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"""
Single/Batch embedding generation endpoints.
This module provides routes for generating embeddings for
multiple texts in a single request.
"""
import time
from typing import Union
from fastapi import APIRouter, Depends, HTTPException, status
from loguru import logger
from src.models.schemas import (
EmbedRequest,
DenseEmbedResponse,
SparseEmbedResponse,
SparseEmbedding,
)
from src.core.manager import ModelManager
from src.core.exceptions import (
ModelNotFoundError,
ModelNotLoadedError,
EmbeddingGenerationError,
ValidationError,
)
from src.api.dependencies import get_model_manager
from src.utils.validators import extract_embedding_kwargs, validate_texts
from src.config.settings import get_settings
router = APIRouter(prefix="/embeddings", tags=["embeddings"])
@router.post(
"/embed",
response_model=Union[DenseEmbedResponse, SparseEmbedResponse],
summary="Generate single/batch embeddings spesialization document",
description="Generate embeddings for multiple texts in a single request",
)
async def create_embeddings_document(
request: EmbedRequest,
manager: ModelManager = Depends(get_model_manager),
settings=Depends(get_settings),
):
"""
Generate embeddings for multiple texts.
This endpoint efficiently processes multiple texts in a single batch,
reducing overhead compared to multiple single requests.
Args:
request: BatchEmbedRequest with texts, model_id, and optional parameters
manager: Model manager dependency
settings: Application settings
Returns:
DenseEmbedResponse or SparseEmbedResponse depending on model type
Raises:
HTTPException: On validation or generation errors
"""
try:
# Validate input
validate_texts(
request.texts,
max_length=settings.MAX_TEXT_LENGTH,
max_batch_size=settings.MAX_BATCH_SIZE,
)
# Extract kwargs
kwargs = extract_embedding_kwargs(request)
# Get model
model = manager.get_model(request.model_id)
config = manager.model_configs[request.model_id]
start_time = time.time()
# Generate embeddings based on model type
if config.type == "sparse-embeddings":
# Sparse batch embeddings
sparse_results = model.embed_documents(
texts=request.texts, prompt=request.prompt, **kwargs
)
processing_time = time.time() - start_time
# Convert to SparseEmbedding objects
sparse_embeddings = []
for idx, sparse_result in enumerate(sparse_results):
sparse_embeddings.append(
SparseEmbedding(
text=request.texts[idx],
indices=sparse_result["indices"],
values=sparse_result["values"],
)
)
response = SparseEmbedResponse(
embeddings=sparse_embeddings,
count=len(sparse_embeddings),
model_id=request.model_id,
processing_time=processing_time,
)
else:
# Dense batch embeddings
embeddings = model.embed_documents(
texts=request.texts, prompt=request.prompt, **kwargs
)
processing_time = time.time() - start_time
response = DenseEmbedResponse(
embeddings=embeddings,
dimension=len(embeddings[0]) if embeddings else 0,
count=len(embeddings),
model_id=request.model_id,
processing_time=processing_time,
)
logger.info(
f"Generated {len(request.texts)} embeddings "
f"in {processing_time:.3f}s ({len(request.texts) / processing_time:.1f} texts/s)"
)
return response
except (ValidationError, ModelNotFoundError) as e:
raise HTTPException(status_code=e.status_code, detail=e.message)
except ModelNotLoadedError as e:
raise HTTPException(status_code=e.status_code, detail=e.message)
except EmbeddingGenerationError as e:
raise HTTPException(status_code=e.status_code, detail=e.message)
except Exception as e:
logger.exception("Unexpected error in create_embeddings_document")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Failed to create batch embeddings: {str(e)}",
)
@router.post(
"/query",
response_model=Union[DenseEmbedResponse, SparseEmbedResponse],
summary="Generate single/batch embeddings spesialization query",
description="Generate embedding for a multiple query text",
)
async def create_query_embedding(
request: EmbedRequest,
manager: ModelManager = Depends(get_model_manager),
):
"""
Generate a single/batch query embedding.
This endpoint creates embeddings optimized for search queries.
Some models differentiate between query and document embeddings.
Args:
request: EmbedRequest with text, model_id, and optional parameters
manager: Model manager dependency
settings: Application settings
Returns:
DenseEmbedResponse or SparseEmbedResponse depending on model type
Raises:
HTTPException: On validation or generation errors
"""
try:
# Validate input
validate_texts(request.texts)
# Extract kwargs
kwargs = extract_embedding_kwargs(request)
# Get model
model = manager.get_model(request.model_id)
config = manager.model_configs[request.model_id]
start_time = time.time()
# Generate embedding based on model type
if config.type == "sparse-embeddings":
# Sparse embedding
sparse_results = model.embed_query(
texts=request.texts, prompt=request.prompt, **kwargs
)
processing_time = time.time() - start_time
# Convert to SparseEmbedding objects
sparse_embeddings = []
for idx, sparse_result in enumerate(sparse_results):
sparse_embeddings.append(
SparseEmbedding(
text=request.texts[idx],
indices=sparse_result["indices"],
values=sparse_result["values"],
)
)
response = SparseEmbedResponse(
embeddings=sparse_embeddings,
count=len(sparse_embeddings),
model_id=request.model_id,
processing_time=processing_time,
)
else:
# Dense batch embeddings
embeddings = model.embed_documents(
texts=request.texts, prompt=request.prompt, **kwargs
)
processing_time = time.time() - start_time
response = DenseEmbedResponse(
embeddings=embeddings,
dimension=len(embeddings[0]) if embeddings else 0,
count=len(embeddings),
model_id=request.model_id,
processing_time=processing_time,
)
logger.info(
f"Generated {len(request.texts)} embeddings "
f"in {processing_time:.3f}s ({len(request.texts) / processing_time:.1f} texts/s)"
)
return response
except (ValidationError, ModelNotFoundError) as e:
raise HTTPException(status_code=e.status_code, detail=e.message)
except ModelNotLoadedError as e:
raise HTTPException(status_code=e.status_code, detail=e.message)
except EmbeddingGenerationError as e:
raise HTTPException(status_code=e.status_code, detail=e.message)
except Exception as e:
logger.exception("Unexpected error in create_query_embedding")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Failed to create query embedding: {str(e)}",
)
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