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use std::collections::HashMap;
use api::rest::{Batch, BatchVectorStruct, PointStruct, PointVectors, Vector, VectorStruct};
use collection::operations::point_ops::{
BatchPersisted, BatchVectorStructPersisted, PointStructPersisted, VectorPersisted,
VectorStructPersisted,
};
use collection::operations::vector_ops::PointVectorsPersisted;
use storage::content_manager::errors::StorageError;
use crate::common::inference::batch_processing::BatchAccum;
use crate::common::inference::infer_processing::BatchAccumInferred;
use crate::common::inference::service::{InferenceData, InferenceType};
pub async fn convert_point_struct(
point_structs: Vec<PointStruct>,
inference_type: InferenceType,
) -> Result<Vec<PointStructPersisted>, StorageError> {
let mut batch_accum = BatchAccum::new();
for point_struct in &point_structs {
match &point_struct.vector {
VectorStruct::Named(named) => {
for vector in named.values() {
match vector {
Vector::Document(doc) => {
batch_accum.add(InferenceData::Document(doc.clone()))
}
Vector::Image(img) => batch_accum.add(InferenceData::Image(img.clone())),
Vector::Object(obj) => batch_accum.add(InferenceData::Object(obj.clone())),
Vector::Dense(_) | Vector::Sparse(_) | Vector::MultiDense(_) => {}
}
}
}
VectorStruct::Document(doc) => batch_accum.add(InferenceData::Document(doc.clone())),
VectorStruct::Image(img) => batch_accum.add(InferenceData::Image(img.clone())),
VectorStruct::Object(obj) => batch_accum.add(InferenceData::Object(obj.clone())),
VectorStruct::MultiDense(_) | VectorStruct::Single(_) => {}
}
}
let inferred = if !batch_accum.objects.is_empty() {
Some(BatchAccumInferred::from_batch_accum(batch_accum, inference_type).await?)
} else {
None
};
let mut converted_points: Vec<PointStructPersisted> = Vec::new();
for point_struct in point_structs {
let PointStruct {
id,
vector,
payload,
} = point_struct;
let converted_vector_struct = match vector {
VectorStruct::Single(single) => VectorStructPersisted::Single(single),
VectorStruct::MultiDense(multi) => VectorStructPersisted::MultiDense(multi),
VectorStruct::Named(named) => {
let mut named_vectors = HashMap::new();
for (name, vector) in named {
let converted_vector = match &inferred {
Some(inferred) => convert_vector_with_inferred(vector, inferred)?,
None => match vector {
Vector::Dense(dense) => VectorPersisted::Dense(dense),
Vector::Sparse(sparse) => VectorPersisted::Sparse(sparse),
Vector::MultiDense(multi) => VectorPersisted::MultiDense(multi),
Vector::Document(_) | Vector::Image(_) | Vector::Object(_) => {
return Err(StorageError::inference_error(
"Inference required but service returned no results",
))
}
},
};
named_vectors.insert(name, converted_vector);
}
VectorStructPersisted::Named(named_vectors)
}
VectorStruct::Document(doc) => {
let vector = match &inferred {
Some(inferred) => {
convert_vector_with_inferred(Vector::Document(doc), inferred)?
