rig-core 0.10.0

//! OpenAI API client and Rig integration
//!
//! # Example
//! ```
//! use rig::providers::openai;
//!
//! let client = openai::Client::new("YOUR_API_KEY");
//!
//! let gpt4o = client.completion_model(openai::GPT_4O);
//! ```
use crate::json_utils::merge;
use crate::streaming::StreamingResult;
use crate::{
    agent::AgentBuilder,
    completion::{self, CompletionError, CompletionRequest},
    embeddings::{self, EmbeddingError, EmbeddingsBuilder},
    extractor::ExtractorBuilder,
    json_utils,
    message::{self, AudioMediaType, ImageDetail},
    one_or_many::string_or_one_or_many,
    streaming,
    streaming::StreamingCompletionModel,
    transcription::{self, TranscriptionError},
    Embed, OneOrMany,
};
use async_stream::stream;
use futures::StreamExt;
use reqwest::multipart::Part;
use reqwest::RequestBuilder;
use schemars::JsonSchema;
use serde::{Deserialize, Serialize};
use serde_json::{json, Value};
use std::collections::HashMap;
use std::{convert::Infallible, str::FromStr};

// ================================================================
// Main OpenAI Client
// ================================================================
const OPENAI_API_BASE_URL: &str = "https://api.openai.com/v1";

#[derive(Clone)]
pub struct Client {
    base_url: String,
    http_client: reqwest::Client,
}

impl Client {
    /// Create a new OpenAI client with the given API key.
    pub fn new(api_key: &str) -> Self {
        Self::from_url(api_key, OPENAI_API_BASE_URL)
    }

    /// Create a new OpenAI client with the given API key and base API URL.
    pub fn from_url(api_key: &str, base_url: &str) -> Self {
        Self {
            base_url: base_url.to_string(),
            http_client: reqwest::Client::builder()
                .default_headers({
                    let mut headers = reqwest::header::HeaderMap::new();
                    headers.insert(
                        "Authorization",
                        format!("Bearer {}", api_key)
                            .parse()
                            .expect("Bearer token should parse"),
                    );
                    headers
                })
                .build()
                .expect("OpenAI reqwest client should build"),
        }
    }

    /// Create a new OpenAI client from the `OPENAI_API_KEY` environment variable.
    /// Panics if the environment variable is not set.
    pub fn from_env() -> Self {
        let api_key = std::env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY not set");
        Self::new(&api_key)
    }

    fn post(&self, path: &str) -> reqwest::RequestBuilder {
        let url = format!("{}/{}", self.base_url, path).replace("//", "/");
        self.http_client.post(url)
    }

    /// Create an embedding model with the given name.
    /// Note: default embedding dimension of 0 will be used if model is not known.
    /// If this is the case, it's better to use function `embedding_model_with_ndims`
    ///
    /// # Example
    /// ```
    /// use rig::providers::openai::{Client, self};
    ///
    /// // Initialize the OpenAI client
    /// let openai = Client::new("your-open-ai-api-key");
    ///
    /// let embedding_model = openai.embedding_model(openai::TEXT_EMBEDDING_3_LARGE);
    /// ```
    pub fn embedding_model(&self, model: &str) -> EmbeddingModel {
        let ndims = match model {
            TEXT_EMBEDDING_3_LARGE => 3072,
            TEXT_EMBEDDING_3_SMALL | TEXT_EMBEDDING_ADA_002 => 1536,
            _ => 0,
        };
        EmbeddingModel::new(self.clone(), model, ndims)
    }

    /// Create an embedding model with the given name and the number of dimensions in the embedding generated by the model.
    ///
    /// # Example
    /// ```
    /// use rig::providers::openai::{Client, self};
    ///
    /// // Initialize the OpenAI client
    /// let openai = Client::new("your-open-ai-api-key");
    ///
    /// let embedding_model = openai.embedding_model("model-unknown-to-rig", 3072);
    /// ```
    pub fn embedding_model_with_ndims(&self, model: &str, ndims: usize) -> EmbeddingModel {
        EmbeddingModel::new(self.clone(), model, ndims)
    }

    /// Create an embedding builder with the given embedding model.
    ///
    /// # Example
    /// ```
    /// use rig::providers::openai::{Client, self};
    ///
    /// // Initialize the OpenAI client
    /// let openai = Client::new("your-open-ai-api-key");
    ///
    /// let embeddings = openai.embeddings(openai::TEXT_EMBEDDING_3_LARGE)
    ///     .simple_document("doc0", "Hello, world!")
    ///     .simple_document("doc1", "Goodbye, world!")
    ///     .build()
    ///     .await
    ///     .expect("Failed to embed documents");
    /// ```
    pub fn embeddings<D: Embed>(&self, model: &str) -> EmbeddingsBuilder<EmbeddingModel, D> {
        EmbeddingsBuilder::new(self.embedding_model(model))
    }

    /// Create a completion model with the given name.
    ///
    /// # Example
    /// ```
    /// use rig::providers::openai::{Client, self};
    ///
    /// // Initialize the OpenAI client
    /// let openai = Client::new("your-open-ai-api-key");
    ///
    /// let gpt4 = openai.completion_model(openai::GPT_4);
    /// ```
    pub fn completion_model(&self, model: &str) -> CompletionModel {
        CompletionModel::new(self.clone(), model)
    }

