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use std::collections::{HashMap, HashSet};
use collection::operations::point_ops::VectorPersisted;
use storage::content_manager::errors::StorageError;
use super::batch_processing::BatchAccum;
use super::service::{InferenceData, InferenceInput, InferenceService, InferenceType};
pub struct BatchAccumInferred {
pub(crate) objects: HashMap<InferenceData, VectorPersisted>,
}
impl BatchAccumInferred {
pub fn new() -> Self {
Self {
objects: HashMap::new(),
}
}
pub async fn from_objects(
objects: HashSet<InferenceData>,
inference_type: InferenceType,
) -> Result<Self, StorageError> {
if objects.is_empty() {
return Ok(Self::new());
}
let Some(service) = InferenceService::get_global() else {
return Err(StorageError::service_error(
"InferenceService is not initialized. Please check if it was properly configured and initialized during startup."
));
};
service.validate()?;
let objects_serialized: Vec<_> = objects.into_iter().collect();
let inference_inputs: Vec<_> = objects_serialized
.iter()
.cloned()
.map(InferenceInput::from)
.collect();
let vectors = service
.infer(inference_inputs, inference_type)
.await
.map_err(|e| StorageError::service_error(
format!("Inference request failed. Check if inference service is running and properly configured: {e}")
))?;
if vectors.is_empty() {
return Err(StorageError::service_error(
"Inference service returned no vectors. Check if models are properly loaded.",
));
}
let objects = objects_serialized.into_iter().zip(vectors).collect();
Ok(Self { objects })
}
pub async fn from_batch_accum(
batch: BatchAccum,
inference_type: InferenceType,
) -> Result<Self, StorageError> {
let BatchAccum { objects } = batch;
Self::from_objects(objects, inference_type).await
}
pub fn get_vector(&self, data: &InferenceData) -> Option<&VectorPersisted> {
self.objects.get(data)
}
}
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