}
None => {
return Err(StorageError::inference_error(
"Inference required but service returned no results",
))
}
};
match vector {
VectorPersisted::Dense(dense) => VectorStructPersisted::Single(dense),
VectorPersisted::Sparse(_) => {
return Err(StorageError::bad_request("Sparse vector should be named"));
}
VectorPersisted::MultiDense(multi) => VectorStructPersisted::MultiDense(multi),
}
}
VectorStruct::Image(img) => {
let vector = match &inferred {
Some(inferred) => convert_vector_with_inferred(Vector::Image(img), inferred)?,
None => {
return Err(StorageError::inference_error(
"Inference required but service returned no results",
))
}
};
match vector {
VectorPersisted::Dense(dense) => VectorStructPersisted::Single(dense),
VectorPersisted::Sparse(_) => {
return Err(StorageError::bad_request("Sparse vector should be named"));
}
VectorPersisted::MultiDense(multi) => VectorStructPersisted::MultiDense(multi),
}
}
VectorStruct::Object(obj) => {
let vector = match &inferred {
Some(inferred) => convert_vector_with_inferred(Vector::Object(obj), inferred)?,
None => {
return Err(StorageError::inference_error(
"Inference required but service returned no results",
))
}
};
match vector {
VectorPersisted::Dense(dense) => VectorStructPersisted::Single(dense),
VectorPersisted::Sparse(_) => {
return Err(StorageError::bad_request("Sparse vector should be named"));
}
VectorPersisted::MultiDense(multi) => VectorStructPersisted::MultiDense(multi),
}
}
};
let converted = PointStructPersisted {
id,
vector: converted_vector_struct,
payload,
};
converted_points.push(converted);
}
Ok(converted_points)
}
pub async fn convert_batch(batch: Batch) -> Result<BatchPersisted, StorageError> {
let Batch {
ids,
vectors,
payloads,
} = batch;
let batch_persisted = BatchPersisted {
ids,
vectors: match vectors {
BatchVectorStruct::Single(single) => BatchVectorStructPersisted::Single(single),
BatchVectorStruct::MultiDense(multi) => BatchVectorStructPersisted::MultiDense(multi),
BatchVectorStruct::Named(named) => {
let mut named_vectors = HashMap::new();
for (name, vectors) in named {
let converted_vectors = convert_vectors(vectors, InferenceType::Update).await?;
named_vectors.insert(name, converted_vectors);
}
BatchVectorStructPersisted::Named(named_vectors)
}
BatchVectorStruct::Document(_) => {
return Err(StorageError::inference_error(
"Document processing is not supported in batch operations.",
))
}
BatchVectorStruct::Image(_) => {
return Err(StorageError::inference_error(
"Image processing is not supported in batch operations.",
))
}
BatchVectorStruct::Object(_) => {
return Err(StorageError::inference_error(
"Object processing is not supported in batch operations.",
))
}
},
payloads,
};
Ok(batch_persisted)
}
pub async fn convert_point_vectors(
point_vectors_list: Vec<PointVectors>,
inference_type: InferenceType,
) -> Result<Vec<PointVectorsPersisted>, StorageError> {
let mut converted_point_vectors = Vec::new();
let mut batch_accum = BatchAccum::new();
for point_vectors in &point_vectors_list {
if let VectorStruct::Named(named) = &point_vectors.vector {
for vector in named.values() {
match vector {
Vector::Document(doc) => batch_accum.add(InferenceData::Document(doc.clone())),
Vector::Image(img) => batch_accum.add(InferenceData::Image(img.clone())),
Vector::Object(obj) => batch_accum.add(InferenceData::Object(obj.clone())),
Vector::Dense(_) | Vector::Sparse(_) | Vector::MultiDense(_) => {}
}
}
}
}
let inferred = if !batch_accum.objects.is_empty() {
Some(BatchAccumInferred::from_batch_accum(batch_accum, inference_type).await?)
} else {
None
};
for point_vectors in point_vectors_list {
let PointVectors { id, vector } = point_vectors;
let converted_vector = match vector {
VectorStruct::Single(dense) => VectorStructPersisted::Single(dense),
VectorStruct::MultiDense(multi) => VectorStructPersisted::MultiDense(multi),
VectorStruct::Named(named) => {
let mut converted = HashMap::new();
for (name, vec) in named {
let converted_vec = match &inferred {
Some(inferred) => convert_vector_with_inferred(vec, inferred)?,
None => match vec {
Vector::Dense(dense) => VectorPersisted::Dense(dense),
Vector::Sparse(sparse) => VectorPersisted::Sparse(sparse),
Vector::MultiDense(multi) => VectorPersisted::MultiDense(multi),
Vector::Document(_) | Vector::Image(_) | Vector::Object(_) => {
return Err(StorageError::inference_error(
"Inference required but service returned no results",
))
}
},
};
converted.insert(name, converted_vec);
}
VectorStructPersisted::Named(converted)
}
VectorStruct::Document(_) => {
return Err(StorageError::inference_error(