    /// Create an agent builder with the given completion model.
    ///
    /// # Example
    /// ```
    /// use rig::providers::openai::{Client, self};
    ///
    /// // Initialize the OpenAI client
    /// let openai = Client::new("your-open-ai-api-key");
    ///
    /// let agent = openai.agent(openai::GPT_4)
    ///    .preamble("You are comedian AI with a mission to make people laugh.")
    ///    .temperature(0.0)
    ///    .build();
    /// ```
    pub fn agent(&self, model: &str) -> AgentBuilder<CompletionModel> {
        AgentBuilder::new(self.completion_model(model))
    }

    /// Create an extractor builder with the given completion model.
    pub fn extractor<T: JsonSchema + for<'a> Deserialize<'a> + Serialize + Send + Sync>(
        &self,
        model: &str,
    ) -> ExtractorBuilder<T, CompletionModel> {
        ExtractorBuilder::new(self.completion_model(model))
    }

    /// Create a completion model with the given name.
    ///
    /// # Example
    /// ```
    /// use rig::providers::openai::{Client, self};
    ///
    /// // Initialize the OpenAI client
    /// let openai = Client::new("your-open-ai-api-key");
    ///
    /// let gpt4 = openai.transcription_model(openai::WHISPER_1);
    /// ```
    pub fn transcription_model(&self, model: &str) -> TranscriptionModel {
        TranscriptionModel::new(self.clone(), model)
    }
}

#[derive(Debug, Deserialize)]
struct ApiErrorResponse {
    message: String,
}

#[derive(Debug, Deserialize)]
#[serde(untagged)]
enum ApiResponse<T> {
    Ok(T),
    Err(ApiErrorResponse),
}

// ================================================================
// OpenAI Embedding API
// ================================================================
/// `text-embedding-3-large` embedding model
pub const TEXT_EMBEDDING_3_LARGE: &str = "text-embedding-3-large";
/// `text-embedding-3-small` embedding model
pub const TEXT_EMBEDDING_3_SMALL: &str = "text-embedding-3-small";
/// `text-embedding-ada-002` embedding model
pub const TEXT_EMBEDDING_ADA_002: &str = "text-embedding-ada-002";

#[derive(Debug, Deserialize)]
pub struct EmbeddingResponse {
    pub object: String,
    pub data: Vec<EmbeddingData>,
    pub model: String,
    pub usage: Usage,
}

impl From<ApiErrorResponse> for EmbeddingError {
    fn from(err: ApiErrorResponse) -> Self {
        EmbeddingError::ProviderError(err.message)
    }
}

impl From<ApiResponse<EmbeddingResponse>> for Result<EmbeddingResponse, EmbeddingError> {
    fn from(value: ApiResponse<EmbeddingResponse>) -> Self {
        match value {
            ApiResponse::Ok(response) => Ok(response),
            ApiResponse::Err(err) => Err(EmbeddingError::ProviderError(err.message)),
        }
    }
}

#[derive(Debug, Deserialize)]
pub struct EmbeddingData {
    pub object: String,
    pub embedding: Vec<f64>,
    pub index: usize,
}

#[derive(Clone, Debug, Deserialize)]
pub struct Usage {
    pub prompt_tokens: usize,
    pub total_tokens: usize,
}

impl std::fmt::Display for Usage {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        write!(
            f,
            "Prompt tokens: {} Total tokens: {}",
            self.prompt_tokens, self.total_tokens
        )
    }
}

#[derive(Clone)]
pub struct EmbeddingModel {
    client: Client,
    pub model: String,
    ndims: usize,
}

impl embeddings::EmbeddingModel for EmbeddingModel {
    const MAX_DOCUMENTS: usize = 1024;

    fn ndims(&self) -> usize {
        self.ndims
    }

    #[cfg_attr(feature = "worker", worker::send)]
    async fn embed_texts(
        &self,
        documents: impl IntoIterator<Item = String>,
    ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
        let documents = documents.into_iter().collect::<Vec<_>>();

        let response = self
            .client
            .post("/embeddings")
            .json(&json!({
                "model": self.model,
                "input": documents,
            }))
            .send()
            .await?;

        if response.status().is_success() {
            match response.json::<ApiResponse<EmbeddingResponse>>().await? {
                ApiResponse::Ok(response) => {
                    tracing::info!(target: "rig",
                        "OpenAI embedding token usage: {}",
                        response.usage
                    );

                    if response.data.len() != documents.len() {
                        return Err(EmbeddingError::ResponseError(
                            "Response data length does not match input length".into(),
                        ));
                    }

                    Ok(response
                        .data
                        .into_iter()
                        .zip(documents.into_iter())
                        .map(|(embedding, document)| embeddings::Embedding {
                            document,
                            vec: embedding.embedding,
                        })
                        .collect())
                }
                ApiResponse::Err(err) => Err(EmbeddingError::ProviderError(err.message)),
            }
        } else {
            Err(EmbeddingError::ProviderError(response.text().await?))
        }
    }
}

impl EmbeddingModel {
    pub fn new(client: Client, model: &str, ndims: usize) -> Self {
        Self {
            client,
            model: model.to_string(),
            ndims,
        }
    }
}

// ================================================================
// OpenAI Completion API
// ================================================================

