swiftide 0.3.1

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use std::sync::Arc;

use crate::{
    ingestion::{IngestionNode, IngestionStream},
    integrations::openai::OpenAI,
    BatchableTransformer, Embed,
};
use anyhow::Result;
use async_trait::async_trait;
use futures_util::{stream, StreamExt};

/// A transformer that uses the OpenAI API to generate embeddings for data.
///
/// This file defines the `OpenAIEmbed` struct and its implementation of the `BatchableTransformer` trait.
/// The primary purpose of this transformer is to embed data using the OpenAI API.
#[derive(Debug)]
pub struct OpenAIEmbed {
    client: Arc<OpenAI>,
    concurrency: Option<usize>,
}

impl OpenAIEmbed {
    /// Creates a new instance of `OpenAIEmbed`.
    ///
    /// # Parameters
    ///
    /// * `client` - An instance of the OpenAI client.
    ///
    /// # Returns
    ///
    /// A new instance of `OpenAIEmbed`.
    pub fn new(client: OpenAI) -> Self {
        Self {
            client: Arc::new(client),
            concurrency: None,
        }
    }

    pub fn with_concurrency(mut self, concurrency: usize) -> Self {
        self.concurrency = Some(concurrency);
        self
    }
}

#[async_trait]
impl BatchableTransformer for OpenAIEmbed {
    /// Transforms a batch of `IngestionNode` objects by generating embeddings for them.
    ///
    /// # Parameters
    ///
    /// * `nodes` - A vector of `IngestionNode` objects to be transformed.
    ///
    /// # Returns
    ///
    /// An `IngestionStream` containing the transformed `IngestionNode` objects with their embeddings.
    ///
    /// # Errors
    ///
    /// If the embedding process fails, the function returns a stream with the error.
    #[tracing::instrument(skip_all, name = "transformers.openai_embed")]
    async fn batch_transform(&self, nodes: Vec<IngestionNode>) -> IngestionStream {
        // TODO: We should drop chunks that go over the token limit of the EmbedModel
        let chunks_to_embed: Vec<String> = nodes.iter().map(|n| n.as_embeddable()).collect();

        stream::iter(
            self.client
                .embed(chunks_to_embed)
                .await
                .map(|embeddings| {
                    nodes
                        .into_iter()
                        .zip(embeddings)
                        .map(|(mut n, v)| {
                            n.vector = Some(v);
                            Ok(n)
                        })
                        .collect::<Vec<Result<IngestionNode>>>()
                })
                .unwrap_or_else(|e| vec![Err(e)]),
        )
        .boxed()
    }

    fn concurrency(&self) -> Option<usize> {
        self.concurrency
    }
}