rig-core 0.28.0

use super::{
    Client,
    client::{ApiErrorResponse, ApiResponse},
    completion::Usage,
};
use crate::embeddings::EmbeddingError;
use crate::http_client::HttpClientExt;
use crate::{embeddings, http_client};
use serde::Deserialize;
use serde_json::json;

// ================================================================
// 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)]
pub struct EmbeddingModel<T = reqwest::Client> {
    client: Client<T>,
    pub model: String,
    ndims: usize,
}

fn model_dimensions_from_identifier(identifier: &str) -> Option<usize> {
    match identifier {
        TEXT_EMBEDDING_3_LARGE => Some(3_072),
        TEXT_EMBEDDING_3_SMALL | TEXT_EMBEDDING_ADA_002 => Some(1_536),
        _ => None,
    }
}

impl<T> embeddings::EmbeddingModel for EmbeddingModel<T>
where
    T: HttpClientExt + Clone + std::fmt::Debug + Default + Send + 'static,
{
    const MAX_DOCUMENTS: usize = 1024;

    type Client = Client<T>;

    fn make(client: &Self::Client, model: impl Into<String>, ndims: Option<usize>) -> Self {
        let model = model.into();
        let dims = ndims
            .or(model_dimensions_from_identifier(&model))
            .unwrap_or_default();

        Self::new(client.clone(), model, dims)
    }

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

    async fn embed_texts(
        &self,
        documents: impl IntoIterator<Item = String>,
    ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
        let documents = documents.into_iter().collect::<Vec<_>>();

        let mut body = json!({
            "model": self.model,
            "input": documents,
        });

        if self.ndims > 0 && self.model.as_str() != TEXT_EMBEDDING_ADA_002 {
            body["dimensions"] = json!(self.ndims);
        }

        let body = serde_json::to_vec(&body)?;

        let req = self
            .client
            .post("/embeddings")?
            .body(body)
            .map_err(|e| EmbeddingError::HttpError(e.into()))?;

        let response = self.client.send(req).await?;

        if response.status().is_success() {
            let body: Vec<u8> = response.into_body().await?;
            let body: ApiResponse<EmbeddingResponse> = serde_json::from_slice(&body)?;

            match body {
                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 {
            let text = http_client::text(response).await?;
            Err(EmbeddingError::ProviderError(text))
        }
    }
}

impl<T> EmbeddingModel<T> {
    pub fn new(client: Client<T>, model: impl Into<String>, ndims: usize) -> Self {
        Self {
            client,
            model: model.into(),
            ndims,
        }
    }

    pub fn with_model(client: Client<T>, model: &str, ndims: usize) -> Self {
        Self {
            client,
            model: model.into(),
            ndims,
        }
    }
}