/// `o3-mini` completion model
pub const O3_MINI: &str = "o3-mini";
/// `o3-mini-2025-01-31` completion model
pub const O3_MINI_2025_01_31: &str = "o3-mini-2025-01-31";
/// 'o1' completion model
pub const O1: &str = "o1";
/// `o1-2024-12-17` completion model
pub const O1_2024_12_17: &str = "o1-2024-12-17";
/// `o1-preview` completion model
pub const O1_PREVIEW: &str = "o1-preview";
/// `o1-preview-2024-09-12` completion model
pub const O1_PREVIEW_2024_09_12: &str = "o1-preview-2024-09-12";
/// `o1-mini completion model
pub const O1_MINI: &str = "o1-mini";
/// `o1-mini-2024-09-12` completion model
pub const O1_MINI_2024_09_12: &str = "o1-mini-2024-09-12";
/// `gpt-4.5-preview` completion model
pub const GPT_4_5_PREVIEW: &str = "gpt-4.5-preview";
/// `gpt-4.5-preview-2025-02-27` completion model
pub const GPT_4_5_PREVIEW_2025_02_27: &str = "gpt-4.5-preview-2025-02-27";
/// `gpt-4o` completion model
pub const GPT_4O: &str = "gpt-4o";
/// `gpt-4o-mini` completion model
pub const GPT_4O_MINI: &str = "gpt-4o-mini";
/// `gpt-4o-2024-05-13` completion model
pub const GPT_4O_2024_05_13: &str = "gpt-4o-2024-05-13";
/// `gpt-4-turbo` completion model
pub const GPT_4_TURBO: &str = "gpt-4-turbo";
/// `gpt-4-turbo-2024-04-09` completion model
pub const GPT_4_TURBO_2024_04_09: &str = "gpt-4-turbo-2024-04-09";
/// `gpt-4-turbo-preview` completion model
pub const GPT_4_TURBO_PREVIEW: &str = "gpt-4-turbo-preview";
/// `gpt-4-0125-preview` completion model
pub const GPT_4_0125_PREVIEW: &str = "gpt-4-0125-preview";
/// `gpt-4-1106-preview` completion model
pub const GPT_4_1106_PREVIEW: &str = "gpt-4-1106-preview";
/// `gpt-4-vision-preview` completion model
pub const GPT_4_VISION_PREVIEW: &str = "gpt-4-vision-preview";
/// `gpt-4-1106-vision-preview` completion model
pub const GPT_4_1106_VISION_PREVIEW: &str = "gpt-4-1106-vision-preview";
/// `gpt-4` completion model
pub const GPT_4: &str = "gpt-4";
/// `gpt-4-0613` completion model
pub const GPT_4_0613: &str = "gpt-4-0613";
/// `gpt-4-32k` completion model
pub const GPT_4_32K: &str = "gpt-4-32k";
/// `gpt-4-32k-0613` completion model
pub const GPT_4_32K_0613: &str = "gpt-4-32k-0613";
/// `gpt-3.5-turbo` completion model
pub const GPT_35_TURBO: &str = "gpt-3.5-turbo";
/// `gpt-3.5-turbo-0125` completion model
pub const GPT_35_TURBO_0125: &str = "gpt-3.5-turbo-0125";
/// `gpt-3.5-turbo-1106` completion model
pub const GPT_35_TURBO_1106: &str = "gpt-3.5-turbo-1106";
/// `gpt-3.5-turbo-instruct` completion model
pub const GPT_35_TURBO_INSTRUCT: &str = "gpt-3.5-turbo-instruct";

#[derive(Debug, Deserialize)]
pub struct CompletionResponse {
    pub id: String,
    pub object: String,
    pub created: u64,
    pub model: String,
    pub system_fingerprint: Option<String>,
    pub choices: Vec<Choice>,
    pub usage: Option<Usage>,
}

impl From<ApiErrorResponse> for CompletionError {
    fn from(err: ApiErrorResponse) -> Self {
        CompletionError::ProviderError(err.message)
    }
}

impl TryFrom<CompletionResponse> for completion::CompletionResponse<CompletionResponse> {
    type Error = CompletionError;

    fn try_from(response: CompletionResponse) -> Result<Self, Self::Error> {
        let choice = response.choices.first().ok_or_else(|| {
            CompletionError::ResponseError("Response contained no choices".to_owned())
        })?;

        let content = match &choice.message {
            Message::Assistant {
                content,
                tool_calls,
                ..
            } => {
                let mut content = content
                    .iter()
                    .filter_map(|c| {
                        let s = match c {
                            AssistantContent::Text { text } => text,
                            AssistantContent::Refusal { refusal } => refusal,
                        };
                        if s.is_empty() {
                            None
                        } else {
                            Some(completion::AssistantContent::text(s))
                        }
                    })
                    .collect::<Vec<_>>();

                content.extend(
                    tool_calls
                        .iter()
                        .map(|call| {
                            completion::AssistantContent::tool_call(
                                &call.id,
                                &call.function.name,
                                call.function.arguments.clone(),
                            )
                        })
                        .collect::<Vec<_>>(),
                );
                Ok(content)
            }
            _ => Err(CompletionError::ResponseError(
                "Response did not contain a valid message or tool call".into(),
            )),
        }?;

        let choice = OneOrMany::many(content).map_err(|_| {
            CompletionError::ResponseError(
                "Response contained no message or tool call (empty)".to_owned(),
            )
        })?;

        Ok(completion::CompletionResponse {
            choice,
            raw_response: response,
        })
    }
}

