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def search_faces(self, request: SearchFacesRequest) -> SearchFacesResponse:
    all_results = []
    
    for query_face in request.query_faces:
        for model_name in query_face.model_names:
            collection_name = f"face_embeddings_{model_name.lower()}"
            
            # Perform vector similarity search
            search_results = self._client.search(
                collection_name=collection_name,

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def store_faces(self, request: StoreFacesRequest) -> StoreFacesResponse:
    stored_count = 0
    face_ids = []
    
    for image_faces_data in request.image_faces_data:
        for face_data in image_faces_data.faces:
            if face_data.embeddings and face_data.model_names:
                for model_name in face_data.model_names:
                    face_record = FaceRecord(
                        face_id=f"{uuid.uuid4()}",

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# This worked almost immediately
def _call_analyze_api(self, image_data: bytes) -> Dict[str, Any]:
    headers = {
        'Ocp-Apim-Subscription-Key': AZURE_VISION_API_KEY,
        'Content-Type': 'application/octet-stream'
    }
    
    params = {
        'features': 'tags,objects,faces,brands,landmarks,celebrities,categories,description,adult,color,imageType',
        'model-version': 'latest'

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# This approach never worked despite multiple attempts
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.core.credentials import AzureKeyCredential

client = ImageAnalysisClient(
    endpoint=AZURE_VISION_ENDPOINT,
    credential=AzureKeyCredential(AZURE_VISION_API_KEY)
)

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# Qdrant collection setup for different embedding models
model_sizes = {
    "VGG-Face": 4096,
    "Facenet": 128,
    "ArcFace": 512
}

def _ensure_collections_exist(self):
    for model_name, vector_size in model_sizes.items():
        collection_name = f"face_embeddings_{model_name.lower()}"

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---
title: Service Architecture Guidelines
description: Strict Serivice Oriented Architecture
globs:
alwaysApply: true
---

# Service-Oriented Architecture (SOA) Guidelines

This project follows a strict Service-Oriented Architecture pattern. All services must adhere to these principles:

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# Example of our service architecture
class FaceDetectionService:
    def detect_faces(self, request: FaceDetectionRequest) -> FaceDetectionResponse:
        # Implementation using DeepFace or Azure Face API
        pass

class ArchiveService:
    def store_faces(self, request: StoreFacesRequest) -> StoreFacesResponse:
        # Store face embeddings in Qdrant vector database
        pass

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$("#order-details tr").on('click', function(evt) {/*do something*/});

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$("#order-details").on('click', ‘tr’, function(evt) {/*do something*/});

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$("#order-details").on('click', ‘tr’, function(evt) {/*do something*/});