmbeddingRequest {
-    /// ID of the model to use. You can use the
-    /// [List models](https://platform.openai.com/docs/api-reference/models/list)
-    /// API to see all of your available models, or see our
-    /// [Model overview](https://platform.openai.com/docs/models/overview)
-    /// for descriptions of them.
-    pub model: String,
-
-    ///  Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
-    pub input: EmbeddingInput,
-
-    /// The format to return the embeddings in. Can be either `float` or [`base64`](https://pypi.org/project/pybase64/). Defaults to float
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub encoding_format: Option<EncodingFormat>,
-
-    /// A unique identifier representing your end-user, which will help OpenAI
-    ///  to monitor and detect abuse. [Learn more](https://platform.openai.com/docs/usage-policies/end-user-ids).
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub user: Option<String>,
-
-    /// The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub dimensions: Option<u32>,
-}
-
-/// Represents an embedding vector returned by embedding endpoint.
-#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
-pub struct Embedding {
-    /// The index of the embedding in the list of embeddings.
-    pub index: u32,
-    /// The object type, which is always "embedding".
-    pub object: String,
-    /// The embedding vector, which is a list of floats. The length of vector
-    /// depends on the model as listed in the [embedding guide](https://platform.openai.com/docs/guides/embeddings).
-    pub embedding: Vec<f32>,
-}
-
-#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
-pub struct Base64EmbeddingVector(pub String);
-
-impl From<Base64EmbeddingVector> for Vec<f32> {
-    fn from(value: Base64EmbeddingVector) -> Self {
-        let bytes = general_purpose::STANDARD
-            .decode(value.0)
-            .expect("openai base64 encoding to be valid");
-        let chunks = bytes.chunks_exact(4);
-        chunks
-            .map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
-            .collect()
-    }
-}
-
-/// Represents an base64-encoded embedding vector returned by embedding endpoint.
-#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
-pub struct Base64Embedding {
-    /// The index of the embedding in the list of embeddings.
-    pub index: u32,
-    /// The object type, which is always "embedding".
-    pub object: String,
-    /// The embedding vector, encoded in base64.
-    pub embedding: Base64EmbeddingVector,
-}
-
-#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
-pub struct EmbeddingUsage {
-    /// The number of tokens used by the prompt.
-    pub prompt_tokens: u32,
-    /// The total number of tokens used by the request.
-    pub total_tokens: u32,
-}
-
-#[derive(Debug, Deserialize, Clone, PartialEq, Serialize)]
-pub struct CreateEmbeddingResponse {
-    pub object: String,
-    /// The name of the model used to generate the embedding.
-    pub model: String,
-    /// The list of embeddings generated by the model.
-    pub data: Vec<Embedding>,
-    /// The usage information for the request.
-    pub usage: EmbeddingUsage,
-}
-
-#[derive(Debug, Deserialize, Clone, PartialEq, Serialize)]
-pub struct CreateBase64EmbeddingResponse {
-    pub object: String,
-    /// The name of the model used to generate the embedding.
-    pub model: String,
-    /// The list of embeddings generated by the model.
-    pub data: Vec<Base64Embedding>,
-    /// The usage information for the request.
-    pub usage: EmbeddingUsage,
-}
+use base64::engine::{general_purpose, Engine};
+use derive_builder::Builder;
+use serde::{Deserialize, Serialize};
+
+use crate::error::OpenAIError;
+
+#[derive(Debug, Serialize, Clone, PartialEq, Deserialize)]
+#[serde(untagged)]
+pub enum EmbeddingInput {
+    String(String),
+    StringArray(Vec<String>),
+    // Minimum value is 0, maximum value is 100257 (inclusive).
+    IntegerArray(Vec<u32>),
+    ArrayOfIntegerArray(Vec<Vec<u32>>),
+}
+
+#[derive(Debug, Serialize, Default, Clone, PartialEq, Deserialize)]
+#[serde(rename_all = "lowercase")]
+pub enum EncodingFormat {
+    #[default]
+    Float,
+    Base64,
+}
+
+#[derive(Debug, Serialize, Default, Clone, Builder, PartialEq, Deserialize)]
+#[builder(name = "CreateEmbeddingRequestArgs")]
+#[builder(pattern = "mutable")]
+#[builder(setter(into, strip_option), default)]
+#[builder(derive(Debug))]
+#[builder(build_fn(error = "OpenAIError"))]
+pub struct CreateEmbeddingRequest {
+    /// ID of the model to use. You can use the
+    /// [List models](https://platform.openai.com/docs/api-reference/models/list)
+    /// API to see all of your available models, or see our
+    /// [Model overview](https://platform.openai.com/docs/models/overview)
+    /// for descriptions of them.
+    pub model: String,
+
+    ///  Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
+    pub input: EmbeddingInput,
+
+    /// The format to return the embeddings in. Can be either `float` or [`base64`](https://pypi.org/project/pybase64/). Defaults to float
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub encoding_format: Option<EncodingFormat>,
+
+    /// A unique identifier representing your end-user, which will help OpenAI
+    ///  to monitor and detect abuse. [Learn more](https://platform.openai.com/docs/usage-policies/end-user-ids).
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub user: Option<String>,
+
+    /// The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub dimensions: Option<u32>,
+}
+
+/// Represents an embedding vector returned by embedding endpoint.
+#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
+pub struct Embedding {
+    /// The index of the embedding in the list of embeddings.
+    pub index: u32,
+    /// The object type, which is always "embedding".
+    pub object: String,
+    /// The embedding vector, which is a list of floats. The length of vector
+    /// depends on the model as listed in the [embedding guide](https://platform.openai.com/docs/guides/embeddings).
+    pub embedding: Vec<f32>,
+}
+
+#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
+pub struct Base64EmbeddingVector(pub String);
+
+impl From<Base64EmbeddingVector> for Vec<f32> {
+    fn from(value: Base64EmbeddingVector) -> Self {
+        let bytes = general_purpose::STANDARD
+            .decode(value.0)
+            .expect("openai base64 encoding to be valid");
+        let chunks = bytes.chunks_exact(4);
+        chunks
+            .map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
+            .collect()
+    }
+}
+
+/// Represents an base64-encoded embedding vector returned by embedding endpoint.
+#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
+pub struct Base64Embedding {
+    /// The index of the embedding in the list of embeddings.
+    pub index: u32,
+    /// The object type, which is always "embedding".
+    pub object: String,
+    /// The embedding vector, encoded in base64.
+    pub embedding: Base64EmbeddingVector,
+}
+
+#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
+pub struct EmbeddingUsage {
+    /// The number of tokens used by the prompt.
+    pub prompt_tokens: u32,
+    /// The total number of tokens used by the request.
+    pub total_tokens: u32,
+}
+
+#[derive(Debug, Deserialize, Clone, PartialEq, Serialize)]
+pub struct CreateEmbeddingResponse {
+    pub object: String,
+    /// The name of the model used to generate the embedding.
+    pub model: String,
+    /// The list of embeddings generated by the model.
+    pub data: Vec<Embedding>,
+    /// The usage information for the request.
+    pub usage: EmbeddingUsage,
+}
+
+#[derive(Debug, Deserialize, Clone, PartialEq, Serialize)]
+pub struct CreateBase64EmbeddingResponse {
+    pub object: String,
+    /// The name of the model used to generate the embedding.
+    pub model: String,
+    /// The list of embeddings generated by the model.
+    pub data: Vec<Base64Embedding>,
+    /// The usage information for the request.
+    pub usage: EmbeddingUsage,
+}