#[derive(Debug, Serialize, Deserialize)]
pub struct Choice {
    pub index: usize,
    pub message: Message,
    pub logprobs: Option<serde_json::Value>,
    pub finish_reason: String,
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
#[serde(tag = "role", rename_all = "lowercase")]
pub enum Message {
    System {
        #[serde(deserialize_with = "string_or_one_or_many")]
        content: OneOrMany<SystemContent>,
        #[serde(skip_serializing_if = "Option::is_none")]
        name: Option<String>,
    },
    User {
        #[serde(deserialize_with = "string_or_one_or_many")]
        content: OneOrMany<UserContent>,
        #[serde(skip_serializing_if = "Option::is_none")]
        name: Option<String>,
    },
    Assistant {
        #[serde(default, deserialize_with = "json_utils::string_or_vec")]
        content: Vec<AssistantContent>,
        #[serde(skip_serializing_if = "Option::is_none")]
        refusal: Option<String>,
        #[serde(skip_serializing_if = "Option::is_none")]
        audio: Option<AudioAssistant>,
        #[serde(skip_serializing_if = "Option::is_none")]
        name: Option<String>,
        #[serde(
            default,
            deserialize_with = "json_utils::null_or_vec",
            skip_serializing_if = "Vec::is_empty"
        )]
        tool_calls: Vec<ToolCall>,
    },
    #[serde(rename = "tool")]
    ToolResult {
        tool_call_id: String,
        content: OneOrMany<ToolResultContent>,
    },
}

impl Message {
    pub fn system(content: &str) -> Self {
        Message::System {
            content: OneOrMany::one(content.to_owned().into()),
            name: None,
        }
    }
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
pub struct AudioAssistant {
    id: String,
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
pub struct SystemContent {
    #[serde(default)]
    r#type: SystemContentType,
    text: String,
}

#[derive(Default, Debug, Serialize, Deserialize, PartialEq, Clone)]
#[serde(rename_all = "lowercase")]
pub enum SystemContentType {
    #[default]
    Text,
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
#[serde(tag = "type", rename_all = "lowercase")]
pub enum AssistantContent {
    Text { text: String },
    Refusal { refusal: String },
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
#[serde(tag = "type", rename_all = "lowercase")]
pub enum UserContent {
    Text { text: String },
    Image { image_url: ImageUrl },
    Audio { input_audio: InputAudio },
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
pub struct ImageUrl {
    pub url: String,
    #[serde(default)]
    pub detail: ImageDetail,
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
pub struct InputAudio {
    pub data: String,
    pub format: AudioMediaType,
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
pub struct ToolResultContent {
    #[serde(default)]
    r#type: ToolResultContentType,
    text: String,
}

#[derive(Default, Debug, Serialize, Deserialize, PartialEq, Clone)]
#[serde(rename_all = "lowercase")]
pub enum ToolResultContentType {
    #[default]
    Text,
}

impl FromStr for ToolResultContent {
    type Err = Infallible;

    fn from_str(s: &str) -> Result<Self, Self::Err> {
        Ok(s.to_owned().into())
    }
}

impl From<String> for ToolResultContent {
    fn from(s: String) -> Self {
        ToolResultContent {
            r#type: ToolResultContentType::default(),
            text: s,
        }
    }
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
pub struct ToolCall {
    pub id: String,
    #[serde(default)]
    pub r#type: ToolType,
    pub function: Function,
}

#[derive(Default, Debug, Serialize, Deserialize, PartialEq, Clone)]
#[serde(rename_all = "lowercase")]
pub enum ToolType {
    #[default]
    Function,
}

#[derive(Debug, Deserialize, Serialize, Clone)]
pub struct ToolDefinition {
    pub r#type: String,
    pub function: completion::ToolDefinition,
}

impl From<completion::ToolDefinition> for ToolDefinition {
    fn from(tool: completion::ToolDefinition) -> Self {
        Self {
            r#type: "function".into(),
            function: tool,
        }
    }
}

#[derive(Debug, Serialize, Deserialize, PartialEq, Clone)]
pub struct Function {
    pub name: String,
    #[serde(with = "json_utils::stringified_json")]
    pub arguments: serde_json::Value,
}

impl TryFrom<message::Message> for Vec<Message> {
    type Error = message::MessageError;

    fn try_from(message: message::Message) -> Result<Self, Self::Error> {
        match message {
            message::Message::User { content } => {
                let (tool_results, other_content): (Vec<_>, Vec<_>) = content
                    .into_iter()
                    .partition(|content| matches!(content, message::UserContent::ToolResult(_)));

                // If there are messages with both tool results and user content, openai will only
                //  handle tool results. It's unlikely that there will be both.
                if !tool_results.is_empty() {
                    tool_results
                        .into_iter()
                        .map(|content| match content {
                            message::UserContent::ToolResult(message::ToolResult {
                                id,
                                content,
                            }) => Ok::<_, message::MessageError>(Message::ToolResult {
                                tool_call_id: id,
                                content: content.try_map(|content| match content {
                                    message::ToolResultContent::Text(message::Text { text }) => {
                                        Ok(text.into())
                                    }
                                    _ => Err(message::MessageError::ConversionError(
                                        "Tool result content does not support non-text".into(),
                                    )),
                                })?,
                            }),
                            _ => unreachable!(),
                        })
                        .collect::<Result<Vec<_>, _>>()
                } else {
                    let other_content = OneOrMany::many(other_content).expect(
                        "There must be other content here if there were no tool result content",
                    );

                    Ok(vec![Message::User {
                        content: other_content.map(|content| match content {
                            message::UserContent::Text(message::Text { text }) => {
                                UserContent::Text { text }
                            }
                            message::UserContent::Image(message::Image {
                                data, detail, ..
                            }) => UserContent::Image {
                                image_url: ImageUrl {
                                    url: data,
                                    detail: detail.unwrap_or_default(),
                                },
                            },
                            message::UserContent::Document(message::Document { data, .. }) => {
                                UserContent::Text { text: data }
                            }
                            message::UserContent::Audio(message::Audio {
                                data,
                                media_type,
                                ..
                            }) => UserContent::Audio {
                                input_audio: InputAudio {
                                    data,
                                    format: match media_type {
                                        Some(media_type) => media_type,
                                        None => AudioMediaType::MP3,
                                    },
                                },
                            },
                            _ => unreachable!(),
                        }),
                        name: None,
                    }])
                }
            }
            message::Message::Assistant { content } => {
                let (text_content, tool_calls) = content.into_iter().fold(
                    (Vec::new(), Vec::new()),
                    |(mut texts, mut tools), content| {
                        match content {
                            message::AssistantContent::Text(text) => texts.push(text),
                            message::AssistantContent::ToolCall(tool_call) => tools.push(tool_call),
                        }
                        (texts, tools)
                    },
                );

