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use qdrant_client::{
Qdrant,
qdrant::{Query, QueryPointsBuilder},
};
use rayon::iter::{IntoParallelIterator, ParallelIterator};
use translators::{GoogleTranslator, Translator};
use crate::{
constants::QDRANT_KEYFRAME_COLLECTION_NAME,
models::{dtos::vectors::keyframes::VectorizedKeyframeDto, states::AppState},
services::embeddings::texts::TextEmbeddingService,
};
#[derive(Clone, Copy)]
pub struct VectorizedKeyframeService<'a> {
client: &'a Qdrant,
translator: &'a GoogleTranslator,
}
impl<'a> From<&'a AppState> for VectorizedKeyframeService<'a> {
fn from(value: &'a AppState) -> Self {
Self {
client: value.qdrant_client(),
translator: value.translator(),
}
}
}
impl<'a> VectorizedKeyframeService<'a> {
async fn embed_text(&self, text: &str) -> anyhow::Result<Vec<f32>> {
let mut embedding_service = TextEmbeddingService::new().await?;
embedding_service.embed_text(text).await
}
pub async fn find_nearest_top_k_by_text(
&self,
text: &str,
top_k: u64,
) -> anyhow::Result<Vec<VectorizedKeyframeDto>> {
let translated_text = self.translator.translate_async(text, "vi", "en").await?;
let embeddings = self.embed_text(&translated_text).await?;
let query_result = self
.client
.query(
QueryPointsBuilder::new(QDRANT_KEYFRAME_COLLECTION_NAME)
.query(Query::new_nearest(embeddings))
.using("images")
.limit(top_k),
)
.await?
.result;
query_result
.into_par_iter()
.map(VectorizedKeyframeDto::try_from)
.collect::<Result<_, _>>()
}
}
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