"Document processing is not supported for point vectors.",
))
}
VectorStruct::Image(_) => {
return Err(StorageError::inference_error(
"Image processing is not supported for point vectors.",
))
}
VectorStruct::Object(_) => {
return Err(StorageError::inference_error(
"Object processing is not supported for point vectors.",
))
}
};
let converted_point_vector = PointVectorsPersisted {
id,
vector: converted_vector,
};
converted_point_vectors.push(converted_point_vector);
}
Ok(converted_point_vectors)
}
fn convert_point_struct_with_inferred(
point_structs: Vec<PointStruct>,
inferred: &BatchAccumInferred,
) -> Result<Vec<PointStructPersisted>, StorageError> {
point_structs
.into_iter()
.map(|point_struct| {
let PointStruct {
id,
vector,
payload,
} = point_struct;
let converted_vector_struct = match vector {
VectorStruct::Single(single) => VectorStructPersisted::Single(single),
VectorStruct::MultiDense(multi) => VectorStructPersisted::MultiDense(multi),
VectorStruct::Named(named) => {
let mut named_vectors = HashMap::new();
for (name, vector) in named {
let converted_vector = convert_vector_with_inferred(vector, inferred)?;
named_vectors.insert(name, converted_vector);
}
VectorStructPersisted::Named(named_vectors)
}
VectorStruct::Document(doc) => {
let vector = convert_vector_with_inferred(Vector::Document(doc), inferred)?;
match vector {
VectorPersisted::Dense(dense) => VectorStructPersisted::Single(dense),
VectorPersisted::Sparse(_) => {
return Err(StorageError::bad_request("Sparse vector should be named"))
}
VectorPersisted::MultiDense(multi) => {
VectorStructPersisted::MultiDense(multi)
}
}
}
VectorStruct::Image(img) => {
let vector = convert_vector_with_inferred(Vector::Image(img), inferred)?;
match vector {
VectorPersisted::Dense(dense) => VectorStructPersisted::Single(dense),
VectorPersisted::Sparse(_) => {
return Err(StorageError::bad_request("Sparse vector should be named"))
}
VectorPersisted::MultiDense(multi) => {
VectorStructPersisted::MultiDense(multi)
}
}
}
VectorStruct::Object(obj) => {
let vector = convert_vector_with_inferred(Vector::Object(obj), inferred)?;
match vector {
VectorPersisted::Dense(dense) => VectorStructPersisted::Single(dense),
VectorPersisted::Sparse(_) => {
return Err(StorageError::bad_request("Sparse vector should be named"))
}
VectorPersisted::MultiDense(multi) => {
VectorStructPersisted::MultiDense(multi)
}
}
}
};
Ok(PointStructPersisted {
id,
vector: converted_vector_struct,
payload,
})
})
.collect()
}
pub async fn convert_vectors(
vectors: Vec<Vector>,
inference_type: InferenceType,
) -> Result<Vec<VectorPersisted>, StorageError> {
let mut batch_accum = BatchAccum::new();
for vector in &vectors {
match vector {
Vector::Document(doc) => batch_accum.add(InferenceData::Document(doc.clone())),
Vector::Image(img) => batch_accum.add(InferenceData::Image(img.clone())),
Vector::Object(obj) => batch_accum.add(InferenceData::Object(obj.clone())),
Vector::Dense(_) | Vector::Sparse(_) | Vector::MultiDense(_) => {}
}
}
let inferred = if !batch_accum.objects.is_empty() {
Some(BatchAccumInferred::from_batch_accum(batch_accum, inference_type).await?)
} else {
None
};
vectors
.into_iter()
.map(|vector| match &inferred {
Some(inferred) => convert_vector_with_inferred(vector, inferred),
None => match vector {
Vector::Dense(dense) => Ok(VectorPersisted::Dense(dense)),
Vector::Sparse(sparse) => Ok(VectorPersisted::Sparse(sparse)),
Vector::MultiDense(multi) => Ok(VectorPersisted::MultiDense(multi)),
Vector::Document(_) | Vector::Image(_) | Vector::Object(_) => {
Err(StorageError::inference_error(
"Inference required but service returned no results",
))
}
},
})
.collect()
}
fn convert_vector_with_inferred(
vector: Vector,
inferred: &BatchAccumInferred,
) -> Result<VectorPersisted, StorageError> {
match vector {
Vector::Dense(dense) => Ok(VectorPersisted::Dense(dense)),
Vector::Sparse(sparse) => Ok(VectorPersisted::Sparse(sparse)),
Vector::MultiDense(multi) => Ok(VectorPersisted::MultiDense(multi)),
Vector::Document(doc) => {
let data = InferenceData::Document(doc);
inferred.get_vector(&data).cloned().ok_or_else(|| {
StorageError::inference_error("Missing inferred vector for document")
})
}
Vector::Image(img) => {
let data = InferenceData::Image(img);
inferred
.get_vector(&data)
.cloned()
.ok_or_else(|| StorageError::inference_error("Missing inferred vector for image"))
}
Vector::Object(obj) => {
let data = InferenceData::Object(obj);
inferred
.get_vector(&data)
.cloned()
.ok_or_else(|| StorageError::inference_error("Missing inferred vector for object"))
}
}
}
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