                // `OneOrMany` ensures at least one `AssistantContent::Text` or `ToolCall` exists,
                //  so either `content` or `tool_calls` will have some content.
                Ok(vec![Message::Assistant {
                    content: text_content
                        .into_iter()
                        .map(|content| content.text.into())
                        .collect::<Vec<_>>(),
                    refusal: None,
                    audio: None,
                    name: None,
                    tool_calls: tool_calls
                        .into_iter()
                        .map(|tool_call| tool_call.into())
                        .collect::<Vec<_>>(),
                }])
            }
        }
    }
}

impl From<message::ToolCall> for ToolCall {
    fn from(tool_call: message::ToolCall) -> Self {
        Self {
            id: tool_call.id,
            r#type: ToolType::default(),
            function: Function {
                name: tool_call.function.name,
                arguments: tool_call.function.arguments,
            },
        }
    }
}

impl From<ToolCall> for message::ToolCall {
    fn from(tool_call: ToolCall) -> Self {
        Self {
            id: tool_call.id,
            function: message::ToolFunction {
                name: tool_call.function.name,
                arguments: tool_call.function.arguments,
            },
        }
    }
}

impl TryFrom<Message> for message::Message {
    type Error = message::MessageError;

    fn try_from(message: Message) -> Result<Self, Self::Error> {
        Ok(match message {
            Message::User { content, .. } => message::Message::User {
                content: content.map(|content| content.into()),
            },
            Message::Assistant {
                content,
                tool_calls,
                ..
            } => {
                let mut content = content
                    .into_iter()
                    .map(|content| match content {
                        AssistantContent::Text { text } => message::AssistantContent::text(text),

                        // TODO: Currently, refusals are converted into text, but should be
                        //  investigated for generalization.
                        AssistantContent::Refusal { refusal } => {
                            message::AssistantContent::text(refusal)
                        }
                    })
                    .collect::<Vec<_>>();

                content.extend(
                    tool_calls
                        .into_iter()
                        .map(|tool_call| Ok(message::AssistantContent::ToolCall(tool_call.into())))
                        .collect::<Result<Vec<_>, _>>()?,
                );

                message::Message::Assistant {
                    content: OneOrMany::many(content).map_err(|_| {
                        message::MessageError::ConversionError(
                            "Neither `content` nor `tool_calls` was provided to the Message"
                                .to_owned(),
                        )
                    })?,
                }
            }

            Message::ToolResult {
                tool_call_id,
                content,
            } => message::Message::User {
                content: OneOrMany::one(message::UserContent::tool_result(
                    tool_call_id,
                    content.map(|content| message::ToolResultContent::text(content.text)),
                )),
            },

            // System messages should get stripped out when converting message's, this is just a
            // stop gap to avoid obnoxious error handling or panic occuring.
            Message::System { content, .. } => message::Message::User {
                content: content.map(|content| message::UserContent::text(content.text)),
            },
        })
    }
}

impl From<UserContent> for message::UserContent {
    fn from(content: UserContent) -> Self {
        match content {
            UserContent::Text { text } => message::UserContent::text(text),
            UserContent::Image { image_url } => message::UserContent::image(
                image_url.url,
                Some(message::ContentFormat::default()),
                None,
                Some(image_url.detail),
            ),
            UserContent::Audio { input_audio } => message::UserContent::audio(
                input_audio.data,
                Some(message::ContentFormat::default()),
                Some(input_audio.format),
            ),
        }
    }
}

impl From<String> for UserContent {
    fn from(s: String) -> Self {
        UserContent::Text { text: s }
    }
}

impl FromStr for UserContent {
    type Err = Infallible;

    fn from_str(s: &str) -> Result<Self, Self::Err> {
        Ok(UserContent::Text {
            text: s.to_string(),
        })
    }
}

impl From<String> for AssistantContent {
    fn from(s: String) -> Self {
        AssistantContent::Text { text: s }
    }
}

impl FromStr for AssistantContent {
    type Err = Infallible;

    fn from_str(s: &str) -> Result<Self, Self::Err> {
        Ok(AssistantContent::Text {
            text: s.to_string(),
        })
    }
}
impl From<String> for SystemContent {
    fn from(s: String) -> Self {
        SystemContent {
            r#type: SystemContentType::default(),
            text: s,
        }
    }
}

impl FromStr for SystemContent {
    type Err = Infallible;

    fn from_str(s: &str) -> Result<Self, Self::Err> {
        Ok(SystemContent {
            r#type: SystemContentType::default(),
            text: s.to_string(),
        })
    }
}

#[derive(Clone)]
pub struct CompletionModel {
    client: Client,
    /// Name of the model (e.g.: gpt-3.5-turbo-1106)
    pub model: String,
}

impl CompletionModel {
    pub fn new(client: Client, model: &str) -> Self {
        Self {
            client,
            model: model.to_string(),
        }
    }

    fn create_completion_request(
        &self,
        completion_request: CompletionRequest,
    ) -> Result<Value, CompletionError> {
        // Add preamble to chat history (if available)
        let mut full_history: Vec<Message> = match &completion_request.preamble {
            Some(preamble) => vec![Message::system(preamble)],
            None => vec![],
        };

