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//!
//! Get a vector representation of a given input that can be easily consumed by
//! machine learning models and algorithms.
//!
//! Source: OpenAI documentation

////////////////////////////////////////////////////////////////////////////////

use crate::openai::{
    endpoint::{endpoint_filter, request_endpoint, Endpoint, EndpointVariant},
    types::{common::Error, embedding::EmbeddingResponse, model::Model},
};
use log::{debug, warn};
use serde::{Deserialize, Serialize};
use serde_with::serde_as;

/// Given a prompt and an instruction, the model will return an edited version
/// of the prompt.
#[serde_as]
#[derive(Serialize, Deserialize, Debug)]
pub struct Embedding {
    /// 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: Model,

    /// Input text to get embeddings for, encoded as a string or array of tokens.
    /// To get embeddings for multiple inputs in a single request, pass an array
    /// of strings or array of token arrays. Each input must not exceed 8192
    /// tokens in length.
    pub input: String,

    /// A unique identifier representing your end-user, which can help OpenAI to
    /// monitor and detect abuse. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
    #[serde(skip_serializing_if = "Option::is_none")]
    pub user: Option<String>,
}

impl Default for Embedding {
    fn default() -> Self {
        Self {
            model: Model::TEXT_EMBEDDING_ADA_002,
            input: String::from(""),
            user: None,
        }
    }
}

impl Embedding {
    /// Set target input.
    ///
    /// # Arguments
    /// - `content` - Target input
    pub fn set_input(&mut self, content: &str) {
        self.input = content.into();
    }

    /// Send embedding request to OpenAI.
    pub async fn embedding(&self) -> Result<EmbeddingResponse, Box<dyn std::error::Error>> {
        if !endpoint_filter(&self.model, &Endpoint::Embedding_v1) {
            return Err("Model not compatible with this endpoint".into());
        }

        let mut embedding_response: Option<EmbeddingResponse> = None;

        request_endpoint(&self, &Endpoint::Embedding_v1, EndpointVariant::None, |res| {
          if let Ok(text) = res {
              if let Ok(response_data) = serde_json::from_str::<EmbeddingResponse>(&text) {
                  debug!(target: "openai", "Response parsed, embedding response deserialized.");
                  embedding_response = Some(response_data);
              } else {
                  if let Ok(response_error) = serde_json::from_str::<Error>(&text) {
                      warn!(target: "openai",
                          "OpenAI error code {}: `{:?}`",
                          response_error.error.code.unwrap_or(0),
                          text
                      );
                  } else {
                      warn!(target: "openai", "Embedding response not deserializable.");
                  }
              }
          }
      })
      .await?;

        if let Some(response_data) = embedding_response {
            Ok(response_data)
        } else {
            Err("No response or error parsing response".into())
        }
    }
}