        // Convert prompt to user message
        let prompt: Vec<Message> = completion_request.prompt_with_context().try_into()?;

        // Convert existing chat history
        let chat_history: Vec<Message> = completion_request
            .chat_history
            .into_iter()
            .map(|message| message.try_into())
            .collect::<Result<Vec<Vec<Message>>, _>>()?
            .into_iter()
            .flatten()
            .collect();

        // Combine all messages into a single history
        full_history.extend(chat_history);
        full_history.extend(prompt);

        let request = if completion_request.tools.is_empty() {
            json!({
                "model": self.model,
                "messages": full_history,

            })
        } else {
            json!({
                "model": self.model,
                "messages": full_history,
                "tools": completion_request.tools.into_iter().map(ToolDefinition::from).collect::<Vec<_>>(),
                "tool_choice": "auto",
            })
        };

        // only include temperature if it exists
        // because some models don't support temperature
        let request = if let Some(temperature) = completion_request.temperature {
            json_utils::merge(
                request,
                json!({
                    "temperature": temperature,
                }),
            )
        } else {
            request
        };

        let request = if let Some(params) = completion_request.additional_params {
            json_utils::merge(request, params)
        } else {
            request
        };

        Ok(request)
    }
}

impl completion::CompletionModel for CompletionModel {
    type Response = CompletionResponse;

    #[cfg_attr(feature = "worker", worker::send)]
    async fn completion(
        &self,
        completion_request: CompletionRequest,
    ) -> Result<completion::CompletionResponse<CompletionResponse>, CompletionError> {
        let request = self.create_completion_request(completion_request)?;

        let response = self
            .client
            .post("/chat/completions")
            .json(&request)
            .send()
            .await?;

        if response.status().is_success() {
            let t = response.text().await?;
            tracing::debug!(target: "rig", "OpenAI completion error: {}", t);

            match serde_json::from_str::<ApiResponse<CompletionResponse>>(&t)? {
                ApiResponse::Ok(response) => {
                    tracing::info!(target: "rig",
                        "OpenAI completion token usage: {:?}",
                        response.usage.clone().map(|usage| format!("{usage}")).unwrap_or("N/A".to_string())
                    );
                    response.try_into()
                }
                ApiResponse::Err(err) => Err(CompletionError::ProviderError(err.message)),
            }
        } else {
            Err(CompletionError::ProviderError(response.text().await?))
        }
    }
}

// ================================================================
// OpenAI Transcription API
// ================================================================
pub const WHISPER_1: &str = "whisper-1";

#[derive(Debug, Deserialize)]
pub struct TranscriptionResponse {
    pub text: String,
}

impl TryFrom<TranscriptionResponse>
    for transcription::TranscriptionResponse<TranscriptionResponse>
{
    type Error = TranscriptionError;

    fn try_from(value: TranscriptionResponse) -> Result<Self, Self::Error> {
        Ok(transcription::TranscriptionResponse {
            text: value.text.clone(),
            response: value,
        })
    }
}

#[derive(Clone)]
pub struct TranscriptionModel {
    client: Client,
    /// Name of the model (e.g.: gpt-3.5-turbo-1106)
    pub model: String,
}

impl TranscriptionModel {
    pub fn new(client: Client, model: &str) -> Self {
        Self {
            client,
            model: model.to_string(),
        }
    }
}

impl transcription::TranscriptionModel for TranscriptionModel {
    type Response = TranscriptionResponse;

    #[cfg_attr(feature = "worker", worker::send)]
    async fn transcription(
        &self,
        request: transcription::TranscriptionRequest,
    ) -> Result<
        transcription::TranscriptionResponse<Self::Response>,
        transcription::TranscriptionError,
    > {
        let data = request.data;

        let mut body = reqwest::multipart::Form::new()
            .text("model", self.model.clone())
            .text("language", request.language)
            .part(
                "file",
                Part::bytes(data).file_name(request.filename.clone()),
            );

        if let Some(prompt) = request.prompt {
            body = body.text("prompt", prompt.clone());
        }

        if let Some(ref temperature) = request.temperature {
            body = body.text("temperature", temperature.to_string());
        }

        if let Some(ref additional_params) = request.additional_params {
            for (key, value) in additional_params
                .as_object()
                .expect("Additional Parameters to OpenAI Transcription should be a map")
            {
                body = body.text(key.to_owned(), value.to_string());
            }
        }

        let response = self
            .client
            .post("audio/transcriptions")
            .multipart(body)
            .send()
            .await?;

        if response.status().is_success() {
            match response
                .json::<ApiResponse<TranscriptionResponse>>()
                .await?
            {
                ApiResponse::Ok(response) => response.try_into(),
                ApiResponse::Err(api_error_response) => Err(TranscriptionError::ProviderError(
                    api_error_response.message,
                )),
            }
        } else {
            Err(TranscriptionError::ProviderError(response.text().await?))
        }
    }
}

// ================================================================
// OpenAI Completion Streaming API
// ================================================================
#[derive(Debug, Serialize, Deserialize, Clone)]
pub struct StreamingFunction {
    #[serde(default)]
    name: Option<String>,
    #[serde(default)]
    arguments: String,
}

#[derive(Debug, Serialize, Deserialize, Clone)]
pub struct StreamingToolCall {
    pub index: usize,
    pub function: StreamingFunction,
}

#[derive(Deserialize)]
struct StreamingDelta {
    #[serde(default)]
    content: Option<String>,
    #[serde(default, deserialize_with = "json_utils::null_or_vec")]
    tool_calls: Vec<StreamingToolCall>,
}

#[derive(Deserialize)]
struct StreamingChoice {
    delta: StreamingDelta,
}

#[derive(Deserialize)]
struct StreamingCompletionResponse {
    choices: Vec<StreamingChoice>,
}

impl StreamingCompletionModel for CompletionModel {
    async fn stream(
        &self,
        completion_request: CompletionRequest,
    ) -> Result<StreamingResult, CompletionError> {
        let mut request = self.create_completion_request(completion_request)?;
        request = merge(request, json!({"stream": true}));

        let builder = self.client.post("/chat/completions").json(&request);
        send_compatible_streaming_request(builder).await
    }
}

pub async fn send_compatible_streaming_request(
    request_builder: RequestBuilder,
) -> Result<StreamingResult, CompletionError> {
    let response = request_builder.send().await?;

    if !response.status().is_success() {
        return Err(CompletionError::ProviderError(format!(
            "{}: {}",
            response.status(),
            response.text().await?
        )));
    }

    // Handle OpenAI Compatible SSE chunks
    Ok(Box::pin(stream! {
        let mut stream = response.bytes_stream();

        let mut partial_data = None;
        let mut calls: HashMap<usize, (String, String)> = HashMap::new();

        while let Some(chunk_result) = stream.next().await {
            let chunk = match chunk_result {
                Ok(c) => c,
                Err(e) => {
                    yield Err(CompletionError::from(e));
                    break;
                }
            };

            let text = match String::from_utf8(chunk.to_vec()) {
                Ok(t) => t,
                Err(e) => {
                    yield Err(CompletionError::ResponseError(e.to_string()));
                    break;
                }
            };


            for line in text.lines() {
                let mut line = line.to_string();



                // If there was a remaining part, concat with current line
                if partial_data.is_some() {
                    line = format!("{}{}", partial_data.unwrap(), line);
                    partial_data = None;
                }
                // Otherwise full data line
                else {
                    let Some(data) = line.strip_prefix("data: ") else {
                        continue;
                    };

                    // Partial data, split somewhere in the middle
                    if !line.ends_with("}") {
                        partial_data = Some(data.to_string());
                    } else {
                        line = data.to_string();
                    }
                }

                let data = serde_json::from_str::<StreamingCompletionResponse>(&line);

                let Ok(data) = data else {
                    continue;
                };

                let choice = data.choices.first().expect("Should have at least one choice");

                let delta = &choice.delta;

                if !delta.tool_calls.is_empty() {
                    for tool_call in &delta.tool_calls {
                        let function = tool_call.function.clone();

                        // Start of tool call
                        // name: Some(String)
                        // arguments: None
                        if function.name.is_some() && function.arguments.is_empty() {
                            calls.insert(tool_call.index, (function.name.clone().unwrap(), "".to_string()));
                        }
                        // Part of tool call
                        // name: None
                        // arguments: Some(String)
                        else if function.name.is_none() && !function.arguments.is_empty() {
                            let Some((name, arguments)) = calls.get(&tool_call.index) else {
                                continue;
                            };

                            let new_arguments = &tool_call.function.arguments;
                            let arguments = format!("{}{}", arguments, new_arguments);

                            calls.insert(tool_call.index, (name.clone(), arguments));
                        }
                        // Entire tool call
                        else {
                            let name = function.name.unwrap();
                            let arguments = function.arguments;
                            let Ok(arguments) = serde_json::from_str(&arguments) else {
                                continue;
                            };

                            yield Ok(streaming::StreamingChoice::ToolCall(name, "".to_string(), arguments))
                        }
                    }
                }

                if let Some(content) = &choice.delta.content {
                    yield Ok(streaming::StreamingChoice::Message(content.clone()))
                }
            }
        }

        for (_, (name, arguments)) in calls {
            let Ok(arguments) = serde_json::from_str(&arguments) else {
                continue;
            };

            yield Ok(streaming::StreamingChoice::ToolCall(name, "".to_string(), arguments))
        }
    }))
}

#[cfg(test)]
mod tests {
    use super::*;
    use serde_path_to_error::deserialize;

    #[test]
    fn test_deserialize_message() {
        let assistant_message_json = r#"
        {
            "role": "assistant",
            "content": "\n\nHello there, how may I assist you today?"
        }
        "#;

        let assistant_message_json2 = r#"
        {
            "role": "assistant",
            "content": [
                {
                    "type": "text",
                    "text": "\n\nHello there, how may I assist you today?"
                }
            ],
            "tool_calls": null
        }
        "#;

        let assistant_message_json3 = r#"
        {
            "role": "assistant",
            "tool_calls": [
                {
                    "id": "call_h89ipqYUjEpCPI6SxspMnoUU",
                    "type": "function",
                    "function": {
                        "name": "subtract",
                        "arguments": "{\"x\": 2, \"y\": 5}"
                    }
                }
            ],
            "content": null,
            "refusal": null
        }
        "#;

        let user_message_json = r#"
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this image?"
                },
                {
                    "type": "image",
                    "image_url": {
                        "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                    }
                },
                {
                    "type": "audio",
                    "input_audio": {
                        "data": "...",
                        "format": "mp3"
                    }
                }
            ]
        }
        "#;

        let assistant_message: Message = {
            let jd = &mut serde_json::Deserializer::from_str(assistant_message_json);
            deserialize(jd).unwrap_or_else(|err| {
                panic!(
                    "Deserialization error at {} ({}:{}): {}",
                    err.path(),
                    err.inner().line(),
                    err.inner().column(),
                    err
                );
            })
        };

        let assistant_message2: Message = {
            let jd = &mut serde_json::Deserializer::from_str(assistant_message_json2);
            deserialize(jd).unwrap_or_else(|err| {
                panic!(
                    "Deserialization error at {} ({}:{}): {}",
                    err.path(),
                    err.inner().line(),
                    err.inner().column(),
                    err
                );
            })
        };

        let assistant_message3: Message = {
            let jd: &mut serde_json::Deserializer<serde_json::de::StrRead<'_>> =
                &mut serde_json::Deserializer::from_str(assistant_message_json3);
            deserialize(jd).unwrap_or_else(|err| {
                panic!(
                    "Deserialization error at {} ({}:{}): {}",
                    err.path(),
                    err.inner().line(),
                    err.inner().column(),
                    err
                );
            })
        };

        let user_message: Message = {
            let jd = &mut serde_json::Deserializer::from_str(user_message_json);
            deserialize(jd).unwrap_or_else(|err| {
                panic!(
                    "Deserialization error at {} ({}:{}): {}",
                    err.path(),
                    err.inner().line(),
                    err.inner().column(),
                    err
                );
            })
        };

        match assistant_message {
            Message::Assistant { content, .. } => {
                assert_eq!(
                    content[0],
                    AssistantContent::Text {
                        text: "\n\nHello there, how may I assist you today?".to_string()
                    }
                );
            }
            _ => panic!("Expected assistant message"),
        }

        match assistant_message2 {
            Message::Assistant {
                content,
                tool_calls,
                ..
            } => {
                assert_eq!(
                    content[0],
                    AssistantContent::Text {
                        text: "\n\nHello there, how may I assist you today?".to_string()
                    }
                );

                assert_eq!(tool_calls, vec![]);
            }
            _ => panic!("Expected assistant message"),
        }

        match assistant_message3 {
            Message::Assistant {
                content,
                tool_calls,
                refusal,
                ..
            } => {
                assert!(content.is_empty());
                assert!(refusal.is_none());
                assert_eq!(
                    tool_calls[0],
                    ToolCall {
                        id: "call_h89ipqYUjEpCPI6SxspMnoUU".to_string(),
                        r#type: ToolType::Function,
                        function: Function {
                            name: "subtract".to_string(),
                            arguments: serde_json::json!({"x": 2, "y": 5}),
                        },
                    }
                );
            }
            _ => panic!("Expected assistant message"),
        }

        match user_message {
            Message::User { content, .. } => {
                let (first, second) = {
                    let mut iter = content.into_iter();
                    (iter.next().unwrap(), iter.next().unwrap())
                };
                assert_eq!(
                    first,
                    UserContent::Text {
                        text: "What's in this image?".to_string()
                    }
                );
                assert_eq!(second, UserContent::Image { image_url: ImageUrl { url: "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg".to_string(), detail: ImageDetail::default() } });
            }
            _ => panic!("Expected user message"),
        }
    }

    #[test]
    fn test_message_to_message_conversion() {
        let user_message = message::Message::User {
            content: OneOrMany::one(message::UserContent::text("Hello")),
        };

        let assistant_message = message::Message::Assistant {
            content: OneOrMany::one(message::AssistantContent::text("Hi there!")),
        };

        let converted_user_message: Vec<Message> = user_message.clone().try_into().unwrap();
        let converted_assistant_message: Vec<Message> =
            assistant_message.clone().try_into().unwrap();

        match converted_user_message[0].clone() {
            Message::User { content, .. } => {
                assert_eq!(
                    content.first(),
                    UserContent::Text {
                        text: "Hello".to_string()
                    }
                );
            }
            _ => panic!("Expected user message"),
        }

        match converted_assistant_message[0].clone() {
            Message::Assistant { content, .. } => {
                assert_eq!(
                    content[0].clone(),
                    AssistantContent::Text {
                        text: "Hi there!".to_string()
                    }
                );
            }
            _ => panic!("Expected assistant message"),
        }

        let original_user_message: message::Message =
            converted_user_message[0].clone().try_into().unwrap();
        let original_assistant_message: message::Message =
            converted_assistant_message[0].clone().try_into().unwrap();

        assert_eq!(original_user_message, user_message);
        assert_eq!(original_assistant_message, assistant_message);
    }

    #[test]
    fn test_message_from_message_conversion() {
        let user_message = Message::User {
            content: OneOrMany::one(UserContent::Text {
                text: "Hello".to_string(),
            }),
            name: None,
        };

        let assistant_message = Message::Assistant {
            content: vec![AssistantContent::Text {
                text: "Hi there!".to_string(),
            }],
            refusal: None,
            audio: None,
            name: None,
            tool_calls: vec![],
        };

        let converted_user_message: message::Message = user_message.clone().try_into().unwrap();
        let converted_assistant_message: message::Message =
            assistant_message.clone().try_into().unwrap();

        match converted_user_message.clone() {
            message::Message::User { content } => {
                assert_eq!(content.first(), message::UserContent::text("Hello"));
            }
            _ => panic!("Expected user message"),
        }

        match converted_assistant_message.clone() {
            message::Message::Assistant { content } => {
                assert_eq!(
                    content.first(),
                    message::AssistantContent::text("Hi there!")
                );
            }
            _ => panic!("Expected assistant message"),
        }

        let original_user_message: Vec<Message> = converted_user_message.try_into().unwrap();
        let original_assistant_message: Vec<Message> =
            converted_assistant_message.try_into().unwrap();

        assert_eq!(original_user_message[0], user_message);
        assert_eq!(original_assistant_message[0], assistant_message);
    }
}